Data Science & Machine Learning Course in Coimbatore

• Excel Basics:

• Workbooks, Worksheets, Cells, and Ranges.
• Basic functions: SUM, AVERAGE, COUNT, MIN, MAX.
• Sorting, Filtering, and Formatting Data.

• Intermediate Excel:

• Conditional Formatting, Data Validation.
• Lookup Functions: VLOOKUP, HLOOKUP, INDEX, MATCH.
• Working with Dates and Time functions
• Basic Charts and Graphs (Bar, Line, Pie)

• Python syntax: Variables, Data Types (int, float, string)
• Data Structures and Methods
• Control flow: if-else, for loops, while loops.
• Functions
• Basic OOPs
• Advanced OOPs
• File and Error Handling
• Modules, Packages and Libraries

• Introduction to relational databases and SQL.
• Basic SQL commands: SELECT, FROM, WHERE, ORDER BY.
• Filtering Data with AND, OR, NOT, and comparison operators.
• Aggregation Functions: COUNT(), AVG(), SUM(), MIN(), MAX().
• Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN.
• Subqueries: SELECT within SELECT.
• GROUP BY and HAVING: Aggregating data by groups.
• Advanced Filtering: Working with LIKE, IN, and wildcards.
• Data Cleaning: Handling NULL values and duplicates.

• Installing Tableau and connecting to data sources (Excel, SQL, etc.).
• Data Preparation in Tableau: Cleaning, filtering, and transforming data.
• Basic Visualization Types: Bar charts, line charts, scatter plots, and pie charts.
• Formatting and customizing visualizations (titles, labels, colors, etc.).
• Calculations in Tableau: Basic calculated fields (e.g., profit margins, growth rates).
• Table Calculations: Running totals, percent of total, moving averages.
• Building Interactive Dashboards: Filters, parameters, and dynamic actions.
• Storytelling with Data: Creating data-driven narratives and dashboards.

• Installing Power BI and connecting to data sources (Excel, SQL, web, APIs).
• Power Query for Data Transformation: Cleaning and reshaping data before analysis.
• Basic Visualization Types in Power BI: Bar, line, pie charts, maps.
• Introduction to DAX (Data Analysis Expressions): Basic calculations (e.g., sum, average, and count).
• Data Modeling: Relationships between tables, star schema, fact and dimension tables.
• Advanced DAX: Calculated columns, measures, and time intelligence functions.
• Building and customizing Dashboards: Filters, slicers, drill-through actions.
• Publishing and Sharing Reports: Export to PDF, share via Power BI Service.

Covers Central Tendency measures, variance, standard deviation, probability, key distributions, hypothesis testing, sampling, and A/B testing for data-driven decisions.

Introduces regression, classification, decision trees, random forests, clustering, PCA, ensemble methods like XGBoost, and hyperparameter tuning.

Teaches neural networks, CNNs, RNNs/LSTMs, training techniques, and NLP concepts including tokenization, embeddings, text classification.

• Capstone Project: Choose a real-world dataset (e.g., sales, marketing, or customer data)

• Includes dashboards, prediction models, sentiment analysis, image classification, and recommendation systems to build industry-ready practical skills.

• Portfolio Creation: Upload your projects to GitHub, LinkedIn, or a personal website.

• Write clear documentation for each project, explaining your process and insights.
• Highlight the tools and techniques used in each project.