Tuesday, June 17, 2025

Data Analysis Course Study Material (Full 3-Month Curriculum)

 

Beginner to Advanced Data Analysis Course Study Material (Full 3-Month Curriculum)


Month 1: Foundations (Excel, Python, SQL Basics)

Week 1: Introduction to Software Development & HTML Basics

1. What is Software Development?

Software development is the process of creating computer programs that perform specific tasks. These can be websites, mobile apps, or software for businesses.

👉 Example: Creating a mobile app like WhatsApp, or a school result calculator.

2. How Software Development Works

Software development usually follows a set of steps called the Software Development Life Cycle (SDLC):

  1. Planning – Understand what the software should do.
  2. Designing – Sketch out how the software will look and work.
  3. Coding – Write the actual program using programming languages.
  4. Testing – Check for mistakes or bugs.
  5. Deployment – Launch the software for people to use.
  6. Maintenance – Keep improving and fixing the software.

3. Tools You Need

  • Text Editors – For writing code. (Example: VS Code, Notepad++)
  • Web Browsers – To view your website. (Example: Chrome, Firefox)
  • Languages – HTML (structure), CSS (style), JavaScript (functionality)

4. Introduction to Websites Using Code

A website is a collection of web pages. It can be:

  • Static (just content)
  • Dynamic (interactive, like login systems)

5. How to Create and View an HTML Page

Open VS Code or Notepad and write:

<!DOCTYPE html>

<html>

<head>

    <title>My First Web Page</title>

</head>

<body>

    <h1>Welcome to My Website</h1>

    <p>This is my first webpage.</p>

</body>

</html>

Explanation:

  • <!DOCTYPE html>: Tells the browser this is an HTML5 page.
  • <html>: Starts the page.
  • <head>: Hidden part (e.g., title, links).
  • <title>: Title shown in the browser tab.
  • <body>: Visible content like headings and text.
  • <h1>: A big heading.
  • <p>: A paragraph.

🔹 Save as index.html, and double-click to open in a browser.

6. Understanding Tags and Elements

  • A tag is a keyword in angle brackets like <p>.
  • An element includes the tag and its content like <p>Hello</p>.

Week 2: Microsoft Excel for Data Analysis

1. Introduction to Excel

Excel helps to store and calculate data easily using rows and columns.

👉 Example Table:

Name

Score

John

80

Mary

45

2. Useful Formulas in Excel

  • =SUM(B2:B3) → Adds numbers in B2 and B3.
  • =AVERAGE(B2:B3) → Finds average.
  • =IF(B2>=50,"Pass","Fail") → Checks if score is a pass.

3. Creating Charts

  • Highlight data.
  • Click “Insert” → Choose “Bar Chart” or “Pie Chart”.

Mini Project:

  • Create a result sheet with 5 students.
  • Add columns: Total, Average, and Result.

Week 3: Python Basics for Data

1. Installing Python & Jupyter Notebook

2. Basic Python Code

name = input("Enter your name: ")

score1 = int(input("Enter score 1: "))

score2 = int(input("Enter score 2: "))

total = score1 + score2

average = total / 2

print("Hello", name)

print("Your average score is:", average)

Explanation:

  • input() asks the user to type something.
  • int() converts input to a number.
  • + adds, / divides.
  • print() displays output.

Mini Project:

Ask name and two scores. Show total and average.

Week 4: SQL Basics

1. What is SQL?

SQL is a language for storing and retrieving data from a database.

2. SQL Commands

CREATE TABLE students (

    id INT,

    name VARCHAR(50),

    score INT

);

 

INSERT INTO students VALUES (1, 'John', 75);

SELECT * FROM students;

Explanation:

  • CREATE TABLE creates a new table.
  • INSERT INTO adds data.
  • SELECT * shows all the data.

Mini Project:

Create a student table, add 3 students, and display all records.


Month 2: Intermediate Level

Week 5: Working with Data in Python (Pandas)

1. Reading CSV Files

import pandas as pd

data = pd.read_csv('students.csv')

print(data.head())

Explanation:

  • import pandas as pd: Brings in the pandas library.
  • read_csv(): Opens your file.
  • head(): Shows first few rows.

Mini Task:

Create students.csv with name and score. Open and display using Python.

Week 6: Cleaning Data with Pandas

1. Fixing Missing Data

data.dropna()        # Removes rows with empty data

data.fillna(0)       # Fills empty spots with 0

Mini Project:

Load a CSV with empty spaces, clean it, and print cleaned data.

Week 7: Using SQL in Python

1. Accessing SQL from Python

from sqlalchemy import create_engine

import pandas as pd

 

engine = create_engine('sqlite:///students.db')

data = pd.read_sql("SELECT * FROM students", engine)

print(data)

Explanation:

  • sqlalchemy lets Python talk to SQL databases.

Mini Task:

Store students in a database and use Python to access the data.

Week 8: Data Visualization

1. Making Charts in Python

import matplotlib.pyplot as plt

 

names = ['John', 'Mary', 'Emma']

scores = [85, 60, 90]

 

plt.bar(names, scores)

plt.title("Student Scores")

plt.xlabel("Names")

plt.ylabel("Scores")

plt.show()

Explanation:

  • bar() draws bar chart.
  • title(), xlabel(), ylabel() – add labels.
  • show() – displays chart.

Mini Task:

Make a chart for student performance.


Month 3: Advanced Projects & Tools

Week 9: Power BI for Reporting

1. Getting Started

  • Install Power BI Desktop
  • Import Excel or CSV files
  • Drag and drop charts and tables

Mini Project:

Create a dashboard showing subject scores.

Week 10: Git & GitHub for Saving Work

1. What is Git?

Git saves different versions of your code. GitHub stores it online.

Commands:

git init

git add .

git commit -m "My first project"

git remote add origin <your_git_url>

git push -u origin main

Mini Task:

Upload your Python projects to GitHub.

Week 11: APIs and Automation

1. Using an API in Python

import requests

response = requests.get("https://api.exchangerate-api.com/v4/latest/USD")

data = response.json()

print("Dollar to Naira:", data['rates']['NGN'])

Explanation:

  • requests.get() gets data from the internet.
  • json() turns it into something Python can use.

Mini Project:

Create a tool to convert USD to Naira using live data.

Week 12: Final Projects

Choose 1 or more:

  • Create a full student result system in Python.
  • Build a sales dashboard using Power BI.
  • Clean and visualize real-world data using Excel + Python.

Would you like me to create quizzes or assignments for each week?

 

3-Month Data Analysis Curriculum

 

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):

  1. Sales Analysis Report with Python (Pandas + Visualization)
  2. Student Result Dashboard in Power BI
  3. API Data Project: Fetch and analyze public data (weather, crypto)
  4. 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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