Are you looking to learn python for data science but have a time crunch? Are you making your career shift into data science and want to learn python? In this blog, we will talk about learning python for data science in just 30 days. Also, we will look at weekly schedules and topics to cover in python.
Before directly jumping to python, let us understand about the usage of python in data science.
Data Science Pipeline
Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. It provides solutions to real-world problems using data available. But, data analysis is not a one-step process. It is a group of multiple techniques employed to reach a suitable solution for a problem. Also, a data scientist may need to go through multiple stages to arrive at some insights for a particular problem. This series of stages collectively is known as a data science pipeline. Let us have a look at various stages involved.
Stages
Problem Definition
Contrary to common belief, the hardest part of data science isn’t building an accurate model or obtaining good, clean data. It is much harder to define feasible problems and come up with reasonable ways of measuring solutions. Problem definition aims at understanding, in depth, a given problem at hand. Multiple brainstorming sessions are organized to correctly define a problem because of your end goal with depending upon what problem you are trying to solve. Hence, if you go wrong during the problem definition phase itself, you will be delivering a solution to a problem which never even existed at first
Hypothesis Testing
Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter. The methodology employed by the analyst depends on the nature of the data used and the reason for the analysis. Hypothesis testing is used to infer the result of a hypothesis performed on sample data from a larger population. In simple words, we form some assumptions during problem definition phase and then validate those assumptions statistically using data.
Data collection and processing
Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. Moreover, the data collection component of research is common to all fields of study including physical and social sciences, humanities, business, etc. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. Furthermore, Data processing is more about a series of actions or steps performed on data to verify, organize, transform, integrate, and extract data in an appropriate output form for subsequent use. Methods of processing must be rigorously documented to ensure the utility and integrity of the data.
EDA and feature Engineering
Once you have clean and transformed data, the next step for machine learning projects is to become intimately familiar with the data using exploratory data analysis (EDA). EDA is about numeric summaries, plots, aggregations, distributions, densities, reviewing all the levels of factor variables and applying general statistical methods. A clear understanding of the data provides the foundation for model selection, i.e. choosing the correct machine learning algorithm to solve your problem. Also, Feature engineering is the process of determining which predictor variables will contribute the most to the predictive power of a machine learning algorithm. The process of feature engineering is as much of an art as a science. Often feature engineering is a give-and-take process with exploratory data analysis to provide much-needed intuition about the data. It’s good to have a domain expert around for this process, but it’s also good to use your imagination.
Modelling and Prediction
Machine learning can be used to make predictions about the future. You provide a model with a collection of training instances, fit the model on this data set, and then apply the model to new instances to make predictions. Predictive modelling is useful for startups because you can make products that adapt based on expected user behaviour. For example, if a viewer consistently watches the same broadcaster on a streaming service, the application can load that channel on application startup.
Data Visualisation
Data visualization is the process of displaying data/information in graphical charts, figures, and bars. It is used as a means to deliver visual reporting to users for the performance, operations or general statistics of data and model prediction.
Insight generation and implementation
Interpreting the data is more like communicating your findings to the interested parties. If you can’t explain your findings to someone believe me, whatever you have done is of no use. Hence, this step becomes very crucial. Furthermore, the objective of this step is to first identify the business insight and then correlate it to your data findings. Secondly, you might need to involve domain experts in correlating the findings with business problems. Domain experts can help you in visualizing your findings according to the business dimensions which will also aid in communicating facts to a non-technical audience.
Python usage in different data science stages
After having a look at various stages in a data science pipeline, we can figure out the usage of python in these stages. Hence, we can now understand the applications of python in data science in a much better way.
To begin with, stages like problem definition and insight generation do not require the use of any programming language as such. Both the stages are more based on research and decision making rather than implementation through code.
Python in data collection
Many data science projects require scraping websites to gather the data that you’ll be working with. The Python programming language is widely used in the data science community, and therefore has an ecosystem of modules and tools that you can use in your own projects.
Python in hypothesis testing
Hypothesis testing requires a lot of statistical knowledge and implementation. Python has libraries which can help users to perform statistical tests and computations easily. Using these libraries, like SciPy, can easily allow users to automate hypothesis testing tasks.
Python in EDA
Multiple libraries are available to perform basic EDA. You can use pandas and matplotlib for EDA. Pandas for data manipulation and matplotlib, well, for plotting graphs. Jupyter Notebooks to write code and other findings. Jupyter notebooks are kind of diary for data analysis and scientists, a web-based platform where you can mix Python, HTML, and Markdown to explain your data insights.
Python in Visualisation
One of the key skills of a data scientist is the ability to tell a compelling story, He should be able to visualize data and findings in an approachable and stimulating way. Also, learning a library to visualize data will also enable you to extract information, understand data and make effective decisions. Furthermore, there are libraries like matplotlib, seaborn which makes it easy for users to build pretty visualizations. Additionally, these libraries are easy to learn in not much time.
Python in modelling and prediction
Python boasts of libraries like sci-kit-learn which is an open source Python library that implements a range of machine learning, pre-processing, cross-validation and visualization algorithms using a unified interface. Such libraries abstract out the mathematical part of the model building. Hence, developers can focus on building reliable models rather than understanding the complex math implementation. If you are new to machine learning, then you can follow this link to know more about it.
Study Timeline
In this section, we will be looking at a week-wise distribution of python topics. This will help you in organizing your schedule and have a dedicated roadmap for 30 days
Week 1
Python Basics
Start with python basics here. You can start learning about variables and control flow. Then you can focus on learning about strings, dictionaries, tuples and other data structures in python.
Reference Links
Python Advanced Once you are done with basic concepts, you can focus on concepts like multithreading, classes, and objects, regular expressions and networking etc. All these concepts may not be very much required at most of the times but it is something good to know.
Reference Links
Follow the link to get started with python basic and advanced.
Week 2
Web scraping in python It refers to gathering data from websites using a code, which is one of the most logical and easily accessible sources of data. Automating this process with a web scraper avoids manual data gathering, saves time and also allows you to have all the data in the required structure. You can start learning about libraries like BeatifulSoup and Scrapy. The libraries in python provide users with functionality to scrape data from websites. Having familiarity with these libraries will help you in utilizing python capabilities in data collection.
Pandas, numPy and SciPy in python Python has its own set of libraries to deal with data management. Library-like Pandas allow you to access data in form of a data frame. This facilitates users with the ability to handle data with complex structures and perform numerical operations on them like data cleaning, data summarization etc. But, numPy is more about handling numerical methods and sciPy about scientific and statistical functions to perform math heavy calculations. These libraries are must to know when you are learning python for data science. Hence, a great deal of attention should be paid while learning these libraries. You can have a look at this link to learn more about above-mentioned libraries.
Week 3
Week 3 is about understanding the machine learning capabilities of python and getting fluent with it
Scikit-learn Package
Week 3 starts with understanding the machine learning capabilities in python. Scikit-learn is the must know package whenever we talk about machine learning and python. Invest your time in learning the methods provided by the scikit-learn package. It provides a uniform way of fitting different models and hence is a great hit among python based ml developers.
Keras
Theano and TensorFlow are two of the top numerical platforms in Python that provide the development in deep-learning. Both are very powerful libraries, but both can be difficult to use directly for creating deep learning models. Hence Keras Python library, which provides a clean and convenient way to create a range of deep learning models on top of Theano or TensorFlow. Keras is a minimalist Python library for deep learning that can run on top of Theano or TensorFlow. It was developed to make implementing deep learning models as fast and easy as possible for research and development. It runs on Python 2.7 or 3.5.
Week 4
Week 4 is more about learning visualizations in python and summarising all the previous learning in the form of a project.
Matplotlib in python
Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Additionally, It can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. Also, it tries to make easy things easy and hard things possible. Furthermore, you can generate plots, histograms, power spectra, bar charts, error charts, scatterplots, etc., with just a few lines of code. For examples, see the sample plots and thumbnail gallery.
Project
After learning most of the things about python for data science, it is time to wrap up all your learnings together in the form of a project. A project will help you to actually implement all your learnings together and visualize a complete picture of the data science pipeline.
A sample project to finish with
You are free to pick up any project you like. In case you are confused and do not know what to take up as a project, you can start with the Titanic problem on the Kaggle. You can find the problem statement here. I will not tell you how to solve it but can give you a few pointers in kickstarting your project
Do not go for the score on Kaggle. The aim is to complete the project and not to go for an extensive model fitting
Do more of EDA and data processing rather than model building
Focus on data processing using libraries you learned (pandas, numpy)
Conclusion
Python is an amazingly versatile programming language. Apart from data science, you can use it to build websites, machine learning algorithms, and even autonomous drones. A huge percentage of programmers in the world use Python, and for good reason. Hence, it is worthwhile to invest in your time in learning python if you are moving into data science. With a plethora of libraries available, python will always have an edge over other languages. Python is a really fun and rewarding language to learn. Also, I think that anyone can get to a high level of proficiency in it if they find the right motivation. Happy Learning!
Interested in Data Science, You can go through a few of the links below
As more businesses look to data-driven technologies like automation and AI, the need for talented workers who can interpret the data is only expected to rise. In fact, IBM predicts that the demand for data scientists will soar 28% by 2020. Due to high demand and lucrative offers, many people are already shifting their career choice towards data science, hence data science live classes are becoming popular.
Are you among those preparing for data science jobs? If you are, you might have also faced a dilemma of either go for self-learning or take up a live course online. In this blog, we will be talking on differences between self-learning and live courses. Furthermore, we will also be looking at why taking a course can be a wiser choice.
Learning Methodologies
With the advent of advanced communicational technology and more diverse content consumption media, training and learning have become easier than ever. Practical training has experienced perhaps the greatest leap forward
Self Paced
With self-paced learning you can make your own decisions instead of completing a specific data science course within a certain amount of time, learners are able to learn concepts within the time that is needed for them. Each participant can decide what time is needed to complete a given course.
Advantages can be like no time pressure, no need for a schedule, suitable to people with different learning styles
Instructor-Led Online Courses
This type of training is facilitated by an instructor either online or in a classroom setting. Instructor-led training allows for learners and instructors or facilitators to interact and discuss the training material, either individually or in a group setting. Online instructor-led training is known as virtual instructor-led training or VILT.
Advantages can be easier to adapt, more social and easier to enforce capabilities
You can also have a look at topmost skills to become a data scientist here
Challenges in learning data science
Data science is not an easy nut to crack. It is difficult to master regardless of your professional experience. You may face a lot of difficulties while learning data science when starting your career. Let us list some reasons and understand why actually data science is a little tougher to learn
A vast content to learn
Data science consists of a large number of concepts and fields. Few examples can be problem definition to data fetching, cleaning, featuring, modeling, visualization and what not. It is impossible to learn all the things at once. This case gets even trickier when you realize that all the separate fields are important and can not be omitted. To be a good data scientist, one has to learn all the things like maths and statistics, machine learning, programming languages, communication, and analytical skills etc. Becoming a data scientist is not a short-term process. It demands years of efforts and diligence.
Rapidly expanding and dynamic field
Data science is relatively new and a major chunk of it is still under research. There have been advancements happening in data science all around the globe. With such fast advancements, it is difficult to catch up to the latest concepts in data sciences. Algorithms are evolving, visualizations are getting smarter, the problem-solving methodology is also under constant development. There are high chances of other frameworks or techniques rolling out by the time you finish with one. Hence, it is a little difficult to learn data science and catch up with all the advancements in short duration. Although basics are nowhere to go, you will need much more than that to tackle real-world problems.
Research Acumen
Data sciences demand research acumen from learners. Data science is more about researching new techniques and methodologies to enhance solutions for real-world problems. Learners should not only be skilled at using available techniques but should also be able to creatively suggest new ones. Furthermore, these tasks get more complex when you are still in a learning stage. Only be able to use available techniques will make you a business analyst rather than a data scientist.
Why Data Science live learning?
Learning data science comes along with its own set of challenges. It becomes practically very difficult to handle above-mentioned challenges alongside learning. A self-paced approach is a little less capable of handling the high learning curve in data science. What do we do then? We have to look at instructor based mode of learning. In this section, we will be looking at reasons why we should go for an instructor based online learning for data science
Easy to maintain motivation
One problem with the self-paced learning program is keeping the motivation high throughout the learning. Learners tend to burn out eventually and it happens even faster when learning is exponential. Motivation is the prime fuel which powers self-paced learning. Without motivation, self-paced learning cannot happen. This the reason why you need an instructor based method. It keeps your schedule and learning under constant check. Furthermore, it also reduces the chances of lagging behind in terms of your pre-defined targets.
Doubts clearance
One of the most important reasons to opt for the instructor-led method is getting the daily doubts cleared. Getting doubts cleared on time is important for higher learning rate. If doubts are not cleared, you won’t be able to move ahead even with all your study material.
Career guidance and assistance
Apart from regular learning schedules, you can also get career advice and guidance from your instructors. Data science opportunity is the only reason that you are learning data science in the first place. With industry experience and contacts, instructors can impart valuable guidance to the learners about their career.
You can also look at data science interview questions here.
Knowing what to learn
Keeping the motivation to learn is one thing and knowing what to learn is other. Data science is a very vast field. One just can not learn all of it at once because it is too big to explore. Instructor-led programs help learners identify the correct topics to study and in the right order. Instructor-led data science programs can help learners focus more on basics and getting them right. Learning advanced topics early can lead to a terrible mixture a bland basics curry and undercooked advanced concepts.
You can have a look at our comprehensive data science course and syllabus here
Real-time projects
Data science projects offer you a promising way to kick-start your career in this field. Not only do you get to learn data science by applying it, but you also get projects to showcase on your CV! Nowadays, recruiters evaluate a candidate’s potential by his/her work and don’t put a lot of emphasis on certifications. It wouldn’t matter if you just tell them how much you know if you have nothing to show them! That’s where most people struggle and miss out. Instructor based data science courses provide you with the opportunity for hands-on industry projects. Learnings while doing a project are immense and can not be matched with any tutorial.
Looking for some good data science projects? You might want to check them out here
High Knowledge intake
Instructor-led courses enable learners to digest concepts at a faster rate. Although a bit more effort has to be put in as compared to self-paced courses, results are much better. You end up learning more and in less time. This result is more important when you are learning something like data science as there is a lot to learn.
Conclusion
Both methods have distinct pros and cons, as well the purposes. In our experience, learners prefer self-paced training for basic conceptual learning and virtual instructor-led training for more advanced training. Since both serve somewhat different purposes, there is no clear winner between the methods. But when learning data science is concerned, instructor-based learning has an edge over self-paced learning. Owing to the vast knowledge in the data science field and continuous advancements happening, instructor based online learning is a good bet to place.
What if I say that there is a way for you to become a data scientist, regardless of your programming skills! Furthermore, most people think that being proficient in a programming knowledge is a must-have for becoming a data scientist. Well, this statement is not completely true! Data science is not all about programming anymore.
In this article, we will be looking at different tools for data scientists. Different tools cover different aspects of data science, hence data scientists can make their work easy by employing these tools for different tasks. Let us understand more about these tools in detail.
What are data science tools?
These are tools that typically obviate the programming aspect and provide user-friendly GUI (Graphical User Interface) hence anyone with minimal knowledge of algorithms can simply use them to build high-quality machine learning models.
Many companies (especially startups) have recently launched GUI driven data science tools. These tools cover different aspects of data science like data storage, data manipulation, data modeling etc.
Why data science tools?
No programming experience required
Better work management
Faster results
Better quality check mechanism
Process Uniformity
Different Data science tools
Data Storage
1. Apache Hadoop
Apache Hadoop is a java based free software framework that can effectively store a large amount of data in a cluster. This framework runs in parallel on a cluster. Hence, it has the ability to allow us to process data across all nodes. Also, Hadoop Distributed File System (HDFS) is the storage system of Hadoop which splits big data and distribute across many nodes in a cluster. This also replicates data in a cluster thus providing high availability.
2. Microsoft HDInsight
It is a Big Data solution from Microsoft powered by Apache Hadoop which is available as a service in the cloud. HDInsight uses Windows Azure Blob storage as the default file system. Also, this also provides high availability with low cost.
3. NoSQL
While the traditional SQL can be effectively used to handle a large amount of structured data, we need NoSQL (Not Only SQL) to handle unstructured data. Also, NoSQL databases store unstructured data with no particular schema. Furthermore, each row can have its own set of column values. Hence, NoSQL gives better performance in storing a massive amount of data. There are many open-source NoSQL DBs available to analyze Big Data.
4. Hive
This is a distributed data management for Hadoop. Also, this supports SQL-like query option HiveSQL (HSQL) to access big data. This can be primarily used for Data mining purpose. Furthermore, this runs on top of Hadoop.
5. Sqoop
This is a tool that connects Hadoop with various relational databases to transfer data. This can be effectively used to transfer structured data to Hadoop or Hive.
6. PolyBase
This works on top of SQL Server 2012 Parallel Data Warehouse (PDW) and is used to access data stored in PDW. Furthermore, PDW is a data warehousing appliance built for processing any volume of relational data and provides integration with Hadoop allowing us to access non-relational data as well.
Data transformation
1. Informatica — PowerCenter
Informatica is a leader in Enterprise Cloud Data Management with more than 500 global partners and more than 1 trillion transactions per month. It is a software Development Company that was found in 1993 with its headquarters in California, United States. In addition, It has a revenue of $1.05 billion and a total employee headcount of around 4,000.
PowerCenter is a product which was developed by Informatica for data integration. It supports data integration lifecycle and also delivers critical data and values to the business. Furthermore, PowerCenter supports a huge volume of data and any data type and any source for data integration.
2. IBM — Infosphere Information Server
IBM is a multinational Software Company found in 1911 with its headquarters in New York, U.S. and it has offices across more than 170 countries. It has a revenue of $79.91 billion as of 2016 and total employees currently working are 380,000.
Infosphere Information Server is a product by IBM that was developed in 2008. It is a leader in the data integration platform which helps to understand and deliver critical values to the business. It is mainly designed for Big Data companies and large-scale enterprises.
3. Oracle Data Integrator
Oracle is an American multinational company with its headquarters in California and was found in 1977. It has a revenue of $37.72 billion as of 2017 and a total employee headcount of 138,000.
Oracle Data Integrator (ODI) is a graphical environment to build and manage data integration. This product is suitable for large organizations which have frequent migration requirement. It is a comprehensive data integration platform which supports high volume data, SOA enabled data services.
Key Features:
Oracle Data Integrator is a commercial licensed RTL tool.
Improves user experience with re-design of flow based interface.
It supports declarative design approach for data transformation and integration process.
Faster and simpler development and maintenance.
4. AB Initio
Ab Initio is an American private enterprise Software Company in Massachusetts, USA. It has offices worldwide in the UK, Japan, France, Poland, Germany, Singapore and Australia. Ab Initio specialises in application integration and high volume data processing.
It contains six data processing products such as Co>Operating System, The Component Library, Graphical Development Environment, Enterprise Meta>Environment, Data Profiler, and Conduct>It. “Ab Initio Co>Operating System” is a GUI based ETL tool with a drag and drop feature.
Key Features:
Ab Initio has a commercial license and a most costlier tool in the market.
The basic features of Ab Initio are easy to learn.
Ab Initio Co>Operating system provides a general engine for data processing and communication between rest of the tools.
Ab Initio products are provided on a user-friendly platform for parallel data processing applications.
5. Clover ETL
CloverETL, by a company named Javlin, with offices across the globe like USA, Germany, and the UK provides services like data processing and data integration.
In addition, CloverETL is a high-performance data transformation and robust data integration platform. Therefore, It can process a huge volume of data and transfers the data to various destinations. Also, it consists of three packages such as — CloverETL Engine, CloverETL Designer, and CloverETL Server.
Key Features:
CloverETL is a commercial ETL software.
CloverETL has a Java-based framework.
Easy to install and simple user interface.
Combines business data in a single format from various sources.
It also supports Windows, Linux, Solaris, AIX and OSX platforms.
It is for data transformation, data migration, data warehousing and data cleansing.
Modelling Tools
1. Infosys Nia
Infosys Nia is a knowledge-based AI platform, built by Infosys in 2017 to collect and aggregate organisational data from people, processes and legacy systems into a self-learning knowledge base.
It is to tackle difficult business tasks such as forecasting revenues and what products need to be built, understanding customer behaviour and more.
Infosys Nia enables businesses to manage customer inquiries easily, with a secure order-to-cash process with risk awareness delivered in real-time.
2. H20 Driverless
H2O is an open source software tool, consisting of a machine learning platform for businesses and developers.
H2O.ai is in the Java, Python and R programming languages. The platform is built with the languages with which developers are familiar with in order to make it easy for them to apply machine learning and predictive analytics.
Also, H2O can analyze datasets in the cloud and Apache Hadoop file systems. It is available on Linux, MacOS and Microsoft Windows operating systems.
3. Eclipse Deep learning 4j
Eclipse Deeplearning4j is an open-source deep-learning library for the Java Virtual Machine. It can serve as a DIY tool for Java, Scala and Clojure programmers working on Hadoop and other file systems. It also allows developers to configure deep neural networks and is suitable for use in business environments on distributed GPUs and CPUs.
The project, by a San Francisco company called Skymind, offers paid support, training and enterprise distribution of Deeplearning4j.
4. Torch
Torch is a scientific computing framework, an open source machine learning library and a scripting language over the Lua programming language. It also provides an array of algorithms for deep machine learning. Furthermore, the torch is used by the Facebook AI Research Group and was previously used by DeepMind before it was acquired by Google and moved to TensorFlow.
5. IBM Watson
IBM is a big player in the field of AI, with its Watson platform housing an array of tools designed for both developers and business users.
Available as a set of open APIs, Watson users will have access to lots of sample code, starter kits and can build cognitive search engines and virtual agents.
Watson also has a chatbot building platform aimed at beginners, which requires little machine learning skills. Watson will even provide pre-trained content for chatbots to make training the bot much quicker.
Model Deployment
1. ML Flow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It tackles three primary functions:
Tracking experiments to record and compare parameters and results (MLflow Tracking).
Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production (MLflow Projects).
Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (MLflow Models).
MLflow is library-agnostic. Also, you can use it with any machine learning library, and in any programming language, since all functions are accessible through a REST API and CLI. For convenience, the project also includes a Python API, R API, and Java API.2. Kubeflow
2. Kubeflow
The Kubeflow project is for making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. The goal is not to recreate other services, but also to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures.
The basic workflow is:
Download the Kubeflow scripts and configuration files.
Customize the configuration.
Run the scripts to deploy your containers to your chosen environment.
In addition, you adapt the configuration to choose the platforms and services that you want to use for each stage of the ML workflow: data preparation, model training, prediction serving, and service management.
3. H20 AI
H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. Also, H2O has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. Furthermore, the H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.
4. Domino Data Lab
Domino provides an open, unified data science platform to build, validate, deliver, and monitor models at scale. This accelerates research, sparks collaboration, increases iteration speed, and removes deployment friction to deliver impactful models.
5. Dataiku
Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build and deliver their own data products more efficiently. Dataiku’s single, collaborative platform powers both self-service analytics and also the operationalization of machine learning models in production. Hence, in simple words, Data Science Studio (DSS) is a software platform that aggregates all the steps and big data tools necessary to get from raw data to production-ready applications. Furthermore, it shortens the load-prepare-test-deploy cycles required to create data-driven applications. Also, thanks to its visual and interactive workspace, it is accessible to both Data Scientists and Business Analysts
Data Visualisation
1. Tableau
One of the major tool in this category. Tableau is famous for his drag and drops features in User Interface. In addition, this data visualization tool is free for some basic versions. Also, it supports multi-format data like xls,csv, XML , database connections etc . Furthermore, for more information on Tableau, You can reach out at Tableau official website.
The Qlik view is again a powerful BI tool for decision making. In addition, It is easily configurable and Deployable. Also, it is scalable with few constraints of RAM. The most loving features of Qlik view is visual drill down. In case you want to read more about Qlik View, You can reach out Qlik View official website. Here you can find all installation guide with other details.
Another powerful tool from Qlik family. Its popularity is because of its user-friendly features like drag and drop. Also, it is designed in such a manner that even a business user can use it. Furthermore, its cloud-based infrastructure makes it strong among other data visualizations tool. You can download the free desktop version of Qlik Sense and use it.
SAS VA is not only a data visualization tool but also it is capable of predictive modeling and forecasting. It is easy to operate with drag and drop features. Also, there is awesome community support for SAS Visual Analytics. In addition, you can directly reach SAS Visual Analytics from here.
D3 is a javascript library. Furthermore, It is an open source library. You can use to bind arbitrary data with the Document Object Model. As it is an open source library so you can find a rich tutorial on D3.js. Also, here is the link for the home page of D3.js.
Conclusion
The success of any modern data analytics strategy depends on full access to all data. Solutions like above simplify and accelerate decision making from massive amounts of data from any data source. Furthermore, we can execute any machine learning models you’ve developed to deepen your knowledge of and engagement with your customers, or other important initiatives.
Whether you are a scientist, a developer or, simply, a data enthusiast, these tools provide features that can cover your every need.
The constant evolution of technology has meant data and information is being generated at a rate unlike ever before, and it’s only on the rise. Furthermore, the demand for people skilled in analyzing, interpreting and using this data is already high and is set to grow exponentially over the coming years. These new roles cover all aspect from strategy, operations to governance. Hence, the current and future demand will require more data scientists, data engineers, data strategists, and Chief Data Officers.
In this blog, we will be looking at different set of interview questions that can certainly help if you are planning to give a shift to your career towards data science.
Category of Interview Questions
Statistics
1. Name and explain few methods/techniques used in Statistics for analyzing the data?
Answer:
Arithmetic Mean: It is an important technique in statistics Arithmetic Mean can also be called an average. It is the number or the quantity obtained by summing two or more numbers/variables and then dividing the sum by the number of numbers/variables.
Median:
Median is also a way of finding the average of a group of data points. It’s the middle number of a set of numbers. There are two possibilities, the data points can be an odd number group or it can be en even number group.
If the group is odd, arrange the numbers in the group from smallest to largest. The median will be the one which is exactly sitting in the middle, with an equal number on either side of it. If the group is even, arrange the numbers in order and pick the two middle numbers and add them then divide by 2. It will be the median number of that set.
Mode: The mode is also one of the types for finding the average. A mode is a number, which occurs most frequently in a group of numbers. Some series might not have any mode; some might have two modes which is called bimodal series.
In the study of statistics, the three most common ‘averages’ in statistics are Mean, Median and Mode.
Standard Deviation (Sigma): Standard Deviation is a measure of how much your data is spread out in statistics.
Regression: Regression is an analysis in statistical modelling. It’s a statistical process for measuring the relationships among the variables; it determines the strength of the relationship between one variable and a series of other changing independent variables.
2. Explain about statistics branches?
Answer:
The two main branches of statistics are descriptive statistics and inferential statistics.
Descriptive statistics: Descriptive statistics summarizes the data from a sample using indexes such as mean or standard deviation.
Descriptive Statistics, methods include displaying, organizing and describing the data.
Inferential Statistics: Inferential Statistics draws the conclusions from data that are subject to random variation such as observation errors and sample variation.
3. List all the other models work with statistics to analyze the data?
Answer:
Statistics along with Data Analytics analyzes the data and help business to make good decisions. Predictive ‘Analytics’ and ‘Statistics’ are useful to analyze current data and historical data to make predictions about future events.
4. List the fields, where statistic can be used?
Answer:
Statistics can be used in many research fields. Below are the lists of files in which statistics can be used
Science
Technology
Business
Biology
Computer Science
Chemistry
It aids in decision making
Provides comparison
Explains action that has taken place
Predict the future outcome
Estimate of unknown quantities.
5. What is a linear regression in statistics?
Answer: Linear regression is one of the statistical techniques used in a predictive analysis, in this technique will identify the strength of the impact that the independent variables show on deepened variables.
6. What is a Sample in Statistics and list the sampling methods?
Answer:
In a Statistical study, a Sample is nothing but a set of or a portion of collected or processed data from a statistical population by a structured and defined procedure and the elements within the sample are known as a sample point.
Below are the 4 sampling methods:
Cluster Sampling: IN cluster sampling method the population will be divided into groups or clusters.
Simple Random: This sampling method simply follows the pure random division.
Stratified: In stratified sampling, the data will be divided into groups or strata.
Systematical: Systematical sampling method picks every kth member of the population.
7. What is P- value and explain it?
Answer:
When we execute a hypothesis test in statistics, a p-value helps us in determine the significance of our results. These Hypothesis tests are nothing but to test the validity of a claim that is made about a population. A null hypothesis is a situation when the hypothesis and the specified population is with no significant difference due to sampling or experimental error.
8. What is Data Science and what is the relationship between Data science and Statistics?
Answer: Data Science is simply data-driven science, also, it involves the interdisciplinary field of automated scientific methods, algorithms, systems, and process to extracts the insights and knowledge from data in any form, either structured or unstructured. Furthermore, It has similarities with data mining, both abstracts the useful information from data.
Data Sciences include Mathematical Statistics along with Computer science and Applications. Also by combing aspects of statistics, visualization, applied mathematics, computer science Data Science is turning the vast amount of data into insights and knowledge.
Similarly, Statistics is one of the main components of Data Science. Statistics is a branch of mathematics commerce with the collection, analysis, interpretation, organization, and presentation of data.
9. What is correlation and covariance in statistics?
Answer:
Covariance and Correlation are two mathematical concepts; these two approaches are widely used in statistics. Both Correlation and Covariance establish the relationship and also measure the dependency between two random variables. Though the work is similar between these two in mathematical terms, they are different from each other.
Correlation: Correlation is considered or described as the best technique for measuring and also for estimating the quantitative relationship between two variables. Correlation measures how strongly two variables are related.
Covariance: In covariance two items vary together and it’s a measure that indicates the extent to which two random variables change in cycle. It is a statistical term; it explains the systematic relation between a pair of random variables, wherein changes in one variable reciprocal by a corresponding change in another variable.
R is data analysis software which is used by analysts, quants, statisticians, data scientists, and others.
2. List out some of the function that R provides?
The function that R provides are
Mean
Median
Distribution
Covariance
Regression
Non-linear
Mixed Effects
GLM
GAM. etc.
3. Explain how you can start the R commander GUI?
Typing the command, (“Rcmdr”) into the R console starts the R Commander GUI.
4. In R how you can import Data?
You use R commander to import Data in R, and there are three ways through which you can enter data into it
You can enter data directly via Data New Data Set
Import data from a plain text (ASCII) or other files (SPSS, Minitab, etc.)
Read a dataset either by typing the name of the data set or selecting the data set in the dialogue box
5. Mention what does not ‘R’ language do?
Though R programming can easily connect to DBMS is not a database
R does not consist of any graphical user interface
Though it connects to Excel/Microsoft Office easily, R language does not provide any spreadsheet view of data
6. Explain how R commands are written?
In R, anywhere in the program, you have to preface the line of code with a #sign, for example
# subtraction
# division
# note order of operations exists
7. How can you save your data in R?
To save data in R, there are many ways, but the easiest way of doing this is
Go to Data > Active Data Set > Export Active dataset and a dialogue box will appear, when you click ok the dialogue box lets you save your data in the usual way.
8. Mention how you can produce co-relations and covariances?
You can produce co-relations by the cor () function to produce co-relations and cov() function to produce covariances.
9. Explain what is t-tests in R?
In R, the t.test () function produces a variety of t-tests. The t-test is the most common test in statistics and used to determine whether the means of two groups are equal to each other.
10. Explain what is With () and By () function in R is used for?
With() function is similar to DATA in SAS, it applies an expression to a dataset.
BY() function applies a function to each level of factors. It is similar to BY processing in SAS.
11. What are the data structures in R that are used to perform statistical analyses and create graphs?
In R missing values are represented by NA (Not Available), why impossible values are represented by the symbol NaN (not a number).
14. Explain what is transpose?
For re-shaping data before, analysis R provides a various method and transpose are the simplest methods of reshaping a dataset. To transpose a matrix or a data frame t () function is used.
15. Explain how data is aggregated in R?
By collapsing data in R by using one or more BY variables, it becomes easy. When using the aggregate() function the BY variable should be in the list.
Machine Learning
1. What do you understand by Machine Learning?
Answer:
Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Also, machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
2. Give an example that explains Machine Leaning in industry.
Answer:
Robots are replacing humans in many areas. It is because robots are programmed such that they can perform the task based on data they gather from sensors. They learn from the data and behaves intelligently.
3. What are the different Algorithm techniques in Machine Learning?
Answer:
The different types of Algorithm techniques in Machine Learning are as follows:
• Reinforcement Learning
• Supervised Learning
• Unsupervised Learning
• Semi-supervised Learning
• Transduction
• Learning to Learn
4. What is the difference between supervised and unsupervised machine learning?
Answer:
This is the basic Machine Learning Interview Questions asked in an interview. A Supervised learning is a process where it requires training labelled data While Unsupervised learning it doesn’t require data labelling.
5. What is the function of Unsupervised Learning?
Answer:
The function of Unsupervised Learning are as below:
• Find clusters of the data of the data
• Low-dimensional representations of the data
• Gaining interesting directions in data
• Interesting coordinates and correlations
• Figuring novel observations
6. What is the function of Supervised Learning?
Answer:
The function of Supervised Learning are as below:
• Classifications
• Speech recognition
• Regression
• Predict time series
• Annotate strings
7. What are the advantages of Naive Bayes?
Answer:
The advantages of Naive Bayes are:
• The classifier will converge quicker than discriminative models
• It cannot learn the interactions between features
8. What are the disadvantages of Naive Bayes?
Answer:
The disadvantages of Naive Bayes are:
• The problem arises for continuous features
• It makes a very strong assumption on the shape of your data distribution
• Does not work well in case of data scarcity
9. Why is naive Bayes so naive?
Answer:
Naive Bayes is so naive because it assumes that all of the features in a dataset are equally important and independent.
10. What is Overfitting in Machine Learning?
Answer:
This is the popular Machine Learning Interview Questions asked in an interview. Overfitting in Machine Learning is defined as when a statistical model describes random error or noise instead of underlying relationship or when a model is excessively complex.
11. What are the conditions when Overfitting happens?
Answer:
One of the important reason and possibility of overfitting is because the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model.
12. How can you avoid overfitting?
Answer:
We can avoid overfitting by using:
• Lots of data
• Cross-validation
13. What are the five popular algorithms for Machine Learning?
Answer:
Below is the list of five popular algorithms of Machine Learning:
• Decision Trees
• Probabilistic networks
• Nearest Neighbor
• Support vector machines
• Neural Networks
14. What are the different use cases where machine learning algorithms can be used?
Answer:
The different use cases where machine learning algorithms can be used are as follows:
• Fraud Detection
• Face detection
• Natural language processing
• Market Segmentation
• Text Categorization
• Bioinformatics
15. What are parametric models and Non-Parametric models?
Answer:
Parametric models are those with a finite number of parameters and to predict new data, you only need to know the parameters of the model.
Non Parametric models are those with an unbounded number of parameters, allowing for more flexibility and to predict new data, you need to know the parameters of the model and the state of the data that has been observed.
16. What are the three stages to build the hypotheses or model in machine learning?
Answer:
This is the frequently asked Machine Learning Interview Questions in an interview. The three stages to build the hypotheses or model in machine learning are:
1. Model building
2. Model testing
3. Applying the model
17. What is Inductive Logic Programming in Machine Learning (ILP)?
Answer:
Inductive Logic Programming (ILP) is a subfield of machine learning which uses logical programming representing background knowledge and examples.
18. What is the difference between classification and regression?
Answer:
The difference between classification and regression are as follows:
• Classification is about identifying group membership while regression technique involves predicting a response.
• Both the techniques are related to prediction
• Classification predicts the belonging to a class whereas regression predicts the value from a continuous set
• Regression is not preferred when the results of the model need to return the belongingness of data points in a dataset with specific explicit categories
19. What is the difference between inductive machine learning and deductive machine learning?
Answer:
The difference between inductive machine learning and deductive machine learning are as follows:
machine learning where the model learns by examples from a set of observed instances to draw a generalized conclusion whereas in deductive learning the model first draws the conclusion and then the conclusion is drawn.
20. What are the advantages decision trees?
Answer:
The advantages decision trees are:
• Decision trees are easy to interpret
• Nonparametric
• There are relatively few parameters to tune
Answer:
The area of machine learning which focuses on deep artificial neural networks which are loosely inspired by brains. Alexey Grigorevich Ivakhnenko published the first general on working Deep Learning network. Today it has its application in various fields such as computer vision, speech recognition, natural language processing.
2. Why are deep networks better than shallow ones?
Answer:
There are studies which say that both shallow and deep networks can fit at any function, but as deep networks have several hidden layers often of different types so they are able to build or extract better features than shallow models with fewer parameters.
3. What is a cost function?
Answer:
A cost function is a measure of the accuracy of the neural network with respect to given training sample and expected output. It is a single value, nonvector as it gives the performance of the neural network as a whole. It can be calculated as below Mean Squared Error function:-
MSE=1n∑i=0n(Y^i–Yi)²
Where Y^ and desired value Y is what we want to minimize.
4. What is a gradient descent?
Answer:
Gradient descent is basically an optimization algorithm, which is used to learn the value of parameters that minimizes the cost function. Furthermore, It is an iterative algorithm which moves in the direction of steepest descent as defined by the negative of the gradient. We compute the gradient descent of the cost function for a given parameter and update the parameter by the below formula:-
Θ:=Θ–αd∂ΘJ(Θ)
Where Θ — is the parameter vector, α — learning rate, J(Θ) — is a cost function.
5. What is a backpropagation?
Answer:
Backpropagation is a training algorithm used for multilayer neural network. In this method, we move the error from an end of the network to all weights inside the network and thus allowing efficient computation of the gradient. It consists of several steps as follows:-
Forward propagation of training data in order to generate output.
Then using the target value and output value error derivative can be computed with respect to output activation.
Then we back propagate for computing derivative of error with respect to output activation on previous and continue this for all the hidden layers.
Using previously calculated derivatives for output and all hidden layers we calculate error derivatives with respect to weights.
And then we update the weights.
6. Explain the following three variants of gradient descent: batch, stochastic and mini-batch?
Answer:
Stochastic Gradient Descent: Here we use only single training example for calculation of gradient and update parameters.
Batch Gradient Descent: Here we calculate the gradient for the whole dataset and perform the update at each iteration.
Mini-batch Gradient Descent: It’s one of the most popular optimization algorithms. It’s a variant of Stochastic Gradient Descent and here instead of single training example, mini-batch of samples is used.
7. What are the benefits of mini-batch gradient descent?
Answer:
Below are the benefits of mini-batch gradient descent
•This is more efficient compared to stochastic gradient descent.
•The generalization by finding the flat minima.
•Mini-batches allows help to approximate the gradient of the entire training set which helps us to avoid local minima.
8. What is data normalization and why do we need it?
Answer:
Data normalization is used during backpropagation. The main motive behind data normalization is to reduce or eliminate data redundancy. Here we rescale values to fit into a specific range to achieve better convergence.
9. What is weight initialization in neural networks?
Answer:
Weight initialization is one of the very important steps. A bad weight initialization can prevent a network from learning but good weight initialization helps in giving a quicker convergence and a better overall error. Biases can be generally initialized to zero. The rule for setting the weights is to be close to zero without being too small.
10. What is an auto-encoder?
Answer:
An autoencoder is an autonomous Machine learning algorithm that uses backpropagation principle, where the target values are set to be equal to the inputs provided. Internally, it has a hidden layer that describes a code used to represent the input.
Some Key Facts about the autoencoder are as follows:-
•It is an unsupervised ML algorithm similar to Principal Component Analysis
•Minimizes the same objective function as Principal Component Analysis
•It is a neural network
•The neural network’s target output is its input
11. Is it OK to connect from a Layer 4 output back to a Layer 2 input?
Answer:
Yes, this can be done considering that layer 4 output is from previous time step like in RNN. Also, we need to assume that previous input batch is sometimes- correlated with the current batch.
12. What is a Boltzmann Machine?
Answer:
Boltzmann Machine is a method to optimize the solution of a problem. The work of the Boltzmann machine is basically to optimize the weights and the quantity for the given problem.
Some important points about Boltzmann Machine −
•It uses recurrent structure.
•Consists of stochastic neurons, which consist one of the two possible states, either 1 or 0.
•The neurons in this are either in adaptive (free state) or clamped (frozen state).
•If we apply simulated annealing on discrete Hopfield network, then it would become Boltzmann Machine.
13. What is the role of the activation function?
Answer:
The activation function is a method to introduce non-linearity into the neural network helping it to learn more complex function. Furthermore, without which the neural network would be only able to learn linear function which is a linear combination of its input data.
Follow this link if you are looking forward to becoming an AI expert
It is the perfect time to move ahead of the curve and position yourself with the skills needed to fill these emerging gaps in data science and analysis. Most importantly, this is not only for people who are at the very beginning of their careers and who decide on the path to study. Hence, professionals already in the workforce can benefit from this data science trend, perhaps even more than their fresh counterparts.
In a 2017 business research article IBM predicted that the need for Data Scientists will increase by 28% by 2020, with nearly 3 million job openings for Data Science professionals. According to a Forbes report, Data Science is the best job in America for three consecutive years, with a median base salary of $110,000 and over 4,524 job openings.
According to Glassdoor’s 50 Best Jobs In America For 2018 research, Data Scientist jobs are among the 50 best jobs based on each job’s overall Glassdoor Job Score. We calculate the Glassdoor Job Score by weighing three key factors equally: earning potential based on the median annual base salary, job satisfaction rating, and the number of job openings. Hence, the need for sharpening Data Scientist skills are at an all-time high.
In this blog, we will be looking at all the technical and non-technical skills that are absolute in mastering the domain of data science.
Technical Skills
R & Python
R is a language for statistical computations, data analysis and graphical representation of data. It is a very popular language in academia. Many researchers and scholars use it for experimenting with data science. Many popular books and learning resources on data science use R for statistical analysis as well. Also, it has an extensive library of tools for database manipulation and wrangling. Data visualization is the visual representation of data in graphical form. This allows analyzing data from angles which are not clear in unorganized or tabulated data. R has many tools that can help in data visualization, analysis, and representation. The packages ggplot2 and ggedit for have become the standard plotting packages. Also, It allows practising a wide variety of statistical and graphical techniques like time-series analysis, classification, classical statistical tests, clustering, etc.
When it comes to data science, Python is a very powerful tool, which is also open sourced and flexible, adding more to its popularity. It has massive libraries for manipulation of data and is extremely easy to learn and use for all data analysts. Anyone who is familiar with programming languages such as, Java, Visual Basic, C++ or C, will find this tool to be very accessible and easy to work with. Apart from being an independent platform, this tool has the ability to easily integrate with the existing Infrastructure and can also solve the most difficult of problems. This tool is powerful, friendly, easy and plays well with others, apart from running everywhere. A lot of banks use this tool for the purpose of crunching data, some institutions use it for analyzing and visualization. This tool offers the great benefit of using one programming language, across multiple application platforms.
Python has already been proven to be as good as R Programming is, in terms of all the process under data analytics. Any novice, entering the field of data analytics can use this programming language to start in the data science industry. As a result of its multipurpose uses, there are a lot of institutes, which offer courses in Python.
Hadoop
Hadoop is an open-source software framework that provides for processing of large data sets across clusters of computers using simple programming models. It can scale up from single servers to thousands of machines.
Hadoop grew out of an open-source search engine called Nutch, developed by Doug Cutting and Mike Cafarella. Back in the early days of the Internet, the pair were looking forward to inventing a way to return web search results faster by distributing data and calculations across different computers so multiple tasks could execute at the same time.
It has a lot to offer. Benefits are :
Computing power: Hadoop’s distributed computing model allows it to process huge amounts of data. The more nodes you use, the more processing power you have.
Flexibility: Hadoop stores data without requiring any preprocessing. Store data — even unstructured data such as text, images, and video — now; decide what to do with it later.
Fault tolerance: Hadoop automatically stores multiple copies of all data, and if one node fails during data processing, jobs are redirected to other nodes and distributed computing continues.
Low cost: The open-source framework is free, and data is stored on commodity hardware.
Scalability: You can easily grow your Hadoop system, simply by adding more nodes.
Although the development of Hadoop came from the need to search millions of web pages and return relevant results, it today serves a variety of purposes. Hadoop’s low-cost storage makes it an appealing option for storing information that is not currently critical but that might be analyzed later.
Spark
Hadoop continues to garner the most name-recognition in big data processing, but Spark is, appropriately, beginning to ignite it’s utility as a vehicle for data analysis and processing, versus simply data storage.
It consists of four core components:
Hadoop Common — Essential utilities and tools referenced by the other modules
Distributed File System — The high-throughput file storage system (HDFS)
Hadoop YARN — The job-scheduling framework for distributed process allocation
MapReduce — The parallel processing module based on YARN
Spark replaces only two of those, YARN and MapReduce. According to a February 2016 article in Information Week, many Spark implementations chug happily away on top of Hadoop Common code and the HDFS. Thanks to the integration, many major companies that have implemented Hadoop clusters to deal with insane amounts of data — the likes of Amazon and Facebook — have kept the data storage elements and simply swapped in Spark as a high-performance alternative to MapReduce.
SQL
SQL, or Structured Query Language, is a special-purpose programming language for managing data held in relational database management systems. Almost all structured data resides in such databases, so, if you want to play with data, chances are you’ll want to know some SQL.
Here are some awesome things you can do with SQL
Generate queries from a query: Basic string concatenation makes it easy to generate en masse queries that use data in a database to fetch data found in another system.
Handle dates: “Fantastic date functions” exist to meet all your formatting and type conversion needs.
Text mining: Yhat recommends going as far as you can with SQL’s built-in string functions before turning to a scripting language.
Find the median: Since there’s no built-in aggregate function for median, Yhat provides the code.
Load data into your database with the \COPY command.
Generate sequences: Use the generate_series function to create ranges of dates and times and to handle time series and funnels.
Machine Learning
Simply put, Machine Learning is the core subarea of artificial intelligence. It makes computers get into a self-learning mode without explicit programming. When fed new data, these computers learn, grow, change, and develop by themselves.
The machine learning field is constantly evolving. And along with evolution comes a rise in the demand and importance. There is one crucial reason why data scientists need machine learning, and that is: ‘High-value predictions that can guide better decisions and smart actions in real time without human intervention’.
Machine learning as a technology helps analyze large chunks of data, easing the tasks of data scientists in an automated process and is gaining a lot of prominence and recognition. Machine learning has changed the way data extraction and interpretation works by involving automatic sets of generic methods that have replaced the traditional statistical techniques.
Non-Technical Skills
Now, the skill set of a successful data scientist will comprise both technical and non-technical skills. While technical skills like programming and quantitative analysis are important, it is easy to undervalue the impact of non-technical skills. So, before we go on to the technical stuff, here is a list of 5 non-technical skills that you must possess:
Communication
Effective business communication is one of the most important abilities. Whether it’s understanding the business requirements or the problem at hand, seeking more data from stakeholders or communicating insights, a data scientist needs to be convincing. ” Storytelling, ” as data scientists call it, means that analytical solutions are communicated in a clear, concise and timely manner in order to benefit both technical and non-technical people. Data visualization and presentation tools are widely employed by data scientists for their graphic appeal and easy absorption by all teams in the organization. Often underestimated, this is one of the most important skills for the simple reason that all statistical computation is useless if the teams can’t act upon it.
Data-Driven Decision Making
A data scientist will not conclude, judge, or decide without adequate data. Scientists need to decide their approach to a business problem in addition to deciding several other things like where to look, what tools and techniques to use, and how to visualize and communicate it in the most effective possible way. The most important thing for them is to ask relevant questions, even if they seem far-fetched. Think of it as a child exploring all his surroundings to draw conclusions. A data scientist is pretty much the same.
Mathematical and Statistical Acumen
A data scientist will never thrive if he/she doesn’t understand what test to run when and how to interpret their findings. They need a solid understanding of algebra and calculus. In good old days, Math was a subject based on common sense and the need to resolve basic problems based on logic. This hasn’t changed much, though the scale has blown up exponentially. A statistical sensibility provides a solid foundation for several analysis tools and techniques, which are used by a data scientist to build their models and analytic routines.
Teamwork
Teamwork is another feather in the cap that data scientists can not do without. Although they may appear to be able to work in isolation, they are closely involved in the organization at various levels. On the one hand, they will have to work with the teams to understand their requirements, collect feedback to achieve beneficial solutions, and on the other hand work with data scientists, data architects and data engineers to perform their tasks well. The culture in a data-driven organization will never be that of the data science team working in isolation; instead, the team will have to use the same characteristics across the organization to make the best use of the insights they draw from various departments.
Intellectual Curiosity and Passion
This is a tad-bit cliched but true. Data scientists are passionate about their work and have an inconsolable itch to use data to find patterns and provide solutions to business problems. They often have to work with unstructured data and rarely know the exact steps they need to take to find valuable insights that lead to business growth. Sometimes, they don’t even have a clear problem to work with, just signs that there is something wrong. That’s where their intellectual curiosity guides them to look in areas no one else has looked in. You don’t need to read “How to think like Sherlock,” just ask a data scientist!
Conclusion
The next question I always get is, “What can I do to develop these skills?” There are many resources around the web, but I don’t want to give anyone the mistaken impression that the path to data science is as simple as taking a few MOOCs. Unless you already have a strong quantitative background, the road to becoming a data scientist will be challenging but not impossible.
However, if it’s something you sincerely want to pursue and have a passion for data and lifelong learning, don’t let your background discourage you from pursuing data science as a career.
Never thought that online trading could be so helpful because of so many scammers online until I met Miss Judith... Philpot who changed my life and that of my family. I invested $1000 and got $7,000 Within a week. she is an expert and also proven to be trustworthy and reliable. Contact her via: Whatsapp: +17327126738 Email:judithphilpot220@gmail.comread more
A very big thank you to you all sharing her good work as an expert in crypto and forex trade option. Thanks for... everything you have done for me, I trusted her and she delivered as promised. Investing $500 and got a profit of $5,500 in 7 working days, with her great skill in mining and trading in my wallet.
judith Philpot company line:... WhatsApp:+17327126738 Email:Judithphilpot220@gmail.comread more
Faculty knowledge is good but they didn't cover most of the topics which was mentioned in curriculum during online... session. Instead they provided recorded session for those.read more
Dimensionless is great place for you to begin exploring Data science under the guidance of experts. Both Himanshu and... Kushagra sir are excellent teachers as well as mentors,always available to help students and so are the HR and the faulty.Apart from the class timings as well, they have always made time to help and coach with any queries.I thank Dimensionless for helping me get a good starting point in Data science.read more
My experience with the data science course at Dimensionless has been extremely positive. The course was effectively... structured . The instructors were passionate and attentive to all students at every live sessions. I could balance the missed live sessions with recorded ones. I have greatly enjoyed the class and would highly recommend it to my friends and peers.
Special thanks to the entire team for all the personal attention they provide to query of each and every student.read more
It has been a great experience with Dimensionless . Especially from the support team , once you get enrolled , you... don't need to worry about anything , they keep updating each and everything. Teaching staffs are very supportive , even you don't know any thing you can ask without any hesitation and they are always ready to guide . Definitely it is a very good place to boost careerread more
The training experience has been really good! Specially the support after training!! HR team is really good. They keep... you posted on all the openings regularly since the time you join the course!! Overall a good experience!!read more
Dimensionless is the place where you can become a hero from zero in Data Science Field. I really would recommend to all... my fellow mates. The timings are proper, the teaching is awsome,the teachers are well my mentors now. All inclusive I would say that Kush Sir, Himanshu sir and Pranali Mam are the real backbones of Data Science Course who could teach you so well that even a person from non- Math background can learn it. The course material is the bonus of this course and also you will be getting the recordings of every session. I learnt a lot about data science and Now I find it easy because of these wonderful faculty who taught me. Also you will get the good placement assistance as well as resume bulding guidance from Venu Mam. I am glad that I joined dimensionless and also looking forward to start my journey in data science field. I want to thank Dimensionless because of their hard work and Presence it made it easy for me to restart my career. Thank you so much to all the Teachers in Dimensionless !read more
Dimensionless has great teaching staff they not only cover each and every topic but makes sure that every student gets... the topic crystal clear. They never hesitate to repeat same topic and if someone is still confused on it then special doubt clearing sessions are organised. HR is constantly busy sending us new openings in multiple companies from fresher to Experienced. I would really thank all the dimensionless team for showing such support and consistency in every thing.read more
I had great learning experience with Dimensionless. I am suggesting Dimensionless because of its great mentors... specially Kushagra and Himanshu. they don't move to next topic without clearing the concept.read more
My experience with Dimensionless has been very good. All the topics are very well taught and in-depth concepts are... covered. The best thing is that you can resolve your doubts quickly as its a live one on one teaching. The trainers are very friendly and make sure everyone's doubts are cleared. In fact, they have always happily helped me with my issues even though my course is completed.read more
I would highly recommend dimensionless as course design & coaches start from basics and provide you with a real-life... case study. Most important is efforts by all trainers to resolve every doubts and support helps make difficult topics easy..read more
Dimensionless is great platform to kick start your Data Science Studies. Even if you are not having programming skills... you will able to learn all the required skills in this class.All the faculties are well experienced which helped me alot. I would like to thanks Himanshu, Pranali , Kush for your great support. Thanks to Venu as well for sharing videos on timely basis...😊
I highly recommend dimensionless for data science training and I have also been completed my training in data science... with dimensionless. Dimensionless trainer have very good, highly skilled and excellent approach. I will convey all the best for their good work. Regards Avneetread more
After a thinking a lot finally I joined here in Dimensionless for DataScience course. The instructors are experienced &... friendly in nature. They listen patiently & care for each & every students's doubts & clarify those with day-to-day life examples. The course contents are good & the presentation skills are commendable. From a student's perspective they do not leave any concept untouched. The step by step approach of presenting is making a difficult concept easier. Both Himanshu & Kush are masters of presenting tough concepts as easy as possible. I would like to thank all instructors: Himanshu, Kush & Pranali.read more
When I start thinking about to learn Data Science, I was trying to find a course which can me a solid understanding of... Statistics and the Math behind ML algorithms. Then I have come across Dimensionless, I had a demo and went through all my Q&A, course curriculum and it has given me enough confidence to get started. I have been taught statistics by Kush and ML from Himanshu, I can confidently say the kind of stuff they deliver is In depth and with ease of understanding!read more
If you love playing with data & looking for a career change in Data science field ,then Dimensionless is the best... platform . It was a wonderful learning experience at dimensionless. The course contents are very well structured which covers from very basics to hardcore . Sessions are very interactive & every doubts were taken care of. Both the instructors Himanshu & kushagra are highly skilled, experienced,very patient & tries to explain the underlying concept in depth with n number of examples. Solving a number of case studies from different domains provides hands-on experience & will boost your confidence. Last but not the least HR staff (Venu) is very supportive & also helps in building your CV according to prior experience and industry requirements. I would love to be back here whenever i need any training in Data science further.read more
It was great learning experience with statistical machine learning using R and python. I had taken courses from... Coursera in past but attention to details on each concept along with hands on during live meeting no one can beat the dimensionless team.read more
I would say power packed content on Data Science through R and Python. If you aspire to indulge in these newer... technologies, you have come at right place. The faculties have real life industry experience, IIT grads, uses new technologies to give you classroom like experience. The whole team is highly motivated and they go extra mile to make your journey easier. I’m glad that I was introduced to this team one of my friends and I further highly recommend to all the aspiring Data Scientists.read more
It was an awesome experience while learning data science and machine learning concepts from dimensionless. The course... contents are very good and covers all the requirements for a data science course. Both the trainers Himanshu and Kushagra are excellent and pays personal attention to everyone in the session. thanks alot !!read more
Had a great experience with dimensionless.!! I attended the Data science with R course, and to my finding this... course is very well structured and covers all concepts and theories that form the base to step into a data science career. Infact better than most of the MOOCs. Excellent and dedicated faculties to guide you through the course and answer all your queries, and providing individual attention as much as possible.(which is really good). Also weekly assignments and its discussion helps a lot in understanding the concepts. Overall a great place to seek guidance and embark your journey towards data science.read more
Excellent study material and tutorials. The tutors knowledge of subjects are exceptional. The most effective part... of curriculum was impressive teaching style especially that of Himanshu. I would like to extend my thanks to Venu, who is very responsible in her jobread more
It was a very good experience learning Data Science with Dimensionless. The classes were very interactive and every... query/doubts of students were taken care of. Course structure had been framed in a very structured manner. Both the trainers possess in-depth knowledge of data science dimain with excellent teaching skills. The case studies given are from different domains so that we get all round exposure to use analytics in various fields. One of the best thing was other support(HR) staff available 24/7 to listen and help.I recommend data Science course from Dimensionless.read more
I was a part of 'Data Science using R' course. Overall experience was great and concepts of Machine Learning with R... were covered beautifully. The style of teaching of Himanshu and Kush was quite good and all topics were generally explained by giving some real world examples. The assignments and case studies were challenging and will give you exposure to the type of projects that Analytics companies actually work upon. Overall experience has been great and I would like to thank the entire Dimensionless team for helping me throughout this course. Best wishes for the future.read more
It was a great experience leaning data Science with Dimensionless .Online and interactive classes makes it easy to... learn inspite of busy schedule. Faculty were truly remarkable and support services to adhere queries and concerns were also very quick. Himanshu and Kush have tremendous knowledge of data science and have excellent teaching skills and are problem solving..Help in interviews preparations and Resume building...Overall a great learning platform. HR is excellent and very interactive. Everytime available over phone call, whatsapp, mails... Shares lots of job opportunities on the daily bases... guidance on resume building, interviews, jobs, companies!!!! They are just excellent!!!!! I would recommend everyone to learn Data science from Dimensionless only 😊read more
Being a part of IT industry for nearly 10 years, I have come across many trainings, organized internally or externally,... but I never had the trainers like Dimensionless has provided. Their pure dedication and diligence really hard to find. The kind of knowledge they possess is imperative. Sometimes trainers do have knowledge but they lack in explaining them. Dimensionless Trainers can give you ‘N’ number of examples to explain each and every small topic, which shows their amazing teaching skills and In-Depth knowledge of the subject. Himanshu and Kush provides you the personal touch whenever you need. They always listen to your problems and try to resolve them devotionally.
I am glad to be a part of Dimensionless and will always come back whenever I need any specific training in Data Science. I recommend this to everyone who is looking for Data Science career as an alternative.
All the best guys, wish you all the success!!read more
