ADVANCE CERTIFICATE IN FULL STACK DATA ANALYTICS ( S-DA2023MT )

**Module 1: Introduction to Data Analysis**

- What is Data Analysis and its Importance

- Types of Data Analysis: Descriptive, Predictive, Prescriptive

- Data Analysis Process: Steps and Framework

- Role of Data Analysts in Decision-Making

**Module 2: Data Collection and Preparing**

- Data Collection Methods and Sources

- Data Cleaning and Quality Assurance

- Data Transformation and Feature Engineering

- Handling Missing Data and Outliers

**Module 3: Excel for Data Analytics**

- Excel Fundamentals for Data Analysis

- Data Cleaning and Formatting in Excel

- Data Visualization with Excel Charts and Graphs

- Using Excel for Basic Statistical Analysis

**Module 4: SQL Fundamentals**

- Introduction to Databases and SQL

- Querying Data with SELECT, WHERE, GROUP BY, JOIN

- Aggregation Functions and Subqueries

- Working with Multiple Tables and Complex Queries

**Module 5: Python Programming for Data Analysis**

- Introduction to Python and its Data Analytics Libraries

- Data Manipulation and Analysis with Pandas

- Data Visualization with Matplotlib and Seaborn

- Basic Statistical Analysis with Python

**Module 6: Power BI Essentials**

- Introduction to Power BI and its Capabilities

- Data Importing and Transformation in Power BI

- Creating Interactive Visualizations: Charts, Graphs, Maps

- Building Dashboards and Reports for Data Insights

**Module 7: Data Visualization and Storytelling**

- Principles of Effective Data Visualization

- Advanced Visualization Techniques: Heatmaps, Treemaps, etc.

- Using Data Visualizations to Convey Insights and Tell a Story

- Designing Engaging and Informative Data Dashboards

**Module 8: Predictive Modeling and Machine Learning**

- Introduction to Predictive Modeling

- Regression Analysis: Linear, Multiple, Polynomial

- Classification Algorithms: Decision Trees, Random Forests, etc.

- Model Evaluation and Selection

**Module 9: Text Analytics and Natural Language Processing (NLP)**

- Processing Text Data: Tokenization, Lemmatization

- Sentiment Analysis and Text Classification

- Introduction to NLP Libraries: NLTK, spaCy

- Extracting Insights from Textual Data

**Module 10: Real-world Data Analytics Projects**

- Applying Data Analytics to Real-world Scenarios

- Project Planning, Data Acquisition, Analysis, and Visualization

- Presenting Findings and Insights

- Peer Review and Feedback on Projects

Feel free to adjust the content and duration of each module based on your course goals and schedule. This expanded syllabus plan covers a wide range of data analytics topics, from foundational concepts to advanced techniques and practical applications.

- Basic computer literacy and mathematics understanding are recommended.

- No prior data analytics experience is necessary.

- Access to a computer with internet connectivity is required.

- Openness to learn software tools like Excel, SQL, Python, and Power BI.

- Average English language proficiency for course materials and communication.

- Suitable for students, professionals, and data enthusiasts seeking practical data skills.