12-Week Data Analytics Mastery Plan
This includes all resources, YouTube videos, and guides directly under each section
Week 1: Orientation & Foundations
Learn what data analytics is and how analysts work.
Set up your environment:
Install: Google Sheets / Excel, MySQL or SQLite, Python (Anaconda / Jupyter Notebook), and Power BI or Tableau Public
Create a GitHub repository for your learning journey
Choose a dataset:
Task: Write 2–3 business questions your dataset might answer.
Week 2: Excel + Business Math / Statistics
Learn Excel for data cleaning and exploration:
Learn basic statistics:
Practice:
Clean dataset (remove duplicates, handle nulls)
Compute descriptive statistics (mean, median, variance, correlation)
Create pivot tables and bar charts
Reference:
Week 3: SQL Basics
Learn SQL fundamentals:
SELECT,FROM,WHERE,GROUP BY,ORDER BYRead:
Task:
Import dataset into SQLite/MySQL
Write basic queries (counts, filters, sorts)
Save and document results on GitHub
Week 4: Intermediate SQL + Data Wrangling
Learn:
JOIN,UNION,SUBQUERIES,CASE, andWINDOW FUNCTIONS
Task:
Add a second dataset and use
JOINsWrite 3–5 complex queries to answer deeper business questions
Document results and insights
Week 5: Data Visualization & BI Tools – Part 1
Learn Power BI or Tableau Public:
Task:
Connect cleaned data to your BI tool
Build 3 visuals (bar, line, pie/map)
Create a simple dashboard with filters and KPIs
Week 6: Data Visualization & BI Tools – Part 2
Dive deeper into dashboard design and data modeling:
Task:
Add advanced visuals and calculated metrics (e.g., % growth, rolling averages)
Publish your dashboard (Power BI Service / Tableau Public)
Share dashboard link on LinkedIn or GitHub
Week 7: Python for Data Analytics
Learn Python basics (variables, functions, loops, importing libraries)
Task:
Set up Python + Pandas
Load dataset with
pd.read_csv()Explore using
.info(),.describe(),.isnull().sum()Compute correlations, mean, min, max
Week 8: Data Cleaning & EDA in Python
Learn advanced Pandas + Seaborn:
Task:
Handle missing values (
fillna(),dropna())Create histograms, boxplots, and correlation heatmaps
Write a short EDA report (Markdown in Jupyter Notebook)
Resource:
Week 9: Statistics & Regression Analysis
Learn inferential stats and simple regression:
Task:
Perform hypothesis testing (
scipy.stats)Run a linear regression (using
statsmodelsorsklearn)Interpret results in business terms
Week 10: Automation & Advanced Analytics
Learn automation & integration:
Task:
Automate data refresh with Python
Add advanced visuals (interactive filters, parameters)
Write a short note on your analytics pipeline architecture
Week 11: Portfolio Project + Storytelling
Build your final capstone project:
Task:
End-to-end pipeline (data cleaning → SQL → Python EDA → dashboard → insights)
Create a detailed write-up or blog post
Publish on GitHub or Medium with visuals
Week 12: Job Prep & Continuous Learning
Prepare for interviews:
Task:
Update your resume with project details
Post your dashboard and learnings on LinkedIn
Apply to at least 3 roles or freelance gigs
Follow top analytics creators (Alex The Analyst, Luke Barousse, Ken Jee)