Data analysis is an essential skill for many careers and personal interests today. With the rise of big data and data-driven decision making, knowing how to analyze data and draw insights from it is more valuable than ever. Thankfully, there are many free resources available online to help you get started with learning data analysis.
In this guide, we’ll provide tips on how to learn key data analysis skills without spending any money. We’ll cover the basics of data analysis terminology, outline the steps of the data analysis process, recommend the best free online courses and resources for mastering Excel, SQL, Python, R, and more. Read on to learn how to become a data analyst for free!
What is Data Analysis?
Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, arriving at conclusions, and supporting decision-making.
It involves applying analytical and statistical techniques to describe and evaluate data, identify patterns and relationships in data to derive insights, and make predictions. From a statistical perspective, it encompasses descriptive statistics, statistical inference, regression analysis, predictive modeling, time series analysis, data mining, and machine learning techniques.
The overall goal of data analysis is to extract knowledge and provide information that can guide decision making and strategy. Businesses utilize data analysis to learn more about their customers, products, competition, and industry. Scientists employ data analysis to prove or disprove hypotheses and advance human knowledge. For individuals, data analysis aids personal finance decisions, fantasy sports strategy, and much more.
Key Types of Data Analysis
Some of the most common and useful types of data analysis techniques include:
Descriptive Analysis: Summarizing and describing the key characteristics of a data set. This provides an overview and helps you visualize what the data looks like. Key descriptive analytics methods include determining measures of central tendency (mean, median, mode) and measures of variability (standard deviation, variance, range). Creating graphs and charts like histograms, pie charts, scatter plots are part of descriptive analysis.
Diagnostic Analysis: Investigating and determining why something happened or assessing performance. You can use statistical tests and benchmarks to identify root cause of issues, underperformance, and anomalies.
Predictive Analysis: Using data to predict future outcomes and trends. Regression analysis, predictive modeling, and machine learning techniques are employed to make data-driven forecasts.
Prescriptive Analysis: Recommending one or more courses of action based on insights derived from data analysis. For example, determining which product pricing strategy will maximize profits based on predictive modeling.
Stages of the Data Analysis Process
While there are various frameworks and methodologies, data analysis typically follows these key stages or steps:
Business/Data Understanding: Determine the project objectives, requirements, data sources, and stakeholder needs. Clearly define the questions you want to answer or problems you want to solve with data analysis.
Data Collection: Gather relevant data from available sources, which may include databases, web servers, APIs, spreadsheets, surveys, sensors, etc.
Data Cleaning: Fix or remove incomplete, duplicate, inaccurate, and irrelevant data.
Data Exploration: Become familiar with the data through initial high-level review and start uncovering patterns, correlations, and potential insights.
Data Preparation: Select, construct, and transform data into the optimal structure for analysis. May involve joining data sets, aggregating data, calculating new metrics, encoding categorical variables, etc.
Modeling and Algorithms: Apply various statistical, machine learning, data mining, and AI techniques and algorithms to analyze patterns in data and make predictions.
Data Visualization: Create charts, graphs, and dashboards to summarize findings and make data easier to understand for stakeholders.
Interpretation: Synthesize results and findings into key takeaways, insights, recommendations, and conclusions.
Communication: Share the analysis, insights, trends, and recommendations with stakeholders in a way that leads to data-driven action.
Now that we’ve provided an overview of data analysis basics, let’s look at the best free learning resources available online.
Learn Data Analysis with Free Online Courses
Online courses are one of the most convenient ways to build data analysis skills for free. Here are some of the top free data analysis courses from leading learning platforms:
Excel
Microsoft Excel for Data Analysis – Coursera
Offered by Duke University, this beginner Coursera course teaches basic Excel skills for data analysis including formatting, formulas, pivot tables, sorting, filtering, statistical functions, and charts.
Excel Skills for Business Specialization – Coursera
Provided by Macquarie University, this 5-course Coursera specialization covers intermediate and advanced Excel functionality for business data analysis.
Introduction to Data Analysis Using Excel – edX
From MIT, this edX course is an introductory level Excel data analysis course that teaches data visualization, descriptive statistics, and predictive modeling.
Intermediate Excel Skills for Data Analysis – DataCamp
DataCamp’s self-paced course focuses on pivot tables, VLOOKUP, data validation, and analytical graphs for intermediate Excel users.
SQL
Introduction to SQL – Khan Academy
This free SQL course for beginners from Khan Academy covers querying, filtering, aggregation, joins, and more using SQLite.
SQL for Data Analysis – UC Davis on Coursera
Dive deeper into using SQL for data analysis including window functions, subqueries, Apache Spark SQL, and more through this Coursera course.
Introduction to Databases and SQL Querying – edX
This introductory database and SQL course from Curtin University teaches SQL querying, database terminology, joins, set operators, and more.
Analyzing Data with SQL – DataCamp
DataCamp’s hands-on course teaches SQL skills for data analysis like window functions, pivoting data, and calculating statistics.
Python
Python for Data Science, AI & Development – edX
IBM’s comprehensive edX course teaches Python for data analysis using libraries like Pandas, Numpy, Matplotlib, Seaborn, TensorFlow, and more.
Python for Data Science – DataCamp
DataCamp’s Python data analysis course covers data manipulation, visualization, pandas Foundations, case studies, and more.
Python for Everybody – Coursera
This popular beginner Python course from University of Michigan also introduces data analysis with Python libraries through hands-on examples.
Programming for Data Science with Python – edX
Part of the Microsoft Professional Program, this edX course focuses on Python for data analysis, visualization, machine learning, and more.
R Programming
Introduction to R for Data Science – edX
From Microsoft, this edX course is designed for beginners looking to utilize R programming for data analysis and visualization.
Introduction to R – DataCamp
DataCamp’s project-based R programming course includes units on data visualization, data manipulation, and statistical analysis in R.
R Programming – Coursera
Provided by Johns Hopkins University, this popular Coursera course covers R basics with a focus on data analysis tools and techniques.
R Programming: Advanced Analytics In R For Data Science – edX
Dive deeper into data analysis functionality in R like multivariate regression, algorithms, predictive modeling, and big data techniques in this advanced edX course.
General Data Analysis & Visualization
Data Analysis with Python – FreeCodeCamp
This free 7-hour YouTube video course teaches the data analysis process end-to-end using Python, including data collection, cleaning, analysis, and visualization.
Introduction to Data Analysis – edX
Learn fundamental data analysis skills including data types, statistical analysis, SQL, data visualization, and more through UC San Diego’s edX course.
Data Visualization with Python – FreeCodeCamp
FreeCodeCamp’s in-depth 5-hour YouTube video course covers Python data visualization libraries like Matplotlib, Seaborn, Plotly, and Pandas.
Data Analysis & Presentation Skills – Udacity
Develop skills for data analysis, visualization, and effective presentation of insights through Udacity’s free course.
Learn with Data Analysis YouTube Channels & Playlists
YouTube has unlimited free data analysis learning content. Check out these great YouTube channels and playlists to continue building data analysis skills:
Alex The Analyst
Alex makes videos teaching Excel, Tableau, SQL, Python, advanced analytics topics, and more for beginners. He also shares tips for getting analyst jobs.
Tina Huang
Tina is a data analyst who shares tutorials on Excel, Power BI, Tableau, SQL, Python, R, and more. She also discusses her data analyst career journey.
Ken Jee
Ken’s channel features Excel tutorials focused on business intelligence and data analytics applications for beginners to advanced users.
Data School
With the motto “learn data science in public,” Data School posts videos teaching concepts in data analysis, visualization, machine learning, statistics, Python, R, and more.
freeCodeCamp.org
Their popular channel has various Python data analysis, data visualization, machine learning, and Pandas tutorials ideal for coders new to data analysis.
Programming with Mosh
Programming with Mosh provides in-depth coding courses including a Python for Data Analysis tutorial spanning over 6 hours for hands-on learning.
Microsoft Excel
From pivot tables to dashboards, Microsoft’s Excel YouTube channel shares official video tutorials on using Excel for data analysis.
Khan Academy Statistics
Sal Khan’s statistics and probability course has nearly 90 videos covering key concepts for data analysis like distributions, statistical studies, regression, etc.
Utilize Free Data Analysis Resources
Beyond courses and videos, there are a wealth of free learning materials and tools available for picking up data analysis skills. We’ve compiled some of the best free resources to supplement your learning:
Kaggle Learn
Kaggle Learn offers free interactive coding courses focused on Python, R, SQL, data visualization, and machine learning techniques for data analysis. You can also compete in data science competitions.
Google Sheets Functions
Like Excel, Google Sheets has formulas and functions for analyzing data. Learn them through Google’s Sheets function reference guide.
Mode Analytics SQL Tutorial
Mode Analytics provides a comprehensive interactive SQL tutorial covering database concepts, queries, joins, statistical analysis functions, and more.
RStudio Primers
Get started with R for data analysis through free interactive primers on RStudio that teach programming basics, data visualization, and working with data.
Tableau Training Videos
Master Tableau data visualization and dashboard creation by watching the official training videos from Tableau on their YouTube channel.
W3Schools SQL Tutorial
Learn SQL queries for accessing and analyzing data through interactive tutorials and examples on W3Schools covering everything from SELECT statements to analytics functions.
analyzing and visualizing data
Here are some key free tools for analyzing and visualizing data:
– Microsoft Excel
– Google Sheets
– OpenOffice Calc
– LibreOffice Calc
– Tableau Public
– RawGraphs
– DataWrapper
– Python (Pandas, Matplotlib, Seaborn)
– R (ggplot2, dplyr)
Join Data Analysis Communities for Feedback
Getting feedback and help from others learning data analysis can greatly accelerate your progress. Join these free communities to discuss concepts, ask questions, see examples, find mentors, and more:
Reddit – Data Analysis
The Data Analysis Subreddit has over 400,000 members sharing insights and discussing all aspects of data analysis. Post questions or browse existing threads for guidance.
Reddit – Learn Data Science
With over 350,000 members, this data science-focused subreddit also contains helpful posts for learning data analysis using Python, R, SQL, statistics, data visualization, and more.
Reddit – Learn Programming
Get feedback on coding questions related to data analysis and utilizing programming languages like Python and R for data analytics through this large subreddit.
Cross Validated on Stack Exchange
Cross Validated is Stack Exchange’s statistics and data analysis Q&A community with over 185,000 members who ask and answer analytical method questions.
KDnuggets Data Science Community
KDnuggets has data science and analytics forums as well as a Slack channel to connect with others and discuss topics like data visualization, programming, career advice, and more.
DataQuest Community
The open DataQuest community forum allows you to get help on courses, discuss data science topics, and interact with 65,000+ data enthusiasts and professionals.
Conclusion
While advanced data analysis may require more specialized skills and tools, you can learn the basics for free through the abundance of online courses, videos, tutorials, and communities. Master Excel, SQL, and an analytical programming language like Python or R using the free resources outlined above.
The key is to start simple and practice continuously by analyzing sample data sets relevant to your interests. Over time you will build proficiency in describing, manipulating, and interpreting data as you learn more advanced techniques for modeling, prediction, and extracting insights from data. The data analysis skills you gain can open up countless opportunities in business, research, technology, and more!