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369 Best + Free Data Science Courses & Certification [2020][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

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283 Filtered Courses
Python for Data Science
provider
Best Course Overall

2 )

Python for Data Science (2017)

4.4
This 10-week course is free to enroll and is structured around a total of 80 hours of active learning. This is not an introductory course. It is an advanced look at applying Python to applications in data science.
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Pros
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Cons
    • 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.
Best YouTube Tutorial

3 )

Learn Python For Data Science (2016)

4.6
This YouTube course by DataCamp is a 40-part video series that explains how to use Python in data science. While it serves as an introduction to data science applications of Python, it is not an introductory coding class. The free YouTube series can be supplemented by a paid course run through DataCamp.
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Pros
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Cons
    • 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.
The Data Scientist's Toolbox
provider

4 )

The Data Scientist's Toolbox (2019)

5.0
This Coursera course was created by Johns Hopkins University. It introduces tools and resources that are essential to working in data science, and it splits lessons into a theoretical and practical half.
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Pros
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Cons
    • 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.
Introduction to Computational Thinking and Data Science
provider

5 )

Introduction to Computational Thinking and Data Science (2015)

4.6
This MIT EdX course covers a brief introduction into data science and computational problem solving. It is not an introduction to computer science; students are expected to have programming experience in Python. Course introduces tools for simulating and modeling with data.
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Pros
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Cons
    • 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.

6 )

Intro to Data Analysis (2016)

4.2
Siraj Raval built this YouTube series as a genuine introduction into data analysis. It covers the basics, and it focuses on the easy ways to accomplish meaningful tasks. The entire series is under two hours, making it a quick and free introduction to the concepts.
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Pros
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Cons
    • 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
provider

7 )

Introduction to Data Science (2017)

4.3
This EdX course was developed by Microsoft. It is part of the Microsoft certification program for computer science. It is completely free (although the certificate will only be provided for a fee), and it can be completed in 12 to 24 hours of work.
Introduction to Data Science in Python
provider

8 )

Introduction to Data Science in Python (2016)

4.4
Coursera brings you this resource that was developed by the University of Michigan. It serves as an introduction to Python and applying it to data science. It will teach students how to manipulate data and make use of Pythons robust libraries. It is the first in a five-course series, and it can be completed in roughly 15 hours.
CS109 Data Science
provider

9 )

CS109 Data Science (2015)

4.9
This Harvard course in data science introduces methodologies for building and using databases with Python and a number of other tools. It is a full 13-week college course, and students who complete it will be primed to pursue greater knowledge and expertise in the field of data science.
Data Science: R Basics
provider

10 )

Data Science: R Basics (2017)

4.0
Built by Harvard and hosted by edX, this class teaches students how to use R in order to work with data. It is an 8-week, 16-hour course aimed at students with some computer science background. It is part of a professional certification program, and it covers the basics of R and applications in data science.

11 )

Introduction to R for Data Science (2018)

4.4
EdX hosts this Microsoft course that takes students through an introduction to R and how to use in data science applications. It is a 4-week, 12-hour course. It is free to enroll, and a Microsoft certificate can be obtained upon completion for a fee.

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