Learn Data Science with the Best Available Free Courses Online

Learn Data Science with the Best Available Free Courses Online

Data Scientist Training Free of Charge

Now, in theory, it is possible to become a data scientist, without paying a dime. What we want to do in this article is to list out the best of the best options to learn what you need to know to become a data scientist. Many articles offer 4-5 courses under each heading. What I have done is to search through the Internet covering all free courses and choose the single best course for each topic.

These courses have been carefully curated and offer the best possible option if you’re learning for free. However – there’s a caveat. An interesting twist to this entire story.  Interested? Read on! And please – make sure you complete the full article.

Topics For A Data Scientist Course

The basic topics that a data scientist needs to know are:

  1. Machine Learning Theory and Applications
  2. Python Programming
  3. R Programming
  4. SQL
  5. Statistics & Probability
  6. Linear Algebra
  7. Calculus Basics (short)
  8. Machine Learning in Python
  9. Machine Learning in R
  10. Tableau

So let’s get to it. Here is the list of the best possible options to learn every one of these topics, carefully selected and curated.

 

Machine Learning – Stanford University – Andrew Ng (audit option)

Machine Learning Course From Stanford University

Machine learning course

The world-famous course for machine learning with the highest rating of all the MOOCs in Coursera, from Andrew Ng, a giant in the ML field and now famous worldwide as an online instructor. Uses MATLAB/Octave. From the website:

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include:

(i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks)

(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning)

(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)

The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

This course is extremely effective and has many benefits. However, you will need high levels of self-discipline and self-motivation. Statistics show that 90% of those who sign up for a MOOC without a classroom or group environment never complete the course.

 

Learn Python The Hard Way – Zed Shaw – Free Online Access

 

learn python

 

Learn Python The Hard Way Online Access

You may ask me, why do I want to learn the hard way? Shouldn’t we learn the smart way and not the hard way? Don’t worry. This ebook, online course, and web site is a highly popular way to learn Python. Ok,  so it says the hard way. Well, the only way to learn how to code is to practice what you have learned. This course integrates practice with learning. Other Python books you have to take the initiative to practice.

Here, this book shows you what to practice, how to practice. There is only one con here – although this is the best self-driven method, most people will not complete all of it. The main reason is that there is no external instructor for supervision and a group environment to motivate you. However, if you want to learn Python by yourself, then this is the best way. But not the optimal one, as you will see at the end of this article since the cost of the book is 30$ USD (2100 INR approx).

Interactive R and Data Science Programming – SwiRl

Interactive R and Data Science Course (In Console)

 

interactive R programming course Swirl

 

Swirlstats is a wonderful tool to learn R and data science scripting in R interactively and intuitively by teaching you R commands from within the R console. This might seem like a very simple tool, but as you use it, you will notice its elegance in teaching you literally how to express yourselves in R and the finer nuances of the language and integration with the console and tidyverse. This is a powerful method of learning R and what is more, it is also a lot of fun!

Descriptive and Inferential Statistics

Course on Statistics and Probability from KhanAcademy

 

khanacademy's profile picture

KhanAcademy is a free non-profit organization on a mission – they want to provide a world-class education to you regardless of where you may be in the world. And they’re doing a fantastic job! This course has been covered in several very high profile blogs and Quora posts as the best online course for statistics – period. What is more, it is extremely high quality and suitable for beginners –  and – free! This organization is doing wonderful work. More power to them!

Mathematics for Data Science

Now the basic mathematics for data science content includes linear algebra, single-variable, discrete mathematics, and multivariable calculus (selected topics) and basics of differential equations.  Now you could take all of these topics separately in KhanAcademy and that is a good option for Linear Algebra and Multivariate Calculus (in addition to Statistics and Probability).

For Linear Algebra, the link of what you need to know given in a course in KhanAcademy is given below:

Course on Linear Algebra From KhanAcademy

Course view with khan academy

 

For Multivariate Calculus

Course on MultiVariate Calculus From KhanAcademy

Mutlivariate calcus from khan academy

These courses are completely free and very accessible to beginners.

Discrete Mathematics

This topic deserves a section to itself because discrete mathematics is the foundation of all computer science. There are a variety of options available to learn discrete mathematics, from ebooks to MOOCs, but today, we’ll focus on the best possible option. MIT (Massachusetts Institute of Technology) is known as one of the best colleges in the world and they have an Open information initiative known as MIT OpenCourseWare (MIT OCW). These are actual videos of the lectures taken by the students at one of the best engineering colleges in the world. You will benefit a lot if you follow the lectures at this link, they give all the basic concepts as clearly as possible. It’s a bit technical because this is open mostly for students at an advanced level. The link is given below:

MIT OpenCourseWare Course: Mathematics for Computer Science

Image result for MIT OCW logo

For beginners, one slightly less technical option is the following course:

Course on Discrete Mathematics for Computer Science

It is also technical and from MIT but might be a little more accessible than the earlier option.

SQL

SQL (see-quel) or Structured Query Language is a must-learn if you are a data scientist. You will be working with a lot of databases, and SQL is the language used to access and generate data from database systems like Oracle and Microsoft SQL Server. The best free course I could find online is undoubtedly the one below:

Udemy Course for SQL Beginners

 

SQL for Newcomers - A Crash Course

SQL For Newcomers – A Free Crash Course from Udemy.com.

5 hours-plus of every SQL command and concept you need to know. And – completely free.

Machine Learning with Scikit-Learn

 

logo for Scikit

scikit learning course

 

Scikit-Learn Online Documentation Main Page

We have covered Python, R, Machine Learning using MATLAB, Data Science with R (SwiRl teaches data science as well), Statistics, Probability, Linear Algebra, and Basic Calculus. Now we just need to get a course for Data Science with Python, and we are done! Now I looked at many options but was not satisfied. So instead of a course, I have provided you with a link to the scikit-learn documentation. Why?

Because that’s as good as an online course by itself. If you read through the main sections, get the code (Ctrl-X, Ctrl-V) and execute it in an Anaconda environment, and then play around with it, experiment, and observe and read up on what every line does, you will already know who to solve standard textbook problems. I recommend the following order:

  1. Classification
  2. Regression
  3. Clustering
  4. Preprocessing
  5. Model Evaluation
  6. 5 classification examples (execute)
  7. 5 regression examples (run them)
  8. 5 clustering examples (ditto)
  9. 6 sample preprocessing functions
  10. Dimensionality Reduction
  11. Model Selection
  12. Hyperparameter Tuning

Machine Learning with R

 

Logo for Oreilly's R for Dsta Science course

 

Online Documentation for Machine Learning in R with Tidyverse

This book is free to learn online. Get the data files, get the script files, use RStudio, and just as with Python, play, enjoy, experiment, execute, and explore. A little hard work will have you up and running with R in no time! But make sure you try as many code examples as possible. The libraries you can focus on are:

  1. dplyr (data manipulation)
  2. tidyr (data preprocessing “tidying”)
  3. ggplot2 (graphical package)
  4. purrr (functional toolkit)
  5. readr (reading rectangular data files easily)
  6. stringr (string manipulation)
  7. tibble (dataframes)

Tableau

To make it short, simple, and sweet, since we have already covered SQL and this content is for beginners, I recommend the following course:

Udemy Course on Tableau for Beginners

This is a course on Udemy rated 4.2/5 and completely free. You will learn everything you need to work with Tableau (the most commonly used corporate-level visualization tool). This is an extremely important part of your skill set. You can make all the greatest analyses, but if you don’t visualize them and do it well, management will never buy into your machine learning solution, and neither will anyone who doesn’t know the technical details of ML (which is a large set of people on this planet). Visualization is important. Please make sure to learn the basics (at least!) of Tableau.

Tableau course image

From Unsplash

 

Kaggle Micro-Courses (Add-Ons – Short Concise Tutorials)

Kaggle Micro-Courses (from www.kaggle.com!)

Kaggle Micro-Courses (from www.kaggle.com!)

 

Kaggle Learn Home Page

Kaggle is a wonderful site to practice your data science skills, but recently, they have added a set of hands-on courses to learn data science practicals. And, if I do say, so myself, it’s brilliant. Very nicely presented, superb examples, clear and concise explanations. And of course, you will cover more than we discussed earlier. Please, if you read through all the courses discussed so far in this article, and if you do just the courses at Kaggle.com, you will have spent your time wisely (though not optimally – as we shall see).

Kaggle Learn

Kaggle Learn

Dimensionless Technologies

 

Dimensonless technologies logo

Dimensionless Technologies

 

Now, if you are reading this article, you might have a fundamental question. This is a blog of a company that offers courses in data science, deep learning, and cloud computing. Why would we want to list all our competitors and publish it on our site? Isn’t that negative publicity?

Quite the opposite. 

This is the caveat we were talking about.

Our course is a better solution than every single option given above!

We have nothing to hide.

And we have an absolutely brilliant top-class product.

Every option given above is a separate course by itself.

And they all suffer from a very prickly problem – you need to have excellent levels of discipline and self-motivation to complete just one of the courses above – let alone all ten.

 

You also have no classroom environment, no guidance for doubts and questions, and you need to know the basics about programming.

Our product is the most cost-effective option in the market for learning data science, as well as the most effective methodology for everyone – every course is conducted live in a classroom environment from the comfort of your home. You can work at a standard job, spend two hours on the internet every day, do extra work and reading on weekends, and become a professional data scientist in 6 months time.

We also have personalized GitHub project portfolio creation, management, and faculty guidance. Not to mention individual attention for each student.

And IITians for faculty who also happen to have 9+ years of industry experience.

So when we say that our product is the best on the market, we really mean it. Because of the live session teaching of the classes, which no other option on the Internet today has.

 

Am I kidding? Absolutely not. And you can get started with Dimensionless Technologies Data Science with Python and R course for just 70-odd USD. Which is the most cost-effective option on the market!

And unlike all the 10 courses and resources detailed above, instead of doing 10 courses, you just need to do one single course, with the extracted meat of all that you need to know as a data scientist. And yes, we cover:

  1. Machine Learning
  2. Python Programming
  3. R Programming
  4. SQL
  5. Statistics & Probability
  6. Linear Algebra
  7. Calculus Basics
  8. Machine Learning in Python
  9. Machine Learning in R
  10. Tableau
  11. GitHub Personal Project Portfolio Creation
  12. Live Remote Daily Sessions
  13. Experts with Industrial Experience
  14. A Classroom Environment (to keep you motivated)
  15. Individual Attention to Every Student

I hope this information has you seriously interested. Please sign up for the course – you will not regret it.

And we even have a two-week trial for you to experience the course for yourself.

Choose wisely and optimally.

Unleash the data scientist within!

 

An excellent general article on emerging state-of-the-art technology, AI, and blockchain:

The Exciting Future with Blockchain and Artificial Intelligence

For more on data science, check out our blog:

Blog

And of course, enjoy machine learning!

7 Technical Concept Every Data Science Beginner Should Know

7 Technical Concept Every Data Science Beginner Should Know

Welcome to Data Science!

 

So you want to learn data science but you don’t know where to start? Or you are a beginner and you want to learn the basic concepts? Welcome to your new career and your new life! You will discover a lot of things on your journey to becoming a data scientist and being part of a new revolution. I am a firm believer that you can learn data science and become a data scientist regardless of your age, your background, your current knowledge level, your gender, and your current position in life. I believe – from experience – that anyone can learn anything at any stage in their lives. What is required is just determination, persistence, and a tireless commitment to hard work. Nothing else matters as far as learning new things – or learning data science – is concerned. Your commitment, persistence, and your investment in your available daily time is enough.

I hope you understood my statement. Anyone can learn data science if you have the right motivation. In fact, I believe anyone can learn anything at any stage in their lives, if they invest enough time, effort and hard work into it, along with your current occupation. From my experience, I strongly recommend that you continue your day job and work on data science as a side hustle, because of the hard work that will be involved. Your commitment is more important than your current life situation. Carrying on a full-time job and working on data science part-time is the best way to go if you want to learn in the best possible manner.

 

Technical Concepts of Data Science

So what are the important concepts of data science that you should know as a beginner? They are, in order of sequential learning, the following:

  1. Python Programming
  2. R Programming
  3. Statistics & Probability
  4. Linear Algebra
  5. Data Preparation and Data ETL*
  6. Machine Learning with Python and R
  7. Data Visualization and Summary

*Extraction, Transformation, and Loading

Now if you were to look at the above list an go to a library, you would, most likely, come back with 9-10 books at an average of 1000 pages each. Even if you could speed-read, 10,000 pages is a lot to get through. I could list the best books for each topic in this post, but even the most seasoned reader would balk at 10,000 pages. And who reads books these days? So what I am going to give you is a distilled extract on each of those topics. Keep in mind, however, that every topic given above could be a series of blog posts in its own right, and these 80-word paragraphs are just a tiny taste of each topic and there is an ocean of depth involved in every topic. You might ask if that is the case, how can everybody be a possible candidate for data scientist role? Two words: Persistence and Motivation. With the right amount of these two characteristics, anyone can be anything they want to be.

 

1) Python Programming:

Python is one of the most popular programming languages in the world. It is the ABC of data science because Python is the language every beginner starts with on data science. It is universally used for any purposes since it is so amazingly versatile. Python can be used for web applications and websites with Django, microservices with Flask, general programming projects with the standard library from PyPI, GUIs with PyQt5 or Tkinter, Interoperability with Jython (Java), Cython (C) and nearly other programming language are available today.

Of course, Python is the also first language used for data science with the standard stack of scikit-learn (machine learning), pandas (data manipulation), matplotlib and seaborn (visualization) and numpy (vectorized computation). Nowadays, the most common technology used is the Anaconda distribution, available from www.anaconda.com. Current version 2018.12 or Anaconda Distribution 5. To learn more about Python, I strongly recommend the following books: Head First Python and the Python Cookbook.

 

2) R Programming

R is The Best Language for statistical needs since it is a language designed by statisticians, for statisticians. If you know statistics and mathematics well, you will enjoy programming in R. The language gives you the best support available for every probability distribution, statistics functions, mathematical functions, plotting, visualization, interoperability, and even machine learning and AI. In fact, everything that you can do in Python can be done in R. R is the second most popular language for data science in the world, second only to Python. R has a rich ecosystem for every data science requirement and is the favorite language of academicians and researchers in the academic domain.

Learning Python is not enough to be a professional data scientist. You need to know R as well. A good book to start with is R For Data Science, available at Amazon at a very reasonable price. Some of the most popular packages in R that you need to know are ggplot2, ThreeJS, DT (tables), network3D, and leaflet for visualization, dplyr and tidyr for data manipulation, shiny and R Markdown for reporting, parallel, Rcpp and data.table for high performance computing and caret, glmnet, and randomForest for machine learning.

 

3)  Statistics and Probability

This is the bread and butter of every data scientist. The best programming skills in the world will be useless without knowledge of statistics. You need to master statistics, especially practical knowledge as used in a scientific experimental analysis. There is a lot to cover. Any subtopic given below can be a blog-post in its own right. Some of the more important areas that a data scientist needs to master are:

  1. Analysis of Experiments
  2. Tests of Significance
  3. Confidence Intervals
  4. Probability Distributions
  5. Sampling Theory
  6. Central Limit Theorem
  7. Bell Curve
  8. Dimensionality Reduction
  9. Bayesian Statistics

Some places on the Internet to learn Statistics from are the MIT OpenCourseWare page Introduction to Statistics and Probability, and the Khan Academy Statistics and Probability Course. Good books to learn statistics: Naked Statistics, by Charles Wheelan which is an awesome comic-like but highly insightful book which can be read enjoyably by anyone including those from non-technical backgrounds and Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce.

 

4) Linear Algebra

Succinctly, linear algebra is about vectors, matrices and the operations that can be performed on vectors and matrices. This is a fundamental area for data science since every operation we do as a data scientist has a linear algebra background, or, as data scientists, we usually work with collections of vectors or matrices. So we have the following topics in Linear Algebra, all of which are covered in the following world-famous book, Linear Algebra and its Applications by Gilbert Strang, an MIT professor. You can also go to the popular MIT OpenCourseWare page, Linear Algebra (MIT OCW). These two resources cover everything you need to know. Some of the most fundamental concepts that you can also Google or bring up on Wikipedia are:

  1. Vector Algebra
  2. Matrix Algebra
  3. Operations on Matrices
  4. Determinants
  5. Eigenvalues and Eigenvectors
  6. Solving Linear Systems of Equations
  7. Computer-Aided Algebra Software (Mathematica, Maple, MATLAB, etc)

 

5) Data Preparation and Data ETL (Extraction, Transformation, and Loading)

By IAmMrRob on Pixabay

 

Yes – welcome to one of the more infamous sides of data science! If data science has a dark side, this is it. Know for sure that unless your company has some dedicated data engineers who do all the data munging and data wrangling for you, 90% of your time on the job will be spent on working with raw data. Real world data has major problems. Usually, it’s unstructured, in the wrong formats, poorly organized, contains many missing values, contains many invalid values, and contains types that are not suitable for data mining.

Dealing with this problem takes up a lot of the time of a data scientist. And your data scientist’s analysis has the potential to go massively wrong when there is invalid and missing data. Practically speaking, unless you are unusually blessed, you will have to manage your own data, and that means conducting your own ETL (Extraction, Transformation, and Loading). ETL is a data mining and data warehousing term that means loading data from an external data store or data mart into a form suitable for data mining and in a state suitable for data analysis (which usually involves a lot of data preprocessing). Finally, you often have to load data that is too big for your working memory – a problem referred to as external loading. During your data wrangling phase, be sure to look into the following components:

  1. Missing data
  2. Invalid data
  3. Data preprocessing
  4. Data validation
  5. Data verification
  6. Automating the Data ETL Pipeline
  7. Automation of Data Validation and Verification

Usually, expert data scientists try to automate this process as much as possible, since a human being would be wearied by this task very fast and is remarkably prone to errors, which will not happen in the case of a Python or an R script doing the same operations. Be sure to try to automate every stage in your data processing pipeline.

 

6) Machine Learning with Python and R

An expert machine learning scientist has to be proficient in the following areas at the very least:

Data Science Topics Listing

Data Science Topics Listing – Thomas

 

Now if you are just starting out in Machine Learning (ML), Python, and R, you will gain a sense of how huge the field is and the entire set of lists above might seem more like advanced Greek instead of Plain Jane English. But not to worry; there are ways to streamline your learning and to consume as little time as possible in learning or becoming able to learn nearly every single topic given above. After you learn the basics of Python and R, you need to go on to start building machine learning models. From experience, I suggest you break up your time into 50% of Python and 50% of R and spend as much time as possible spending time without switching your languages or working between languages. What do I mean? Spend maximum time learning one programming language at one time. That will prevent syntax errors and conceptual errors and language confusion problems.

Now, on the job, in real life, it is much more likely that you will work in a team and be responsible for only one part of the work. However, if your working in a startup or learning initially, you will end up doing every phase of the work yourself. Be sure to give yourself time to process information and to spend sufficient time for your brain to rest and get a handle on the topics you are trying to learn. For more info, do check out the Learning How to Learn MOOC on Coursera, which is the best way to learn mathematical or scientific topics without ending up with burn out. In fact, I would recommend this approach to every programmer out there trying to learn a programming language, or anything considered difficult, like Quantum Mechanics and Quantum Computation or String Theory, or even Microsoft F# or Microsoft C# for a non-Java programmer.

I strongly recommend the book, Hands-On Machine Learning with Scikit-Learn and TensorFlow to learn Python for Data Science. The R book was given earlier in the section on R.

 

7) Data Visualization and Summary

Common tools that you have with which you can produce powerful visualizations include:

  1. Matplotlib
  2. Seaborn
  3. Bokeh
  4. ggplot2
  5. plot.ly
  6. D3.js
  7. Tableau
  8. Google Data Studio
  9. Microsoft Power BI Desktop

Some involve coding, some are drag-and-drop, some are difficult for beginners, some have no coding at all. All of these tools will help you with data visualization. But one of the most overlooked but critical practical functions of a data scientist has been included under this heading: summarisation. 

Summarisation means the practical result of your data science workflow. What does the result of your analysis mean for the operation of the business or the research problem that you are currently working on? How do you convert your result to the maximum improvement for your business? Can you measure the impact this result will have on the profit of your enterprise? If so, how? Being able to come out of a data science workflow with this result is one of the most important capacities of a data scientist. And most of the time, efficient summarisation = excellent knowledge of statistics. Please know for sure that statistics is the start and the end of every data science workflow. And you cannot afford to be ignorant about it. Refer to the section on statistics or google the term for extra sources of information.

How Can I Learn Everything Above In the Shortest Possible Time?

You might wonder – How can I learn everything given above? Is there a course ora pathway to learn every single concept described in this article at one shot? It turns out – there is. There is a dream course for a data scientist that contains nearly everything talked about in this article.

Want to Become a Data Scientist? Welcome to Dimensionless Technologies! It just so happens that the course: Data Science using Python and R, a ten-week course that includes ML, Python and R programming, Statistics, Github Account Project Guidance, and Job Placement, offers nearly every component spoken about above, and more besides. You don’t know to buy the books or do any of the courses other than this to learn the topics in this article. Everything is covered by this single course, tailormade to convert you to a data scientist within the shortest possible time. For more, I’d like to refer you to the following link:

Data Science using R & Python

Does this seem too good to be true? Perhaps, because this is a paid course. With a scholarship concession, you could end up paying around INR 40,000 for this ten-week course, two weeks of which you can register for 5,000 and pay the remainder after two weeks trial period to see if this course really suits you. If it doesn’t, you can always drop out after two weeks and be poorer by just 5k. But in most cases, this course has been found to carry genuine worth. And nothing worthwhile was achieved without some payment, right?

In case you want to learn more about data science, please check out the following articles:

Data Science: What to Expect in 2019

and:

Big Data and Blockchain

Also, see:

AI and intelligent applications

and:

Evolution of Chatbots & their Performance

All the best, and enjoy data science. Every single day of your life!

How to Land a Job As a Data Scientist in 2019

How to Land a Job As a Data Scientist in 2019

It’s the buzz!

Everyone is talking about data science as the dream job that they want to have!

Yes, the “100K $USD annual package” is a big draw.

Furthermore, the key focus of self-help and self-improvement literature coming out in the last decade speak about doing what you enjoy and care about – in short, a job you love to do – since there is the greatest possibility that you will shine the brightest in those areas.

Hence many students and many adventurous challenge-hunting individuals from other professions and other (sometimes related) roles are seeking jobs that involve problem-solving. Data science is one solution since it offers both the chance to disrupt a company’s net worth and profits for the better by focusing on analytics from the data they already have as well as solving problems that are challenging and interesting. Especially for the math nerds and computer geeks with experience in problem-solving and a passionate thirst to solve their next big challenge.

So what can you do to land yourself in this dream role?

 

Fundamentals of Data Science

Data science comprises of several roles. Some involve data wrangling. Some involve heavy coding expertise. And all of them involve expert communication and presentation skills. If you focus on just one of these three aspects, you’re already putting yourself at a disadvantage. What you need is to follow your own passion. And then integrate it into your profession. That way you earn a high amount while still doing work you love to do, even at the level of going above and beyond all the expectations that your employer has of you.  So if you’re reading this article, I assume that you are either a student who is intrigued by data science or a working professional who is looking for a more lucrative profession. In such a case, you need to understand what the industry is looking for.

From http://news.mit.edu/2018/mitx-micromasters-program-statistics-and-data-science

a) Coding Expertise

If you want to land a job in the IT or data science fields, understand that you will have to deal with code. Usually, that code will already have been written by some other people or company in the first place. So being intimate with programming and readiness to spend hours and hours of your life sitting before a computer and writing code is something you have to get used to. The younger you start, the better. Children pick up coding fastest compared to all other age groups so there is a very real use-case for getting your kids to code and to see if they seem to like it as young as possible. And there is not just coding – the best choices in these cases will involve people who know software engineering basics and even source control tools and platforms (like Git and GitHub) and have already started their career in coding by contributing to open source projects.

If you are a student, and you want to know what all the hype is about, I suggest that you visit a site that teaches programming – preferably in Python – and start developing your own projects and apps. Yes – apps. The IT world is now mobile, and anyone without knowledge of how to build a mobile app for his product will be left in the dust as far as the highest level of earning is concerned. Even deep learning frameworks, that were once academic, have migrated to the mobile and app ecosystem. That was unthinkable a mere five years ago. If you already know the basics of programming, then learn source control (Git), and how to build programs for open source projects. And then contribute to those projects while you’re still a student. In this case, you will actually become an individual that companies go hunting for before you even complete your schooling or college education. Instead of the other way around!

Mentoring

If you are a student or a professional who is interested in this domain, but don’t know where to start – well – the best thing to do is to find a mentor. You can define a mentor or a coach as someone who has achieved what you aim to achieve in your life. You learn from their experience, their networking capabilities, and their tough sides – the way to keep up your ambition and motivation when you feel the least motivated. If you want to learn data science, what better way than to learn from someone who has done that already? And you will gain a lot of traction when you show promise, especially on your networking side for job placement. For more on that topic (mentoring) – I highly recommend that you study the following article:

https://dimensionless.in/how-to-find-mentors-for-data-science/ 

b) Cogent Communication (Writing and Speaking skills)

Even if you have the world’s best programming expertise, ACM awards, Mathematics Olympiad winning background, you name it – even if you are the best data scientist available in the industry today for your domain – you will go nowhere without communication skills. Communication is more than speaking, reading and typing English – it is the way you present yourself to others in the digital world. That is why blogging, content creation, and focused interaction with your target industry – say, on StackOverflow.com – are so important. A blog really resonates with those to whom you seek a job. It shows that you have genuine, original knowledge about your industry. And if your blog receives critical acclaim through several incoming links from the industry, expect a job interview offer in your email before too long. In many countries but especially in India, the market is flooded with graduates, postgraduates, and PhDs who might have top marks on paper but have no marketable skills as far as their job requirements demand.

Overcome your fears!

Right now it is difficult to see the difference between a 100th percentile skilled data scientist and a 30th percentile skill level by just looking at documents that you submit to a company. A blog testifies that you know your field authoritatively. It also means that you have gained attention from industry leaders (when you receive comments). A StackOverflow answer that is highly rated or even a mention in technology sites like GitHub indicate that you are an expert in your field. Communication is so critical that I recommend that you try to make the best use of every chance you get to speak in public. This is the window the world has on you. Make yourself heard. Be original. Be creative. And the best data scientist in the world will go nowhere unless he or she knows how to communicate effectively. In the industry, this capacity is known as soft skills. And it can be your single biggest advantage over the competition. If you are planning to join a training course for your dream job, make sure the syllabus covers it!

c) Social Networking and Building Industry Connections through LinkedIn

Many sources of information don’t focus on this issue, but it is an absolute must. Your next job could be waiting for you on LinkedIn through a connection. Studies show that less than 1% of resume submissions are selected for the final job offer and lucrative placement. But the same studies show that at least 30% of internal referrals from within a company get placed into the job of their dreamsNetworking is important – so important that if you know the job you’re after, please reach out and research. Understand the company’s problems. Try to address some of their key issues. The more focused, you are the more likely it is that you will get placed in the company you aim for. But always have a plan B – a fallback system, so that in case you do not get placed, you will know what to do. This is especially important today with the competition being so intense.

The Facebook of the Workplace

One place where you can be noticed is through industry connections in social networks. You might miss this, even if you are an M.S. from a college in the US. LinkedIn profiles – the Facebook of the technology world – are especially important today. More and more, in an environment saturated with high-quality talent, who you know can sometimes be even more important as what you know. Connecting to professionals in the industry you plan to work in is critical. This can occur through meetups, through conferences, through technological symposiums and even through paid courses. Courses who have instructors with industry connections are worth their weight in gold – even platinum. Students of such courses who show outstanding promises will be directed to their industry leaders early. If you have a decent GitHub profile but don’t know where to go after that, one way is to go for a course with industry experienced experts. These are the people who are the most likely to be able to land you a job in such a competitive environment. Because the market for data scientists – in fact for IT professionals in general – is highly saturated, including locations like the US.

Conclusion

We have not covered all topics required on this issue, there is much more to speak about. You need to know Statistics – even at PhD levels sometimes, especially Inferential Statistics, Bayes Theorem, Probability and Analysis of Experiments. You should know Linear Algebra in-depth. Indeed, there is a lot to cover. But the best place to learn can be courses tailored to produce Data Scientists. Some firms have really gone the extra mile to convert industry knowledge and key results in each subtopic to create noteworthy training courses specially designed for data science students. In the end, no college degree alone will land you a dream job. What will land you a dream job is hard work and experience through internships and industry projects. Some courses like the ones offered by www.Dimensionless.in have resulted in stellar placement and guidance even after the course duration is finished and when you are a working professional in the job of your dreams. These courses offer – 

  1. Individual GitHub Profile Creation & Mentoring (Coding Expertise)
  2. Training in Soft Skills
  3. Networking Connections on LinkedIn
  4. Instructors with Industry Experience (not academic professors!)

It’s a simple yet potent formula to land you the job of your dreams. Compare the normal route to a data science dream job – a PhD from the US (starting cost Rs. 1,40,28,000.00 INR for five years total, as a usual range) – to a simple course at Rs. 50K to Rs. 25K (yes, INR) from the comfort of taking the course from wherever you may be in the world (remote but live tuition – not recorded videos) with a mic on your end to ask the instructor every doubt you have – and you have a remarkable product guaranteed to land you a dream job within six months. Think the offer’s too good to be true? Well; visit the link below, and pay special attention to the feedback from past students of these same courses on the home page.

Last words – you never know what the future holds – economy and convenience are both prudent and praiseworthy. All the best!

 

 

Must-Have Resources to Become a Data Scientist

Must-Have Resources to Become a Data Scientist

Become a Professional

Now there are a multitude of data science resources out there, all of whom claim to be the “best possible introductory to advanced material and courseware on the subject of data science”. Now I’ve made mistakes in choosing my data science references to buy and keep (and use) but I’ll be sharing what I’ve learned through experience to be the most effective for these particular topics. This list is both effective and born out of experience by going through them one at a time. The list of resources contains the following items:

eBooks (or Books – your choice):

  1. Developing Analytic Talent: Becoming a Data Scientist by Vincent Granville
  2. Introduction to Machine Learning with Python by Muller & Guido.
  3. R for Data Science by Wickham & Grolemund.
  4. Hands-On Machine Learning with Scikit-Learn & TensorFlow by Aurelion Geron
  5. Statistics by Freedman, Pisani, & Purves

Websites:

  1. www.stackoverflow.com (for doubts and errors during coding)
  2. www.kaggle.com (for Data Science Competitions and worldwide rankings)

Online Courses:

  1. Dimensionless.in
    1. Data Science with Python and R: https://dimensionless.in/data-science-using-r-python/ 
    2. Big Data Analytics and NLP: https://dimensionless.in/big-data-analytics-nlp/
    3. Deep Learning: https://dimensionless.in/deep-learning/

 Books for Data Science

1. Developing Analytic Talent: Becoming a Data Scientist by Vincent Granville
Now there are not many books that I would recommend for a professional data scientist, but this book is written by an authority with 15 years of experience in the data science field working on some seriously large-scale projects for the best companies in the world. And it shows. This single book contains some of the latest and the best methods to achieve what you need to be a professional data scientist. And it’s not just teaching theory. Every chapter has multiple case studies taken from the experiences in the industry. Vincent Granville is recognized worldwide as one of the best-known resource talents in data science. The level is a little advanced, and it is not recommended for beginners. But this is the perfect book for advanced-intermediate to professional data scientists. If you want to know how to work professionally as a data scientist, this book is for you. But this is only for intermediate, advanced, and professional data scientists since you need to know the basics before starting on this book.

2. Introduction to Machine Learning with Python – A Guide for Data Scientists (Muller & Guido)

Now, this is a book for beginners, with just a basic knowledge of numpy, pandas and matplotlib required. This is perhaps the most effective way to learn the Scikit-Learn data science library since the authors are two of the core contributors to the scikit-learn package as an open source project. They literally know the library inside out, since they both contributed heavily to creating it! The explanations are simple and the time spent working on the exercises and source code in this book will be highly beneficial if you want to master scikit-learn and its associated libraries.

3. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Wickham & GrolemundThis is another beginner-friendly book, teaching all the basics of R clearly and concisely for those with basic programming skills. R is a language intended for manipulation of raw data, and it is an excellent complement to your toolset if you already know Python and are preparing for a career as a data scientist. The IDE used is RStudio, which is bundled with the Anaconda distribution of Python and ML libraries. Both authors are chief scientists involved in the RStudio software development team and are also members of the R Foundation. This book gets you up and running in R effectively and quickly.

4. Hands-On Machine Learning with Scikit-Learn & TensorFlow by Aurelion Geron

 

This book has received massive acclaim from the data science community for the breadth of knowledge which it provides and is one of the best books on this topic till date. TensorFlow coverage is excellent, and there are methodologies that this book teaches to get your data science project perfectly executed immediately. The TensorFlow (with some Keras) coverage is the most simple and easy to understand among all the various TensorFlow tutorials I personally have found both on the Web as well as in the few available ebooks. If you want to work in Deep Learning but don’t know how to get started, this book is for you (it covers Deep Learning as well)!

5. Statistics, 4th Ed. by Freedman, Pisani, and Purves

There is a wide selection of Statistics books for data scientists, but this book is highly recommended. For many simple reasons:

  1. It does not use equations but real-life examples
  2. All the material is based on real-world scenarios
  3. Inferential Statistics coverage is excellent
  4. Study of Design of Experiments is the entry point for the book
  5. Tests for significance and p-values is clearly explained without formulas through examples
  6. The material is presented in such a way that the reader becomes excellent at off-the-cuff estimation.
  7. Most graphics in the book can be drawn easily by hand.
  8. Excellent set of exercises and solutions with real-world applications.
  9. Plain English words, real-life stories, understanding concepts through applications and simple explanations.
  10. Few to almost very little formulae.
  11. Perfect for someone who is approaching the subject for the first time.

And the icing on the cake is that it is a very enjoyable read! 🙂  

Websites for Data Science

1. www.stackoverflow.com

Once upon a time, if a developer was stuck on a programming problem, he would have to go through several textbooks each over 500 pages long to find the answer to his problem. Not any more! StackOverflow is a site that is a platform for questions and doubts on nearly every type of programming language available, including Python, R, scikit-learn, TensorFlow, Keras, pandas, numpy, scipy, Theano, PyTorch, matplotlib and dozens more (both languages and libraries). It shows the power of crowd-sourcing problems since it is much easier to find the answer to a problem from 50,000 people than just four or five which would be the case if you were studying from a few teachers. You can simply copy-paste your error message in your data science compiler tool into StackOverflow and the site will return fully worked out and clearly explained solutions to your problem.

The way I see it – StackOverflow was a defining moment in programming. Once upon a time, debugging was a challenge. Now for nearly 90% or more of all bugs and errors, StackOverflow has your answer, explained in clear English, with the corrected source code. What more could you want? These days, anyone can become a developer in any language, thanks to this single site. And the concept has become so popular that there are now a multitude of crowd-sourcing answers to questions platform websites such as www.math-exchange.com, www.stack-exchange.com, and around 10 to 20 more sites that provide this functionality for that particular field, be it Mathematics, English, and even Christianity!

2. www.kaggle.com

These days, just having an impressive profile on Kaggle will be enough to land you a job interview at the very least. Kaggle is a site that has been hosting data science competitions for many years. The competition is immense and intense, but so are the tutorials and the articles are also equally powerful and instructive. If you want to be a data scientist, not having a decent Kaggle profile is inexcusable. Kaggle will be like a showcase of your data science skills to the entire world. Even if you don’t rank very high, consistency and practice can get you there more often than not.

And there is another side to it, a course designed purely for the purpose of winning data science competitions, available on Coursera (How to Win Data Science Competitions: Learn from Top Kagglers), available on this link: https://www.coursera.org/learn/competitive-data-scienceHowever, this course is not for beginners, it is only for those who already have a strong background in machine learning and machine learning libraries with practical programming knowledge experience.

Courses in Data Science

Dimensionless.in

Dimensionless.in is an elite data science training company that imparts industry level experience and knowledge to those with a real thirst to learn. Training is given from the basics, leading to a strong foundation. Now, anyone with discipline and persistence can learn data science and become a data scientist. All the faculty in the courses are IIT alumni. The training received is personalized to cater to the needs of each student. There are only 20-30 students in a single batch.

Going by popular wisdom, this level of detail and attention is not available in any of the major data science training centers and course curators. Most organizations focus on marketing and volume (quantity). But Dimensionless Technologies has their focus not on quantity but in quality. The following three major courses are offered:

  1. Data Science with Python and R: https://dimensionless.in/data-science-using-r-python/ 
  2. Big Data Analytics and NLP: https://dimensionless.in/big-data-analytics-nlp/
  3. Deep Learning: https://dimensionless.in/deep-learning/

An in-depth explanation for each course is given at each of the links. Do check them out and have an in-depth view of the potential that is here to be tapped, for your benefit.

And Finally…

AI & ML (Artificial Intelligence & Machine Learning) is the future for nearly every single industry. The question on every CEO’s and CIO’s mind will be this: Why should we set up a staffed division in our company for any role when an automated machine with just a high one-time investment (for which operating costs are literally non-existent (compared to paying a salary to 100 staff members – you can do the math) can do the same job for us more reliably, more efficiently, more consistently, and more accurately than people when they do it as staff or employees? That burning question is racing through nearly every industry in the world right now. Thousands of jobs will be automated and the biggest demand in all sectors will be for machine learning experts who are also highly skilled in domain knowledge of that company (say hospitals, for e.g.).

Don’t be scared of the incoming changes. Change is humanity’s best friend. Without change, life would be boring. Changes are not just challenges, they are also opportunities for much higher-paying and much less laborious jobs than the jobs you hold currently. And, if by chance, you happen to be a student reading this article, you now know which industry you should focus on –  completely. All the best, and remember to enjoy the process of learning. Regardless of your age, this is the best time to be alive – ever. Because domain knowledge is available more widely today than at any time in the past. Be enthusiastic. Be positive. Be disciplined. Be focused. And make the right choices at the right times – and no, its never too late when you have quality trainers ready to mentor you. May the thrill of learning a completely new concept with truly enlightened insight never leave you. Once again, all the best.

Robots and Automation

Advantages of Online Data Science Courses

Online Data Science Courses with Dimensionless

Data Science has been hailed as the transformative trend that is set to re-wire the industries and re-invent the ways people do things. Products and applications are being developed in agriculture, healthcare, urban planning, trade, commerce, finance, and the possibilities are growing.

It has been a buzzword for a while now with more people aiming to look ahead for the career opportunities it provides. If you are looking for a career change to data science or you want to build a career in it or you are really passionate about it, then Dimensionless Technologies has some great courses just for you.

Data Science is an umbrella term which is used to describe pretty much everything, from data engineering to data processing to data analysis to machine learning, pattern recognition, and deep learning.

Dimensionless provides online data science training that provides in-depth course coverage, case study based learning, entirely Hands-on driven sessions with personalized attention to every participant. We provide only instructor-led LIVE online training sessions and not classroom training.

You can have a sneak peek into how classes are conducted which will help you make a wiser choice
Demo tutorial: Link

Why Dimensionless Technologies?

Dimensionless Technologies provide instructor-led LIVE online training with hands-on different problems. We do not provide classroom training but we deliver more as compared to what a classroom training could provide you with

Are you skeptical of online training or you feel that online mode is not the best platform to learn? Let us clear your insecurities about online training!

  1. Live and Interactive sessions
    We conduct classes through live sessions and not pre-recorded videos. The interactivity level is similar to classroom training and you get it in the comfort of your home. If you miss any class or didn’t understand some concepts, you can’t go through the class again. However, in online courses, it’s possible to do that. We share the recordings of all our classes after each class with the student. Also, there’s no hassle of long-distance commuting and disrupting your schedule.
  2. Highly Experienced Faculty
    We have very highly experienced faculty with us (IIT`ians) to help you grasp complex concepts and kick-start your career success journey
  3. Up to Data Course content
    Our course content is up to date which involves all the latest technologies and tools. Our course is well equipped for learners to grasp the knowledge required to solve real-world problems through their data analytical skills
  4. Availability of software and computing resource
    Any laptop with 2GB RAM and Windows 7 and above is perfectly fine for this course. All the software used in this course are Freely downloadable from the Internet. The trainers help you set it up in your systems. We also provide access to our Cloud-based online lab where these are already installed.
  5. Industry-Based Projects
    During the training, you will be solving multiple case studies from different domains. Once the LIVE training is done, you will start implementing your learnings on Real Time Datasets. You can work on data from various domains like Retail, Manufacturing, Supply Chain, Operations, Telecom, Oil and Gas and many more. You would be working on multiple projects so that you can gain enough content and confidence to enter into the field of Data Science.
  6. Course Completion Certificate
    Yes, we will be issuing a course completion certificate to all individuals who successfully complete the training.
  7. Placement Assistance
    We provide you with real-time industry requirements on a daily basis through our connection in the industry. These requirements generally come through referral channels, hence the probability to get through increases manifold.

Our Courses

Through our 3 well designed coursed, we have covered most of the aspects which data science encompasses and even have gone deeper into other domains like maths and computer science.

Comparison of Dimensionless Tech with Other E-learning platforms for data science

Our unique approach to conducting session sets us apart from other e-learning platforms for data science

data science teaching process

Demo video: Link

Following are the courses available

  1. Data Science using R and Python (Link)
    This is our complete data science course which is specifically designed for all the people looking for a career in the data science. This course requires no pre-requisites and walks learners from basics to depth in every topic.
    This course includes
    1. Descriptive Statistics(Variability, Distributions, Central tendency etc)
    2. Inferential Statistics(Hypothesis Testing, ANOVA, Regression, T-tests etc)
    3. R (functions, libraries, dplyr, apply etc)
    4. Python (Functions, pandas, numpy, sci-kit learn etc)
    5. Machine Learning (Regression, SVM, Naive Bayes, Time Series Forecasting etc)
    6. Tableau
    7. Final Project
  2. Big Data and NLP (Link)
    Data is everywhere and there is a lot of it actually. Big Data gives us the capability to work on this large amount of data. This course is designed for learners who want to understand how data science is applied in the industry over the big data setup. Also, this course will help learners to understand Natural Language Processing and making learners perform analytics when encountered with a lot of textual data
    This course includes
    1. Spark basics and its architecture
    2. Data Manipulation with Spark
    3. Applied Machine Learning with Spark
    4. Text Processing using NLTK
    5. Building Text Classifiers with Machine Learning
    6. Semantic Analysis
  3. Deep Learning (Link)
    Machine Learning has been booming off late. This course is designed for all the people who are looking for machine learning engineer profile. This covers the concept starting from the basics of neural networks to building them to solve different case studies
    This course includes
    1. Understanding Neural networks and Deep Learning
    2. Tuning Deep Neural Networks
    3. Convolutional Neural Networks
    4. Recursive Neural Network

Our Faculty

Himanshu (IIT, Bombay – 10+ years experience in Data Science), A machine-learning practitioner, fascinated by the numerous application of Artificial Intelligence in the day to day life. I enjoy applying my quantitative skills to new large-scale, data-intensive problems. I am an avid learner keen to be the frontrunner in the field of AI.

Kushagra, (IIT Delhi – 8+ years experience in Analytics & data science), has a keen interest in Problem Solving, Deriving insights & Improving the efficiency of processes with new age technologies. Trained 500+ participants in R, Machine Learning, Tableau and Python, Big Data Analytics at Dimensionless Conducted workshops and training on Data Analytics for Corporate and Colleges.

The Final Picture

Our courses have been designed considering the overall growth of the learners in the data science field. We not only cover the data science domain but we have courses for you to learn text mining or production level technologies like Apache Spark. We have  a deep learning course for all the machine learning enthusiasts to extend their knowledge and step into the world of AI

data science venn diagram

From machine learning to Data processing, from multi-domain problems to writing production grade code on Apache Spark, Dimensionless Technologies have covered it all for you!

Visit Dimensionless and enroll now to give your career a kick-start in data science! [LINK]