Workshop

Week 1: Introduction to Data Analytics

Session 1: Overview of Data Analytics

1. Introduction to Data Analytics

2. Importance of Data Analytics in various industries

3. Types of data and their characteristics

Session 2: Basics of Excel for Data Analytics

1. Introduction to Excel for data analysis

2. Data importing, sorting, and filtering

3. Basic functions (SUM, AVERAGE, COUNT, etc.)

Session 3: Data Visualization with Excel

1. Introduction to data visualization in Excel

2. Creating charts and graphs

3. Visual representation of data trends

Week 2: Data Cleaning and Preparation

Session 4: Data Cleaning Techniques

1. Importance of data cleaning

2. Identifying and handling missing data

3. Removing duplicates and inconsistencies

Session 5: Data Preparation in Excel

1. Data formatting and transformation

2. Data validation and error handling

3. Introduction to pivot tables for data summarization

Session 6: Project: Data Cleaning and Preparation in Excel

1. Apply data cleaning and preparation techniques on real datasets

Week 3: Introduction to SQL

Session 7: Introduction to Databases and SQL

1. Basics of databases and SQL

2. Understanding tables, rows, and columns

3. SQL syntax (SELECT, WHERE, GROUP BY, etc.)

Session 8: Querying Data using SQL

1. Writing basic SQL queries

2. Filtering and sorting data

3. Joining tables for data retrieval

Session 9: Project: SQL Data Querying

1. Practice SQL queries on sample databases

Week 4: Exploratory Data Analysis (EDA)

Session 10: Exploratory Data Analysis Concepts

1. Importance of EDA in Data Analytics

2. Statistical summary of data

3. Data visualization for EDA using Excel

Session 11: EDA with Visualization Tools

1. Introduction to visualization tools (Tableau, Power BI)

2. Creating interactive visualizations for EDA

3. Analyzing data patterns and relationships

Session 12: Project: Exploratory Data Analysis

1. Perform EDA on datasets using visualization tools

Week 5: Introduction to Statistical Analysis

Session 13: Basic Statistical Concepts

1. Overview of statistical analysis

2. Measures of central tendency and dispersion

3. Probability distributions

Session 14: Hypothesis Testing

1. Introduction to hypothesis testing

2. Types of tests (t-test, chi-square, etc.)

3. Conducting hypothesis tests in Excel

Session 15: Project: Basic Statistical Analysis in Excel

1. Apply basic statistical tests on datasets in Excel

Week 6: Data Analytics Techniques

Session 16: Regression Analysis

1. Introduction to regression analysis

2. Linear regression in Excel

3. Interpretation of regression results

Session 17: Classification Techniques

1. Basics of classification

2. Introduction to decision trees

3, Implementing classification algorithms in Excel

Session 18: Project: Regression and Classification in Excel

1. Apply regression and classification techniques on sample datasets

Week 7: Advanced Data Analytics

Session 19: Time Series Analysis

1. Understanding time series data

2. Time series visualization in Excel

3. Forecasting using time series data

Session 20: Clustering and Segmentation

1. Introduction to clustering algorithms

2. K-means clustering in Excel

3. Segmenting data for analysis

Session 21: Project: Time Series Analysis and Clustering

1. Apply time series analysis and clustering techniques on datasets

Week 8: Capstone Project and Conclusion

Session 22: Capstone Project

1. Work on a comprehensive data analytics project

2. Apply all learned concepts and techniques

Session 23: Project Presentation

1. Present the capstone project findings

2. Review key concepts and takeaways

3. Next steps in Data Analytics