python data science online course

Last Updated on August 8, 2022 by Team College Learners

This data science with python certification course will teach you how to code with Python and do data science. It’s suitable for beginners who want to learn data science, programming, or both.

Learn Python, NumPy, Pandas, Matplotlib, PyTorch, and Linear Algebra from a professional programmer who has worked in Silicon Valley, Toronto, and New York. Designed for professionals with no prior programming experience, this course will teach you the skills you need to analyze data using Python. By the end of this course, you will be able to create machine learning models using a variety of algorithms and be able to understand which algorithm is best suited for each problem.

Python Libraries For Data Science

best python course for data science

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:

  1. The Data Scientist’s Toolbox
  2. R Programming
  3. Getting and Cleaning Data
  4. Exploratory Data Analysis
  5. Reproducible Research
  6. Statistical Inference
  7. Regression Models
  8. Practical Machine Learning
  9. Developing Data Products
  10. 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:

  1. Computer Science, Statistics, Linear Algebra Short Course
  2. Exploratory Data Analysis and Visualization
  3. Data Modeling: Supervised/Unsupervised Learning and Model Evaluation
  4. Data Modeling: Feature Selection, Engineering, and Data Pipelines
  5. Data Modeling: Advanced Supervised/Unsupervised Learning
  6. 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:

  1. Introduction to Data Science in Python
  2. Applied Plotting, Charting & Data Representation in Python
  3. Applied Machine Learning in Python
  4. Applied Text Mining in Python
  5. 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:

  1. Python for Data Science
  2. Probability and Statistics in Data Science using Python
  3. Machine Learning Fundamentals
  4. 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.

Video-based learning is more “passive” — it’s very easy to think you understand a concept after watching a 2-hour long video, only to freeze up when you actually have to put what you’ve learned in action. — Dataquest FAQ

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:

  1. Python – Basic to Advanced
  2. Python data science libraries – Pandas, NumPy, Matplotlib, and more
  3. Visualization and Storytelling
  4. Effective data cleaning and exploratory data analysis
  5. Command-line and Git for data science
  6. SQL – Basic to Advanced
  7. APIs and Web Scraping
  8. Probability and Statistics – Basic to Intermediate
  9. Math for Machine Learning – Linear Algebra and Calculus
  10. Machine Learning with Python – Regression, K-Means, Decision Trees, Deep Learning, and more
  11. Natural Language Processing
  12. 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:

  1. Probability – The Science of Uncertainty and Data
  2. Data Analysis in Social Science—Assessing Your Knowledge
  3. Fundamentals of Statistics
  4. Machine Learning with Python: from Linear Models to Deep Learning
  5. 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

Also available using R.

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.