Tech Launch Program Data science pr.pptx

data science

1. T ECH L AUNCH P ROGRAM D ATA S CIENCE

2. P rogram O verview This intensive 90-day course provides a comprehensive journey through Data Science, incorporating Deep Learning, SQL Databases , and Business Intelligence tools . The curriculum is divided into three phases, each building upon the previous knowledge. M ajor C ourse O utcomes T echnical S kills D evelopment : Proficiency in Python programming with focus on data science libraries Strong SQL database management capabilities Advanced data analysis using NumPy, Pandas, and statistical tools Deep learning and machine learning model development Expertise in BI tools (Tableau, Power BI)

3. M ajor C ourse O utcomes D ata P rocessing & A nalysis Ability to clean, preprocess, and transform complex datasets Big data handling using tools like Spark Time series analysis and forecasting capabilities V isualization & R eporting Creation of interactive dashboards Advanced data visualization techniques Enterprise-level BI implementation Custom visualization development

4. M achine L earning & A I Implementation of supervised and unsupervised learning algorithms Neural network architecture design and implementation CNN and RNN model development Transfer learning applications E nterprise I ntegration Database optimization and performance tuning Security implementation and governance Server deployment and maintenance Enterprise-level system integration M ajor C ourse O utcomes

5. L evels of P rogram Phase 1 – Basic Level – P ython B asic F or D ata S cience – S QL F oundations – D ata A nalysis using P ython – B usiness I ntelligence F oundations Phase 2 - Intermediate – A dvanced D ata A nalysis – A dvanced B usiness I ntelligence – M achine L earning F oundations – D eep L earning B asics

6. Phase 1 – Basic Level P ython B asic F or D ata S cience Installation and Setup Install Python 3.x IDEs: VS Code / PyCharm Using Jupyter Notebook Virtual environments ( venv ) Package management with pip Variables & Data Types Data Types: Int, Float, String, Boolean Type Conversion & Checking Variable Naming & Comments String Operations: Formatting (f-strings), Methods (split, join), Slicing Numeric Operations: Arithmetic, Division (/, //), Modulo Basic Operations & Functions Operators: Arithmetic, Comparison, Logical Input/Output: print(), input(), Formatted Output Data Structures Lists: Creation, Methods (append, extend), Slicing, Comprehensions Tuples: Immutable Lists, Named Tuples Dictionaries: Key-Value Operations, Methods, Comprehensions Sets: Unique Elements, Union/Intersection Arrays: NumPy Basics Control Structures Conditionals: if, elif , else Loops: for, while, break, continue, Nested Loops

7. P ython B asic F or D ata S cience Functions Definitions & Calls, Default/Keyword Arguments * args , ** kwargs Lambda Functions Scope & Lifetime of Variables Modules Importing & Creating Modules Package Structure, Popular Modules Virtual Environments & pip Object-Oriented Programming (OOP) Classes & Objects: Definitions, Methods (Instance, Class, Static) Inheritance: Single/Multiple, Overriding Encapsulation: Private/Protected Attributes, Property Decorators Polymorphism: Overloading, Duck Typing Error Handling Try-Except, Exception Types Raising & Custom Exceptions Debugging Techniques Practice & Mini-Project Algorithms, Data Structures, OOP, Error Handling Build a Mini-Project combining concepts Phase 1 – Basic Level

8. S QL F oundations Database Concepts DBMS Overview: Relational Databases, Tables, Rows, Columns Keys: Primary & Foreign Keys, Basics of Normalization SQL Installation Install MySQL/PostgreSQL GUI Tools: MySQL Workbench, pgAdmin Create and Set Up Sample Databases Basic Queries SELECT Statements: Column Selection, Aliases WHERE Clause: Comparison, Logical Operators, BETWEEN, IN, LIKE ORDER BY: Ascending/Descending, Multiple Columns, NULL Handling JOIN Operations Types: INNER, LEFT, RIGHT, FULL OUTER, CROSS, Self JOIN Handling Multiple Tables Aggregation Functions COUNT(), SUM(), AVG(), MAX(), MIN() String Aggregations, Custom Aggregations GROUP BY & HAVING Single/Multiple Column Grouping Aggregate Filtering Complex Grouping Scenarios Phase 1 – Basic Level

9. S QL F oundations Subqueries Single & Multiple Row Subqueries Correlated Subqueries, EXISTS Operator CTEs (Common Table Expressions) Window Functions Ranking: ROW_NUMBER(), RANK(), DENSE_RANK() LAG(), LEAD(), Partitioning, Moving Averages Views & Stored Procedures Views & Materialized Views Stored Procedures & Functions Triggers Basics Phase 1 – Basic Level

10. D ata A nalysis T ools NumPy Array Operations Array Creation, Indexing & Slicing Array Operations & Broadcasting Universal Functions Pandas DataFrame Creation: From Files, Lists, Dicts Operations: Column, Row, Indexing (loc, iloc ) Boolean Indexing & Selection Data Cleaning Missing Values Handling Removing Duplicates Data Type Conversion String & Date/Time Operations Data Validation Data Visualization Matplotlib: Basic Plots: Line, Scatter, Bar, Histogram Plot Customization, Subplots, Saving Seaborn: Statistical Plots, Heatmaps Categorical & Regression Plots Style and Color Basic Charts Line, Bar, Pie, Box, Violin Correlation Matrices Phase 1 – Basic Level

11. D ata A nalysis T ools Descriptive Statistics Central Tendency: Mean, Median, Mode Dispersion: Variance, Standard Deviation Distribution Shapes, Percentiles, Quartiles Probability Basics Key Concepts, Random Variables Common Distributions Sampling Techniques Statistical Testing Hypothesis Testing : Null vs. Alternative Tests: t-tests, Chi-square Interpreting p-values Confidence Intervals Phase 1 – Basic Level

12. B usiness I ntelligence F oundations Power BI Desktop Interface Overview: Data, Model, and Report Views Data Modeling Star Schema Design Relationships, Calculated Columns, Measures DAX Basics Syntax and Common Functions Time Intelligence and Filter Context Report Creation Visualizations : Charts, Tables, and Cards Filters, Interactions, and Formatting Publishing Reports Phase 1 – Basic Level

13. Phase 1 Project & Assessments Phase 1 Project Database Design : Requirements Analysis, Schema Design, Table Creation Data Population Data Analysis : Exploratory & Statistical Analysis Data Preparation and Insights Generation Dashboard Creation : Planning, Visual Selection, Interactivity, Presentation Learning Resources Documentation, Practice Datasets, Tutorials, Video Courses, Forums Assessment Criteria Code Quality, Problem-Solving, Documentation Presentation, Peer Review Feedback Practical Assignments Daily Coding Exercises & Weekly Mini-Projects SQL Query Challenges & Visualization Tasks Final Phase Project Phase 1 – Basic Level

14. A dvanced D ata A nalysis Complex Data Manipulation : MultiIndex , Pivot Tables, Stack/Unstack Advanced Grouping: Custom Aggregations, Rolling/Expanding Windows Memory Optimization: Chunking, Optimized DataTypes Time Series Analysis : Basics: DateTime Indexing, Resampling, Rolling Stats Seasonal Decomposition: Trend Analysis, Moving Averages Time Zone Handling: Conversions, Localization Data Preprocessing Cleaning: Outlier Handling, Feature Engineering Text Processing: String Operations, Regular Expressions Encoding: One-hot, Label, Target Encoding Scaling: StandardScaler , MinMaxScaler , RobustScaler Phase 2 – Intermediate Level

15. A dvanced D ata A nalysis Database Optimization Query Optimization: Execution Plans, Query Profiling Design: Normalization, Denormalization, Partitioning Memory: Buffer Pool, Query Cache Indexing Types: B-Tree, Hash, Full-Text Strategies: Composite, Covering, Index Hints Maintenance: Fragmentation, Rebuilding Performance Tuning Query: JOIN/Subquery Optimization, WHERE Clause Tuning Server Config: Resource Allocation, Caching Strategies Monitoring: Performance Metrics, Slow Query Log Phase 2 – Intermediate Level

16. A dvanced B usiness I ntelligence Complex DAX Advanced Functions: Time Intelligence, Filter, Iterator Functions Context Manipulation: CALCULATE, CALCULATETABLE, Context Transition Calculations: Running Totals, YTD, Previous Period Comparison Custom Visuals Development: Custom Visual Types, Interactivity, Formatting Python Integration: Script Visuals, Custom Visual Integration Advanced Visualizations: Matrix, Decomposition Tree, AI Visuals Row-Level Security (RLS) Models: Static RLS, Dynamic RLS, Role Hierarchy Implementation: DAX Filters, Security Roles, Testing Deployment Workspace Management, App Deployment, Gateway Configuration Administration Capacity Management, User Management, Usage Monitoring Sharing & Collaboration Report & Dashboard Sharing, App Distribution Phase 2 – Intermediate Level

17. M achine L earning F oundations Supervised Learning Linear Regression: Simple & Multiple Regression (Feature Selection, Multicollinearity) Regularization: Ridge, Lasso, Elastic Net Logistic Regression: Binary & Multiclass Classification (One-vs-Rest, ROC Curve) Model Evaluation: Precision-Recall, Confusion Matrix Decision Trees & Random Forests: Tree Construction, Pruning, Splitting Criteria Random Forests: Ensemble Methods, Feature Importance Unsupervised Learning Clustering: K-means (Elbow Method, Silhouette Analysis) Hierarchical (Agglomerative, Dendrograms), DBSCAN Dimensionality Reduction & PCA: PCA (Variance Explained, Component Selection) Feature Selection (Filter, Wrapper, Embedded Methods) Model Evaluation Cross-Validation Techniques: K-fold, Stratified K-fold, Leave-One-Out Time Series CV: Forward Chaining, Rolling Forecast Implementation: Validation Curves, Model Selection Metrics & Hyperparameter Tuning Metrics: Classification: Accuracy, Precision, Recall, AUC-ROC Regression: MSE, RMSE, R-squared Hyperparameter Tuning: Methods: Grid Search, Random Search, Bayesian Optimization Phase 2 – Intermediate Level

18. D eep L earning B asics Neural Network Architecture Components : Neurons, Layers, Weights, Biases Network Types : Feedforward, Convolutional, Recurrent Forward & Backward Propagation Forward Propagation : Input Processing, Layer Computation, Output Generation Backward Propagation : Chain Rule, Gradient Descent, Learning Rate Activation Functions Common : ReLU , Sigmoid, Tanh Advanced : Leaky ReLU , ELU, SELU Deep Learning Frameworks TensorFlow/ Keras TensorFlow Basics: Tensors, Variables, Operations Keras API: Sequential, Functional, Model Subclassing Model Building: Layers, Loss Functions, Optimizers PyTorch Basics Tensors, Autograd , Neural Network Modules Model Development: DataLoader , Training Loops, Evaluation Simple Neural Networks Implementation: Binary & Multiclass Classification, Regression Training: Batch Processing, Epochs, Early Stopping Phase 2 – Intermediate Level

19. Phase 2 Project Overview ML Model Development Project Planning : Problem Definition, Data Collection, Model Selection Implementation : Data Preprocessing, Model Training, Evaluation BI Dashboard Integration Data Pipeline : ETL, Feature Engineering, Model Predictions Visualization : Interactive Dashboards, Real-Time Updates, Performance Metrics Database Integration Data Storage : Model Artifacts, Predictions, Metrics API Development : REST Endpoints, Real-Time Scoring, Monitoring Learning Resources Technical Documentation, Research Papers, Online Courses Industry Case Studies, GitHub Repositories Assessment Methods Coding Assignments, Project Milestones, Peer Reviews Technical Presentations, Model Performance Metrics Practical Applications Real-World Datasets, Industry Projects Kaggle Competitions, Portfolio Development Phase 2 – Intermediate Level

20. L earning R esources & T ools Enterprise tools documentation Cloud platform documentation Industry best practices Research papers Case studies Online courses A ssessment M ethods Code reviews Architecture reviews Performance metrics Documentation quality Presentation skills Project completion 20

21. B est P ractices Code quality standards Security guidelines Performance optimization Documentation standards Testing procedures Deployment protocols 21

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