1. Responsibilities of a Data Analyst
2. The Data Ecosystem
3. Spreadsheet Basics – Part 1
4. Spreadsheet Basics – Part 2
5. Analyzing Data Using Spreadsheets
6. Career Path in Data Analytics and Prospects with Python
7. What Employers Look for in a Data Analyst

1. Learning to master Excel formulas, the secret sauce to master them all
2. Understanding the syntax of IF, IFERROR, VLOOKUP and other formulas
3. Using the formulas to do real life data analytics
4. Leveraging excel tools to create dashboars

1. Data Visualization and Dashboards
2. Using Visualizations to Tell a Data Story
3. Creating Basic Charts in Excel (Line, Pie, Bar)

1. Introduction to Data Analytics using SQL
2. Importance of Data-Driven Decision Making
3. Business Intelligence Tools and Techniques
4. Basic Concepts of Data Visualization

1. Database Concepts & SQL Overview
2. Concepts of Database
3. Database vs. Relational Database
4. What is DBMS?
5. Data Mapping

1. Overview of SQL and Its Importance in Data Science
2. Creating Databases and Tables
3. Inserting Data into Tables, Importing Data
4. SELECT Queries and Filtering Rows (WHERE, AND, OR, NOT, LIKE)
5. Querying Multiple Tables with JOINS
6. Understanding Table Relationships
7. INNER JOIN, LEFT JOIN, RIGHT JOIN
8. Using Aliases for Table Names

1. Aggregate Functions (COUNT, SUM, AVG, MAX, MIN)
2. Grouping Data with GROUP BY and HAVING
3. ROW_NUMBER(), RANK(), DENSE_RANK()
4. Understanding PARTITION BY and ORDER BY
5. Common Table Expressions (CTEs)
6. Bulk Operations with BULK INSERT and BULK DELETE

1) Connect to real-world datasets like Excel, CSV, and databases with ease.
2) Understand the difference between Live and Extract connections for performance and
flexibility
3) Build essential charts like bar, line, pie, and highlight tables using Tableau’s
drag-and-drop interface
4) Use filters, sorting, and groups to focus your visuals and uncover insights
5) Create calculated fields to build custom metrics like profit margin, rankings, and
performance b

1)Interactive Dashboards & Publishing like a Pro
2)Build beautiful, interactive dashboards with filters, maps, and multiple charts
3)Use parameters to give users control and dynamically update visuals
4)Explore advanced charts like dual-axis, tree maps, histograms, and Gantt charts
5)Add storytelling elements and dashboard actions for a professional-grade experience
6)Publish your work to Tableau Public and create a live online portfolio

1. Introduction to Arrays, Tuple, List, Dictionary
2. Methods, Parameters, Return Types
3. Problem Solving: Range or Prime Number
4. Introduction to Object-Oriented Programming
5. Classes and Objects
6. Constructors
7. OOP Principles
8. Creating Classes

1. File Handling in Python
2. Python Libraries Overview
3. NumPy and Pandas
4. Loading Data from CSV, Excel, SQL

1. Extracting Stock Data Using Python
2. Intro to Web Scraping with BeautifulSoup
3. Analyzing Historical Stock Data

1. Using Libraries (Pandas, NumPy, SciPy)
2. Data Cleaning and Preparation
3. Handling Missing Values, Formatting, Normalization
4. Exploratory Data Analysis Techniques
5. Manipulating and Summarizing Data
6. Correlation Analysis and Data Pipelines

1. Libraries: Matplotlib, Seaborn, Folium
2. Chart Types: Line, Area, Histogram, Bar, Pie, Box, Scatter, Bubble
3. Advanced Visuals: Waffle Charts, Word Clouds, Regression Plots, Geographic Maps

1. Introduction to Machine Learning
2. Understanding Data Distribution
3. Training vs. Test Datasets
4. Building and Training ML Models
5. Making Predictions with Models
6. Advanced ML Concepts: NLP, Deep Learning

1. Extended Learning: Python, SQL, and Career Guidance Opportunities
2. Optional Enrichment: QA, Technical Projects & Career Path Support
3. Additional Support: Python/SQL Practice & Career Mentorship
4. On-Demand Sessions: Python, SQL Projects & Career Readiness