Currently, there are many curricula in universities, courses, or other places that provide curricula to become a data scientist. One of the tools that are widely used to learn data science is by taking online courses (Udemy, Coursera, Datacamp, etc.). The reason is that the time is relatively short compared to if we have to attend lectures, and we can access it wherever and whenever we want.
Online courses for data scientist do not make you a data scientist right away. However, online data science courses can help you get started especially if you don’t have a background in data. By taking the online course in data science, you can find out what data scientists are doing.
However, if you want to become a professional data scientist, you also have to train your soft skills and these cannot be obtained just by taking data scientist online courses. You have to train it by working on a real data science project.
Best Data Scientist Courses & Certifications Online
Resources you should use when learning
When learning data science online it’s important to not only get an intuitive understanding of what you’re actually doing but also to get sufficient practice using data science on unique problems.
In addition to the courses listed below, I would suggest reading two books:
- Introduction to Statistical Learning — available for Free — one of the most widely recommended books for beginners in data science. Explains the fundamentals of machine learning and how everything works behind the scenes
- Applied Predictive Modeling — a breakdown of the entire modeling process on real-world datasets with incredibly useful tips each step of the way
These two textbooks are incredibly valuable and provide a much better foundation than just taking courses alone. The first book is incredibly effective at teaching the intuition behind much of the data science process, and if you are able to understand almost everything in there, then you’re more well off than most entry-level data scientists.
Furthermore, since both of these books utilize R in their exercises and examples, a great learning experience would be to work through them in R and then convert them to Python.
1. Data Science Specialization — JHU @ Coursera
This course series is one of the most enrolled and highly rated course collections on this list. JHU did an incredible job with the balance of breadth and depth in the curriculum. One thing that’s included in this series that’s usually missing from many data science courses is a complete section on statistics, which is the backbone of data science.
Overall, the Data Science specialization is an ideal mix of theory and application using the R programming language. As far as prerequisites go, you should have some programming experience (doesn’t have to be R) and you have a good understanding of Algebra. Previous knowledge of Linear Algebra and/or Calculus isn’t necessary, but it is helpful.
Price – Free or $49/month for certificate and graded materials
Provider – Johns Hopkins University
Curriculum:
- The Data Scientist’s Toolbox
- R Programming
- Getting and Cleaning Data
- Exploratory Data Analysis
- Reproducible Research
- Statistical Inference
- Regression Models
- Practical Machine Learning
- Developing Data Products
- Data Science Capstone
If you’re rusty with statistics and/or want to learn more R first, check out the Statistics with R Specialization as well.
2. Introduction to Data Science — Metis
An extremely highly rated course — 4.9/5 on SwichUp and 4.8/5 on CourseReport — which is taught live by a data scientist from a top company. This is a six-week-long data science course that covers everything in the entire data science process, and it’s the only live online course on this list. Furthermore, not only will you get a certificate upon completion, but since this course is also accredited, you’ll also receive continuing education units.
Two nights per week, you’ll join the instructor with other students to learn data science as if it was an online college course. Not only are you able to ask questions, but the instructor also spends extra time for office hours to further help those students that might be struggling.
Price — $750
The curriculum:
- Computer Science, Statistics, Linear Algebra Short Course
- Exploratory Data Analysis and Visualization
- Data Modeling: Supervised/Unsupervised Learning and Model Evaluation
- Data Modeling: Feature Selection, Engineering, and Data Pipelines
- Data Modeling: Advanced Supervised/Unsupervised Learning
- Data Modeling: Advanced Model Evaluation and Data Pipelines | Presentations
For prerequisites, you’ll need to know Python, some linear algebra, and some basic statistics. If you need to work on any of these areas, Metis also has Beginner Python and Math for Data Science, a separate live online course just for learning Python, Stats, Probability, Linear Algebra, and Calculus for data science. If you’re interested in taking a dedicated Python course, see my Python course article for the best offerings according to data analysis.
3. Applied Data Science with Python Specialization — UMich @ Coursera
The University of Michigan, which also launched an online data science Master’s degree, produce this fantastic specialization focused on the applied side of data science. This means you’ll get a strong introduction to commonly used data science Python libraries, like matplotlib, pandas, nltk, scikit-learn, and networkx, and learn how to use them on real data.
This series doesn’t include the statistics needed for data science or the derivations of various machine learning algorithms but does provide a comprehensive breakdown of how to use and evaluate those algorithms in Python. Because of this, I think this would be more appropriate for someone that already knows R and/or is learning the statistical concepts elsewhere.
If you’re rusty with statistics, consider the Statistics with Python Specialization first. You’ll learn many of the most important statistical skills needed for data science.
Price – Free or $49/month for certificate and graded materials
Provider – University of Michigan
Courses:
- Introduction to Data Science in Python
- Applied Plotting, Charting & Data Representation in Python
- Applied Machine Learning in Python
- Applied Text Mining in Python
- Applied Social Network Analysis in Python
To take these courses, you’ll need to know some Python or programming in general, and there are actually a couple of great lectures in the first course dealing with some of the more advanced Python features you’ll need to process data effectively.
4. Data Science MicroMasters — UC San Diego @ edX
MicroMasters from edX are advanced, graduate-level courses that count towards a real Master’s at select institutions. In the case of this MicroMaster’s, completing the courses and receiving a certificate will count as 30% of the full Master of Science in Data Science degree from Rochester Institute of Technology (RIT).
Since these courses are geared towards prospective Master’s students, the prerequisites are higher than many of the other courses on this list. Since the first course in this series doesn’t spend any time teaching basic Python concepts, you should already be comfortable with programming. Spending some time going through a platform like Treehouse would probably get you up to speed for the first course.
Overall, I found this MicroMaster’s to be a perfect mix of theory and application. The lectures are comprehensive in scope and balanced superbly with real-world applications.
Price – Free or $1,260 for certificate and graded materials
Provider – UC San Diego
Courses:
- Python for Data Science
- Probability and Statistics in Data Science using Python
- Machine Learning Fundamentals
- Big Data Analytics using Spark
The one downside of this MicroMaster’s, and many courses on edX, is that they aren’t offered as frequently as other platforms. If your schedule aligns with the start date of the first course, definitely consider jumping in.
5. Dataquest
Dataquest is a fantastic resource on its own, but even if you take other courses on this list, Dataquest serves as a superb complement to your online learning.
Dataquest foregoes video lessons and instead teaches through an interactive textbook of sorts. Every topic in the data science track is accompanied by several in-browser, interactive coding steps that guide you through applying the exact topic you’re learning.
To me, Dataquest stands out from the rest of the interactive platforms because the curriculum is very well organized, you get to learn by working on full-fledged data science projects, and there’s a super active and helpful Slack community where you can ask questions.
The platform has one main data science learning curriculum for Python:
Data Scientist In Python Path
This track currently contains 31 courses, which cover everything from the very basics of Python, to Statistics, to math for Machine Learning, to Deep Learning, and more. The curriculum is constantly being improved and updated for a better learning experience.
Price – 1/3 of content is Free, 29/monthforBasic,49/month for Premium
Here’s a condensed version of the curriculum:
- Python – Basic to Advanced
- Python data science libraries – Pandas, NumPy, Matplotlib, and more
- Visualization and Storytelling
- Effective data cleaning and exploratory data analysis
- Command-line and Git for data science
- SQL – Basic to Advanced
- APIs and Web Scraping
- Probability and Statistics – Basic to Intermediate
- Math for Machine Learning – Linear Algebra and Calculus
- Machine Learning with Python – Regression, K-Means, Decision Trees, Deep Learning, and more
- Natural Language Processing
- Spark and Map-Reduce
Additionally, there are also entire data science projects scattered throughout the curriculum. Each project’s goal is to get you to apply everything you’ve learned up to that point and to get you familiar with what it’s like to work on an end-to-end data science strategy.
Lastly, if you’re more interested in learning data science with R, then definitely check out Dataquest’s new Data Analyst in R path. The Dataquest subscription gives you access to all paths on their platform, so you can learn R or Python (or both!).
6. Statistics and Data Science MicroMasters — MIT @ edX
The inclusion of probability and statistics courses makes this series from MIT a very well-rounded curriculum for being able to understand data intuitively. This MicroMaster’s from MIT dedicates more time towards statistical content than the UC San Diego MicroMaster’s mentioned earlier in the list.
Due to its advanced nature, you should have experience with single and multivariate calculus, as well as Python programming. There isn’t any introduction to Python or R like in some of the other courses in this list, so before starting the ML portion, they recommend taking Introduction to Computer Science and Programming Using Python to get familiar with Python. If you’d rather utilize an on-demand interactive platform to learn Python, check out Treehouse’s Python track.
Price – Free or $1,350 for certificate and graded materials
Provider – University of Michigan
Courses:
- Probability – The Science of Uncertainty and Data
- Data Analysis in Social Science—Assessing Your Knowledge
- Fundamentals of Statistics
- Machine Learning with Python: from Linear Models to Deep Learning
- Capstone Exam in Statistics and Data Science
The ML course has several interesting projects you’ll work on, and at the end of the whole series, you’ll focus on one exam to wrap everything up.
7. CS109 Data Science — Harvard
With a great mix of theory and application, this course from Harvard is one of the best for getting started as a beginner. It’s not on an interactive platform, like Coursera or edX, and doesn’t offer any sort of certification, but it’s definitely worth your time and it’s totally free.
Curriculum:
- Web Scraping, Regular Expressions, Data Reshaping, Data Cleanup, Pandas
- Exploratory Data Analysis
- Pandas, SQL and the Grammar of Data
- Statistical Models
- Storytelling and Effective Communication
- Bias and Regression
- Classification, kNN, Cross-Validation, Dimensionality Reduction, PCA, MDS
- SVM, Evaluation, Decision Trees and Random Forests, Ensemble Methods, Best Practices
- Recommendations, MapReduce, Spark
- Bayes Theorem, Bayesian Methods, Text Data
- Clustering
- Effective Presentations
- Experimental Design
- Deep Networks
- Building Data Science
Python is used in this course, and there are many lectures going through the intricacies of the various data science libraries to work through real-world, interesting problems. This is one of the only data science courses around that actually touches on every part of the data science process.
8. Python for Data Science and Machine Learning Bootcamp — Udemy
A very reasonably priced course for the value. The instructor does an outstanding job explaining the Python, visualization, and statistical learning concepts needed for all data science projects. A huge benefit to this course over other Udemy courses is the assignments. Throughout the course you’ll break away and work on Jupyter notebook workbooks to solidify your understanding, then the instructor follows up with a solutions video to thoroughly explain each part.
Curriculum:
- Python Crash Course
- Python for Data Analysis – Numpy, Pandas
- Python for Data Visualization – Matplotlib, Seaborn, Plotly, Cufflinks, Geographic plotting
- Data Capstone Project
- Machine learning – Regression, kNN, Trees and Forests, SVM, K-Means, PCA
- Recommender Systems
- Natural Language Processing
- Big Data and Spark
- Neural Nets and Deep Learning
This course focuses more on the applied side, and one thing missing is a section on statistics. If you plan on taking this course it would be a good idea to pair it with a separate statistics and probability course as well.
An honorary mention goes out to another Udemy course: Data Science A-Z. I do like Data Science A-Z quite a bit due to its complete coverage, but since it uses other tools outside of the Python/R ecosystem, I don’t think it fits the criteria as well as Python for Data Science and Machine Learning Bootcamp.
Other top data science courses for specific skills
Deep Learning Specialization — Coursera
Created by Andrew Ng, maker of the famous Stanford Machine Learning course, this is one of the highest-rated data science courses on the internet. This course series is for those interested in understanding and working with neural networks in Python.
Complete SQL Mastery — CodeWithMosh
Pair this with Coursera’s SQL for Data Science course for a very well-rounded introduction to SQL, an important and necessary skill for data science.
Computational Thinking using Python XSeries — edX
Although this series only runs once every several months, if you’re new to Computer Science and Python this a great series to jump into if you get the chance. I found the lecturers to be really passionate about what they teach, making it a pleasant experience taking the courses.
Mathematics for Machine Learning — Coursera
This is one of the most highly rated courses dedicated to the specific mathematics used in ML. Take this course if you’re uncomfortable with the linear algebra and calculus required for machine learning, and you’ll save some time over other, more generic math courses.
How to Win a Data Science Competition — Coursera
One of the courses in the Advanced Machine Learning Specialization. Even if you’re not looking to participate in data science competitions, this is still an excellent course for bringing together everything you’ve learned up to this point. This is more of an advanced course that teaches you the intuition behind why you should pick certain ML algorithms, and even goes over many of the algorithms that have been winning competitions lately.
Bayesian Statistics: From Concept to Data Analysis — Coursera
Bayesian, as opposed to Frequentist, statistics is an important subject to learn for data science. Many of us learned Frequentist statistics in college without even knowing it, and this course does a great job comparing and contrasting the two to make it easier to understand the Bayesian approach to data analysis.
Spark and Python for Big Data with PySpark — Udemy
From the same instructor as the Python for Data Science and Machine Learning Bootcamp in the list above, this course teaches you how to leverage Spark and Python to perform data analysis and machine learning on an AWS cluster. The instructor makes this course really fun and engaging by giving you mock consulting projects to work on, then going through a complete walkthrough of the solution.
