3 Months Full Data Analytics Learning Guide

Here is the 3 Months Full Data Analytics Learning Guide:

Month 1: Fundamentals and Tools

Week 1: Introduction to Data Analytics

• Objective: Understand the basics of data analytics and its importance.
• Topics:
• Overview of Data Analytics: What it is and its application.
• Types of Data (Structured vs. Unstructured).
• Understanding the data lifecycle.
• Introduction to key roles: Data Analyst, Data Scientist, etc.
• Resources:
• Online courses (e.g., Coursera, Udemy).
• Read “Data Science for Business” by Foster Provost and Tom Fawcett.
• Blogs and articles.

Week 2: Excel for Data Analysis

• Objective: Learn Excel, a foundational tool for data analytics.
• Topics:
• Data cleaning and preparation.
• Formulas and Functions (e.g., VLOOKUP, INDEX-MATCH).
• Pivot Tables and Charts.
• Data Analysis ToolPak.
• Resources:
• Excel courses (LinkedIn Learning, YouTube).
• Practice datasets (Kaggle).

Week 3: Introduction to SQL

• Objective: Learn SQL for querying databases.
• Topics:
• Basics of SQL (SELECT, WHERE, JOIN).
• Filtering and Sorting data.
• Aggregating data (GROUP BY, HAVING).
• Subqueries and Nested queries.
• Resources:
• SQLZoo, Mode Analytics SQL Tutorial.
• Practice on platforms like LeetCode, HackerRank.

Week 4: Introduction to Python

• Objective: Start with Python, focusing on data analytics.
• Topics:
• Python basics (variables, loops, functions).
• Introduction to libraries: Pandas, NumPy.
• Data manipulation with Pandas.
• Basic data visualization with Matplotlib.
• Resources:
• Python for Data Science courses (Coursera, DataCamp).
• Jupyter Notebook for hands-on practice.

Month 2: Deepening Your Knowledge

Week 5: Advanced Excel and Data Visualization

• Objective: Master advanced Excel features and data visualization techniques.
• Topics:
• Advanced formulas (e.g., Array formulas).
• Data validation, Conditional formatting.
• Introduction to Excel Macros.
• Data visualization principles.
• Creating effective charts and dashboards in Excel.
• Resources:
• Excel MVP blogs, YouTube advanced tutorials.
• Books like “Storytelling with Data” by Cole Nussbaumer Knaflic.

Week 6: Advanced SQL

• Objective: Enhance SQL skills for complex data analysis.
• Topics:
• Advanced Joins (Self-joins, CTEs).
• Window functions (ROW_NUMBER, RANK, etc.).
• Stored Procedures and Functions.
• Performance tuning (Indexes, Query optimization).
• Resources:
• Practice on real-world datasets from Kaggle.
• Advanced SQL tutorials on Mode Analytics, LeetCode.

Week 7: Advanced Python for Data Analysis

• Objective: Get comfortable with advanced Python techniques.
• Topics:
• Data cleaning and preprocessing with Pandas.
• Merging and Joining datasets.
• Advanced data visualization with Seaborn, Plotly.
• Introduction to data analysis projects.
• Resources:
• Kaggle datasets for practice.
• “Python for Data Analysis” by Wes McKinney.

Week 8: Statistics for Data Analysis

• Objective: Learn statistical concepts essential for data analysis.
• Topics:
• Descriptive statistics (mean, median, mode).
• Probability theory.
• Hypothesis testing, p-values.
• Correlation and Regression analysis.
• Resources:
• Online courses on statistics (Khan Academy, Coursera).
• Books like “Naked Statistics” by Charles Wheelan.

Month 3: Projects and Specialized Topics

Week 9: Data Visualization with Power BI/Tableau

• Objective: Learn to create interactive dashboards.
• Topics:
• Introduction to Power BI/Tableau.
• Connecting to data sources.
• Creating visuals and dashboards.
• Best practices for dashboard design.
• Resources:
• Power BI and Tableau official tutorials.
• Practice building dashboards with public datasets.

Week 10: Introduction to Machine Learning

• Objective: Understand basic machine learning concepts.
• Topics:
• Supervised vs. Unsupervised learning.
• Introduction to algorithms (Linear Regression, Decision Trees).
• Training and evaluating models.
• Introduction to Scikit-Learn.
• Resources:
• Kaggle tutorials and competitions.

Week 11: Real-World Projects

• Objective: Apply knowledge to real-world problems.
• Activities:
• Choose a dataset from Kaggle or any open-source platform.
• Define a problem statement and objectives.
• Conduct data cleaning, exploration, and analysis.
• Present findings in a report or dashboard.
• Resources:
• Kaggle, GitHub for project ideas and datasets.
• Peer reviews and feedback.

Week 12: Final Review and Next Steps

• Objective: Consolidate knowledge and plan for continued learning.
• Activities:
• Review all tools and techniques learned.
• Polish and showcase your projects (GitHub, portfolio).
• Explore advanced topics for future learning (e.g., Big Data, Deep Learning).
• Network with professionals and join Data Analytics communities.
• Resources:
• LinkedIn, Medium for networking and sharing your work.
• Look into certifications (Google Data Analytics, Microsoft Certified: Data Analyst Associate).

Additional Tips:

• Consistency: Study and practice regularly to build a habit.
• Projects: Focus on hands-on projects; they’re the best way to learn.
• Community: Engage in forums like Stack Overflow, Kaggle, and LinkedIn groups.