Introduction to Data Science

• • What is Data Science?
  • Evolution and Importance of Data Science
  • Applications of Data Science in Industries
  • Data Science Lifecycle & Workflow
  • Structured vs. Unstructured Data
  • Key Tools and Technologies in Data Science
  • Career Opportunities in Data Science
  • Setting Up the Data Science Environment
  • Understanding Business Problems and Data-Driven Solutions
  • Introduction to Jupyter Notebook and Google Colab
  • Data Science vs. Business Analytics vs. Data Engineering
  • Case Study: Real-World Data Science Applications

• Introduction to Python Programming
• Variables, Data Types, and Operators
• Control Flow: Conditional Statements and Loops
• Functions and Lambda Expressions
• Working with Lists, Tuples, and Dictionaries
• File Handling (Reading and Writing Files)
• Introduction to NumPy for Numerical Computing
• Introduction to Pandas for Data Manipulation
• Working with Pandas DataFrames
• Data Visualization with Matplotlib
• Data Analysis with Seaborn
• Hands-on Practice: Python Coding Challenges

• Handling Missing Data
• Data Cleaning Techniques
• Feature Engineering Basics
• Data Transformation & Normalization
• Handling Categorical Variables (One-Hot Encoding, Label Encoding)
• Outlier Detection & Treatment
• Scaling and Standardization Techniques
• Data Wrangling with Pandas

• Understanding the Importance of EDA
• Descriptive Statistics (Mean, Median, Mode)
• Data Visualization using Matplotlib and Seaborn
• Histograms, Boxplots, and Scatter Plots
• Correlation and Covariance Analysis
• Detecting Skewness and Kurtosis
• Feature Selection Techniques
• Case Study: Real-World Data Analysis

• Basics of Descriptive and Inferential Statistics
• Probability Distributions (Normal, Binomial, Poisson)
• Hypothesis Testing (Z-Test, T-Test, Chi-Square Test)
• Confidence Intervals and P-Values
• Correlation vs. Causation
• Regression Analysis (Simple & Multiple)
• ANOVA and F-Test
• Case Study: Statistical Analysis on a Dataset

• Introduction to Databases and SQL
• Writing Basic SQL Queries
• Filtering, Sorting, and Aggregation
• Joins and Subqueries
• Common Table Expressions (CTE) and Views
• Window Functions in SQL
• Optimizing Queries for Performance
• Case Study: Querying a Large Dataset

• Basics of Machine Learning and AI
• Supervised vs. Unsupervised Learning
• ML Model Lifecycle & Pipeline
• Data Splitting (Train-Test, Cross-Validation)
• Model Evaluation Metrics
• Bias-Variance Tradeoff
• Overfitting and Underfitting
• Hyperparameter Tuning Strategies
• Overfitting and Regularization
• Implementing Machine Learning Models in Python
• Cross-Validation Techniques
• Case Study: Building a Basic ML Model

• Linear Regression and Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Naive Bayes Classifier
• Gradient Boosting Algorithms (XGBoost, LightGBM)
• Model Hyperparameter Tuning
• Case Study: Predicting House Prices
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• Introduction to Clustering
• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Anomaly Detection Techniques
• DBSCAN Clustering
• Feature Extraction Methods
• Case Study: Customer Segmentation