Data Analytics Courses

These data analytics courses focus on teaching the essential techniques used in day-to-day analysis with practical exercises and real-world data. You’ll learn to prepare data, calculate metrics, and communicate insights through dashboards and visuals.

1M+ learners
Hands-on projects
No credit card required
4.8

Recommended Path for Beginners

Start your data analytics journey with these expert-curated learning paths.

Data Analyst (Python)

Build end-to-end analytics skills with Python and SQL—cleaning data, visualizing insights, and delivering business answers.

27 courses 18 projects 426.2k

Data Analyst (R)

Analyze, clean, and visualize data using R and SQL to perform end-to-end statistical analysis and communicate insights effectively.

23 courses 18 projects 91.8k

Junior Data Analyst (Excel + SQL)

Analyze and communicate insights by preparing, querying, and visualizing data with Excel, SQL, and Python to support data-driven decisions.

19 courses 14 projects 449k

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Explore All Data Analytics Courses

Linear Regression Modeling in R

Apply linear regression in R to build, interpret, and evaluate predictive models, understanding when linear assumptions hold and fail.

3 hours 3.2k

Introduction to Data Cleaning in R

Develop practical data cleaning skills in R by reshaping tables, fixing missing values, and preparing relational data for analysis.

7 hours 6.2k

Advanced Data Cleaning in R

Work with regular expressions in R to precisely match, clean, and transform text data as part of advanced, real-world data cleaning workflows.

6 hours 2.8k

Introduction to APIs in R

Acquire data from external APIs in R, handling JSON responses, authentication, and status codes to support real-world analysis workflows.

3 hours 2.4k

Introduction to Web Scraping in R

Collect structured data from websites by scraping and parsing web pages in R to support downstream analysis and insights.

3 hours 1.5k

Introduction to Statistics in R

Apply core statistical sampling techniques in R—including random, stratified, and cluster sampling—using hands-on analysis scenarios.

5 hours 5.1k

Intermediate Statistics in R

Apply measures of central tendency and variability in R, using means, medians, standard deviation, and z-scores to compare data.

3 hours 2k

Introduction to Probability in R

Compare theoretical and experimental probability in R while calculating event likelihoods using permutations, combinations, and real examples.

2 hours 1.8k

Conditional Probability in R

Apply conditional probability and Bayes’ theorem in R to model dependent events, reason under uncertainty, and build practical Naive Bayes classifiers.

2 hours 1.5k

Hypothesis Testing in R

Use hypothesis testing in R to assess real-world data with chi-square tests, probability distributions, and statistical significance.

1 hours 2.1k

Recursion and Trees for Data Engineering

Explore recursion, binary trees, binary heaps, and more with ready-to-use tactics for real projects.

6 hours 1.8k

Introduction to Data Analysis in R

Establish core R programming skills to analyze data by writing basic code, working with vectors, and performing calculations.

3 hours 47.7k

Data Structures in R

Manipulate core R data structures to store, index, and transform analysis-ready data using vectors, lists, matrices, and DataFrames.

6 hours 15.4k

Control Flow, Iteration, and Functions in R

Apply control flow, iteration, and functions in R to structure reusable workflows, reduce repetition, and handle complex data logic.

4 hours 13k

Specialized Data Processing in R

Transform text, dates, and times in R by applying string operations, date-time tools, and functional mapping to support real analysis workflows.

4 hours 6.6k

Introduction to Data Visualization in R

Create clear, insightful data visualizations in R using ggplot2 to explore trends, compare groups, and communicate findings effectively.

5 hours 8.4k

Introduction to Python for Data Engineering

Develop core Python skills used in data engineering, including working with data, control flow, and notebooks.

4 hours 55k

Dictionaries and Functions in Python

Build reusable Python programs by working with dictionaries, functions, and Jupyter Notebook to support data engineering and analysis workflows.

5 hours 15.2k

Learn Data Analytics Courses by Building Projects

Apply your skills to real-world scenarios with these guided projects

Project
Free

Word Raider

For this project, you’ll step into the role of a Python developer to create “Word Raider,” an interactive word-guessing game using core programming concepts like loops, conditionals, and file handling.

11 Steps
Project

Profitable App Profiles for the App Store and Google Play Markets

For this project, we’ll assume the role of data analysts for a company that builds free Android and iOS apps. Our revenue depends on in-app ads, so our goal is to analyze data to determine which kinds of apps attract more users.

14 Steps
Project
Free

Analyzing Kickstarter Projects

For this project, you’ll assume the role of a data analyst at a startup considering launching a Kickstarter campaign. You’ll analyze data to help the team understand what might influence a campaign’s success.

8 Steps
Project
Free

Investigative Statistical Analysis – Analyzing Accuracy in Data Presentation

For this project, you’ll be a data journalist analyzing Fandango’s movie ratings to determine if there was any change after a 2015 analysis found evidence of bias. You’ll use R and statistics skills to compare movie ratings data from 2015 and 2016.

8 Steps

Frequently Asked Questions

How do you choose the right data analytics course for your goals?

Choose a data analytics course by matching it to the skills required for analytics roles. Most roles expect SQL, Excel, Python or R, statistics, and dashboard tools like Tableau or Power BI.

If you are new to analytics, pick a structured program that starts with fundamentals and builds toward real projects. Dataquest’s guided paths show you which courses to take and teach each skill through hands-on practice.

What is data analytics?

Data analytics is the process of organizing, analyzing, and interpreting data to understand performance and support decision-making. It focuses on turning raw data into clear, measurable insights teams can act on.

Analysts use tools like SQL, spreadsheets, and data visualization software to calculate metrics, identify trends, and communicate results. Dataquest teaches these skills through interactive lessons where you work directly with real business data.

Is data analytics hard to learn?

Data analytics can feel hard at first, but hands-on practice makes it easier to learn. Working with real data helps you understand core concepts and develop data storytelling skills.

Dataquest breaks topics into small, practical steps and gives immediate feedback. This approach makes advanced concepts easier to grasp and helps you learn data analytics faster than video-based courses.

What are the best data analytics courses online?

The best data analytics courses focus on practical skills like SQL, Excel, Python or R, and dashboards, and let you apply them to real data.

Dataquest stands out because every course is interactive and project-focused. Learners consistently highlight how this approach helped them build confidence and create strong portfolios.

Are data analytics skills still in demand?

Yes, and demand continues to grow as companies rely more on data-driven decisions. While AI tools can automate some tasks, skilled analysts are needed to define metrics, interpret results, validate AI output, and communicate insights clearly. Dataquest’s projects help you build these skills so you stay relevant as the field evolves.

What jobs can you get with data analytics skills?

Data analytics skills prepare you for data analyst roles such as:

  • Data Analyst
  • Business Analyst
  • Product Analyst
  • Marketing Analyst
  • Operations Analyst
  • Business Intelligence Analyst

As you add tools like SQL, Excel, Python, Tableau, or Power BI, your opportunities expand. Dataquest paths help you learn these skills step-by-step.

Which data skills are essential for data analytics roles?

Data analytics roles require core skills that cover how data is collected, structured, cleaned, and analyzed. These skills help you work with raw data and turn it into clear, useful insights.

Key data analytics skills include data management, data modeling, data manipulation, and data mining. Together, they support tasks like preparing datasets, spotting patterns, and answering business questions with data.

What is the difference between data analytics, data analysis, and data science?

  • Data analysis focuses on exploring data and presenting findings.
  • Data analytics includes analysis but also involves performance tracking, dashboards, and business insights.
  • Data science builds predictive models and uses machine learning.

Dataquest offers courses for each, so you can choose the direction that fits your goals.

Do you need a technical background before starting data analytics courses?

No, many Dataquest learners start with no coding experience. Our courses begin with the basics and use hands-on practice and real datasets to help you build confidence as you go.

What tools are commonly used in data analytics?

Analysts rely on SQL, Excel, Python, R, Tableau, Power BI, Git/GitHub, and libraries like pandas and NumPy. Dataquest teaches many of these tools directly in your browser so you can learn by doing, not by watching.

What is the best way to learn data analytics fast?

Follow a structured curriculum, practice regularly, and build real projects that show your skills. Dataquest speeds up learning data analytics with interactive lessons, guided paths, and portfolio-ready projects that mirror real analyst tasks.

How long will it take to become job-ready in data analytics?

Most learners reach job-ready skill level within 3–9 months, depending on how much time they study each week. Dataquest’s analytics paths are designed to help you progress quickly with real-world projects and consistent hands-on learning.

How much do data analytics courses cost?

Costs vary widely, from free introductory courses to monthly subscriptions on learning platforms to university programs costing thousands.

Dataquest offers an affordable subscription with full access to all data science, analytics, engineering, and AI courses. It also includes free lessons and a 14-day money-back guarantee, so you can start learning risk-free.

Will you get a certificate, and does it help you stand out?

Yes. Dataquest awards a certificate for every course and path you complete. Certificates help show your progress.

Your real advantage comes from the projects you build as you learn. Employers value practical experience, and learners often mention projects as one of the biggest benefits.