3-Month Data Analysis Curriculum
Below is a 3-Month Data Analysis
Curriculum that starts from basic and gradually builds up to advanced
level using Excel, Python, MySQL, and other important software tools
like Power BI, Git, Pandas, and Jupyter Notebook.
📊
3-Month Data Analysis Course (Beginner to Advanced)
🧰 Tools Covered: Excel,
Python, MySQL, Jupyter Notebook, Pandas, Power BI, Git, SQLAlchemy, Plotly
📅
Month 1: Foundations – Excel, Python & SQL Basics
✅
Week 1: Introduction to Data Analysis
- What is Data Analysis?
- Types of data (structured/unstructured,
qualitative/quantitative)
- Stages: Collection → Cleaning → Analysis →
Visualization → Reporting
- Overview of key tools (Excel, Python, SQL, Power BI)
📘 Mini Task: List 5
fields that use data analysis.
✅
Week 2: Excel for Data Entry & Analysis
- Excel interface overview
- Entering and formatting data
- Basic formulas: SUM, AVERAGE, MIN, MAX, IF
- Sorting, filtering, conditional formatting
- Creating charts (bar, line, pie)
📘 Project: Student
score calculator with pass/fail grading using Excel.
✅
Week 3: Python Basics for Data Analysis
- Installing Python and Jupyter Notebook
- Variables, data types (int, float, string, boolean)
- Lists, tuples, dictionaries
- Loops and conditional statements
- Basic input/output and calculations
📘 Project: Write a
Python script to calculate and display biodata or average scores.
✅
Week 4: SQL Basics (MySQL or SQLite)
- What is a database?
- SQL syntax: SELECT, INSERT, UPDATE, DELETE
- Filtering using WHERE,
sorting with ORDER
BY
- Creating and modifying tables
- Simple joins
📘 Project: Create a
MySQL database for employee records and query them.
📅
Month 2: Intermediate Level – Python + Pandas + SQL
✅
Week 5: Working with Files & Data in Python
- Reading/writing CSV, Excel using openpyxl and pandas
- Importing data with pandas.read_csv()
or .read_excel()
- DataFrame basics: head, tail, describe, info
- Selecting rows/columns
📘 Project: Read
student results from Excel, clean and display the top 5 scores.
✅
Week 6: Data Cleaning with Pandas
- Handling missing values (dropna, fillna)
- Removing duplicates, changing data types
- String operations and date formatting
- Filtering with conditions
📘 Project: Clean a
sales dataset and summarize total sales per product.
✅
Week 7: Data Analysis with Pandas & SQL
- groupby, agg, and pivot tables
- Merge and join DataFrames
- Running SQL queries from Python with sqlite3 or SQLAlchemy
- Exporting cleaned data to Excel
📘 Project: Build a
customer order summary from a raw dataset.
✅
Week 8: Data Visualization in Python
- Using matplotlib for bar, line, scatter charts
- seaborn for heatmaps, boxplots, pairplots
- Styling and saving plots
- Dashboards with multiple plots
📘 Project: Visualize
COVID-19 or student performance data.
📅
Month 3: Advanced Tools – Power BI, Git, API, Automation
✅
Week 9: Power BI for Business Intelligence
- Install and explore Power BI Desktop
- Import data from Excel or MySQL
- Build dashboards with filters and visuals
- Use basic DAX for calculated fields
📘 Project: Create a
sales dashboard using Power BI with Excel data.
✅
Week 10: Introduction to Git & Version Control
- What is Git and why use it?
- init, add, commit, push, pull
- Setting up a GitHub account and repo
- Using GitHub to showcase your data projects
📘 Project: Push your
Python project to GitHub with a README file.
✅
Week 11: Working with APIs & Automation
- Using Python requests
to pull data from APIs
- Understanding JSON data
- Automating tasks like email reports with smtplib
- Scheduling tasks with schedule
or Windows Task Scheduler
📘 Project: Pull
weather or stock price data and email a report.
✅
Week 12: Final Projects & Career Prep
📘 Capstone Projects
(Choose One or More):
- Sales Analysis Report
with Python (Pandas + Visualization)
- Student Result Dashboard in Power BI
- API Data Project:
Fetch and analyze public data (weather, crypto)
- Database Project:
Manage customer records in MySQL + Python interface
🎓 Bonus:
- Tips on building a portfolio for freelancing or job
applications
- Mock interview and resume guidance for data analyst
roles
🧰
Final Software List (Skills You’ll Master):
|
Tool/Library |
Use |
|
Excel |
Entry-level analysis, charts, data
formatting |
|
Python |
Scripting, data handling,
automation |
|
Pandas |
Data wrangling and analysis |
|
MySQL/SQLite |
Data storage, querying |
|
Matplotlib/Seaborn |
Data visualization |
|
Power BI |
Dashboards and reporting |
|
Git & GitHub |
Version control and collaboration |
|
Jupyter Notebook |
Clean code and documentation |
|
SQLAlchemy |
Python-MySQL integration |
|
Plotly/Dash |
Interactive dashboards |
|
APIs (Requests, JSON) |
External data fetching |
Would you like this as a PDF
syllabus, lesson slides, or worksheet templates? I can also
include a certificate design template and project report format if
needed.
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