Learn Data Science Online for Free (Full Tutorial)

This guide is for self-tough individuals would would like to learn data science for free. In this online tutorial, you will have plenty of resources to help you learn the skills for data science. We will start learning Python and Machine learning for data science from scratch.

What is Data Science

Data science is a field that extracts insights from large amount of data using techniques from statistics, mathematics, computer science, and domain expertise. Data scientists help solve complex problems and discover insights to make informed decisions for business or scientific progress. This involves collecting and cleaning data, exploring and modelling it statistically, applying machine learning, and using data visualization to support decision-making based on data.

Why Learn Data Science?

Learning data science useful to make better decisions, understand customers, and drive business growth.

It helps to analyze data, uncover insights, and improve your strategies.

With data science skills, you can succeed in the data-driven world and stay ahead of the competition, and stay ahead in the job market.

Why Become a Data Scientist?

Data scientists use statistical and programming skills to collect, analyze and interpret large datasets. Data scientist is a high-paying in-demand career, and still a growing field. Harvard Business Reviews places this job as the Sexiest Job of the 21st Century.

Can a Non-Technical Person Learn Data Science?

Yes, data science is open to people from very varied backgrounds. People with skills in analytics can improve their knowledge in programming, and programmers can improve their skills in data analysis, all coming back to learning data science.

Data Science Salaries

According to Glassdoor, a Data scientist makes 127K$ per year on average.

Is it Possible to Learn Data Science Online?

It is possible to learn data science online, though most organizations will require some kind of degree when hiring a data scientists. Individuals who already have a relevant degree can find work by completing data science certifications. Plenty of well known brands (IBM, Microsoft) as well as Universities (Harvard, Stanford) offer data science programs online that will provide credible certifications to get you hired.

Alternatives such like the online data science certifications offered by Coursera and DataCamp may get you hired, but you will likely need to showcase other academic achievements related to data analysis before being hired.

Learning Data Science for Beginners

Absolute beginners in data science should start by learning some basic statistical concepts and start learning a programming language such as Python or R. By experience, it is preferable for beginners to have some kind of structured course where they can cover the basics required to get started in Data Science and then move piece by piece to topics of interests once the foundations are made.

Courses and Applications to Learn Data Science for Beginners

We recommend starting with a structured course through applications such as Datacamp, Coursera or Elevate. Here is Datacamp’s Data Science with Python track.

Learn Data Science Roadmap

The data science roadmap is a strategic plan to learn data science. It can be through a paid university curriculum, a paid bootcamp, or completely free, on your own and online.

Yet, it is still very important to have a roadmap plan for your data science learning so that you don’t spend all your time learning a machine learning algorithm and zero time learning statistical methods to optimize your data.

The following roadmap is a guideline to help you learn the important tools, skills and building blocks of data science:

Data Science Subject	Why Study?
Learn the Data Science Process	The first step in learning data science is understanding the systematic process of using scientific methods to solve data-related problems and extract knowledge from data.
Learn Python programming	Python is widely used in data science for its versatility, vast library ecosystem, and readability. It allows efficient data manipulation, analysis, and machine learning.
Learn SQL	SQL is essential for working with databases. It enables data extraction, transformation, and querying, which are crucial in Data science for data analysis and obtaining insights from structured data.
Learn Git and Version Control	Version control, such as Git, helps manage and track changes in code and data. It facilitates collaboration, experiment management, and reproducibility in data science projects.
Learn Machine Learning	Machine learning is at the core of data science. Understanding algorithms, techniques, and model evaluation is crucial for building predictive models and extracting insights from data.
Learn Shell	Shell scripting allows automation and efficient management of data processing tasks. It helps data scientists in handling large datasets, performing system operations, and integrating workflows.
Learn Mathematics and Statistics for Data Science	Mathematics and statistics provide the foundation for data science. Concepts like linear algebra, probability, and hypothesis testing are essential for understanding and developing data models.
Learn Web Scraping	Web scraping enables data extraction from documents and websites, which can be valuable for various data science applications such as gathering data for analysis or building datasets.
Learn how to Use APIs	APIs allow data retrieval from various online sources. Learning to use APIs helps access real-time data, integrate external services, and enrich data-driven analyses.

Learn the Data Science Process (methodology)

The first step to learning data science is to understand the data science process. The data science process is a systematic methodology used to solve data-related problems starting from the problem definition to the communication with the stakeholders. It uses scientific methods, processes and system to draw knowledge from data.

Here is the data scientist’s framework process according to Stanford University.

1. Define the Problem
2. Collect the data
3. Explore the Data
4. Clean the data
5. Model the data
6. Communicate the Findings

Define the Data Science Problem

First, the data scientist clearly defines the problem or question we want to answer. Problem definition could be anything, like predicting sales or understanding customer behaviour.

Collect the Data

Data collection involves collecting data from various sources, such as surveys, databases, or online sources. APIs and web scraping are common data collection techniques in Data Science. Alternatively, data scientists can gather internal data using tools such as log data, Google Analytics or Google Search Console data.

Explore the Data

Once we have the data, we explore it using a concept called Exploration Data Analysis (EDA). Exploration Data Analysis is used to understand the characteristics of the data. Data Scientists look for patterns, trends, and anomalies that can provide valuable insights.

Clean the Data

Data is rarely clean enough to be used in data science problems. It often contains errors or missing values. In the data preprocessing stage, data scientists clean the data to remove inconsistencies, fill in missing values, and ensure data quality. Data cleaning is one of the most important steps of the process to improve the accuracy and efficiency of machine learning problems

Model the Data

In Data modelling, we use statistical and machine learning techniques to build models. These models help data scientists to find patterns, make predictions, or gain a deeper understanding of the data.

Communicate the Findings

Finally, data scientists have communicate findings and insights to stakeholders in their team. Data visualization and reporting techniques are critical to effective communication of the results.

Learn Python Programming for Data Science

Python programming is one of the two most popular programming languages for data science. Most data science programs will start with some introduction to a programming language. It is OK to choose a path using R or Python.

Python VS R Programming for Data Science

Python and R are popular languages in data science. Python is versatile with extensive libraries for various applications, while R excels in statistical analysis with rich packages and functions. The choice depends on personal preference, project requirements, and data analysis needs.

Python has a larger adoption than R altogether and offers tool that go well beyond data science. Thus, this course outline focuses on using Python as the basis of our data science learning path.

How to Start Learning Python for Data Science

To start learning Python for data science, select a structured introduction course on Python that will teach you the basics of the Python syntax and the Python libraries that can be used in Data Science. Datacamp has a paid Data Scientist with Python track that you can follow to get you started.

Free Python for Data Science Tutorials

I created this free series of online tutorials to get you started with Python for data science. Some of the tutorials are available on my YouTube channel.

You can get started instantly by using Python with Google Colab which comes with Python already installed, or install Python on your machine.

Once set-up with Python you can follow this series of Python tutorials to guide you towards learning the basics of Python for Data Science:

1. Introduction: Getting started with Python
2. Ways that you can Run Python code
3. Learn Python Data Types (string, lists, tuples, etc.)
4. Learn Python Control Flows (if/else, for, match, etc.)
5. Learn Python Loops (while, for)
6. Learn Python Functions and Methods
7. Learn Python file handling
8. Learn Python libraries for Data Science
   • Python NumPy (external)
   • Python Matplotlib (external)
   • Python Seaborn
   • Python Pandas

Learn Version Control for Data Science

Inevitably, any data scientist will end-up working with some kind of version control system (e.g. Gitlab, Github, etc.) to collaborate organize and keep track of code changes.

If you want to be working in data science, you have to learn Git and version control for data science. I recommend that you follow a structured course such as this one on DataCamp which guides you through everything you need to understand version control.

Free Git and Version Control for Data Science Tutorials

In this Free series, I will guide you through the basics of git and version control for data science and how to get started with Github.

1. Getting Started with Git and Github
   • Install Git on Windows and Mac OS X
   • Setting Up Git For the First Time (git config)
   • Get Started With Github
2. Basics of Git and Version Control
   • What is Version Control
   • What is Git
3. How to Use Git and Github
   • Create a Repository on Github
   • How to Clone a Git Repository (Remote and Local)
     • Clone a Github Repository on Windows
   • How to Push Code to Github (From Git Bash and VSCode)
   • How to Use Gitignore Files in Github

Learn Machine Learning for Data Science

It is important for data scientists to learn machine learning. We recommend learning the basics of machine learning in a structured course such as the Data Scientist with Python track as it provides a walkthrough of all the basics knowledge that you will need as a data scientist and also allows for a certified path.

Free Machine Learning for Data Science Tutorials

For budget conscious individuals, I created this free series of online tutorials to get you started with Machine Learning for data science.

I recommend that you have some basics of Python programming before diving into these machine learning tutorials.

In this series, I will guide you through the most important concepts of Machine Learning as well as the most popular machine learning algorithms for data science.

1. Introduction to statistics and probability for data science (external)
2. Fundamentals Python Programming
3. Exploratory data analysis with Python (external)
4. Data visualization with Python
5. Introduction to Scikit-learn with Python
6. Supervised learning
   • Regression algorithms
     • Python Linear Regression
     • Python Logistic Regression
   • Classification Algorithms
     • Python k-nearest Neighbors (KNN)
7. Unsupervised Learning
   • Dimensionality reduction algorithms
     • Principal component analysis (PCA)
   • Clustering algorithms
     • K-means
     • Hierarchical Clustering
8. Decision Trees algorithms
9. Ensemble algorithms
10. Evaluation metrics to assess model performance
    • Split Training and Testing Data in Scikit-learn
    • Confusion Matrix in Python
    • Classification report in Python
11. Feature engineering and selection methods
12. Advanced machine learning algorithms
13. Deep learning concepts and neural networks
14. Model optimization and hyperparameter tuning
15. Handling imbalanced datasets and dealing with missing data
16. Model deployment and serving in production environments

Learn Web Scraping for Data Science

Web scraping is a fundamental skill for data scientists to have to gather data to improve their current datasets or train machine learning models.

To learn web scraping, data scientists should have a basic understand of how the web is structured, how data is transferred on the Internet and the main Python libraries that can be used in Web Scraping.

Free Web Scraping for Data Science Tutorials

In this series of web scraping tutorials with Python, I will guide you through the most important concepts of web scraping as well as the most commonly used web scraping Python libraries.

We recommend individuals that learn data science to learn at least some basics of how to scrape data from the web.

1. Introduction to Python Programming
2. Fundamentals of the Web for web scraping
   • Learn the basics of HTTP requests
   • Introduction of HTML for web scraping
   • Introduction CSS Selectors for Web Scraping
   • Introduction to XPath for Web Scraping
3. Learn Python Web Scraping Libraries
   • Web Scraping with the Python Requests Library in Python
   • Web Scraping with BeautifulSoup in Python
   • Web Scraping with Selenium in Python
   • Web Scraping with Scrapy in Python

You don’t have to cover everything above, but at least make sure to:

• understand the basics of HTTP and HTML
• learn how to use CSS selectors and/or XPath in web scraping.
• learn Python requests with a parsing library (BeautifulSoup or lxml)

Learn SQL for Data Science

Aspiring data scientists have to learn SQL since it is the language that most systems today capture data today. SQL is the language used to interact with relational databases (e.g MySQL, Redshift, etc.). SQL is needed in data science to extract data from these systems. We recommend following a structured series of course like the ones given on DataCamp to learn how to use SQL.

For budget conscious users, I have created some tutorials to help you learn SQL.

Free SQL for Data Science Tutorials

1. Learn SQL by Building Your First SQLite Database
2. Basic SQL commands
3. Simple SQLite3 Tutorial With Python
4. Create a MySQL Database using Python (pymySql)
5. Install MySQL and PHPMyAdmin With XAMPP
6. Backup Google Search Console Data Into MySQL With Python

Learn Shell

Learning the shell, also known as command-line interface (CLI) or terminal, is beneficial for data scientists because it allows efficient data manipulation and automation, enabling tasks like cleaning, preprocessing, and automating workflows.

The shell facilitates the access to data, the management and organization of files, and makes it easier to handle and merge datasets. The more advanced you get in data science, the more you will end-up using the shell.

This Datacamp course teaches you how to use the Shell from started to advanced.

Free Command-Line and Terminal Shell for Data Science Tutorial

Follow this tutorial to learn how to use the command-line for Free. In this introduction to the shell we discuss the basics that data scientists should know:

• What is the Shell
• How the Shell Works
• How to Navigate in the Shell
  • Locate yourself in the shell
  • List Files in the Shell
  • Move Directories in the Command-line Shell
• How to Manipulate Files and Directories
  • Copy Files in the Shell
  • Move Files in the Shell
  • Rename Files in the Shell
  • Delete Files in the Shell
  • Delete Directories in the Shell
  • Create Directories in the Shell
  • Create Files in the Shell
• How to Manipulate Data in the Shell

Learn How to use APIs

APIs are an essential data source for data scientists and can take multiple formats that apply to many parts of the data science process. Thus, it is very important for aspiring data scientist to know how to use application programming interfaces.

Free API for Data Science Tutorials

In this series of how to use APIs with Python, I will guide you through what APIs are, the types of APIS and how to use the most important APIs available for data scientists with Python.

There are no other places in the entire web where you will find a more extensive series of how to use various APIs with Python tutorials, than on my own blog (jcchouinard.com). Not a single website.

• What is an API
• How to use Wikimedia APIs with Python
  • How to use Wikipedia API with Python
  • How to use Wikidata API with Python
• How to use Social Media APIs with Python
  • How to use Reddit API with Python (6 part series)
  • How to Use Facebook API with Python
  • How to Use Twitter API with Python (6 part series)
  • How to use LinkedIn API with Python (5 part series)
  • How to use Slack API with Python (3 parts series)
• How to Use Google APIs with Python
  • How to use Google Search Console API with Python (10 part series)
  • How to use Google Analytics API with Python
  • How to use Google Ads API with R
  • How to use Google My Business API (deprecated)
  • How to use Google PageSpeed API with Python
  • How to use Google Indexing API with Python
  • How to use Google URL Inspection API with Python
  • How to Use Gmail API with Python
• How to Use Other Data APIs
  • How to use WordPress API with Python
  • How to use Teleport API with Python
  • How to use Flickr API with Python

Trust me, I have over 50 tutorials on how to use APIs with Python from Google APIs, to social media APIs and ending up with Wikipedia and WordPress APIs. Nowhere else you’ll find a better place to learn to use APIs with Python.

Learn Mathematics and Statistics for Data Science

Mathematics and Statistics are a fundamental piece of knowledge to have when dealing with data science.

Free Statistics for Data Science Tutorials

Individuals interested in data science should consider one of these free course from EdX:

• Introduction to Statistics for Data Science using Python
• Probability and Statistics in Data Science using Python

Alternatively, check out StatQuest with Josh Starmer YouTube Channel.

We are coming up with a free course on statistics related to data science, but is not yet ready where we will cover these main aspects:

• Introduction to Statistics
  • What is statistics?
  • Importance and applications of statistics
• Introduction to Variables
  • Definition and types of variables
  • Categorical and numerical variables
• Population and Sample
  • Difference between population and sample
  • Sampling techniques
• Measures of Central Tendency
  • Mean, median, and mode
  • Using measures of central tendency to describe data
• Measures of Variability
  • Variance and standard deviation
  • Interpreting variability in data
• Normal Data Distribution
  • Understanding the normal distribution
  • Z-scores and standardizing data
• Exploring Relationships
  • Scatter plots and correlation
  • Interpreting correlation coefficients
• Hypothesis Testing
  • Basics of hypothesis testing
  • Formulating null and alternative hypotheses
• Statistical Tests in Python
  • Performing hypothesis tests using Python
  • Examples and practical applications
• Comparative Measures
  • One-sample t-test and interpretation
  • Two-sample t-test and paired sample t-test
• Analysis of Variance (ANOVA)
  • Introduction to ANOVA
  • One-way ANOVA and interpreting results
• Predictive Measures
  • Linear regression fundamentals
  • Calculating regression coefficients
• Regression Analysis with statsmodels
  • Implementing regression models in Python using statsmodels
  • Assessing model fit and significance

Data Science Projects

To learn data science through projects, here is a list of data science projects (and tutorials) that you can start experimenting with:

• Classification Machine Learning Project on the Titanic Dataset in Scikit-Learn
• Build a recommender system on Wikipedia Data with TF-IDF and NMF
• Sentiment Analysis with Python, BeautifulSoup and TextBlob
• Classification on the Breast Cancer Dataset
• Dimension reduction with PCA on the Iris Dataset

Best Free Github Repositories for Data Science

In the article on the 6 BEST Github Repositories to learn Data Science, I listed these awesome repositories to learn data science for Free.

• Microsoft – Machine Learning for Beginners – A Curriculum
• Complete Machine Learning Package
• Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow
• Deep Learning Drizzle
• Awesome Machine Learning
• 500+ Artificial Intelligence Project List with Code

Best Data Science YouTube Channels

The best free YouTube channels for data science are

• FreeCodeCamp
• Simplilearn
• Krish Naik
• StatQuest with Josh Starmer

Finally, while not as complete as the channels that I just mentioned, you can also head over to my own channel where I teach how to use Python for beginners.

On What Websites Can You Learn Data Science

The top websites where to learn data science for free are:

• EdX
• Kaggle
• MachineLearningMastery.com
• DataScienceCentral.com
• jcchouinard.com

The top paid websites to learn data science are:

• DataCamp
• Coursera
• Towards Data Science

Conclusion

We hope that this free online course on Data Science will help you towards your path to becoming a data scientist. Let us know by sharing or support us on Buy Me a Coffee.