Data Analysis.
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Module 1: Introduction to Data Analysis

Understanding Data Analysis

• What is Data Analysis?
• Importance of Data Analysis in Decision-Making
• The Data Analysis Lifecycle

Types of Data

• Structured, Semi-Structured, and Unstructured Data
• Qualitative vs. Quantitative Data

Introduction to Tools

• Overview of Excel, Python, R, SQL, Tableau, and Power BI

Module 2: Data Analysis with Excel

Data Cleaning and Preparation

• Importing and Organizing Data
• Removing Duplicates and Handling Missing Data

Using Formulas and Functions

• Statistical Functions (AVERAGE, MEDIAN, STDEV)
• Lookup Functions (VLOOKUP, HLOOKUP, INDEX-MATCH)

Data Visualization

• Creating Charts and Graphs
• Conditional Formatting

Pivot Tables and Dashboards

• Summarizing Data with Pivot Tables
• Building Interactive Dashboards

Module 3: Data Analysis with Python

Introduction to Python for Data Analysis

• Installing Python and Jupyter Notebook
• Introduction to Pandas, NumPy, and Matplotlib

Data Cleaning and Manipulation

• Loading and Exploring Data with Pandas
• Handling Missing Data and Duplicates
• Filtering and Sorting Data

Exploratory Data Analysis (EDA)

• Descriptive Statistics (Mean, Median, Variance)
• Data Visualization with Matplotlib and Seaborn
• Filtering and Sorting Data

Data Transformation

• Grouping and Aggregating Data
• Merging and Joining Datasets

Module 4: Data Analysis with R

Getting Started with R

• Installing R and RStudio
• Basic R Syntax and Data Types

Data Cleaning and Exploration

• Importing Data into R
• Data Wrangling with dplyr and tidyr

Statistical Analysis

• Hypothesis Testing and Confidence Intervals
• Regression Analysis

Data Visualization

• Creating Plots with ggplot2

Module 5: SQL for Data Analysis

Introduction to SQL

• SQL Basics: SELECT, WHERE, GROUP BY, HAVING
• Data Types in SQL

Working with Databases

• Connecting to Databases (PostgreSQL/MySQL)
• Joins and Subqueries

Data Aggregation and Transformation

• Using Aggregate Functions (SUM, COUNT, AVG)
• Creating Views and Temporary Tables

Hands-on SQL Queries for Real-World Scenarios

Module 6: Data Visualization

Introduction to Data Visualization

• Importance of Visualization in Data Analysis
• Choosing the Right Visualization

Using Tableau for Visualization

• Creating and Customizing Charts
• Building Dashboards and Stories

Using Power BI

• Data Import and Transformation in Power BI
• Building Interactive Dashboards

Module 7: Advanced Data Analysis Techniques

Introduction to Machine Learning for Analysts

• Basics of Predictive Analysis
• Linear and Logistic Regression

Time Series Analysis

• Understanding Time Series Data
• Forecasting Techniques (ARIMA, Exponential Smoothing)

Big Data Basics

• Introduction to Hadoop and Spark (Optional)
• Working with Large Datasets

Module 8: Real-World Data Analysis Project

Problem Definition and Data Collection

• Identifying Business Problems
• Sourcing and Cleaning Data

Exploratory and Statistical Analysis

• Deriving Insights and Trends

Building Reports and Dashboards

• Creating Deliverables for Stakeholders

Module 9: Best Practices and Career Guidance

Data Analysis Best Practices

• Maintaining Data Integrity
• Documenting and Presenting Findings

Career Guidance and Interview Preparation

• Common Data Analysis Interview Questions
• Resume Building and Portfolio Development