Here is the 3 Months 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:
• Coursera’s “Machine Learning” by Andrew Ng.
– 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.
