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Top 5 Udemy Online Data Scientist Courses that help you move to a Data Analyst/Scientist position

1. Applied Statistical Modeling for Data Analysis in R

Applied Statistical Modeling for Data Analysis in R

What you’ll learn

  • Analyze their own data by applying appropriate statistical techniques
  • Interpret the results of their statistical analysis
  • Identify which statistical techniques are best suited to their data and questions
  • Have a strong foundation in fundamental statistical concepts
  • Implement different statistical analysis in R and interpret the results
  • Build intuitive data visualizations
  • Carry out formalized hypothesis testing
  • Implement linear modeling techniques such as multiple regressions and GLMs
  • Implement advanced regression analysis and multivariate analysis




 Confounded by Confidence Intervals? Pondering Over p-values? Hankering Over Hypothesis Testing? 

Hello, My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a Ph.D. at Cambridge University (Tropical Ecology and Conservation).

With this course, I want to help you save time and learn what the arcane statistical concepts have to do with the actual analysis of data and the interpretation of the bespoke results. Frankly, this is the only course you need to complete in order to get a head start in practical statistical modeling for data analysis using R. 

My course has 9.5 hours of lectures and provides a robust foundation to carry out PRACTICAL, real-life statistical data analysis tasks in R, one of the most popular and FREE data analysis frameworks.


This course is your sure-fire way of acquiring the knowledge and statistical data analysis skills that I acquired from the rigorous training I received at 2 of the best universities in the world, a perusal of numerous books and publishing statistically rich papers in renowned international journals like PLOS One.

To be more specific, here’s what the course will do for you:

  (a) It will take you (even if you have no prior statistical modeling/analysis background) from a basic level to performing some of the most common advanced statistical data analysis tasks in R.

  (b) It will equip you to use R for performing the different statistical data analysis and visualization tasks for data modeling.

  (c) It will introduce some of the most important statistical concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.

  (d) You will learn some of the most important statistical modeling concepts from probability distributions to hypothesis testing to regression modeling and multivariate analysis.

  (e) You will also be able to decide which statistical modeling techniques are best suited to answer your research questions and applicable to your data and interpret the results.

The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results.

After each video, you will learn a new concept or technique which you may apply to your own projects immediately!

2. Learn Python – Python Programming For Beginners From Scratch

Learn Python – Python Programming For Beginners From Scratch


Are you interested in learning Python?

Python is the future of software development. This high-level programming language is commonly regarded as the best programming language to learn for beginners.  And now you can learn it all from the comfort of your home.. in your own time.. without having to attend class. 

Learning Python will give you more opportunities for jobs and career advancement because Python is one of the most requested skills today.

5-Star Testimonials

I know this that this is a beginner’s course. The instructor has a calm and patient voice. It appears that he wants you to learn how to do it. 5 stars earned! I am glad that I took the first step!

Extremely good instructor. His pace is very normal and his explanation is really great. When he was explaining the concepts, Python looked really simple for me, even though this was the first overview i had.

Yes, the videos are very easy to follow and understand.

Why learn Python?

  • It’s fun and easy to learn
  • High salaries
  • Python is used for many kinds of development
  • Python is the future of AI and Machine Learning.
  • Diversity and flexibility
  • And many more

Who is this for?

This course is for anyone who wants to take their skills to the next level. Python is a programming language that many believe to be the future of software development. No previous programming experience required. This course is also a great reference for those who are experienced with Python.

3. Tensorflow Deep Learning – Data Science in Python

Tensorflow Deep Learning – Data Science in Python

What you’ll learn

  • Harness The Power Of Anaconda/iPython For Practical Data Science
  • Learn How To Install & Use Tensorflow Within Anaconda
  • Implement Statistical & Machine Learning With Tensorflow
  • Implement Neural Network Modelling With Tensorflow
  • Implement Deep Learning-Based Unsupervised Learning With Tensorflow
  • Implement Deep Learning-Based Supervised Learning With Tensorflow


  • Be Able To Operate & Install Software On A Computer
  • Prior Exposure To Python Programming Will Be Beneficial
  • Have Prior Exposure To Common Machine Learning Terms
  • Prior Exposure To Basic Statistical Concepts Will be Useful


Complete Tensorflow Mastery For Machine Learning & Deep Learning in Python


It is a full 7-Hour Python Tensorflow Data Science Boot Camp that will help you learn statistical modeling, data visualization, machine learning and basic deep learning using the Tensorflow framework in Python.                         


This course is your complete guide to practical data science using the Tensorflow framework in Python.

This means, this course covers all the aspects of practical data science with Tensorflow (Google’s powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow based data science.  

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and the advent of Tensorflow is revolutionizing Deep Learning.

By storing, filtering, managing, and manipulating data in Python and Tensorflow, you can give your company a competitive edge and boost your career to the next level.


But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a Ph.D. at Cambridge University (Tropical Ecology and Conservation).

This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the Tensorflow framework.

Unlike other Python courses, we dig deep into the statistical modeling features of Tensorflow and give you a one-of-a-kind grounding in Python-based Tensorflow Data Science!


• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about Tensorflow installation and a brief introduction to the other Python data science packages
• A brief introduction to the working of Pandas and Numpy
• The basics of the Tensorflow syntax and graphing environment
• Statistical modeling with Tensorflow
• Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow framework
• You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow


You’ll start by absorbing the most valuable Python Tensorflow Data Science basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real -life.

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow. I will even introduce you to deep learning models such as the Convolution Neural network (CNN) !!

The underlying motivation for the course is to ensure you can apply Python-based data science on real data into practice today, start analyzing data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.

This course will take students without a prior Python and/or statistics background from a basic level to performing some of the most common advanced data science techniques using the powerful Python-based Jupyter notebooks

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, the majority of the course will focus on implementing different techniques on real data and interpret the results.

After each video, you will learn a new concept or technique which you may apply to your own projects!

4. Taming Big Data with Apache Spark and Python – Hands On!

What you’ll learn

  • Use DataFrames and Structured Streaming in Spark 2
  • Frame big data analysis problems as Spark problems
  • Use Amazon’s Elastic MapReduce service to run your job on a cluster with Hadoop YARN
  • Install and run Apache Spark on a desktop computer or on a cluster
  • Use Spark’s Resilient Distributed Datasets to process and analyze large data sets across many CPU’s
  • Implement iterative algorithms such as breadth-first-search using Spark
  • Use the MLLib machine learning library to answer common data mining questions
  • Understand how Spark SQL lets you work with structured data
  • Understand how Spark Streaming lets your process continuous streams of data in real time
  • Tune and troubleshoot large jobs running on a cluster
  • Share information between nodes on a Spark cluster using broadcast variables and accumulators
  • Understand how the GraphX library helps with network analysis problems


  • Access to a personal computer. This course uses Windows, but the sample code will work fine on Linux as well.
  • Some prior programming or scripting experience. Python experience will help a lot, but you can pick it up as we go.


“Big data” analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark. Employers including AmazonEBayNASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You’ll learn those same techniques, using your own Windows system right at home. It’s easier than you might think.

Learn and master the art of framing data analysis problems as Spark problems through over 15 hands-on examples, and then scale them up to run on cloud computing services in this course. You’ll be learning from an ex-engineer and senior manager from Amazon and IMDb.

  • Learn the concepts of Spark’s Resilient Distributed Datastores
  • Develop and run Spark jobs quickly using Python
  • Translate complex analysis problems into iterative or multi-stage Spark scripts
  • Scale up to larger data sets using Amazon’s Elastic MapReduce service
  • Understand how Hadoop YARN distributes Spark across computing clusters
  • Learn about other Spark technologies, like Spark SQL, Spark Streaming, and GraphX

By the end of this course, you’ll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes. 

This course uses the familiar Python programming language; if you’d rather use Scala to get the best performance out of Spark, see my “Apache Spark with Scala – Hands On with Big Data” course instead.

We’ll have some fun along the way. You’ll get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you’ve got the basics under your belt, we’ll move to some more complex and interesting tasks. We’ll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We’ll analyze a social graph of superheroes, and learn who the most “popular” superhero is – and develop a system to find “degrees of separation” between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You’ll find the answer.

This course is very hands-on; you’ll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon’s Elastic MapReduce service. 5 hours of video content is included, with over 15 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and GraphX.

Wrangling big data with Apache Spark is an important skill in today’s technical world. Enroll now!

  • ” I studied “Taming Big Data with Apache Spark and Python” with Frank Kane, and helped me build a great platform for Big Data as a Service for my company. I recommend the course!  ” – Cleuton Sampaio De Melo Jr.

Who this course is for:

  • People with some software development background who want to learn the hottest technology in big data analysis will want to check this out. This course focuses on Spark from a software development standpoint; we introduce some machine learning and data mining concepts along the way, but that’s not the focus. If you want to learn how to use Spark to carve up huge datasets and extract meaning from them, then this course is for you.
  • If you’ve never written a computer program or a script before, this course isn’t for you – yet. I suggest starting with a Python course first, if programming is new to you.
  • If your software development job involves, or will involve, processing large amounts of data, you need to know about Spark.
  • If you’re training for a new career in data science or big data, Spark is an important part of it.

5. Python for Statistical Analysis

Master applied Statistics with Python by solving real-world problems with state-of-the-art software and libraries

Python for Statistical Analysis

What you’ll learn

  • Gain deeper insights into data
  • Use Python to solve common and complex statistical and Machine Learning-related projects
  • How to interpret and visualize outcomes, integrating visual output and graphical exploration
  • Learn hypothesis testing and how to efficiently implement tests in Python


  • Python basics


Welcome to Python for Statistical Analysis!

This course is designed to position you for success by diving into the real-world of statistics and data science.

  1. Learn through real-world examples: Instead of sitting through hours of theoretical content and struggling to connect it to real-world problems, we’ll focus entirely upon applied statistics. Taking theory and immediately applying it through Python onto common problems to give you the knowledge and skills you need to excel.
  2. Presentation-focused outcomes: Crunching the numbers is easy, and quickly becoming the domain of computers and not people. The skills people have are interpreting and visualising outcomes and so we focus heavily on this, integrating visual output and graphical exploration in our workflows. Plus, extra bonus content on great ways to spice up visuals for reports, articles and presentations, so that you can stand out from the crowd.
  3. Modern tools and workflows: This isn’t school, where we want to spend hours grinding through problems by hand for reinforcement learning. No, we’ll solve our problems using state-of-the-art techniques and code libraries, utilising features from the very latest software releases to make us as productive and efficient as possible. Don’t reinvent the wheel when the industry has moved to rockets.

Who this course is for:

  • Data Scientists who want to add to their skillset statistical analysis
  • Data Scientists who want to do machine learning but want some more statistical foundations before jumping in
  • Students wanting to learn applied statistics for research, coursework or business

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