DATA SCIENCE & MACHINE LEARNING

• • Why Python?
  • Setting up Python and IDEs
  • Writing your first Python program

Core Concepts

• • Variables & Data Types
  • Strings, Lists, Tuples, Sets, and Dictionaries
  • Conditional Statements
  • Loops: For and While
  • Built-in Functions (Numbers and Math)
  • User-defined Functions
  • Modules and Packages
  • Handling Common Errors

• • List Comprehensions
  • File Handling
  • Debugging Techniques
  • Object-Oriented Programming: Classes and Objects
  • Lambda, Filter, and Map
  • Regular Expressions
  • Working with External Packages (Python PIP)
  • Handling Excel Data in Python
  • Iterators, Decorators, and Generators
  • Pickling & Python JSON

• • Understanding Algorithmic Thinking
  • Algorithm Efficiency & Time Complexity
  • Example Algorithms (Binary Search, Euclid’s Algorithm)
  • Data Structures: Stack, Heap, Binary Trees
  • Memory Management Techniques
  • Best Coding Practices: Simple, DRY, Naming Conventions

• • Series and Data Frames
  • Data Querying, Indexing, and Loading
  • Merging, Grouping, and Pivot Tables
  • Time and Date Manipulations
  • Data Frame Manipulation Examples

• • Counting and Probability Theory Basics
  • Axioms of Probability & Theorems
  • Discrete Distributions: Bernoulli, Binomial, Geometric
  • Continuous Distributions: Uniform, Exponential, Normal
  • Simulation Techniques with NumPy

• • Inferential Statistics & Sampling
  • Hypothesis Testing (I, II, III)
  • Central Limit Theorem (CLT) & Chi-Square Distribution
  • Estimation Techniques (MLE, Interval Estimators)

• • Plotting with Matplotlib & Seaborn
  • Creating Histograms, Box Plots, Scatter Plots, Bar Charts, Line Plots
  • Advanced Visualization Techniques (Pie, Donut, Stacked Bar, Area Plots)

• • Data Import, Management, and Filtering
  • Creating Basic Plots (Trend Analysis, Area, Ribbon, Scatter)
  • Power BI Reports and Interactive Dashboards
  • Deploying Dashboards

• Overview of Machine Learning
• Key Terminology: Concepts, Inputs & Attributes

• Types of Variables
  • Categorical Variables
  • Ordinal Variables
  • Numerical Variables

Core Concepts

• Cost Functions and Gradient Descent
• Overfitting and Underfitting
• Training, Validation, and Test Data
• Precision vs. Recall
• Bias and Variance
• Lift

• Supervised Learning (Regression Techniques)
  • Linear Regression
  • Poisson Regression
• Classification Techniques
  • Classification Rate
  • Decision Trees
  • Logistic Regression
  • Naive Bayes Classifiers
  • K-Nearest Neighbor
  • Support Vector Machines (SVM)
  • Gaussian Mixture Models
• Unsupervised Learning (Clustering Techniques)
  • Hierarchical Clustering
  • K-Means Clustering
  • DBSCAN
  • HDBSCAN
  • Fuzzy C-Means
  • Mean Shift
  • Agglomerative Clustering
  • OPTICS
• Unsupervised Learning (Association Rule Learning)
  • Apriori Algorithm
  • ECLAT Algorithm
  • FP Trees
• Unsupervised Learning (Dimensionality Reduction)
  • Principal Component Analysis (PCA)
  • Random Projection
  • Nonnegative Matrix Factorization (NMF)
  • T-SNE
  • UMAP
• Ensemble Learning
  • Boosting
  • Bagging
  • Stacking
• Reinforcement Learning
  • Q-Learning

• Sentiment Analysis
• Collaborative Filtering
• Tagging
• Prediction

• Scikit learn
• Spacy (NLP)

• • Introduction to Relational Databases
  • Basic SQL Syntax
  • SELECT Statements
  • Filtering Data with WHERE
  • Sorting Results with ORDER BY
  • Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
  • GROUP BY and HAVING Clauses
  • JOIN Operations
  • Subqueries
  • Creating and Modifying Tables
  • Inserting, Updating, and Deleting Data