Top 10 reasons why Dimensionless is the Best Data Science Course Provider Online

Top 10 reasons why Dimensionless is the Best Data Science Course Provider Online

Introduction

Data Science was called “The sexiest work of the 21st Century” by the Harvard Review. Data researchers as problematic solvers and analysts identify patterns, notice developments and make fresh findings and often use real-time information, machine learning, and IA. This is where Data Science Course comes into the picture.

There is a strong demand for information researchers and qualified data scientists. Projections from IBM suggest that by 2020 the figure of information researchers will achieve 28%. In the United States alone, there will be 2,7 million positions for all US information experts. In addition, we were provided more access to detailed analyzes by strong software programs.

Dimensionless Tech offers the finest online data science course and big data coaching to meet the requirement, offering extensive course coverage and case studies, completely hands-on-driven meetings with personal attention to each individual. This assessment is a gold mine with invaluable insights. To satisfy the elevated requirement. We only provide internet LIVE instruction for instructors and not instruction in the school.

About Dimensionless Technologies

Dimensionless Technologies is a training firm providing online live training in the sector of data science. Courses include–R&P data science, deep learning, large-scale analysis. It was created in 2014, with the goal of offering quality data science training for an inexpensive cost, by 2 IITians Himanshu Arora & Kushagra Singhania.
Dimensionless provides a range of internet Data Science Live lessons. Dimensionless intends to overcome the constraints by giving them the correct skillset with the correct methodology, versatile, adaptable and versatile at the correct moment, which will assist learners to create informed business choices and sail towards a successful profession.

Why Dimensionless Technologies

Experienced Faculty and Industry experts

Data science is a very vast field and hence a comprehensive grasp over this subject requires a lot of effort. With our experienced faculties, we are committed to impart quality and practical knowledge to all the learners. Our faculty through their vast experience (10 plus industry experience) in the data science industry is best suited to show the right path to all students towards their success journey on the path of data science. Our trainer’s boast of their high academic career as well (IITian’s)!

End to End domain-specific projects

We, at Dimensionless, believe that concepts can be learned best when all the theory learned in the classroom can actually be implemented. With our meticulously designed courses and projects, we make sure our students get hands-on the projects ranging from pharma, retail, and insurance domains to banking and financial sector problems! End-to-end projects make sure that students understand the entire problem-solving lifecycle in data science

Up to date and adaptive courses

All our courses have been developed based on the recent trends in data science. We have made sure to include all the industry requirements for data scientists. Courses start from level 0 and assume no prerequisites. Courses make learners traverse from basic introductions to advanced concepts gradually with the constant assistance of our experienced faculties. Courses cover all the concepts to a great depth such that learners are never left wanting for more! Our courses have something or other for everyone whether you are a beginner or a professional.

Resource assistance

Dimensionless technologies have all the required hardware setup from running a regression equation to training a deep neural network. Our online-lab provides learners with a platform where they can execute all their projects. A laptop with bare minimum configuration (2GB RAM and Windows 7) is sufficient enough to pave your way into the world of deep learning. Pre-setup environments save a lot of time of learners in installing all the required tools. All the software requirements are loaded right in front of the accelerated learning

Live and interactive sessions

Dimensionless provides classes through live interactive classes on our platform. All the classes are taken live by instructors and are not in any pre-recorded format. Such format enables our learners to keep up their learning in the comfort of their own homes. You don’t need to waste your time and expenses in any travel and can take classes from any location of your preference. Also, after each class, we provide the recorded video of it to all our learners so that they can go through it to clear all their doubts. All trainers are available to post classes to clear the doubts as well

Lifetime access to study materials

Dimensionless provides lifetime access to the learning material provided in the course. Many other course providers provide access only till the time one is continuing with classes. With all the resources available thereafter, learnings for our students will not stop even after they have taken up our entire course

Placement assistance

Dimensionless technologies provide placement assistance to all its students. With highly experienced faculties and contacts in the industry, we make sure our students get their data science job and kick start their career. We help in all stages of placement assistance. From resume-building to final interviews, Dimensionless technologies is by your side to help you achieve all your goals

Course completion certificate

Apart from the training, we issue a course completion certificate once the training is complete. The certificate brings credibility to the resume of the learners and will help them in fetching their data science dream jobs

Small batch sizes

We make sure that we have small batch sizes of students. Keeping the batch size small allows us to focus on students individually and impart them a better learning experience. With personalized attention, we make sure students are able to learn as much possible and helps us to clear all their doubts as well

Conclusion

If you want to start a profession in data science, dimensionless systems have the correct classes for you. Not just all key ideas and techniques are covered but they are also implemented and used in real-world company issues.

Follow this link, if you are looking to learn data science online!

You can follow this link for our Big Data course! This course will equip you with the exact skills required. Packed with content, this course teaches you all about AWS tools and prepares you for your next ‘Data Engineer’ role

Additionally, if you are having an interest in learning Data Science, click here to start the Online Data Science Course

Furthermore, if you want to read more about data science, read our Data Science Blogs

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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!

Data Visualization with R

Data Visualization with R

Introduction

Visualizing the data is important as it makes it easier to understand large amount of complex data using charts and graphs than studying documents and reports. It helps the decision makers to grasp difficult concepts, identify new patterns and get a daily or intra-daily view of their performance. Due to the benefits it possess, and the rapid growth in analytics industry, businesses are increasingly using data visualizations; which can be assessed from the prediction that the data visualization market is expected to grow annually by 9.47% to $7.76 billion by 2023 from $4.51 billion in 2017.

R is a programming language and a software environment for statistical computing and graphics. It offers inbuilt functions and libraries to present data in the form of visualizations. It excels in both basic and advanced visualizations using minimum coding and produces high quality graphs on large datasets.

This article will demonstrate the use of its packages ggplot2 and plotly to create visualizations such as scatter plot, boxplot, histogram, line graphs, 3D plots and Maps.

 

1. ggplot2

 

There are a lot of datasets available in R in package ‘datasets’, you can run the command data() to list those datasets and use any dataset to work upon. Here I have used the dataset named ‘economics’ which gives the monthly U.S. data of various economic variables like unemployment for the time period 1967-2015.

You can view the data using view function-

 

data set

 

Scatter Plot

We’ll make a simple scatter plot to view how unemployment has fluctuated over the years by using plot function-

 

Scatter plot

ggplot() is used to initialize the ggplot object which can be used to declare the input dataframe and set of plot aesthetics. We can add geom components to it that acts as its layer and are used to specify the plot’s features.

We would use its feature geom point which is used to create scatter plots.  

 

scatter plot

 

Modifying Plots

We can modify the plot like its color, shape, size etc. using geom_point aesthetics.

 

modifying plot

Lets view the graph by modifying its color-

 

modifying plot after colour

 

Boxplot

Boxplot is a method of graphically depicting groups of numerical data through their quartiles. a geom boxplot layer of ggplot is used to create boxplot of the data.

 

boxplot graph

When there is overplotting, one or more points are in the same place and we can’t tell by looking at the plot that how many points are there. In that case, we can use the jitter geom which adds a small amount of variation to the location of each point that is it slightly moves the point, which is used to spread out the points that would otherwise be overplotted.

 

overplotted boxplot

 

Line Graph

We can view the data in the form of a line graph as well using geom_line.

 

line graph

To change the names of the axis and to give a title to the graph, use labs feature-

 

labeled line graph

Let’s group the data according to year and view how average unemployment fluctuated through these years.

We will load dplyr package to manipulate our data and lubridate package to work with date column.

 

Now we will use mutate function to create a column year from the date column given in economics dataset by using the year function of lubridate package. And then we will group the data according to year and summarise it according to average unemployment-

 

Now, lets view the data as a line plot using line geom of ggplot2

 

(Since here we want the height of the bar be equal to avg_unempl, so we need to specify stat equal to identity)

line plot using line geom of ggplot2
                    This graph shows the average unemployment in each year

 

Plotting Time Series Data

In this section, I’ll be using a dataset that records the number of tourists who visited India from 2001 to 2015 which I have rearranged such that it has 3 columns, country, year and number of tourists arrived.

Data Set

To visualize the plot of the number of tourists that visited the countries over the years in the form of line graph, we use geom_line-

 

Geom Line graph

Unfortunately, we get this graph which looks weird because we have plotted all the countries data together.

So, we group the graph by country by specifying it in aesthetics-

 

geom line graph
This graph is showing the country wise line graph which shows the trend of a number of tourists arrived from these countries over the years.

To better view the graph that distinguishes countries and is bigger in size, we can specify color and size-

 

Geom Line graph with color

 

Faceting

Faceting is a feature in ggplot which enables us to split one plot into multiple plots based on some factor. We can use it to visualize one-time series for each factor separately-

 

faceting graph

For convenience purpose, you can change the theme of the background as well, here I am keeping the theme as white-

 

theme changed faceting graph

These were some basic functions of ggplot2, for more functions, check out the official guide.

 

2. Plotly

Plotly is deemed to be one of the best data visualization tools in the industry.

 

Line graph

Lets construct a simple line graph of two vectors by using plot_ly function that initiates a visualization in plotly. Since we are creating a line graph, we have to specify type as ‘scatter’ and mode as ‘lines’.

 

ploty line graph

Now let’s create a line graph using the economics dataset that we used earlier-

 

ploty line graph using dataset

Now, we’ll use the dataset ‘women’ that is available in R which records the average height and weight of American women.

dataset example

 

Scatter Plot

Now lets create a scatter plot for which we need to specify mode as ‘markers’ – 

 

Scatter plot

 

Bar Chart

Now, to create a bar chart, we need to specify the type as ‘bar’.

 

bar chart

 

Histogram

To create a histogram in plotly, we need to specify the type as ‘histogram’ in plot_ly.

1. Normal distribution

Let x follow a normal distribution with n=200

 

We then plot this normal distribution in histogram,

 

histogram

Since its a normally distributed data, so the shape of this histogram is bell-shaped.

2. Chi-Square Distribution

Let y follow a chi square distribution with n = 200 and df = 4,

Then, we construct a histogram of y-

 

                                               This histogram represents a chi square distribution, so it is positively skewed

 

Boxplot

We will build a boxplot of a normally distributed data, fr that we need to specify the type as ‘box’.

 

here x follows a normal distribution with mean 0 and sd 1,

boxplot                                       So in this box plot, the median is at 0 as in normal distribution, median is equal to mean.

 

Adding Traces

We can add multiple traces to the plot using pipelines and add_trace feature-

boxplot with traces

Now let’s construct two boxplots from two normally distributed datasets, one with mean 0 and other with mean 1-

 

boxplots from two normally distributed datasets

 

Modifying the Plot

Now, let’s modify the size and color of the plot, since the mode is a marker, so we would specify the marker as a list with the modifications that we require.

 

modifying the plot with marker

We can modify points individually as well if we know the number of points in the graph-

 

individual marker plot

We can modify the plot using the layout function as well which allows us to customize the x-axis and y-axis. We can specify the modifications in the form of a list-

 

Here, we have given a title to the graph and the x-axis and y-axis as well. Also, we have the X-axis line and Y-axis line

title to axis in scatter plot

Let’s say we want to distinguish the points in the plot according to a factor-

 

here, if we don’t specify the mode, it will set the mode to ‘markers’ by default

Scatter plot distribution according to factors

 

Mapping Data to Symbols

We can map the data into differentiated symbols so that we can view the graph better for different factors-

 

here, the points pertaining to 3 factors are distinguished by symbols that R assigned to it.

Mapping data by symbols

We can customize the symbols as well-

 

coustomization of symbols in graph

 

3D Line Plot

We can construct a 3D plot as well by specifying it in type. Here we are constructing a 3D line plot-

 

3D Line plot

 

Map Visualization

We can visualize map as well by specifying in type as ‘scattergeo’. Since its a map, so we need to specify lattitude and longitude.  

map visulaization

We can modify the map as well. Here we have increased the size of the points and changed its color. We have also added text that is the location of the point which would show the location name when the cursor is placed on it.

 

map visualization with increased size of points

These were some of the visualizations from package ggplot2 and plotly. R has various other packages for visualizations like graphics and lattice. Refer to the official documentation of R to know more about these packages.

To know more about our Data Science course, click below

Follow this link, if you are looking to learn data science online!

You can follow this link for our Big Data course!

Additionally, if you are having an interest in learning Data Science, click here to start the Best Online Data Science Courses

Furthermore, if you want to read more about data science, read our Data Science Blogs