Data Analysis

Transform raw data into insights using analytical tools and techniques.

Course Content

Course Objective

Understand the data analysis lifecycle
Use tools like Excel, Power BI, Python, and SQL
Learn how to interpret data, visualize trends, and support data-driven decision-making

Module 1: Introduction to Data Analysis

Module 2: Data Collection & Cleaning

Module 3: Excel for Data Analysis

Module 4: SQL for Data Handling

Module 5: Python for Data Analysis (Intro Level)

Module 6: Data Visualization

Module 7: Statistical Concepts

Module 8: Exploratory Data Analysis (EDA)

Module 9: Final Project

A course by

waseem khan

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Price:Free
Instructor:waseem khan
Lessons:38
Students:0
Level:All Levels

Data Analysis

Course Content

Course Objective

Understand the data analysis lifecycle

Use tools like Excel, Power BI, Python, and SQL

Learn how to interpret data, visualize trends, and support data-driven decision-making

Module 1: Introduction to Data Analysis

What is Data Analysis?

Types of Data: Qualitative vs Quantitative

Structured vs Unstructured Data

Data lifecycle and its stages

Module 2: Data Collection & Cleaning

Data sources (files, databases, APIs, web scraping)

Data cleaning techniques

Handling missing data and outliers

Data types and conversions

Module 3: Excel for Data Analysis

Functions: VLOOKUP, IF, INDEX/MATCH, etc.

Pivot Tables & Charts

Data validation and conditional formatting

What-If Analysis and Goal Seek

Module 4: SQL for Data Handling

Basics of relational databases

SELECT, WHERE, JOIN, GROUP BY

Data aggregation and filtering

Subqueries and views

Module 5: Python for Data Analysis (Intro Level)

Using libraries: Pandas, NumPy

DataFrames and Series operations

Importing and exporting data (CSV, Excel, JSON)

Data transformation and summary stats

Module 6: Data Visualization

Principles of effective visualization

Excel charts, Power BI, and Python (Matplotlib/Seaborn)

Dashboards using Power BI/Tableau (optional)

Storytelling with data

Module 7: Statistical Concepts

Descriptive statistics: mean, median, mode, std deviation

Inferential statistics: correlation, regression

Hypothesis testing basics

Data distributions and probability

Module 8: Exploratory Data Analysis (EDA)

Identifying patterns and relationships

Detecting anomalies and trends

Summary reports and insights generation

EDA using Python or Power BI

Module 9: Final Project

Choose a real-world dataset

Perform end-to-end analysis

Present findings via a dashboard or report

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