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

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11 Filtered Courses
Exploratory Data Analysis
provider
Best Free Course

1 )

Exploratory Data Analysis (2016)

4.9
Created by Johns Hopkins University, this Coursera course gets into the nuts and bolts of summarizing data. It covers varying techniques that help with modeling and statistical exploration. Its part of a program aimed at fostering expertise in data science.
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Pros
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Cons
    • 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.
Best Practical Course

2 )

Fundamentals of Data Analytics (2017)

0.0
Developed by LEAPS, this course covers the fundamentals of statistical concepts. It shows students how to apply analytics to solve business cases with real data. By the time students are finished, they will be proficient in several analytical methods.
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Pros
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Cons
    • 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
provider
Best Crash Course

3 )

Intro to Data Science - Crash Course for Beginners (2019)

0.0
From freeCodeCamp.org this YouTube tutorial introduces beginners to data science over the course of a video that lasts 1 hour and 40 minutes. It breaks the lesions into sections of statistics, data visualization and programming to arm students with the fundamental tools of data science.
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Pros
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Cons
    • 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.
Data Science Full Course - Learn Data Science in 10 Hours
provider
Best NEW Course

5 )

Data Science Full Course - Learn Data Science in 10 Hours (2019)

0.0
This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms.
Python Data Science Handbook
provider
Best Text Based Course

6 )

Python Data Science Handbook (2016)

0.0
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, youll learn how to use:IPython and Jupyter: provide computational environments for data scientists using PythonNumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in PythonPandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in PythonMatplotlib: includes capabilities for a flexible range of data visualizations in PythonScikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Best Advanced Course

7 )

The Analytics Edge (2014)

0.0
In the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life. We will examine real world examples of how analytics have been used to significantly improve a business or industry. These examples include Moneyball, eHarmony, the Framingham Heart Study, Twitter, IBM Watson, and Netflix. Through these examples and many more, we will teach you the following analytics methods: linear regression, logistic regression, trees, text analytics, clustering, visualization, and optimization. We will be using the statistical software R to build models and work with data. The contents of this course are essentially the same as those of the corresponding MIT class (The Analytics Edge). It is a challenging class, but it will enable you to apply analytics to real-world applications.The class will consist of lecture videos, which are broken into small pieces, usually between 4 and 8 minutes each. After each lecture piece, we will ask you a "quick question" to assess your understanding of the material. There will also be a recitation, in which one of the teaching assistants will go over the methods introduced with a new example and data set. Each week will have a homework assignment that involves working in R or LibreOffice with various data sets. (R is a free statistical and computing software environment we'll use in the course. See the Software FAQ below for more info). At the end of the class there will be a final exam, which will be similar to the homework assignments.
Data Analyst with Python
provider

8 )

Data Analyst with Python (2017)

0.0
Gain the career-building Python skills you need to succeed as a data analyst. No coding experience required. In this track, youll learn how to import, clean, manipulate, and visualize dataall integral skills for any aspiring data professional or researcher. Through interactive exercises, youll get hands-on with some of the most popular Python libraries, including pandas, NumPy, Matplotlib, and many more. Youll also gain experience of working with real-world datasets, including data from the Titanic and from Twitters streaming API, to grow your data manipulation and exploratory data analysis skills, before moving on to learn the SQL skills you'll need to query data from databases and join tables. Start this track, grow your Python and SQL skills, and begin your journey to becoming a confident data analyst.
Data Analyst in Python
provider

9 )

Data Analyst in Python (2015)

0.0
This path covers everything you need to learn to work as a data analyst using Python.You'll learn the Python fundamentals, dig into data analysis and data viz using popular packages like pandas, query databases with SQL, and study statistics, among other things!It's designed so that there are no prerequisites and no prior experience required. Everything you need to learn to work as a data analyst, you'll learn on this path!As you learn, you'll apply each concept immediately by writing code right in your browser that's automatically checked by our system to give you near-instant feedback on your progress.We think the best way to learn is to learn by doing, so you'll be challenged every step of the way to really apply the concepts you're learning, and you'll build a variety of projects using real-world data to solve real data science problems.By the end of this path, you'll have the skills you need to work as a data analyst, and you'll be comfortable with things like:Basic and intermediate programming conceptsHow to clean and visualize data.Probability and statistics for data analysis.Collaboration tools like git and SQL databases.
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
provider

10 )

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2017)

0.0
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing.

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