After analyzing them, I created this list of The Best 16 Data Analysis Courses and Tutorials of 2021 that you should know about. I took the courses that I didn’t take earlier and ranked them according to their quality and completeness.

**Course Provider: **
Organization

**Course Provider Name: **
DataCamp

**Course Provider URL: **
https://www.datacamp.com/tracks/data-manipulation-with-python?tap_a=5644-dce66f&tap_s=1352853-117e8d&utm_medium=affiliate&utm_source=smfahim

4.8

Tons of courses on Data Analysis out there, but unsure about where to spend your time to make the best use of your time and money? Or maybe looking for the best **FREE** courses on Data Analysis topics?

I’ve analyzed **around 50 courses** and tutorials on Data Analysis with Python that people are talking about on Reddit, Quora, and Facebook groups; most enrolled courses on top platforms like Coursera, EdX, Udemy, DataCamp, LinkedIn Learning; and most viewed tutorials on YouTube.

After analyzing them, I created this list of **The Best 16 Data Analysis Courses and Tutorials of 2021** that you should know about. I took the courses that I didn’t take earlier and ranked them according to their quality and completeness.

The main topics covered in these Data Analysis courses are **Numpy, Pandas, and SciPy**. As you must need Data Visualization skills to get the best out of Data Analysis, the **Matplotlib** and **Seaborn** topics were also considered, mostly Matplotlib.

You’ll find some complete courses on Data Science, as I’ve discussed in Top Data Science Courses On The Internet in Python and R 2021. But I love to take courses that are dedicated to smaller topics, because they dive very deep into each topic, and contain lots of practice problems and projects.

I couldn’t make this list of the Best Resources for Data Analysis any shorter because of the **quality** and **uniqueness** each serves. This is my ultimate list. Just take the ones you find best suits you. If you still can’t decide, take a look at the special recommendation section at the end of the article.

- Numpy Data Science Essential Training by LinkedIn Learning
- Using Python for Research by Harvard University on Edx
- Working with Multidimensional Data Using NumPy by PluralSight

- The Complete Pandas Bootcamp 2021: Data Science with Python by Udemy
- Data Analysis with Pandas and Python by Udemy
- pandas Foundations by DataCamp
- Data Manipulation with Python [Skill Track] by DataCamp
- Doing Data Science with Python by PluralSight
- Data Analysis with Python by IBM on Coursera
- The Ultimate Pandas Bootcamp: Advanced Python Data Analysis by Udemy
- Pandas Essential Training by LinkedIn Learning
- Exploratory Data Analysis With Python and Pandas - Guided Project on Coursera

- Complete Python NumPy Tutorial by Keith Galli
- Pandas Tutorials by Corey Schafer
- Python SciPy Tutorial For Beginners by Edureka
- Data Analysis with Python by FreeCodeCamp

One thing you should know is that the official documentation is your best buddies after you’ve taken these courses and want to dive even deeper. Here are the documentations:

- Numpy documentation
- Pandas documentation
- SciPy documentation
- Matplotlib documentation
- Seaborn documentation

**Rating**: 4.8/5**Total Number of Students**: 27k**Number of Courses**: 1 (7 Chapters)**Certificate**: Available**Level**: Beginner, Intermediate**Time to Complete**: 1 week at 10 hours per week**Video Material Length**: 3 hours 54 minutes**Projects**: 1 project**Prerequisites**: Python Programming**Cost**: $30 per month or $20 per month annually

- Using Jupyter Notebooks, including basic operations, markdown, and mathematical typesetting
- Basics of numpy arrays including data types
- Indexing, slicing, broadcasting, boolean masking techniques
- Matplotlib for drawing different plots to visualize data
- Numpy methods and their usage
- Linear algebra and statistics with numpy

- Explains each topic in details and shows lots of examples
- Teaches enough that you can start using numpy fluently
- A huge number of numpy methods covered
- Explains the usage of jupyter notebook, which is very helpful in Data Analysis

At first, this course seemed very plain to me. But then it started throwing lots of methods and examples that will help to build you a strong base on numpy. The best thing about this course is that the instructor explains the numpy methods by showing you the official documentation.

This way, you better understand why something works while others don’t. On the other hand, this is helpful for absolute beginners who don’t have experience in learning from the documentation.

**Rating**: 4.8/5**Total Number of Students**: 280k**Number of Courses**: 1 (5 weeks)**Certificate**: Available**Level**: Beginner, Intermediate**Time to Complete**: 1 month at 10 hours per week**Projects**: 7 case studies and 1 final project**Prerequisites**: Python programming**Cost**: Free on audit (limited access) or $169

- Python basics warm-up
- Numpy library to manipulate mathematical data
- Matplotlib library for visualizing data
- How to use Python for research purpose with
**7 different famous case studies** - Case studies on statistical learning like regression and random forest

- Contains real-world case studies that help to learn numpy and matplotlib by practicing
- Each video tutorial follows up with lots of quiz problems. So you understand the topics very efficiently
- The only quality course till now that focuses more on practical learning than showing various methods, which you can read from the official documentation
- You get to know how really Python helps in research!

This is an amazing course to learn numpy for Data Analysis because of the bunch of real-world case studies it offers and the huge number of practice problems after each video tutorial.

I recently solved a project problem for a client where the project was very similar to one of the case studies discussed here. So this course surely contains some real-world values! Note that to participate in the case studies, you must purchase the course.

**Rating**: 4.6/5**Number of Courses**: 1 (4 sections)**Certificate**: Available**Level**: Beginner, Intermediate**Time to Complete**: 1 week at 10 hours per week**Video Material Length**: 1 hour 43 minutes**Projects**: None**Prerequisites**: Python programming**Cost**: $29/m or $25/m annually

- Basic to advanced applications of numpy in Data Analysis
- Image manipulation with numpy
- Complex indexing, broadcasting, and miscellaneous operations
- Relation of numpy library with other Data manipulation libraries like Pandas and SciPy.

- Focuses more on multidimensional data
- Learn how images are actually 3-dimensional arrays of numbers and how they are manipulated in computer vision
- Various complex but commonly used high dimensional data indexing and slicing methods

The course instructor is a Google engineer and the course content is really satisfactory. It felt like a premium YouTube tutorial, to be honest. Because there’s no quiz or project that you can attend to sharpen your understanding. You surely will get some new aspects of data analysis, so it won’t be a waste of money.

**Rating**: 4.6 (2k ratings)**Total Number of Students**: 13k**Number of Courses**: 1 (31 sections)**Certificate**: Available**Level**: Beginner**Time to Complete**: 1.5 months at 10 hours per week**Video Material Length**: 33.5 hours**Projects**: Available**Prerequisites**: None/ Python programming**Cost**: $12

- Basics of Jupyter Notebook, Python programming, Numpy, and Statistical concepts
- Deep dive into Pandas library with lots of examples
- Data cleaning, manipulating, and visualizing
- Matplotlib and Seaborn for Data Visualization
- Time series analysis
- Machine Learning concepts

- By far the most complete course on Data Manipulation
- Includes the prerequisites as an appendix to discuss Python, Numpy, and Statistical concepts (I still prefer learning them separately)
- Extends to various machine learning concepts to show the application of Pandas library
- The newest course on Data Analysis with Python till now
- Covers a lot of topics of Pandas

extremely thorough & comprehensive. it is obvious that a lot of care and attention has gone into the preparation of the course and material. good, prompt, support to any questions raised as well.

- Douglas Smith

This course is a gem for Data Analysis with Pandas. The only thing you may dislike is the accent of the instructor. Other than that, contains a huge amount of information. Although the main focus is Pandas library, the instructor spent a lot of time making it the perfect course. Covers all the prerequisites and also follows some Machine Learning topics to add some extra spice.

Although this course seems a complete course to Data Analytics, still, you’ve to take different courses on other topics. Because, the course surely dives deep into Pandas and serves necessary knowledge to cope up with the flow, but it’s not possible to cover those extra topics within a few hours of lectures.

**Rating**: 4.7/5 (13k ratings)**Total Number of Students**: 150k**Number of Courses**: 1 (15 sections)**Certificate**: Available**Level**: Beginner, Intermediate**Time to Complete**: 1 month at 10 hours per week**Video Material Length**: 20.5 hours**Projects**: Available**Prerequisites**: Numpy library, Python programming (optional)**Cost**: $12

- A crash course on Python and Jupyter notebook
- A huge number of topics in Pandas library with lots of examples
- Time series analysis
- Data visualization with Matplotlib
- Options and settings in Pandas library

- A complete course on Data Analysis with Pandas library
- The most famous course on Data Analysis on Udemy till now
- An in-depth explanation of each topic
- Includes a special section:
**options and settings in Pandas**

Fantastic class for someone coming in with no experience in pandas. I was able to utilize what I was learning as I went (along with my best friend google) to solve a business problem at work.

- Philip Higgins

This is the most enrolled and a bestseller course for Data Analysis with Pandas on Udemy. It ties with the previous course in my view except that it is a bit older. The course quality is amazing, so is the instructor. This course is more engaging than the previous one. Instead of adding a bunch of topics to make the course more appealing, the instructor focused more on the best practices of using pandas.

**Rating**: 4.8/5**Total Number of Students**: 180k**Number of Courses**: 1 (4 chapters)**Certificate**: Available**Level**: Beginner, Intermediate**Time to Complete**: 1 week at 10 hours per week**Projects**: 1 case study**Prerequisites**: Python programming**Cost**: $12/m

- Basics of DataFrame in Pandas including visualization with pandas
- Statistical exploratory data analysis
- Basics of Time Series analysis
- Solving a case study with common data analysis and visualization techniques

- It prepares you for the data analysis with pandas environment so that you can easily dive deep into the topic
- Teaches all the necessary basics within a very short time including EDA and Time Series
- The best environment for those who want to learn by doing
- Before even diving deep into data analysis, you’ll already have solved a case study with data manipulation and visualization

DataCamp has a different approach to Data Analysis. This course builds the foundation and then proceeds to the main skill track listed below. A huge number of practice problems build a strong foundation in the Pandas library. The content quality is the best till now. Best pandas tutorial for beginners who like learning by doing.

**Rating**: 4.8/5**Total Number of Students**: 80k**Number of Courses**: 4**Certificate**: Available**Level**: Beginner, Intermediate**Time to Complete**: 1 month at 10 hours per week**Projects**: Available**Prerequisites**: Python programming**Cost**: $12/m

- Data analysis from basics to advanced including visualization and working with databases
- Data manipulation, aggregation, slicing-indexing, and visualizing DataFrames in pandas
- Combining information from multiple sources, merging data tables, and advanced merging like ordered and time-series data merging
- Exploratory data analysis with visualization using a project
- Basics of SQL with Python using SQLAlchemy

- A very well structured skill track and provides high-quality materials
- The cheat sheets are very helpful as a guideline when you start solving a project
- A huge number of high-quality practice problems, which is the best thing about DataCamp
- Talks about how to manage Databases with Python, which is a very common skill of Data Analysts
- Very efficient video tutorials that teach a lot of methods within a very short time without making it overwhelming

This skill track is one of my favorites from DataCamp. Anyone who loves **learning by doing**, this is the best choice for you. They provide some cheat sheets that consist of all the methods you may need in different types of problems. In fact, these cheat sheets are very popular. Also, you’ll have lecture slides as PDFs which will be very helpful when you complete the skill track but can’t remember anything.

You must take this skill track after the previous course. I think this is the best value for money. One thing to keep in mind, **don’t rush**! The practice problems are mostly self-explanatory, so you may complete them quickly to earn a certificate. Don’t do that! In fact, try to start over all the practice problems once you finish the skill track, without watching the videos again.

**Rating**: 4.6**Number of Courses**: 1 (9 sections)**Certificate**: Available**Level**: Beginner, Intermediate**Time to Complete**: 1 month at 10 hours per week (Make sure to normalize to 10hrs per week)**Video Material Length**: 6.5 hours**Projects**: Available**Prerequisites**: Python programming**Cost**: $29/m or $25/m annually

- Extracting data from databases, using APIs, and by web scraping
- Exploratory data analysis with statistics and distribution theorems
- Feature engineering in detail
- Building and evaluating predictive models with a Machine Learning approach
- Using Git for projects and submitting to Kaggle

- The most practical approach for learning data analysis
- Covers some unique topics like Git and Kaggle
- Shows various data extraction methods that you will use in your projects
- Dives deeper into both Data Analysis and Machine Learning models which is exactly what I wanted
- The end section shows your way next so that you can keep improving yourself

I liked the data extraction part the most. These are the most common ways you’ll be collecting data. You’ll also be using Git for updating your projects. Similarly, the Machine Learning models for the data analysis part was very detailed. A couple of uniqueness of this course is using Git and Kaggle. So this course should be a good choice for you.

**Rating**: 4.7/5 (15k ratings)**Total Number of Students**: 170k**Number of Courses**: 1 (7 weeks)**Certificate**: Available**Level**: Beginner, Intermediate**Time to Complete**: 2 weeks at 10 hours per week**Projects**: 1 project, 5 assessments**Prerequisites**: Python programming**Cost**: Free (Audit) or $39/month or $33/month annually

- Importing and exporting data from and to files and databases
- Data wrangling including pre-processing, dealing with missing data, data formatting, normalization, binning, etc.
- Exploratory data analysis with statistics
- Various data analysis model development like linear regression, polynomial regression, ridge regression, prediction, decision making, etc
- Model evaluation and refinement to tackle common problems

- Offers the most practical environment compared to all the other courses
- Not only teaches data analysis models but also how to evaluate and refine the models
- Quizzes within and after each video tutorial, at the end of every week, assessment after each week, and a final project; that’s a lot of practice problems!
- Peer graded assessments help you to see how others solved a similar problem, hence a broader view on topics
- The community help to answer your questions, which is very important to know many things that you may not get from any courses. Try to read the top discussions on the community each week

To get the best out of this course, make sure to practice them by yourself as most of the lab problems are self-explanatory. And there are some typos, ignore them. Otherwise, this course offers a lot of practical approaches. Especially, the Model Development week and the Model Evaluation week will be very helpful for you. A little bit of statistics knowledge is recommended.

**Rating**: 4.6 (150 ratings)**Total Number of Students**: 20k**Number of Courses**: 1 (15 section)**Certificate**: Available**Level**: Beginner, Intermediate**Time to Complete**: 2 months at 10 hours per week**Video Material Length**: 32 hours**Projects**: 50+ skill challenges**Prerequisites**: Python Programming (Optional)**Cost**: $12

- Basics of Jupyter Notebook, Numpy library, and necessary Python programming
- Data manipulation with Pandas library A-Z, the same topics as you’ve seen in earlier Udemy courses
- Regex: A very helpful skill for data scraping and analyzing
- Common visualization techniques with Matplotlib
- Some formats of data (JSON, HTML, Excel, etc) and how to handle them

- Covers a lot of topics. One of the most completed tutorials
- Around 50+ skill challenges help to learn and implement the topics
- Covers some extra topics than others like Regex and Data Formats
- Prerequisites are also covered and so it will be easy for you to cope up with the course

excellent course, very good explanation and walk through the courses and mini-projects.

- Bouredja Amine

This is another very promising course on Data Analysis. The instructions are very detailed and in-depth! Though you might want to watch it at 1.5X speed. The course contains some prerequisites sections on Python programming, numpy library, data visualization with Matplotlib, though very basic, but helpful to continue the course. Worth trying it out.

**Rating**: 4.6/5**Total Number of Students**: 20k**Number of Courses**: 1 (12 sections)**Certificate**: Available**Level**: Beginner, Intermediate**Time to Complete**: 1 week at 10 hours per week**Video Material Length**: 2 hours 14 minutes**Projects**: 1 project**Prerequisites**: Python programming**Cost**: $30 per month or $20 per month annually

- Basics of series and DataFrames of Pandas library
- Reading CSV files, validation, and basic analysis
- Plotting the data using pandas and seaborn
- Details on indexing, groupby, and reshaping data
- Solving a final project to apply the knowledge

- A quick course with examples of various methods
- Teaches enough to start working with Data Analysis
- Good for exploring the data analysis field

This course is kind of the same quality as the numpy course discussed at the top. But with lots of quality and detailed courses on Pandas data analysis, this course couldn’t outperform others like the numpy course. Still, I’m listing it here because it contains enough knowledge to start working with data analysis. If you enroll in both the numpy course and this one, then it will be value for money.

**Rating**: 4.7 (200 ratings)**Total Number of Students**: 6k**Number of Courses**: 1**Certificate**: Available**Level**: Beginner, Intermediate**Time to Complete**: 1 week at 10 hours per week**Projects**: 1 guided projects**Prerequisites**: Python programming**Cost**: $10

- Exploratory data analysis step by step using Pandas, Numpy, Matplotlib, and Seaborn
- Basic data exploration
- Univariate and Bivariate analysis
- Dealing with duplicate and missing data, a very common scenario in Data Analysis
- Correlation analysis of numerical variables

- It’s kind of one on one project
- See how a data Scientist solves an exploratory data analysis problem
- Working on a cloud computer gives a real-life experience

Simple yet very organized, and elegant Data Analysis project. I really liked it!

- Salih K

Guided project is a relatively new topic on Coursera. It’s a project focusing on only one major topic, and shows how exactly you need to solve that problem. On your browser, there will be a split-screen. In one section, you will write and run your code, on the other section, the instructor will show you the steps. Note that the instructions are pre-recorded video, not live.

The best thing about guided projects is that you work in exactly the same environment as the instructor. And the project is focused on one topic. This one is a good practice project once you’ve taken any of the detailed courses listed above.

**Rating**: 4.7/5**Level**: Beginner**Time to Complete**: <1 week at 10 hours per week**Video Material Length**: 1 hour**Prerequisites**: Python programming**Cost**: Free

Keith Galli is relatively a new YouTuber and I have been following him since his <10k subs. He created some valuable tutorials. I have some pretty good feelings about this channel!

This 1-hour tutorial contains the numpy basics with some engaging examples. In fact, it has been uploaded to FreeCodeCamp for the quality he created! The best YouTube tutorial for those who want to explore the numpy library.

**Rating**: 4.8/5**Level**: Beginner**Time to Complete**: 2 weeks at 10 hours per week**Video Material Length**: 11 tutorials, each around 30 minute**Prerequisites**: Python programming, Numpy**Cost**: Free

Corey Schafer, a very famous Python Instructor on YouTube, created this amazing detailed tutorial on Data Analysis with Pandas tutorial series. If only the Pandas section is taken under consideration, this tutorial playlist might be one of the best 5 courses of this article. The only downside is the lack of practice problems or projects, which is common for all YouTube tutorials.

**Rating**: 4.5/5**Level**: Beginner**Time to Complete**: 1 day**Video Material Length**: 20 minutes**Prerequisites**: Python programming, Numpy**Cost**: Free

This is the only course or tutorial I could find that focuses only on the SciPy library and worth listing here. It’s just the basics of the SciPy library. So after completing this tutorial, you should start reading the official documentation of the SciPy library.

There are very few tutorials on SciPy, and none of them is as detailed as I want. You may keep an eye on my programming website Machine Learning Wiki, where I will be writing about SciPy soon.

**Rating**: 4.7**Level**: Beginner**Time to Complete**: 1 month at 10 hours per week**Video Material Length**: 4.5 hours**Projects**: Jupyter notebooks available**Prerequisites**: Python programming**Cost**: Free

FreeCodeCamp brings premium courses for free for everyone to use. This course briefly contains almost all the topics of Data Analysis. So this is the best tutorial for exploring the field! Make sure to use the jupyter notebooks to get the best out of this tutorial.

As I’ve listed the best 16 online courses for Data Analysis, and each of them is better than the others, it might be confusing for you to choose where to spend your valuable time. Here are some suggestions from my experience assuming you’re a complete beginner in Data Analysis:

- Start with Numpy Data Science Essential Training by LinkedIn Learning as it’s the most detailed course on Numpy
- Continue by applying the numpy knowledge with Using Python for Research by Harvard University on Edx. This will create a huge difference with those who won’t be taking it.
- Start learning Pandas with Pandas Tutorials by Corey Schafer. Yes, it’s a free youTube playlist, but it’s for getting you familiar with the environment.
- Take pandas Foundations by DataCamp and then proceed with the skill track Data Manipulation with Python by DataCamp. This is for the huge amount of practice problems and topics they covered.
- Finally, take any of the complete courses, either The Complete Pandas Bootcamp 2021: Data Science with Python by Udemy, or Data Analysis with Pandas and Python by Udemy
- For the uniqueness, I'd prefer ending the journey with Doing Data Science with Python by PluralSight

If you’ve any better suggestions for Data Analysis courses than mine, feel free to contact me and I’ll update the list after reviewing.

- Top Data Science Courses On The Internet in Python and R 2021
- Choosing from the Top 10 C++ Courses: The Ultimate Guide 2021

By far, the best data analysis course on Coursera is Data Analysis with Python by IBM. It does not only teach data analysis models but also how to evaluate and refine the models

Data Analysis with Python by FreeCodeCamp on YouTube is the best free Data Analysis course. It briefly contains almost all the topics you need to learn. Best for those who want to explore the field.

Data Manipulation with Python Skill Track by DataCamp contains the most completed and quality courses for Data Analysis. Make sure to start with the pandas Foundations course. Although I prefer taking separate course on each major topic.

When I planned to learn Data Science, I was so confused about which are the best Data Science courses and in what order I should take them. So I started enrolling in courses and also leaving them in the middle when I found I’m not ready for that course yet. It took a while to cope up with the topics and to find the best courses.

In this article, I’ll be sharing with you the best Data Science courses on the internet right now and in both tracks, Python and R language. To make this list, I spent a week reading articles like “my journey to data science”, joining discussions on Reddit, exploring the top course platforms, and analyzing them with my own experience.

After analyzing around 35 famous Data Science courses, I made this shortlist for my well-wishers. The ranking has been done based on completeness and quality. You don’t have to take all of the courses listed below. Find the best one/s for you, or read my special recommendation section to help you decide.

- MicroMasters® Program in Statistics and Data Science by MIT on EdX
- Machine Learning by Andrew Ng on Coursera
- Data Scientist With Python by DataCamp
- MicroMasters® Program in Data Science by UCSanDiego on EdX
- Data Scientist NanoDegree by Udacity
- IBM Data Science Professional Certificate by Coursera
- The Data Science Course 2020: Complete Data Science Bootcamp by Udemy
- Python for Data Science and Machine Learning Bootcamp by Udemy
- Applied Data Science with Python Specialization by UMich on Coursera

- Professional Certificate in Data Science by Harvard on Edx
- Data Science Specialization by John Hopkins on Coursera
- Data Scientist with R by DataCamp
- Data Science and Machine Learning Bootcamp with R by Udemy

Let’s dive into each of them and analyze their quality and completeness.

**Rating**: 5/5**Number of Courses**: 5**Certificate**: Yes. Considered to be 20% to 30% of total credit in Master’s programs at many well-known universities. The most valuable certificate on the list**Level**: Beginner to Advanced. Most suited for intermediate level**Time to Complete**: 1 year 2 months at 10-14 hours per week**Projects**: Full of Projects and exams**Prerequisites**: Basic knowledge in Data Science will be very helpful**Cost**: Free or $1350 with certificate

- EVERYTHING starting from Python, Statistics and Probability, fundamentals of Data Science and Machine Learning.
- Create decision-making models by analyzing big data
- Handling structured and unstructured data
- Creating supervised and unsupervised models
- Building Machine Learning and Deep Learning models

- It’s from MIT. Do I need to tell you anything more?
- A complete journey divided into 5 different courses
- A community of intellectual moderators and peers to help you out whenever you’re stuck.
- Challenging projects to make you ready for industry
- The certificate is recognized by many top universities and equals 20%-30% credit of the master’s program!

Our business has data at its core. In a competitive marketplace, we have a growing need for employees trained in the critical field of data science with strong analytical skills. The MITx MicroMasters in Statistics and Data Science provides an excellent opportunity for learners to have rigorous training in statistics, data analysis, and machine learning and to develop the skills necessary to be competitive in a world increasingly shaped by data.

Joseph Logue, Executive Vice President, Booz Allen Hamilton

*MITx Micromasters Program in Statistics and Data Science *is my best choice in this review list. A bit of previous knowledge in every major topic will be very helpful for you. And if you keep digging into each topic after you've taken a class, this program will be the only one you should take before diving into your projects.

Just **one thing to keep in mind** when you’re taking this MicroMaster's program, **don’t rush and spend as much time as you can!** The course might get a bit difficult for you sometimes. But be active in the community, use Google if something is not clear to you, and solve the problems on your own. At least this is how I proceed. It will take time, but you can finish it properly within 6 months.

Why do I consider it to be the top one? If you’ve already taken some Data Science courses, just look at their syllabus. How complete it is! It has got everything from start to finish. Although you won’t find any end while learning, this program gives you everything you need to proceed. This is worth it!

**Rating**: 4.9/5 (154k+ ratings)**Total Number of Students**: 3.9M**Number of Courses**: 1**Certificate**: Available on Purchase**Level**: Beginner**Time to Complete**: 3 months at 5 hours per week**Projects**: Lots of projects and exams**Prerequisites**: None**Cost**: Free (Audit) or all courses at $49/month or $33/month when bought yearly

- EVERY SINGLE THEORY in Machine Learning.
- Supervised Learning
- Unsupervised Learning
- Best Practices in Machine Learning

- The best course on the planet that teaches every theoretical aspect that you need to know if you’re interested in Data Science or Machine Learning
- Mathematics for Machine Learning made easy.
- Taught by the co-founder of Coursera, founder of deeplearning.ai, co-founder and leader of Google Brain, my favorite Andrew Ng!

Excellent starting course on machine learning. Beats any of the so-called programming books on ML. Highly recommend this as a starting point for anyone wishing to be an ML programmer or data scientist.

Murali N

This is the best course to learn the theory and mathematics behind Machine Learning and Data Science. Although it’s a bit old course, there’s still no course made as simple and complete on theories as it is. Almost anyone you find learning Data Science or Machine Learning for a while has already taken this course.

The only drawback of this course is that it’s taught in MatLab, instead of Python or R. That’s why I keep saying it’s the best for theories. Instead of updating this course in Python, Andrew Ng and his team are focusing on launching new and updated courses on various topics of Deep Learning. If you’re interested, take the Deep Learning AI Specialization after this course.

**Rating**: 4.8/5**Number of Courses**: 29 (mini-courses)**Certificate**: Available**Level**: Beginner**Time to Complete**: 2 months at 10 hours per week**Projects**: Good number of projects, but a huge amount of practice problems. The most in the list**Prerequisites**: None**Cost**: $12/month

- Python Programming
- Data Manipulation with Pandas
- Data Visualization with Matplotlib, Seaborn
- Statistics
- Supervised and Unsupervised learning

- DataCamp skill tracks are best suited for beginners because of how easy they made it and the huge amount of exercises after each video tutorial
- Starts from Python and serves a good amount of knowledge on different libraries that are regularly used in Data Science
- The lecture slides and cheat sheets are very useful when you’re trying to implement a project on your own.
- The track is very engaging that I finished 1 course per day on average. But don’t rush like me.
- Delivers the maximum amount of knowledge in a minimum amount of time. So very efficient.

This skill track by DataCamp is best suited for beginners. It’s so engaging and easy that you’ll start loving the platform. Although I missed 2 things on DataCamp. They don’t go that much deep into Data Science, and they don’t have a community. So you might need to take more than 1 skill track to master the topic. Still, I love the platform and would highly recommend you if you’re just starting out.

**Rating**: 4.8/5**Number of Courses**: 4**Certificate**: Yes. Considered to be 20% to 30% of total credit in Masters programs at some well-known universities.**Level**: Beginner to Advanced. Most suited for intermediate level**Time to Complete**: 10 months at 10 hours per week**Projects**: Full of Projects and exams**Prerequisites**: None. But a basic understanding of any programming language will be very helpful**Cost**: Free or $1260 with certificate

- Everything from Python, Statistics and Probability, data cleaning and visualizing, creating machine learning models, and working with big data in Apache Spark!
- Loading and cleaning real word data with Python
- Visualizing complex data
- Creating machine learning models to extract information from noisy data
- How to use Apache Spark to analyze data that does not fit within the memory of a single computer
- Programming Spark with PySpark

- Focused on an industry level and real-world problems and solutions
- Dedicated course on Big Data, an increasing demand for Data Science
- A broad range of topics such as Generative and Discriminative models, clustering, dimensionality reduction, autoencoders, Deepnet, etc.
- Consists of both theories behind the models and real-world projects

Mitchell International highly values professionals with proficiency in Data Science and recognizes the need for more individuals to obtain this knowledge and skill set. It is my belief that a candidate credentialed through the edX MicroMasters program in Data Science would have a marked advantage for the following positions in Mitchell International: Business Systems Analyst, Data Analyst, Data Scientist, Data Informatics Analyst, Machine Learning Engineer.

Erez Nir , SVP and CTO, Mitchell International

I consider this MicroMasters program as an alternative to the first one in the list, the MicroMasters® Program in Statistics and Data Science by MITx. The main focus of this program is the course Big Data Analytics Using Spark. The program starts with Python programming, then proceeds to Statistics and Probability with Python. Next talks about the models in Machine Learning. Finally jumps into working with Big Data.

The program details might look minimal to you, but once you start it, you’ll see how broad it is. I recommend this to those who are mostly interested in working with Big Data. But I personally like taking the MicroMasters® Program in Statistics and Data Science by MITx, and then taking only the last course of this program which is Big Data Analytics Using Spark.

**Rating**: 4.7/5**Number of Courses**: 5**Certificate**: Available on completion**Level**: Intermediate**Time to Complete**: 4 months**Projects**: 4 projects and a lot of quizzes**Prerequisites**: Python. SQL, Statistics, Basic Machine Learning**Cost**: $1356 (Discount available)

- Solving Data Science problems and presenting them to a various audience
- Software engineering skills that are essential for data scientists
- Working with data through the
**entire**data science process - Designing a recommendation engine with IBM
- Creating a data science project of your own

- The projects are designed by industry experts and are very popular right now
- Truly prepares you for a Data Science job
- Solving 4 big projects, including a unique one of your choice
- The support from the community, project reviewers, and technical mentor support is unique from other platforms
- The career services like resume support, Github review, and LinkedIn profile optimization will ease your way of getting a job

I enjoyed the Data Scientist Nanodegree. I especially like the portfolio projects. They are sufficiently challenging, but they come with helpful instructions so that I can actually finish them and put them on my resume. The GitHub and LinkedIn review requirements also pushed me to polish my online presence. I am a lot more confident to look for a data scientist position than I was four months ago.

Ying G.

There’s one thing I highly like and one thing I highly dislike about the NanoDegree. The program costs a lot, but still, it has a big list of prerequisites. If I’m spending more than $500, I’d expect it to be a complete program. I’m okay if it takes more than a year to complete, but when the program is a complete bundle, it contains a reliable flow.

On the other hand, I like how it truly prepares for the industry. To become a Data Scientist takes a lot of effort. So instead of stacking lots of courses at once, they focused only on preparing you for a job with some challenging projects. Still, if it was a zero to hero program, I’d rank it as number 1 or 2 on the list.

**Rating**: 4.6/5 (150k ratings)**Total Number of Students**: 240k**Number of Courses**: 9**Certificate**: After each course, and also at the end of the full program**Level**: Beginner**Time to Complete**: 5 months at 10 hours per week**Projects**: n projects/ Available**Prerequisites**: None**Cost**: All courses at $39/month or $33/month when bought yearly

- How to use the tools needed for Data Science
- Data Science methodology like the major steps involved in tackling a Data Science problem
- Fundamentals of Python programming for Data Science and AI
- Databases and SQL, a must-have knowledge for Data Scientist
- Data analysis and visualization
- Machine Learning models, including supervised and unsupervised learning
- A capstone real-life Data Science project

- A vast program consisting of almost everything you need to learn
- Dedicated courses on Data Manipulation, Visualization, and SQL
- A wide range of tools used: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio
- A wide range of libraries used: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.
- Except for the first course, all other courses include a series of hands-on labs in the IBM Cloud

This professional certificate program serves a wide range of knowledge. The projects give a real-life problem-solving feel. I just missed one thing in this certification, there’s no course on the mathematics required for Data Science. Maybe that’s because Coursera has an outstanding specialization, Mathematics for Machine Learning by Imperial College London.

You might find the first part of the course not that engaging. But if you stick to it, you definitely will get some unique knowledge and experience. The same program is on EdX, Professional Certificate in IBM Data Science. But the one on Coursera is relatively cheaper.

**Rating**: 4.5/5 (85k ratings)**Total Number of Students**: 370k**Number of Courses**: 1 (All in one)**Certificate**: Available**Level**: Beginner**Time to Complete**: 2 months**Video Material Length**: 28.5 hours**Projects**: Available**Prerequisites**: None**Cost**: $9.99

- Probability: Combinatorics, Bayesian Interface, Distributions
- Statistics: Descriptive statistics, Inferential statistics, Hypothesis testing
- Linear Algebra necessary for Data Science
- Python programming
- Advanced statistical methods
- Deep Learning using Tensorflow
- Case studies

- A lot of offers within a very cheap price
- Touches a lot of things you must know in your Data Science journey
- Focuses a lot on the mathematics behind Data Science, which is very important
- No renew price like other platforms

Very informative, fun to get through, learned a lot about the basics of what I'll be diving into more. There's a lot more to learn but this was a good foundation.

Kyler Gould

I recommend this course to those who are just starting with Data Science or Machine Learning, and not sure about whether it is really a good field for them. You’ll only have the foundational knowledge of most sectors you need to master to truly become a Data Scientist. I really missed the data visualization part.

Unfortunately, this is the best Data Science course on Udemy, and lots of reviewers say it will make you a Data Scientist, which is a lie. Just build your foundation with it, and then grab one of the top programs stated above. This will help you understand those courses very easily. So it’s worth trying for absolute beginners.

**Rating**: 4.6/5 (n ratings)**Total Number of Students**: 420k**Number of Courses**: 1 (All in one)**Certificate**: Available**Level**: Beginner**Time to Complete**: 2 months**Video Material Length**: 25 hours**Projects**: Available**Prerequisites**: None**Cost**: $9.99

- Basic Python Programming
- A satisfactory amount of knowledge on Numpy, Pandas, Matplotlib, Seaborn libraries.
- Machine Learning models starting from linear regression up to recommender system
- Natural Language Processing
- Neural Networks, Deep Learning, and Big Data

- Popular topics covered within a very cheap price
- Focuses a lot on Data Manipulation and Data Visualization, which I like a lot
- Most of the topics are covered and it’s enough for you to start exploring Data Science
- Includes NLP, Big Data, and Spark with Python
- A very well structured program

The amount of topics covered in this course is very good. This course is very good for someone to get knowledge of data science. The only thing this course is missing is the theoretical explanation of various concepts.

Jashan Uppal

This course is an alternative to the previous one. It fills the gap of the previous course. It focuses a lot on Data Manipulation and Visualization. And also includes topics like NLP and Big Data, which are very popular in the industry.

However, the course does not have any section for the mathematics behind Data Science. So you’ll miss a major section of Data Science. This is the top-rated course on Udemy, still, it will only serve you the basic idea of the DS field. But if you’re familiar with Probability, Statistics, and Linear Algebra, and you want to explore the Data Science field, the course is worth the price.

**Rating**: 4.5/5 (42k ratings)**Total Number of Students**: 270k**Number of Courses**: 5**Certificate**: Available**Level**: Beginner.**Time to Complete**: 5 months at 7 hours per week**Projects**: Some projects and a bunch of quizzes**Prerequisites**: Basic Python programming language will be helpful**Cost**: Free on audit or all courses at $49/month or $33/month when bought yearly

- Basics of Python, importing tabular data, cleaning it, manipulating, and running basic inferential statistical analysis
- Data visualization, plotting, charting, data representation, and best practices using the Matplotlib library
- Supervised and Unsupervised learning theories and implementation using Scikit Learn
- Text mining and manipulation using NLTK framework, regex, NLP
- Social network analysis with NetworkX library

- Will be very helpful for those who are interested in text manipulation
- Offers a unique course which is Applied Social Network Analysis with Python
- The community support and graded programming assignments will attract you a lot
- Serves enough knowledge to start working with your own projects
- I consider it to be kind of a complete guideline, although you’ve to learn a lot on your own

I like this specialization because it focuses on text manipulation a lot. And the Social Network Analysis course is a unique one and you’ll like it. I recommend this course if you have some previous Python and Statistics knowledge, and the Social Network Analysis course attracts you.

**Rating**: 4.9/5**Number of Courses**: 9**Certificate**: Available**Level**: Beginner**Time to Complete**: 6 months at 10 hours per week**Projects**: Lots of projects and quizzes**Prerequisites**: None**Cost**: $792

- Basics of R programming, including data wrangling, sorting, and making plots
- Data Visualization using ggplot2 and differentiating weakness of several widely used plots
- Probability and Statistics, and the theorems necessary for Data Science
- How to use various productivity tools needed for Data Science, such as Linux, RStudio, and Github
- Machine Learning concepts starting from Linear Regression to Movie Recommender System

- If you wish to learn Data Science with R, then this is the best program on the internet right now
- A full program starting from the basics of R programming to Machine Learning models, including the mathematics behind it!
- It has lots of projects, specially the capstone project will be very challenging.
- It has a community. Get help from moderators and peers around the world.

This is my top pick for Data Science courses in the R programming track. It has everything starting from programming, data manipulation, visualization, mathematics, machine learning models, and ending with a capstone project. This is the most popular program among Data Science enthusiasts with an R track.

**Rating**: 4.5/5 (83k ratings)**Total Number of Students**: 420k**Number of Courses**: 10**Certificate**: After each course and also at the end of the specialization**Level**: Beginner**Time to Complete**: 7 months at 10 hours per week**Projects**: Full of projects and quizzes**Prerequisites**: None**Cost**: All courses at $39/month or $33/month when bought yearly

- Tools for a Data Scientist, such as version control, markdown, git, R and RStudio
- Foundation in R programming language
- Collecting data, cleaning, analyzing, visualizing in graphs, and reporting in a reproducible manner
- Fundamentals of inference in a practical approach
- Regression models, prediction functions, and various Machine Learning models
- The statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience

- It’s a complete program for learning Data Science with R and does not have any prerequisites
- 10 different courses focusing on 10 different important sectors of Data Science
- This program is different because of its unique practical approach.
- It shows how to collect data, how to report modern data analysis in a reproducible manner, how to create a data product that can be used to tell a story about the data. These are unique.

You might have already noticed that I like the programs that are complete with resources and well structured. The Data Science Specialization by John Hopkins on Coursera is one of them. It’s huge, well structured, full of knowledge, quizzes, and projects. On top of that, it offers some unique practical knowledge as stated above. This specialization will definitely help you in your professional life. However, I wanted more topics in Machine Learning.

**Rating**: 4.6/5**Number of Courses**: 19**Certificate**: Available**Level**: Beginner**Time to Complete**: 3.5 months at 10 hours per week**Projects**: Some projects and a lot of practice problems**Prerequisites**: None**Cost**: $12/month

- Fundamentals of R programming
- Data manipulation with dplyr and visualization with Tidyverse and ggplot2
- Correlation of data and Regression model
- Supervised learning (classification and regression) and unsupervised learning (clustering and dimensionality reduction)
- Cluster analysis

- The easiest and engaging way to start learning Data Science in R
- A rich amount of practice problems make it suitable for absolute beginners
- Different courses on dplyr, Tidyverse, and ggplot2 helps to master these skills very easily
- Covers the most important topics of Data Science in an engaging manner

This skill track is best suited for absolute beginners. It’s so engaging and has a huge amount of practice problems. I like everything it offers. But unfortunately, it does not offer everything you need. For example, it does not cover the mathematics behind Data Science. And it covers only the most important topics of Machine Learning. The track will take you from absolute beginner to intermediate learner, and you’ll have enough knowledge to start working on your own projects.

**Rating**: 4.6 (13k ratings)**Total Number of Students**: 70k**Number of Courses**: 1 (All in one)**Certificate**: Available**Level**: Beginner/Intermediate**Time to Complete**: 1 month at 10 hours per week**Video Material Length**: 17.5 hours**Projects**: Projects and quizzes on every major topic**Prerequisites**: Basic math skills**Cost**: $9.99

- Fundamentals of R programming
- Data Manipulation and Visualization
- Machine Learning models
- NLP
- Neural Nets

- It includes NLP and Neural Nets, which are absent in the courses listed above
- Best for beginners who have some knowledge in probability and statistics
- Covers almost all the major topics for Data Science in R
- Best course for the cheapest price

The best thing about this course is that it includes NLP and Neural Nets. You’ll easily grasp the R programming part of the course, but it will become a bit tough for the Machine Learning section if you’re not already familiar with Probability and Statistics. Well, you can learn them from other places, but I would rank this course 1 or 2 only if there was enough explanation of the NLP and Neural Nets topics.

I tried my best to present to you the top data science courses that you should care about. You don’t have to take all of them. I suggest taking 2 to 3 courses from the list for the best outcome. Here’s a more specific suggestion for you.

- Complete beginners and want to explore the field before spending a good amount of time and money: Either The Data Science Course 2020: Complete Data Science Bootcamp by Udemy or Python for Data Science and Machine Learning Bootcamp by Udemy.
- An absolute beginner who loves engaging environments and lots of practice problems to grasp the basics properly: Data Scientist With Python by DataCamp
- Determined to start the journey: Machine Learning by Andrew Ng on Coursera to sharpen the theories
- Undergrad student looking for a complete program and also use the certificate as a 20%-30% credit in the Master’s program: Either MicroMasters® Program in Statistics and Data Science by MIT on EdX or MicroMasters® Program in Data Science by UCSanDiego on EdX.
- Have strong knowledge of Python, SQL, and Mathematics and now want to be ready for a job: Data Scientist NanoDegree by Udacity
- Willing to learn with industry tools experience: IBM Data Science Professional Certificate by Coursera
- Have a passion for Social Network Analysis or looking to audit: Applied Data Science with Python Specialization by UMich on Coursera

- Absolute beginners looking for an engaging environment and lots of practice problems: Data Scientist with R by DataCamp
- Undergrad student looking for a complete program, and also want to use the certificate as a 20%-30% credit of Masters program: Professional Certificate in Data Science by Harvard on Edx. It also contains the Best Data Science Courses in R on the internet right now.
- The best Data Science courses at a very reasonable price for absolute beginners: Data Science Specialization by John Hopkins on Coursera
- Looking for NLP and Neural Nets knowledge in R: Data Science and Machine Learning Bootcamp with R by Udemy

If you have any suggestions, let me know. I’ll try to update this list for The Best Data Science Courses On The Internet In Python And R!

- Choosing from the Top 10 C++ Courses: The Ultimate Guide 2021
- 16 Best Data Analysis Courses For Beginners 2021 [Reviewed]

It's tough to declare a course or a program to be the best on the internet. After analyzing around 35 online courses, I believe MicroMasters® Program in Statistics and Data Science by MIT on EdX is the most complete course set.

It's undoubtedly the Machine Learning course by Andrew Ng on Coursera. Although the course is on MatLab, Andrew Ng still teaches the Machine Learning theories better than anyone.

If you want to get some value from any online Data Science course certificate, then MicroMasters® Program in Statistics and Data Science by MIT on EdX is your best choice to go for. This is considered to be around 20% to 30% of total credits in Master’s programs at many well-known universities.

After the Corona Pandemic, you might have experienced the importance of Data Science and Machine Learning the most! Its demand is increasing day by day. Data is money in 2021!

2 things you must know. One, if you're a boss of a company, will you hire someone based on how many certificate they have? I'm sure you'll look for the skill. Secondly, it's easy to skip the course materials and submit others course work to get a certificate. So ignore the certificate thing, don't deceive yourself, and get the knowledge from the courses and build your skill.

It's like cooking foods without fire! You will get done a lot of your works, but not enough to actually play with the data and get the best use of it. Just learn the basics of Probability and Statistics, and you'll be very comfortable with Data Science.

When I planned to learn Data Science, I was so confused about which are the best Data Science courses and in what order I should take them. So I started enrolling in courses and also leaving them in the middle when I found I’m not ready for that course yet. It took a while to cope up with the topics and to find the best courses.

**Course Provider: **
Organization

**Course Provider Name: **
MicroMasters® Program in Statistics and Data Science by MIT on EdX

**Course Provider URL: **
https://www.edx.org/micromasters/mitx-statistics-and-data-science

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