• Understanding Data Sources (Databases, APIs, Web Scraping)
• Methods of Data Collection
• Survey Design and Data Collection Tools
• Data Quality and Integrity
• Ethical Considerations in Data Collection

 • Importance of Data Cleaning
• Handling Missing Data
• Data Transformation Techniques
• Data Normalization and Standardization
• Outlier Detection and Treatment

 • Introduction to EDA
• Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
• Data Visualization Techniques (Histograms, Box Plots, Scatter Plots)
• Identifying Patterns, Trends, and Anomalies
• Correlation and Covariance Analysis

 • Probability Theory and Distributions
• Hypothesis Testing and Confidence Intervals
• T-tests, Chi-Square Tests, and ANOVA
• Regression Analysis (Linear and Multiple Regression)
• Time Series Analysis

 • Principles of Data Visualization
• Tools for Data Visualization (Tableau, Power BI, Matplotlib, Seaborn)
• Creating Effective Charts and Graphs
• Dashboards and Interactive Visualizations
• Storytelling with Data

 • Introduction to Relational Databases
• SQL for Data Analysis
• Writing Complex Queries (Joins, Subqueries, Aggregations)
• Data Warehousing Concepts
• NoSQL Databases (MongoDB, Cassandra)

 • Introduction to Python/R for Data Analysis
• Data Manipulation with Pandas (Python) or dplyr (R)
• Working with DataFrames
• Data Munging and Wrangling
• Automation of Data Analysis Tasks

 • Introduction to Machine Learning for Data Analysts
• Clustering and Classification Algorithms
• Dimensionality Reduction (PCA)
• Predictive Modeling and Forecasting
• A/B Testing and Experimentation

 • Introduction to Business Analytics
• Key Performance Indicators (KPIs) and Metrics
• Financial Data Analysis
• Market Basket Analysis
• Customer Segmentation and Lifetime Value Analysis

 • Introduction to Big Data Concepts
• Tools and Technologies for Big Data (Hadoop, Spark)
• Handling Large Datasets
• Distributed Computing and Data Processing
• Data Lakes vs. Data Warehouses

 • Data Privacy and Protection (GDPR, HIPAA)
• Ethical Considerations in Data Analysis
• Data Governance Frameworks
• Data Security and Compliance
• Responsible Use of Data

 • Defining a Data Analysis Problem
• Data Collection and Preparation
• Data Analysis and Interpretation
• Visualization and Reporting
• Presenting Findings and Recommendations

 • Overview of Popular Data Analysis Tools (Excel, SAS, Python, R)
• Introduction to Cloud Data Platforms (AWS, Google Cloud, Azure)
• Data Integration Tools (Talend, Informatica)
• Version Control with Git/GitHub
• Collaborative Tools for Data Analysts

 • Building a Data Analyst Portfolio
• Resume and LinkedIn Profile Optimization
• Interview Preparation (Technical and Behavioral)
• Mock Interviews and Feedback
• Networking and Industry Insights