How to Learn Data Analytics for Free?- Step-by-Step Roadmap [2024]

Do you have a question, “How to Learn Data Analytics for Free?“… If yes, this article is for you. In this article, you will get a step by step guidance along with the free resources to learn data analytics.

So without any further ado, let’s get started-

How to Learn Data Analytics for Free?

Before I answer this question, let’s have a look at mandatory skills for Data Analysts.

Skills Required for Data Analysts

1. Programming Languages
2. Statistics
3. Mathematics
4. Data Wrangling
5. Data Visualization.
6. Machine Learning

So, these are must-have skills for a Data Analysts.

Now, let’s see the Roles and Responsibilities of Data Analysts-

Roles and Responsibilities of Data Analyst

Data Analyst performs these jobs daily-

• Collecting Data and Interpreting Data.
• Data Cleaning, a data analyst clean the data. Cleaning requires removing noise from the data. The collected data contains noise, so data analysts clean the data before analysis.
• Data Analysts find out important insights from a huge amount of data. This is the main role of the Data Analyst.
• Data Analysts also find out the Trends and Patterns from Data. Data Analysts look for short-term as well as long-term trends for the company. Trend Analysis helps Data Analyst to find business strategies for their company.
• Another important role of the Data Analyst is creating data reports and visualization of patterns with the help of various reporting tools. Data reports made by Data Analyst helps business executives to make better business decisions.
• Visualization of data is also part of a Data Analyst. Data Analysts use catchy graphs and charts to visualize their findings.
• A data analyst writes SQL queries for data extraction from the Data warehouse.

Now let’s see in what order you should start learning these concepts and what are Free Resources to learn these skills.

Step 1. Learn Programming Language

Programming knowledge is a must-have skill for a Data Analyst. This is the core skill that makes a Data analyst apart from a Business analyst.

You must have knowledge of one or more programming languages like Python, R, or SaaS. Along with that, you should be familiar with data science libraries and packages (such as ggplot2, reshape2, NumPy, pandas, and scipy).

Knowledge of all programming languages is not required. You can choose any language.

Now, you might be thinking about where to learn these languages?. So, don’t worry, I have chosen some best free resources for you.

Let’s see some Free Resources to learn Python-

Free Resources to Learn Python Programming

1. Introduction to Python Programming(Udacity Free Course)
2. The Python Tutorial (PYTHON.ORG)
3. CS DOJO (YouTube)
4. Python 3 Tutorial (SOLOLEARN)
5. Python For Data Science(Udemy Free Course)
6. Programming with Mosh (YouTube)
7. Corey Schafer (YouTube)

So, these are the best free resources to learn Python. You can learn Python programming with the help of these free resources.

I am also going to list some free resources to learn R Programming. So, If you want to learn R, you can learn from these Free resources-

Free Resources to Learn R-

1. R Basics – R Programming Language Introduction(Udemy Free Course)
2. R Programming (Coursera Free to Audit Course)
3. Learn R Quickly (Udemy Free Course)
4. R, ggplot, and Simple Linear Regression (Udemy Free Course)
5. R Programming Tutorial (YouTube Tutorial)
6. R Programming Full Course In 7 Hours (YouTube Tutorial)

Step 2- Learn Statistics

In order to become a successful data analyst, you should have knowledge of Statistics. Statistics knowledge will give you the ability to decide which algorithm is good for a certain problem.

Statistics knowledge includes statistical tests, distributions, and maximum likelihood estimators. All are essential in data analysis.

As a Data Analyst, you have to find useful insights from the data, so, that’s why statistics knowledge is crucial for you. Now, you may be thinking, Ok fine! Statistics knowledge is required, but from where to learn?

So, if you are thinking the same, don’t worry. I have chosen some Best FREE courses for Statistics.

Free Resources to Learn Statistics-

1. Intro to Statistics (Udacity Free Course)
2. Introduction to Statistics (Coursera Free to Audit Course)
3. Intro to Inferential Statistics(Udacity Free Course)
4. Intro to Descriptive Statistics(Udacity Free Course)
5. Statistics and probability (Khan Academy)
6. Bayesian Statistics: From Concept to Data Analysis (Coursera Free to Audit Course)
7. Introduction to Bayesian Statistics (Udemy)
8. Python and Statistics for Financial Analysis (Coursera Free to Audit Course)
9. Statistics literacy for non-statisticians (Udemy)

Step 3- Learn Maths

As a data analyst, you have to deal with numbers. That’s why strong knowledge of Math is required.

You should be familiar with multivariate calculus and linear algebra.

Along with that, you should have an understanding of matrix manipulations, dot product, eigenvalues and eigenvectors, and multivariable derivatives.

Free Resources to Learn Maths-

1. Mathematics for Machine Learning: Linear Algebra(Coursera Free to Audit Course)
2. Mathematics for Machine Learning: Multivariate Calculus(Coursera Free to Audit Course)
3. Linear Algebra Refresher Course(Udacity Free Course)
4. Multivariable calculus(Khan Academy)
5. Learn Linear Algebra(Khan Academy)
6. A Survey of Optimization Methods from a Machine Learning Perspective (Research Paper)
7. Optimization Methods for Large-Scale Machine Learning (Research Paper)
8. How optimization for machine learning works (YouTube Video)

Step 4- Learn Data Science Libraries

Libraries are the collection of pre-existing functions and objects. You can import these libraries into your script to save time.

If you learn Python, then you need to learn the following Python Libraries for Data Science & Data Analytics-

• Numpy- NumPy will help you to perform numerical operations on data. With the help of NumPy, you can convert any kind of data into numbers. Sometimes data is not in a numeric form, so we need to use NumPy to convert data into numbers.
• Pandas- pandas is an open-source data analysis and manipulation tool. With the help of pandas, you can work with data frames. Dataframes are nothing but similar to Excel files.
• Matplotlib– Matplotlib allows you to draw a graph and charts of your findings. Sometimes it’s difficult to understand the result in tabular form. That’s why converting the results into a graph is important. And for that, Matplotlib will help you.
• Scikit-Learn- Scikit-Learn is one of the most popular Machine Learning Libraries in Python. Scikit-Learn has various machine learning algorithms and modules for pre-processing, cross-validation, etc.

Now, let’s see the Free Resources to learn these Python Libraries-

Free Resources to Learn Python Libraries

1. Learn NumPy Fundamentals (Python Library for Data Science)(Udemy Free Course)
2. NumPy for Data Science Beginners: 2024(Udemy Free Course)
3. NumPy Tutorial by freeCodeCamp
4. Pandas (Kaggle)
5. NumPy user guide
6. pandas documentation
7. Matplotlib Guide
8. scikit-learn Tutorial

Step 5- Learn Data Wrangling

Data wrangling is all about data collection and data cleaning. So, for that, you should have knowledge of database systems- both SQL-based and NoSQL-based.

You should also be familiar with relational databases such as PostgreSQL, MySQL, Netezza, and Oracle, as well as Hadoop, Spark, and MongoDB.

You can manipulate data using both SQL and Pandas. But there are certain data manipulation tasks that can be easily performed using SQL.

Free Resources to Learn SQL

1. W3Schools
2. SQL for Data Analysis(Udacity Free Course)
3. SQL for Data Science (edX Free to Audit Course)
4. SQL for Data Analysis: Solving real-world problems with data(Udemy Free Course)
5. SQL Crash Course for Aspiring Data Scientist(Udemy Free Course)
6. SQL Tutorial

Step 6- Learn Data Visualization

As a Data Analyst, you have to showcase your findings in a visual form, so that stakeholders can understand them properly. 

That’s why the knowledge of Data Visualization is important. And for that, you should be familiar with data visualization libraries like ggplot, matplotlib, Seaborn, and D3.js.

You should have knowledge of various Reporting tools like Tableau and power bi. 

Free Resources to Learn Data Visualization

1. Data Visualization in Tableau(Udacity Free Course)
2. Data Visualization with Tableau Specialization(Coursera Free to Audit Course)
3. Complete Tableau Training for Absolute Beginners(Udemy Free Course)
4. Data Analysis and Visualization(Udacity Free Course)
5. Data Visualization (Kaggle)
6. Data Visualization and D3.js(Udacity Free Course)
7. Data Visualization in Python Masterclass™ for Data Scientist(Udemy Free Course)
8. Free Training Videos (Tableau)
9. Creating Dashboards and Storytelling with Tableau (Coursera Free to Audit Course)
10. Tableau | A Quick Start Guide(Udemy Free Course)

Step 7- Learn Machine Learning Algorithms

After having all previous skills, it’s good to have a basic knowledge of Machine Learning. Not all Data Analysts have Machine Learning knowledge, but if you want to get the extra privilege, it’s better to have Machine Learning skills.

You don’t need to learn the theory and implementation details behind all ML algorithms. All you need to know is its pros and cons, as well as when to and when not to apply these algorithms to a dataset.

These are some important algorithms of ML, you can learn- principal component analysis, neural networks, support vector machines, decision tree, logistic regression, and k-means clustering.

Free Resources to Learn Machine Learning

1. Machine Learning by Georgia Tech(Udacity Free Course)
2. Introduction to Machine Learning Course(Udacity Free Course)
3. Machine Learning: Unsupervised Learning (Udacity Free Course)
4. Machine Learning by Stanford University(Coursera Free to Audit Course)
5. Machine Learning for All by University of London(Coursera Free to Audit Course)
6. What is Machine Learning?(Udemy Free Course)
7. Machine Learning Fundamentals(edX Free to Audit Course)

Step 8- Work on Projects & Build Portfolio

Once you learn all the required data analysis skills, start working on data analysis projects. The more your work on projects, the more you will learn.

You can also take part in competitions. Competitions will make you even more proficient in Data Analysis.

When we talk about top data science competitions, Kaggle is one of the most popular platforms for data science. Kaggle has a lot of competitions where you can participate according to your knowledge level.

You can also check these platforms for data analytics competitions-

• Driven Data
• Codalab
• Iron Viz
• Topcoder
• CrowdANALYTIX Community
• Bitgrit

Data Analysis Project Ideas for beginners-

• Fake News Detection
• Build a Chatbots
• Recommendation System
• Driver Drowsiness Detection
• Sentiment Analysis 
• Credit Card Fraud Detection Project
• Road Lane line detection
• Color Detection with Python
• Stock Price Predictor
• Forest Fire Prediction

That’s all!. If you follow these steps, you can learn Data Analytics for Free. But the most important thing is to keep enhancing your skills by working on more and more challenges.

The more you practice, the more knowledge of data analytics you will gain. So after completing these steps, don’t stop, just find new challenges and try to solve them.

Now it’s time to wrap up!

Conclusion

I hope you got an answer to the question, “How to Learn Data Analytics for Free?”. If you have any doubts or queries, feel free to ask me in the comment section. I am here to help you.

All the Best for your Career!

Happy Learning!

Thank YOU!

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Though of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden