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

Do you have a question about how to learn data analytics for free? If yes, this article is for you. Here, you will get step-by-step guidance along with free resources to learn data analytics.

Let's get started!

Skills Required for Data Analysts

Before diving into learning data analytics, it's essential to understand the skills required:

1. Programming Languages: Python, R, or SaaS.
2. Statistics: Understanding statistical tests, distributions, and estimators.
3. Mathematics: Multivariate calculus and linear algebra.
4. Data Wrangling: Collecting and cleaning data.
5. Data Visualization: Creating visual representations of data.
6. Machine Learning: Basic knowledge of machine learning algorithms.

Roles and Responsibilities of Data Analysts

Data analysts perform several key tasks daily:

• Collecting and Interpreting Data: Gathering data and making sense of it.
• Data Cleaning: Removing noise from data before analysis.
• Finding Insights: Extracting important insights from large datasets.
• Trend Analysis: Identifying short-term and long-term trends.
• Creating Reports: Using reporting tools to help business executives make decisions.
• Data Visualization: Creating graphs and charts to showcase findings.
• Writing SQL Queries: Extracting data from databases.

Step-by-Step Guide to Learning Data Analytics for Free

Step 1: Learn a Programming Language Programming is essential for data analysts. You can choose any language, but Python and R are popular choices. Here are some free resources to learn Python:

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)

For R programming, consider these resources:

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 Statistics is crucial for analyzing data. Here are 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)

Step 3: Learn Mathematics Strong math skills are necessary for data analysis. Focus on multivariate calculus and linear algebra. Free resources include:

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)

Step 4: Learn Data Science Libraries If you choose Python, familiarize yourself with these libraries:

• NumPy: For numerical operations.
• Pandas: For data manipulation.
• Matplotlib: For creating graphs and charts.
• Scikit-Learn: For machine learning algorithms.

Free resources for learning these libraries include:

1. Learn NumPy Fundamentals (Python Library for Data Science)(Udemy Free Course)
2. NumPy for Data Science Beginners: 2022(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 involves collecting and cleaning data. Learn SQL and database systems. Free resources include:

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 Showcasing your findings visually is important. Learn visualization libraries like ggplot, matplotlib, Seaborn, and tools like Tableau. Free resources include:

1. Data Visualization in Tableau(Udacity Free Course)
2. Fundamentals of Visualization with Tableau(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 Having basic knowledge of machine learning is beneficial. Learn important algorithms such as PCA, neural networks, SVM, decision tree, logistic regression, and k-means clustering. Free resources include:

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)

Step 8: Work on Projects & Build a Portfolio Apply your skills by working on projects and participating in competitions. Platforms like Kaggle, Driven Data, and Topcoder offer various challenges. Project ideas include:

• Fake News Detection
• Chatbots
• Recommendation System
• Sentiment Analysis
• Credit Card Fraud Detection

Conclusion

Following these steps, you can learn data analytics for free. Keep practicing and working on new challenges to enhance your skills. If you have any questions, feel free to ask in the comments. All the best for your career!

Happy Learning!

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