Data analytics is the science of taking raw data, cleaning it, and analyzing it to inform conclusions and support decision making. From business to health care to social media, data analytics is changing the way organizations operate.
“It’s not hyperbole to say that data analytics has really taken over the world,” says Brian Caffo, professor of biostatistics at Johns Hopkins University’s Bloomberg School of Public Health and director of academic programs for the university’s Data Science and AI Institute. “Every domain has become increasingly quantitative to inform decision making.”
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Visit WebsiteAnd this space isn’t slowing down anytime soon: The U.S. Bureau of Labor Statistics projects that employment for data scientists will grow 35% from 2022 to 2032, with 17,700 new job openings projected each year on average during that decade.
Interested in becoming a data analyst? Below, we’ve compiled ten free data analytics courses to help give you a firmer grasp of this rapidly growing field.
A/B Testing
About: This course covers the design and analysis of A/B tests, which are online experiments that compare two versions of content to see which one appeals to viewers more. A/B tests are used throughout the tech industry by companies like Amazon and Google. This course is offered through Udacity.
Course length: Six self-paced modules
Who this course is for: Beginners
What you’ll learn: In this course you’ll learn about A/B testing, experiment ethics, how to choose metrics, design an experiment, and analyze results.
Prerequisites: None
Data Analytics Short Course
About: In this quick, five-tutorial course you’ll get a broad overview of data analytics. You’ll learn about the different types of roles in data analytics, a summary of the tools and skills you’ll need to develop, and a hands-on introduction to the field. This course is offered by CareerFoundry.
Course length: 75 minutes, divided into five 15-minute lessons
Who this course is for: Beginners
What you’ll learn: In this course you’ll get an introduction to data analytics. You’ll also analyze a real dataset to solve a business problem through data cleaning, visualizations, and garnering final insights.
Prerequisites: None
Data Science: R Basics
About: This program gives you a foundational knowledge of programming language R. Offered by HarvardX through the EdX platform, this course is offered for free; the paid version includes a credential. It’s the first of ten courses HarvardX offers as part of its Professional Certificate in Data Science.
Course length: Eight weeks, 1–2 hours per week
Who this course is for: Beginners
What you’ll learn: In this course you’ll learn basic R syntax and foundational R programming concepts, including data types, vectors arithmetic, and indexing. You’ll also perform operations that include sorting, data wrangling using dplyr, and making plots.
“It’s the basics of how to wrangle, analyze, and visualize data in R,” says Dustin Tingley, Harvard University’s deputy vice provost for advances in learning and a professor of government in the school’s government department. “That gets you writing a little bit of code, but you’re not doing anything that heavy.”
Prerequisites: HarvardX recommends having an up-to-date browser to enable programming directly in a browser-based interface
Fundamentals of Qualitative Research Methods
About: This course will teach you the fundamentals of qualitative research methods. Qualitative research provides deeper insights into real-world problems that might not always be immediately evident. This course is offered through Yale University on YouTube.
Course length: 90 minutes spread out over six modules
Who this course is for: Beginners
What you’ll learn: In this course you’ll learn how qualitative research is a way to systematically collect, organize, and interpret information that is difficult to measure quantitatively. This includes developing qualitative research questions, gathering data through interviews and focus groups, and analyzing this data.
“Qualitative research is the systematic, rigorous application of narratives and tools to better understand a complex phenomenon,” says Leslie Curry, a professor of public health and management at the Yale School of Public Health and a professor of management at the Yale School of Management. She adds that this approach can help understand flaws in large data sets. “It can be used as an adjunct to a lot of the really important work that’s happening in large data analysis.”
Prerequisites: None
Getting and Cleaning Data
About: This course covers the basic ways that data can be obtained and how that data can be cleaned to make it “tidy.” It will also teach you the components of a complete data set, such as raw data, codebooks, processing instructions, and processed data. This course is offered by Johns Hopkins University through Coursera, and is part of a 10-course Data Science Specialization series.
Course length: Four weeks, totaling approximately 19 hours
Who this course is for: Beginners
What you’ll learn: Through this course you’ll learn about common data storage systems, how to use R for text and date manipulation, how to use data cleaning basics to make data “tidy,” and how to obtain useable data from the web, application programming interfaces (APIs), and databases.
“It’s the starting point” when it comes to data analysis, Caffo says. “Without a good data set that is cleaned and appropriate for use, you have nothing. You can talk all you want about doing models or whatnot—underlying that has to be the data to support it.”
Prerequisites: None
Introduction to Data Science with Python
About: This course teaches you concepts and techniques to give you a foundational understanding of data science and machine learning. Offered by HarvardX through the EdX platform, this course can be taken for free. The paid version offers a credential.
Course length: Eight weeks, 3–4 hours a week
Who this course is for: Intermediate
What you’ll learn: This course will give you hands-on experience using Python to solve real data science challenges. You’ll use Python programming and coding for modeling, statistics, and storytelling.
“It gets you up and running with the main workhorse tools of data analytics,” says Tingley. “It helps to set people up to take more advanced courses in things like machine learning and artificial intelligence.”
Prerequisites: None, but Tingley says having a basic background in high school-level algebra and basic probability is helpful. Some programming experience—particularly in Python—is recommended
Introduction to Databases and SQL Querying
About: In this course you’ll learn how to query a database, create tables and databases, and be proficient in basic SQL querying. This free course is offered through Udemy.
Course length: Two hours and 17 minutes
Who this course is for: Beginners
What you’ll learn: This course will acquaint you with the basic concepts of databases and queries. This course will walk you through setting up your environment, creating your first table, and writing your first query. By the course’s conclusion, you should be able to write simple queries related to dates, string manipulation, and aggregation.
Prerequisites: None
Introduction to Data Analytics
About: This course offers an introduction to data analysis, the role of a data analyst, and the various tools used for data analytics. This course is offered by IBM through Coursera.
Course length: Five modules totaling roughly 10 hours
Who this course is for: Beginners
What you’ll learn: This course will teach you about data analytics and the different types of data structures, file formats, and sources of data. You’ll learn about the data analysis process, including collecting, wrangling, mining, and visualizing data. And you’ll learn about the different roles within the field of data analysis.
Prerequisites: None
Learn to Code for Data Analysis
About: This course will teach you how to write your own computer programs, access open data, clean and analyze data, and produce visualizations. You’ll code in Python, write analyses and do coding exercises using the Jupyter Notebooks platform. This course is offered through the United Kingdom’s Open University on its OpenLearn platform.
Course length: Eight weeks, totaling 24 hours
Who this course is for: Intermediate
What you’ll learn: In this course you’ll learn basic programming and data analysis concepts, recognize open data sources, use a programming environment to develop programs, and write simple programs to analyze large datasets and produce results.
Prerequisites: A background in coding—especially Python—is helpful
The Data Scientist’s Toolbox
About: This course will give you an introduction to the main tools and concepts of data science. You will learn the ideas behind turning data into actionable knowledge and get an introduction to tools like version control, markdown, git, GitHub, R, and RStudio. This course is offered by Johns Hopkins University through Coursera, and is part of a 10-course Data Science Specialization series.
Course length: 18 hours
Who this course is for: Beginners
What you’ll learn: This course will teach you how to set up R, RStudio, GitHub, and other tools. You will learn essential study design concepts, as well as how to understand the data, problems, and tools that data analysts use.
“That course is a very accessible introduction for anyone who wants to get started in this,” Caffo says. “It’s an overview that covers the full pipeline, from things like collecting and arranging data to asking good questions, all the way to creating a data deliverable.”
Prerequisites: None
The takeaway
From businesses estimating demand for their products to political campaigns figuring out where they should run advertisements to health care professionals running clinical trials to judge a drug’s efficacy, data analytics has a wide variety of applications. Getting a better understanding of the field on your own time can be done easily and freely. And the field is only growing.
“Just about every field is having a revolution in data analytics,” Caffo says. “In fields like medicine that have always been data driven, it’s become more data-driven.”
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