11 Best + Free Data Analysis Courses & Certificates [2021]
- 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]
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
- Udemy and Eduonix are best for practical, low cost and high quality Data Analysis courses.
- Coursera, Udacity and EdX are the best providers for a Data Analysis certificate, as many come from top Ivy League Universities.
- YouTube is best for free Data Analysis crash courses.
- PluralSight, SkillShare and LinkedIn are the best monthly subscription platforms if you want to take multiple Data Analysis courses.
- Independent Providers for Data Analysis courses & certificates are generally hit or miss.
Provider
University
Tags
Rating
Duration
Difficulty
Publication Year
Language
The Data Science Course 2022: Complete Data Science Bootcamp (2022)
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- 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.
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- 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.
1 )
Exploratory Data Analysis (2016)
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- 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.
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- 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.
2 )
Fundamentals of Data Analytics (2017)
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- 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.
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- 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.
3 )
Intro to Data Science - Crash Course for Beginners (2019)
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- 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.
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- 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.
4 )
Learning Python for Data Analysis and Visualization (2019)
What You'll Learn
- Have an intermediate skill level of Python programming.
- Use the Jupyter Notebook Environment.
- Use the numpy library to create and manipulate arrays.
- Use the pandas module with Python to create and structure data.
- Learn how to work with various data formats within python, including: JSON,HTML, and MS Excel Worksheets.
- Create data visualizations using matplotlib and the seaborn modules with python.
- Have a portfolio of various data analysis projects.
5 )
Data Science Full Course - Learn Data Science in 10 Hours (2019)
Quality Score
Overall Score : 99 / 100
6 )
Python Data Science Handbook (2016)
Quality Score
Overall Score : 99 / 100
7 )
The Analytics Edge (2014)
Quality Score
Overall Score : 99 / 100
8 )
Data Analyst with Python (2017)
Quality Score
Overall Score : 99 / 100
9 )
Data Analyst in Python (2015)
Quality Score
Overall Score : 99 / 100
10 )
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2017)
Quality Score
Overall Score : 99 / 100