30 Best + Free Data Science Courses & Certification [UPDATED]
As featured on Harvard EDU, Stackify and Inc - CourseDuck identifies and rates the Best Data Science Courses, Tutorials, Providers and Certifications, based on 12,000+ student reviews, public mentions, recommendations, ratings and polling 5,000+ highly active StackOverFlow members. Learn more
- Udemy and Eduonix are best for practical, low cost and high quality Data Science courses.
- Coursera, Udacity and EdX are the best providers for a Data Science certificate, as many come from top Ivy League Universities.
- YouTube is best for free Data Science crash courses.
- PluralSight, SkillShare and LinkedIn are the best monthly subscription platforms if you want to take multiple Data Science courses.
- Independent Providers for Data Science courses & certificates are generally hit or miss.
13% of jobs $41,000 - $58,999
10% of jobs $61,500 is the 25th percentile. Salaries below this are outliers.
$59,000 - $76,999
7% of jobs $77,000 - $94,999
6% of jobs $95,000 - $112,999
10% of jobs The average salary is $119,031 a year
$113,000 - $130,999
11% of jobs $131,000 - $148,999
10% of jobs $163,000 is the 75th percentile. Salaries above this are outliers.
$149,000 - $166,999
10% of jobs $167,000 - $184,999
10% of jobs $185,000 - $202,999
7% of jobs $203,000 - $221,000
2% of jobs
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- 1. Python for Data Science [edX] - Best Course Overall
- 2. Data Science A-Z: Real-Life Data Science Exercises Included [Udemy] - Best Paid Course
- 3. The Data Scientist's Toolbox [Coursera] - Best Coursera Course
- 4. Python for Data Science and Machine Learning Bootcamp [Udemy] - Editor's Choice
- 5. Learn Python For Data Science [YouTube] - Best YouTube Tutorial
- 6. Introduction to Computational Thinking and Data Science [edX]
- 7. Data Science And Machine Learning Masterclass [Eduonix] - Best Value Course
- 8. Intro to Data Analysis [YouTube]
- 9. Introduction to Data Science [edX]
- 10. Introduction to Data Science in Python [Coursera]
Python for Data Science (2017)
- Course teaches by example, giving you a realistic glimpse into legitimate data science.
- Course encourages peer interaction to build a supportive community that expands learning.
- All lecture videos include subtitles.
- Interaction with instructors can be slow and limited.
- Jupyter section is UNIX heavy and can be troubling to perform on Windows machines.
- Completing course does not guarantee reception of the certificate.
Data Science A-Z: Real-Life Data Science Exercises Included (2020)
What You'll Learn
- Successfully perform all steps in a complex Data Science project
- Create Basic Tableau Visualisations
- Perform Data Mining in Tableau
- Understand how to apply the Chi-Squared statistical test
- Apply Ordinary Least Squares method to Create Linear Regressions
- Assess R-Squared for all types of models
- Assess the Adjusted R-Squared for all types of models
- Create a Simple Linear Regression (SLR)
- Create a Multiple Linear Regression (MLR)
- Create Dummy Variables
- Interpret coefficients of an MLR
- Read statistical software output for created models
- Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
- Create a Logistic Regression
- Intuitively understand a Logistic Regression
- Operate with False Positives and False Negatives and know the difference
- Read a Confusion Matrix
- Create a Robust Geodemographic Seg
The Data Scientist's Toolbox (2019)
- Course takes a light introduction on a broad range of topics that all apply to data science. Great preparation for a full-dive, multicourse adventure into data science.
- Covers mindset of data science in a way most courses skip.
- Course ensures that you have the tools to take on a journey to truly master data science.
- Course covers a substantial range of data science tools. Accessing all of them can potentially add a hefty price tag to completing the course.
- Course is not a Capstone project. It is intended to prepare for a data science Capstone project.
- Course teaches very little data science itself. It is more like going over the syllabus and prerequisites before diving into real learning.
Python for Data Science and Machine Learning Bootcamp (2020)
What You'll Learn
- Use Python for Data Science and Machine Learning
- Use Spark for Big Data Analysis
- Implement Machine Learning Algorithms
- Learn to use NumPy for Numerical Data
- Learn to use Pandas for Data Analysis
- Learn to use Matplotlib for Python Plotting
- Learn to use Seaborn for statistical plots
- Use Plotly for interactive dynamic visualizations
- Use SciKit-Learn for Machine Learning Tasks
- K-Means Clustering
- Logistic Regression
- Linear Regression
- Random Forest and Decision Trees
- Natural Language Processing and Spam Filters
- Neural Networks
- Support Vector Machines
Learn Python For Data Science (2016)
- YouTube tutorials are always free and repeatable - one of their greatest strengths.
- Course delves well beyond the basics of Python and gets deep into databases for data science.
- Course caters to an audience that already understands the fundamentals of programming and doesn't waste time on things you already know.
- Content covers the usage of powerful resources. Accessing and installing them can provide barriers in completing the course.
- Interaction with instructor is extremely limited unless you pay for the additional DataCamp course.
Introduction to Computational Thinking and Data Science (2015)
- Covers some of the most common and important algorithms in all of programming.
- Supplemental MIT resources are available that make this into a master class in computational science.
- Completing the substantial challenges in this course is richly rewarding and will help develop a strong understanding of data science.
- Prerequisites are not to be taken lightly. This course covers deep topics related to data scientists and assumes a strong background in Python.
- Course is difficult and will overwhelm students who are not prepared to be challenged.
- Trying this course without first completely 6.00.1x can prove prohibitively difficult.
Data Science And Machine Learning Masterclass (2020)
What You'll Learn
- Machine Learning
- R programming
- Data science
- Learn Machine Learning from scratch
- Unsupervised learning, Supervised learning, Reinforcement learning, Neural networks, and so on
- Integrate algorithms in Python Projects
- Perform the most important pre-processing tasks needed prior to machine learning in R
- Use machine learning for unsupervised classification in R
- Evaluate the accuracy of supervised machine learning algorithms and compare their performance in R
- Carry out data visualization in R
Intro to Data Analysis (2016)
- It's short. Taking up less than two hours of your life, this course will not feel grueling or overwhelming.
- It's on YouTube. That means it's free and accessible.
- Demo-based teaching allows you to see concepts in motion.
- It's short. Truly living up to the word "intro", this course does not delve deeply into analytical techniques. It only covers the bare basics and will not teach the tools for professional analysis.
- Tutorial is more fun than serious. While information is accurate, it does not reflect the data science profession.
- Series is more of an overview of a few ideas than a true course in data science.
Introduction to Data Science (2017)
Overall Score : 88 / 100
Introduction to Data Science in Python (2016)
Overall Score : 90 / 100
CS109 Data Science (2015)
Overall Score : 100 / 100