Data Analysis
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
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