Data Analysis Courses

  • Social Sciences

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Introduction to Digital Humanities

Develop skills in digital research and visualization techniques across subjects and fields within the humanities.

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Data Science: Inference and Modeling

Learn inference and modeling: two of the most widely used statistical tools in data analysis.

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Data Science: Productivity Tools

Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

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Data Science: Linear Regression

Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

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Data Science: Probability

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

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Data Science: Capstone

Show what you’ve learned from the Professional Certificate Program in Data Science.

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Data Science: R Basics

Build a foundation in R and learn how to wrangle, analyze, and visualize data.

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Data Science: Visualization

Learn basic data visualization principles and how to apply them using ggplot2.

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Case Studies in Functional Genomics

Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

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Introduction to Bioconductor

The structure, annotation, normalization, and interpretation of genome scale assays.

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Advanced Bioconductor

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.

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High-Dimensional Data Analysis

A focus on several techniques that are widely used in the analysis of high-dimensional data.

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Statistical Inference and Modeling for High-throughput Experiments

A focus on the techniques commonly used to perform statistical inference on high throughput data.

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Introduction to Linear Models and Matrix Algebra

Learn to use R programming to apply linear models to analyze data in life sciences.

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  • Data Science
  • Data Analytics
  • Machine Learning

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The 5 Best Courses to Learn Data Analytics for 2024

The increasing demand for data analysts has spurred a proliferation of data-related courses. However, many of these programs do not offer a well-rounded learning experience that covers all the essential analytics skills.

For this article, I've sifted through the best online course platforms to find the best classes for learning data analytics. Since many skills are involved in an analyst's job, I've provided the best course option for specific categories. Hopefully, this categorization will help you more easily decide which course is the best for you .

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If you're still having trouble picking a course, feel free to check out the Learning Guide at the end of this article for information about what data analysts do, what skills they need, and recommendations on how to get started.

Course selection criteria

There're thousands of data courses these days, so to narrow down the options to only the most qualified, I considered the following data points:

  • Organization and quality of content
  • Student reviews
  • Staff and student discussions
  • Quizzes and assignments
  • Inclusion of statistical concepts

These criteria helped reduce the number of potential courses to only a handful, of which I then compared and contrasted to bring you my recommendations listed below. These final few are what I found to be the best for learning data analytics online today.

TL;DR Best Data Analytics courses for 2024

Google data analytics professional certificate — google, coursera.

Complete beginners looking for a broad introduction to data analytics focused on Google products.

This highly-rated course from Google covers a wide range of topics and is designed to "have you job-ready in less than six months," according to their description. Google states that certificate holders will have access to an Employer Consortium, which comprises 150 U.S. companies committed to considering graduates for entry-level analytics positions.

Out of all the courses I could find, this series by Google is the most comprehensive. The course video content is well-organized, professional, and exciting, and with so many students enrolled, there's an active community for questions and answers. Through the course, you're exposed to the most popular analytics tools: Google Sheets, SQL, R, and Tableau. These topics cover the entire data analytics pipeline and give you the skills to develop your own projects.

Course 1: Foundations: Data, Data, Everywhere

  • Intro to data analytics
  • Intro to analytics tools (Sheets, SQL)

Course 2: Ask Questions to Make Data-Driven Decisions

  • Problem-solving
  • Asking good questions
  • Spreadsheet basics
  • Communicating effectively

Course 3: Prepare Data for Exploration

  • Data formats, types, modeling, collection
  • Data ethics
  • Using spreadsheets with databases
  • Intro to BigQuery
  • Data security

Course 4: Process Data from Dirty to Clean

  • Data integrity
  • Data cleaning with spreadsheets and SQL
  • Resume/career info

Course 5: Analyze Data to Answer Questions

  • Organizing data for Sheets and Bigquery
  • Formatting and transforming data
  • Data aggregation functions in Sheets and SQL
  • More formulas, functions, and pivot tables
  • Intermediate SQL

Course 6: Share Data Through the Art of Visualization

  • Intro to data visualization concepts
  • Creating visualizations with Tableau
  • Developing data stories
  • Creating effective presentations

Course 7: Data Analysis with R Programming

  • Intro to the R language and RStudio
  • Cleaning, organizing, and transforming data with R
  • Creating visualizations with R
  • Making reports and docs for R analyses

Course 8: Google Data Analytics Capstone: Complete a Case Study

  • Developing your own project to display in your portfolio and resume
  • Info on building a compelling portfolio

The curriculum is divided into "courses," but some of the content can be completed far shorter than a typical Coursera course. Some learners report they completed the entire Specialization in under a month. So, depending on your background, you may well finish the course series quicker than advertised.

Enroll in the Google Data Analytics Professional Certificate

Become a Data Analyst — Linkedin Learning

Those looking for broad exposure to many data analytics tools, but with more of a focus on Microsoft products

The data analyst learning path from Linkedin Learning in a collection of courses organized in a way that provides you with a well-rounded education. The course path is similar in scope to that of Google's listed above, but focusing on Microsoft products, namely Excel and Power BI.

One benefit to this course series over Google's is the inclusion of statistics modules, which is excellent for learners that would like to strengthen their math for analytics.

Course 1: The Non-Technical Skills of Effective Data Scientists

  • Imperative non-technical skills

Course 2: Learning Excel: Data Analysis

  • Basic statistics in Excel
  • Visualizing data
  • Hypothesis testing
  • Using distributions
  • Covariance and correlation
  • Bayesian analysis

Course 3: Data Fluency: Exploring and Describing Data

  • Data fluency
  • How to use the most common chart types
  • Descriptive statistics

Course 4: Learning Data Analytics: 1 Foundations

  • Importing and cleaning data
  • Creating and maintaining datasets
  • Intro to Power Query

Course 5: Learning Data Analytics Part 2: Extending and Applying Core Knowledge

  • Working with business data
  • Building datasets with queries
  • Building pivot tables
  • Intro to Power BI
  • Presenting data in meetings

Course 6: Excel Statistics Essential Training: 1

  • Types of data
  • Probability
  • Central tendency
  • Variability
  • Distributions
  • Analysis of variance (ANOVA)
  • Repeated measure analysis
  • Correlation

Course 7: Predictive Analytics Essential Training: Data Mining

  • Defining problems
  • Understanding data requirements
  • Problems and solutions you'll face with data
  • Deploying models
  • Cross-Industry Standard for Data Mining (CRISP-DM)

Course 8: Power BI Essential Training

  • Getting data into Power BI
  • Reports and visualizations
  • Creating dashboards
  • Sharing data
  • Power BI mobile

Course 9: Learning Data Visualization

  • Information hierarchy
  • Storytelling
  • Visual paradigms
  • Interactivity

Course 10: Tableau Essential Training

  • Managing data sources
  • Tableau worksheets and workbooks
  • Creating custom calculations and fields
  • Analyzing data in Tableau
  • Mapping geographic data
  • Creating dashboards and actions

Course 11 SQL: Data Reporting and Analysis

  • Using SQL to report data
  • Grouping SQL results
  • Merging data
  • Some advanced syntax

Course 12: R Essential Training: Wrangling and Visualizing Data

  • Intro to R and RStudio
  • Importing data
  • Visualizing data in R
  • Wrangling data
  • Recoding data

Course 13 Data Cleaning in Python Essential Training

  • Causes of errors
  • Detecting, preventing, and fixing errors

You can acquire many in-demand skills from the data analyst path on Linkedin Learning. There is some overlap with the statistics and visualization content, but for a beginner, this can only reinforce your newly acquired analytics skills as a beginner.

The one gripe I have with this path is that the Python course at the end already assumes Python experience, but nowhere in the path is there a Python syntax course. If you intend to complete this path, I'd also recommend learning Python syntax on Codecademy , the top Python course according to the data.

Enroll in the Become a Data Analyst path

Excel Skills for Data Analytics and Visualization Specialization — Coursera

Those with some Excel experience looking to analyze data as fast as possible.

This course from Macquarie University covers many of Excel's intermediate to advanced concepts, allowing you to clean, analyze, and visualize data efficiently. If you're rusty on statistics, it may be a good idea to complement this course with an appropriate statistics course since the math side of analytics isn't covered here.

Overall, the quality of instruction is fantastic, and you'll find plenty of assessments and assignments to hone your Excel and Power BI skills.

Course 1: Excel Fundamentals for Data Analysis

  • Cleaning and manipulating text
  • Working with numbers and dates
  • Defined Names
  • Tables for automating data manipulation
  • Logical and lookup functions

Course 2: Data Visualization in Excel

  • Conditional formatting, sparklines, and number formats
  • Various charting techniques
  • Specialized charts
  • Interactive dashboards using pivot charts, slicers, and dynamic charts

Course 3: Excel Power Tools for Data Analysis

  • Get and transform data with Power Query
  • Power Pivot and data models
  • Visualize data with Power BI

Once you've completed this course and are comfortable analyzing data in Excel, it may make sense to start learning SQL or R. For SQL, see the previous course; for R see the following course.

Enroll in Excel Skills for Data Analytics and Visualization Specialization

Data Analyst in R — Dataquest

Beginners who are more interested in the programming side of data analytics, as opposed to a spreadsheet software, like Excel.

Dataquest is one of the most popular interactive data science learning platforms. Despite not having videos for each topic, their teaching methodology and project-based learning style are very effective.

The Data Analyst with R path, which also has a Python version , brings learners with no programming experience through the entire analytics pipeline using R and SQL.

Python is often the first choice for working with data, but if your goal is to become proficient in analytics, R plays more nicely with data and statistics out of the box. The language's straightforwardness lets you be productive with data faster than Python in many cases.

  • Intro to R syntax
  • Data structures
  • Loops, iterations, functions, and control flow
  • Data visualization
  • Data cleaning
  • SQL fundamentals
  • Getting data from APIs and web scraping in R
  • Beginner to intermediate statistics using R
  • Probability fundamentals and conditional probability
  • Linear regression
  • Machine learning fundamentals
  • Interactive web apps with R

Dataquest has a much stronger curriculum in statistics and probability than other courses, so if you feel like your math needs work, this course path will help. Furthermore, Dataquest can also serve as a great resource for practicing your skills through their guided projects, regardless of which other course you decide to take.

Enroll in the Data Analyst in R path

Business and Data Analysis with SQL — Skillshare

Those who are more interested in learning and applying SQL to analytics, or anyone wishing to expand past spreadsheets

Geoff Devitt, the instructor for this course, is a 20+ year veteran of the Big Data industry and created this Skillshare class to share what he's learned building projects and analyzing data over his career.

Unlike other courses mentioned in this list, this course focuses solely on solving analytics problems using Structured Query Language (SQL), a programming language built to interface with databases. Since SQL runs many businesses, it was one of the most highly requested skills mentioned in job posts during my research for this article.

  • Intro to databases
  • Normalizing data
  • Fact tables
  • Aggregating data
  • Entity Relationship Diagrams (ERD)
  • Setting up and connecting to MySQL
  • Beginner to intermediate SQL
  • Data analysis example project
  • Query explain plans
  • MySQL administration

Once you've finished this course, you should be reasonably comfortable with SQL and can start working on your portfolio projects.

Enroll in Business and Data Analysis with SQL

Learning guide

What do data analysts do.

Data analysts' responsibilities vary widely from company to company, but most have the following primary objectives: 1) Pull data from a variety of sources, such as databases, Excel workbooks, and CSVs 2) Apply statistical analysis to the data 3) Condense information into easily digestible formats 4) Create visualizations, summary reports, and dashboards

A data analyst creates value for a company by helping them make better decisions and, ultimately, to generate more revenue.

data analytics coursework

Data Analyst vs. Data Scientist

Becoming a data scientist often starts with first becoming a data analyst. If we were to look at a Venn diagram of the skillsets of both roles, the data scientist's circle would fully encompass the data analyst's.

The Northeastern University blog sums up the differences nicely:

While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists , on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

Essentially, data analysts work with established data and pipelines to draw insight, while data scientists create new data pipelines and use more advanced tools and techniques to solve more complex problems.

Data analyst tools and skills

Data analysts use various tools and processes, so the courses you take will depend on your desired position and what knowledge you already have. One company may need an analyst primarily for Excel and Power BI, while another business needs someone with SQL and Tableau experience.

data analytics coursework

If you're coming into data analytics with no programming experience, your quickest path to job-ready would be to build proficiency in a spreadsheet program like Excel or Google Sheets and visualization software, such as Tableau or Google Data Studio.

Alternatively, if you have any programming experience, starting with SQL, Python, or R would be a good choice. Most job posts mention SQL, so if that's your primary interest, feel free to check out my picks for the best SQL courses , which rank the best courses for learning SQL in general. In this article, I focus my SQL suggestions primarily on using SQL for analytics.

Which technology (Excel, SQL, Tableau, etc.) do I start with?

Learning Excel is one of the best ways to get started in data analytics since many people already have exposure to the software. You can more easily build on this familiarity through an Excel-specific course, like Excel Skills for Data Analytics and Visualization Specialization from Coursera.

Despite using Python to analyze data for years, I still reach for Excel when I need to do a quick analysis, share results with the team, and make educated decisions. Many companies still run their entire analytics pipeline through Excel, so many job opportunities are awaiting skilled Excel data analysts.

Knowing more about Excel (or Google Sheets) can only benefit you in analytics, but if you already have a decent Excel foundation, going on to SQL would be a safe bet. Many businesses store their data in a SQL database and need analysts to pull, summarize, and make sense of that data. When researching job posts for this article, I found more jobs requiring someone with solid SQL experience than any other technology.

Since SQL is a programming language, it makes sense that most SQL courses focus solely on syntax and data modeling, which is why I've included Business and Data Analysis with SQL here since it has more of an analytics approach.

Finally, if you already have some spreadsheet experience and would rather move on to programming language with more applications, then learning R is an excellent choice. Unlike SQL, which is confined to databases, R is a regular programming language with many advantages in statistics and visualization. R is more straightforward than Python for pure analytics work, but Python skills are still in high-demand. Either way, I would recommend working through the free R or Python material on Dataquest to start learning as quickly as possible.

Apply what you learn

No matter which skill, technology, or path you start, it's imperative you practice everything you learn. Solidify your knowledge by analyzing something you're interested in, and produce reports or dashboards to display your findings. Many employers consider unique, completed projects an equivalent replacement for work experience. Not only does talking through a passion project you've completed aid interviewers with gauging your abilities, but it also helps you escape imposter syndrome and build self-confidence.

Are certificates worth it?

Every course in this list offers a certificate, and they are a fantastic way to prove to yourself that you completed a course and solidified knowledge of a topic. Despite that, you should approach building your resume as if the certificates you earn don't exist. You should focus solely on building projects that demonstrate your knowledge and aptitude, especially when you lack past job experience.

Pick a niche you're interested in, whether it's sports, finance, health, or marketing, and build something that interests you. It doesn't have to be groundbreaking, but it should showcase your abilities to interviewers. If you're looking for inspiration, check out Kaggle datasets and community notebooks, or follow a few of the Dataquest or Coursera guided projects.

Good luck, have fun!

Learning data analytics can be a challenging journey, but whether for a career or hobby, I'm confident analytics will lead you down a path of exciting projects, tangents, and fortuitous findings.

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The Top 14 Data Analytics Training Courses

The global economy is facing seismic shifts and traditionally steady jobs no longer offer the stability they once did. As many people now look to diversify their skills, data analytics is proving a compelling solution.

This once-niche discipline is rapidly expanding into all areas of the modern economy—from IT to healthcare and finance, the sciences, construction, and more. Plus, beyond the core technical skills, data analytics roles increasingly require creative, analytical thinkers; from those with a meticulous eye for detail to others capable of seeing the bigger picture.

In short, data analytics is for everyone who wants to give it a try.

In this post, we explore 14 of the best data analytics training courses available right now. Whether you’re a career shifter or a recent graduate, we’ll look at everything you need to get the basics down.

Times are tough and not everyone has money to spare, so we’ve included some options: data analytics training courses you can find for free online; paid online bootcamps, and, for the committed; a sample of full-time graduate programs.

We’ll cover:

Beginner: Free data analytics training courses

  • Intermediate: Paid data analytics training courses
  • Expert: College-based data analytics training courses

Ready to find a data analytics training course that’s right for you? Then let’s dive in.

If you’re new to data analytics and want to explore some of its themes and tools before forking out for a more comprehensive training program, here are five great free training courses you might want to consider:

1. Data Analytics Short Course – CareerFoundry

Best for: Those wanting to get a quick taster of the world of data analytics

Cost: Free, but with the option to enroll into the full Data Analytics Program afterward.

Completion time: 5 days, self-paced

Our free, five-tutorial data analytics short course is ideal if you want a digestible introduction to data analytics. When you sign up to the platform, you’ll get access to five hands-on lessons delivered by email—each focused on a separate step of the data analytics process. The course provides a broad view of data analytics, setting you up to explore the topic further if you choose.

This short course covers everything to get a broad overview of the field: from the different types of data analytics roles, to a summary of tools and skills you’ll need to develop if you pursue a career in the field. You’ll also have a few opportunities to get a hands-on experience with the basics of the data analytics process.

2. Data Analytics Basics for Everyone – edX:

Best for: Those seeking an all-round, high-level taste of data analytics.

Cost: Free, with optional upgrades for certifications or add-on training.

Completion time: 5 weeks, 2-3 hours of study a week.

Led by expert professionals from IBM, this self-paced course covers all the need-to-know information about data analytics across 10-15 hours of video lectures. The introductory course offers a straightforward explanation of what data analytics involves, including the different steps in the process.

Although very high-level, it covers both the practical elements of the topic (such as various types of data structure and file formats) as well as career-related content. For example, the course clearly outlines the difference between data engineers, data scientists, and business intelligence roles, while also exploring different career paths.

You won’t get any in-depth training here on the tools and software that data analysts typically use. However, you will enjoy a whistle-stop tour through the major big data platforms used to gather, mine, analyze and visualize data. Overall, a great all-around introduction to get any beginner started.

3. SQL for Data Analysis – Udacity

Best for: Those who want to learn the basics of SQL.

Cost: Free (but expect regular plugs for their paid courses!)

Completion time: About 30 hours of self-paced learning across 4 weeks.

Relational databases are one essential building block of data analytics, and mastering SQL is necessary for effectively managing them. This free course from Udacity covers all the basics of SQL, gradually building your knowledge across five standard and two advanced lessons. Starting with an introduction to SQL, you’ll learn the key commands for querying a database. You’ll proceed to work with multiple tables, picking up additional functions on the way via practical examples.

Finally, you can expect to move on to more advanced techniques, such as how to quickly query data across giant data lakes. We love the step-by-step approach of this course, which ensures that learners follow along with it, rather than being thrown right in at the deep end and being expected to swim.

4. Python for Data Science – Cognitiveclass.ai

Best for: Those who want an introduction to Python programming.

Cost: Free.

Completion time: About 20 hours of self-paced learning.

This beginner-friendly introduction to Python is suitable for all, the only prerequisite being the basic math skills that anyone should have from school. Split across five modules with a final exam, the course introduces the basic concept of Python and how it’s used, before progressing to its core data analytics functionality.

Like the SQL course, it builds on knowledge as it goes. You’ll start with entry-level content, like how to define variables and set conditional statements, all carried out within Cognitive Class’s Jupyter Notebook sandpit (which is also beneficial since data analysts often use this software).

Once you’ve picked up the standalone functionality, the course shifts focus to one of Python’s many popular data analytics libraries, pandas, which is commonly used for data analysis, data cleaning , and machine learning tasks. While you’ll still have plenty to learn after completing the course, it offers a concrete foundation to build upon.

5. Excel Basics for Data Analysis – Coursera

Best for: Those looking to unleash the statistical power of MS Excel.

Cost: Free, so long as you complete it within the 7-day trial period.

Completion time: Approximately 12 hours of learning.

Another course from IBM via Coursera, this data analytics training course focuses on everyone’s favorite spreadsheet software, MS Excel. Spread across 9 modules, the first three focus specifically on MS Excel, starting with a beginner’s introduction to spreadsheets. Next, it progresses to topics such as how to insert, filter, and sort data.

Finally, more complex themes emerge, including an exploration of Excel’s most valuable data analytics functions, (like how to create visualizations and dashboards). Modules 5 to 8 focus on the R programming language and aren’t exclusively Excel-focused. However, R can be used with Excel, and is a useful tool in its own right, so you may decide to continue on.

While you’ll need to complete the R modules to gain a certification, you can easily skip them if you’re using Coursera’s 7-day free trial. The whole course is capped off with a quiz-based assessment. But again, you can skip this if you want to access the rest of the course content for free.

6. Machine Learning for Data Analytics – Coursera

Best for: Those who want to dive deep into machine learning.

Cost: Free if you complete it within Coursera’s 7-day trial period.

Completion time: Approximately 61 hours of learning.

Machine learning isn’t a topic beginner data analysts need to master but it’s so fascinating we couldn’t resist including one course on our list! This particular one is also delivered by Andrew Ng who is kind of a big deal in data science circles.

Although not technically free—and at 61 hours of learning, a squeeze to complete within a 7-day trial—we reckon it’s worth completing at least the introductory module. This is only 42 minutes of learning and covers the basics of machine learning theory, such as the difference between supervised and unsupervised learning.

If you progress, however, you’ll dive into the technical stuff, such as multivariate linear regression, logistic regression, and how to train neural networks. While this stuff is not for beginners, it’s a great option if you’re a math nut who finds the potential of artificial intelligence intriguing.

2. Intermediate: Paid data analytics bootcamps

Done dabbling and want to invest in the no-nonsense skills you’ll need for an entry-level data analytics role? Online data analytics bootcamps are becoming increasingly popular for this.

While these types of data analytics training courses don’t always come cheap, they’re much more affordable than full degrees and are usually flexible enough to fit around your schedule. Some even come with job guarantees, making them a much safer investment. Here are five top courses to consider:

1. Data Analytics Program – CareerFoundry

Best for: Beginners looking for comprehensive training, mentoring, and career support.

Cost : $6,900 USD (via payment plan, or with a discount if you pay upfront).

Completion time: 5 months studying full-time (30-40 hours a week) or up to 8 months studying part-time (15-20 hours a week).

Do you know that data analytics is the path you want to take? Do you also want the guarantee of a job at the end of your course? Then look no further than CareerFoundry’s Data Analytics Program. Regardless of background, the course is designed to take learners from beginner to job-ready in 8 months. While longer than some data analytics training courses, it’s fully comprehensive, covering the tools, skills, and processes you’ll need in detail.

To effectively prepare you, CareerFoundry’s Data Analytics Program uses a project-based curriculum to get you working hands-on from the very start in a professional environment. It offers unrivaled mentoring from active industry professionals and the Career Services team offers job coaching.

CareerFoundry’s offering comes in at $6,900 for the entire program, b ut the cost of the tuition is dependent on your location and is competitively priced. A range of flexible payment options include paying upfront, or getting a small course discount. Contact them to find out your local pricing and if there are any partial scholarships available.

Once you’ve completed the course, you’ll not only be skilled-up—you’ll have a portfolio of projects, a polished resume, and be ready for interviews. And if you don’t have a job within six months of completing the course? Provided you fulfil some clearly stated conditions, you get your money back, making CareerFoundry about as safe an investment as you can get.

2. Online Data Analytics Course – General Assembly

Best for: Non-data analytics professionals looking to supplement their skills.

Cost : $3,950 USD (with employer sponsorship options).

Completion time: 10 weeks part-time or one week intensive.

As data analytics starts to permeate all sectors, you might decide to supplement your present role (e.g. in marketing or finance) rather than changing careers completely.

In this case, General Assembly’s data bootcamp is aimed at busy professionals looking to upskill in their current roles. This flexible, self-paced course covers all the essential functions of Excel and SQL, along with the need-to-know aspects of data visualization and software tools like Tableau.

It includes 40 hours of learning, project coursework, and a final assignment with individualized feedback and guidance from an expert instructor. You’ll get a completion certificate, too, ideal for showing off your newfound skills to your employers or on LinkedIn. While the course costs just under $4,000 USD, General Assembly welcomes employer sponsorship, so it’s definitely an option worth considering.

3. Data Science Career Tracks – Datacamp

Best for: Those who want a good selection of specific (and affordable) modules.

Cost : $25 USD per month.

Completion time: From 12-25 hours of learning per course.

Unlike many course providers, Datacamp specializes specifically in data science. The best part about their courses is the number available. Rather than providing a single generic course, you can sort Datacamp’s offering by the technology you want to focus on. From spreadsheets in Excel to data analysis using Python and R, specialized industry software such as Tableau for data viz, or Microsoft Power BI (an industry staple for many big companies), the choice is yours.

For $25 USD per month, Datacamp’s Career Tracks are also very affordable, and their bitesize courses cover everything from collecting and cleaning data, to theories like statistical inference, and even highly-specific training such as how to analyze genomic data. You’ll learn by doing, working on real projects, and collecting rewards as you go. And if you’re looking to shift into a specific role, Datacamp’s dedicated career service can help you select the best set of courses to get you on the right track.

4. Data Analytics Bootcamp – Springboard

Best for: Career changers with existing experience.

Cost: $10,140 USD or $8,500 USD if you pay upfront.

Completion time: 420 hours across 8 months(15-20 hours per week).

Already got some experience using office, design, or programming tools? Then Springboard’s Data Analytics Bootcamp offers the additional technical skills you need to take things up a notch. Going beyond mere data analytics, the course also focuses on areas where employers find the most significant competency gaps—things like critical thinking, problem-solving, and communication.

The Springboard bootcamp aims to develop these so-called ‘soft skills’ while helping you devise a job search strategy. They even provide career support for six months after completing the program. This aftercare is particularly practical as it includes things like interview tips and negotiating salaries.

Unlike other courses, you will require a little experience to enroll, and it’s also one of the pricier options on our list. However, like CareerFoundry, Springboard offers a job guarantee, with a full refund if you still don’t have a job six months after graduating.

5. Data Science Learning Paths – Dataquest

Best for: Those looking for flexible and affordable career-focused modules.

Cost: $24.50 per month for an annual subscription.

Completion time: Depends on the modules you choose.

Another flexible and affordable option is Dataquest’s Data Science Learning Paths. Reflecting the increasingly broad career paths available in data analytics, Dataquest offers 70+ courses on topics ranging from Python to R, SQL, Excel, and much more.

These modular courses can be compiled in any way you wish, but Dataquest also provides 5 pre-structured career paths to help prepare you for specific roles in areas like business analytics or data engineering. They offer an additional 12 paths for specialized skills development, too, in areas like machine learning, data pipelines, or statistics and probability. Check out their full list of 70+ courses here .

At the lower end of the price range, Dataquest’s offering doesn’t come with mentoring. But for those who are happy studying under their own steam, it’s a great way to build skills. They also regularly add new courses, keeping things industry-relevant.

3. Expert: College-based data analytics programs

If you already work in the field you might be looking to jump the tracks from data analytics into more senior data science roles.

If so, a master’s degree is usually a minimum prerequisite. You’ll find countless options available, varying widely depending on your location and particular area of interest. These aren’t for beginners but to give you a flavor, here are three possible options from North America. Check local institutions first, though.

1. Master of Business Analytics (MBAn) at MIT

If you’ve got business specifically in mind, MIT’s MBAn program is a 12-month master’s course delivered by some of the leading minds in data science. It will fully prepare you with the skills necessary to drive smarter business decisions and solve some of the biggest problems that companies face.

Aimed at engineers, mathematicians, physicists, computer programmers , and other high-tech professionals, the program’s core focus is on machine learning; how this emerging technology is currently being used, and what its future applications are likely to be. This includes all the practical aspects and theory you’ll need to become an expert on the topic. As a prerequisite, you’ll either need an undergraduate degree in a related subject or significant hands-on experience in the field.

2. Master of Science (MSc) in Applied Computing at the University of Toronto

If you prefer not to hone in purely on data science for business, the University of Toronto’s MSc in Applied Computing is a good example of a course that diversifies a data analyst’s skills for all industries.

Concentrating on computer and data science, applied math, and quantum computing, this is one of many such degrees aimed at preparing tomorrow’s data scientists for the high-tech economy. Four semesters over 16 months, this particular course includes an applied-research internship with one of the university’s employer partners (something to consider looking out for if you want hands-on, practical experience).

Once again, all applicants need to have completed (or be in the process of completing) a relevant undergraduate degree with certain grade expectations, which is also a common requirement for this type of course. Although this is just one example, you’ll find many similar MScs available in locations all over the globe.

3. Master of Statistics at Stanford University

All master’s degrees in data-related fields require some expertise in math. However, Stanford’s Master’s in Statistics focuses specifically on mathematical topics and how these can be applied to an individual learner’s specific area of interest. On the Stanford course, core modules include probability theory, stochastic processes, and applied statistics. Once complete, students will then apply these to their elective area of interest via additional modules of their choosing.

Elective topics are diverse, ranging from the biological sciences to computation, mathematical engineering, computer science, economics, operational management, and much more. Stanford’s course is about 15-18 months, but students have three years to complete it, making it a very flexible and career-focused option.

While these are just three of many master’s degrees out there, they should offer a taste of just how niche you can go with your data career over time. 

From broad-stroke tutorials for beginners to career-focused bootcamps and niche university specializations, there are many courses available for would-be data analysts to choose from. The options might seem staggering at first but don’t be put off. One of the best things about data analytics is that you can carve your career in any way that appeals.

There’s no single prescribed path that you have to take. We hope this list of data analytics training courses gave you a good idea of the scope that’s available to you, no matter what stage of your career you’re currently at.

To learn more about a potential future career in data analytics, check out this free, 5-day Intro to Data Analytics short course or read the following introductory guides for more:

  • What Is Power BI?
  • Creating Data Visualizations in Tableau (A Beginner’s Guide)
  • What Is Prescriptive Analytics? A Complete Guide

Google Career Certificate in Data Analytics

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Prepare for a new career in Data Analytics and develop confidence in navigating the data lifecycle. Learn how to use tools and platforms to gain insights from data and help inform important business decisions. After completing the Data Analytics Certificate, learners can go deeper in the field by taking the Google Advanced Data Analytics Certificate or the Google Business Intelligence Certificate, available in English.

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No relevant experience necessary

Develop skills in data analysis & visualization. Master tools like sheets, SQL, R. and Tableau.

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Complete the certificate online at your own pace in under 6 months.

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Numbers are the name of the game

Statistics have always been part of sports. But the digital age has altered the playing field, as organizations seek out those with the skills to use statistics as a tool for success. Combine sports marketing skills with the analysis and management of data when you earn a master’s in Applied Data Science with a specialization in Sports Analytics at IU Indianapolis.

  • Degrees & Courses
  • Applied Data Science Master's

Sports Analytics Specialization

Succeed with a winning combination.

Analytics is a crucial part of decision-making in amateur and professional athletics . Teams rely on those with the knowledge to interpret data and relate it to the world of athletics. (Nikhil Morar—pictured above—earned his Applied Data Science master's degree with a specialization in Sports Analytics from our program, and became Manager of Business Analytics & Strategy for the Los Angeles Lakers basketball franchise.)

Indianapolis boasts 10 professional sports teams. The city is home to the National Collegiate Athletic Association (NCAA), the National Federation of State High School Associations, and is widely considered the Capital of Amateur Sports.

By teaming up, the Luddy School in Indianapolis and the Department of Tourism, Event, and Sport Management  draw on a unique mix of resources to offer B.S./M.S. in this exciting field.

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Rishi Chandran

I was able to use machine learning and descriptive statistics to create actionable scouting reports focused on finding strategies that will give a team a better chance of winning. Rishi Chandran, M.S. '23 & Basketball Operations Seasonal Assistant with the Cleveland Cavaliers

Careers in Sports Analytics

Our sports analytics alumni work for some of the greatest teams in the NBA. On game day, it's all about the numbers!

Nikhil Morar at the office of the LA Lakers

Nikhil Morar

Manager of Business Analytics & Strategy for the Los Angeles Lakers

“Sports organizations need analytics experts who can turn data about their customers and teams into revenue-generating strategies."

Gabriel Wachowski with the NBA Championship trophy

Gabriel Wachowski

Research and Innovation Analyst for the Milwaukee Bucks

"Overall, the job has been absolutely incredible. I definitely feel like having my master’s was extremely important to being ready for the job that I have. My classes at IUPUI and my internship (with the Indiana Pacers) were both instrumental to where I am today."

A skill set tailored to sports

Students who earn a Master of Science in Applied Data Science with a specialization in Sports Analytics learn core skills in data analysis, data management and infrastructure, and client–server application development, and ethical and professional management of data projects.

Earn additional competencies in sports sales, the management of massive, high-throughput data stores, cloud computing, and the data life cycle.

Degree requirements

The plan of study is 30 credit hours. It includes six core courses and four specialization/ elective courses. Transfer students may be able to transfer in approved graduate courses from an accredited institution.

F-1 students can only take one online course per semester. They must take a minimum of 8 credit hours per semester; the exception being in their final semester. These limitations apply to fall and spring semesters but not summer sessions.

Core Courses (18 credits)

  • INFO-H 501 Introduction to Data Science Programming
  • LIS-S 511 Database Design
  • INFO-H 510 Statistics for Data Science
  • INFO-H 515 Statistical Learning  (Prerequisite: Graduate Statistics course)
  • INFO-H 516 Cloud Computing for Data Science  (Prerequisites: Graduate Database course)
  • INFO-H 517 Visualization Design, Analysis, and Evaluation  (Prerequisite: Programming experience)

Students may test out of LIS-S 511. Students do not receive credit toward their required 30 credit hours by testing out of a course. However, they may instead replace the course with a specialization course or approved elective.

Specialization + Elective Courses (12 credits)

Specialization Courses

  • TESM-T 562 Economics of Event Tourism (Fall)
  • TESM-T 582 Applied Sport Event Research (Spring)
  • TESM-T 598 Master’s Consulting Project (Summer)

Elective Courses

  • INFO-B 505 Informatics Project Management
  • INFO-H 518 Deep Learning Neural Networks
  • INFO-H 519 Natural Language Processing with Deep Learning  
  • INFO-H 695 Thesis/Project in Applied Data Science (MS Thesis students only)
  • INFO-I 575 Informatics Research Design
  • INFO-I 595 Professional Internship
  • INFO-I 698 Research in Informatics (Independent Study)
  • INFO-P 502 Modeling Crisis
  • NEWM-N 510 Web Database Development

Thesis or Project

The Thesis/Project is available to highly motivated students ready to carry out publishable research. Students must prepare a prospectus and gain a commitment from a  primary faculty advisor  with research interests in data science by the end of the first semester. By the end of the second semester, students must complete a course on research design and methods (e.g.,  INFO-I 575 or  LIS-S 506 ).

The thesis or project must be completed in two semesters or in a semester and summer. Thesis students register for a total of 6 credits and project students register for a total of 3–6 credits of  INFO-H 695 Thesis/Project in Data Science . Students are required to prepare and defend a research proposal with a timeline of deliverables in addition to the thesis or project.

Plan of study for fall admissions

Fall year 1, spring year 1.

  • INFO-H 515 Statistical Learning
  • INFO-H 516 Cloud Computing for Data Science
  • INFO-H 517 Visualization Design, Analysis, and Evaluation
  • Sports Analytics Specialization or Elective Course

Summer Year 1 (Optional)

Fall year 2.

  • Sports Analytics Specialization or Elective Course (If not taken in Summer)

Plan of study for spring admissions

Spring year 2, ready to get started.

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