The Best Courses to Learn Data Analysis in 2020
As featured on Harvard EDU, Stackify and Inc - CourseDuck identifies and rates the Best Data Analysis 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
- 1. Exploratory Data Analysis [Coursera] - Best Free Course
- 2. Fundamentals of Data Analytics [Analyttica TreasureHunt] - Best Practical Course
- 3. Intro to Data Science - Crash Course for Beginners [YouTube] - Best Crash Course
- 4. Learning Python for Data Analysis and Visualization [Udemy] - Best Paid Course
- 5. Data Science Full Course - Learn Data Science in 10 Hours [YouTube] - Best NEW Course
- 6. Python Data Science Handbook [GitHub] - Best Text Based Course
- 7. The Analytics Edge [edX] - Best Advanced Course
- 8. Data Analyst with Python [DataCamp]
- 9. Data Analyst in Python [Dataquest]
- 10. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython [O'Reilly Media]
The Data Science Course 2020: Complete Data Science Bootcamp (2020)
- Course is as comprehensive as a first exposure course can be. It covers a little of everything and a lot of some things.
- Takes very complicated subject matter and makes it just plain easy to understand.
- Q&A responses are fast and reliable.
- There is no such thing as a complete data science boot camp. The field is too vast. This is an introduction.
- Course does a lot more talking about what data science looks like than how to do data science.
- Course tries to casually pack incredibly deep topics like statistical analysis and deep learning into small modules.
Exploratory Data Analysis (2016)
- Emphasis on graphical analysis creates a strong point that many other courses overlook or minimize.
- Covers a wide range of analytic techniques.
- Course deeply covers R, which is vital to analytic presentation.
- Course doesnt really get out of graphics systems, severely limiting the amount of exploratory data analysis that really happens.
- Theory is lacking in this course.
- Course outlines what data analysis consists of more than how to perform exploratory analysis.
Fundamentals of Data Analytics (2017)
- Course takes a streamlined look at foundational statistical methods, like probability distributions and frequently applied statistical tests.
- Course strongly covers hypothesis testing.
- Course is not overly long, helping students avoid statistics fatigue.
- Course does not go beyond the stated fundamentals at all. Students will need many more resources to truly understand data analytics.
- While the course uses real examples, it is overly focused on narrow business applications.
- Website hosting the course is clunky.
Intro to Data Science - Crash Course for Beginners (2019)
- Course scores an even distribution of the three sections. Students will not have a glaring weakness in the three pillars of data science after this course.
- The programming section offers something that is overlooked in most data science crash courses for beginners.
- Course takes a light approach to statistical nomenclature to help students focus on concepts over vocabulary.
- The lack of nomenclature can make it difficult for students to transition knowledge from this course to scholastic and professional applications.
- Course creator has not responded to a single question in over a year.