.Data Science Essentials

Module 1: Introduction to Data Science

• What is Data Science?
• The Data Science Lifecycle
• Applications of Data Science in different industries
• Overview of tools used in Data Science (Python, R, Jupyter Notebook, Anaconda)

Module 2: Working with Data

• Types of Data: Structured vs. Unstructured
• Data Sources and Data Collection Methods
• Data Cleaning Techniques
• Handling Missing Data
• Data Manipulation using Pandas

Module 3: Exploratory Data Analysis (EDA)

• Understanding Distributions, Histograms, and Boxplots
• Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
• Identifying Outliers and Anomalies
• Data Correlation and Feature Relationships

Module 4: Data Visualization

• Introduction to Data Visualization
• Visualization Tools: Matplotlib & Seaborn
• Creating Line, Bar, Scatter, and Pie Charts
• Heatmaps and Pairplots for Data Insights

Module 5: Introduction to Statistics for Data Science

• Probability Theory Basics
• Statistical Distributions
• Hypothesis Testing & p-values
• Central Limit Theorem
• Confidence Intervals and Significance Testing

Module 6: Introduction to Machine Learning

• Overview of Machine Learning and its types
• Supervised vs. Unsupervised Learning
• Understanding Regression and Classification
• Basics of Decision Trees and k-Nearest Neighbors
• Model Evaluation: Accuracy, Precision, Recall, F1-score

Module 7: Introduction to Python for Data Science

• Python Basics: Variables, Data Types, and Operators
• Loops and Conditional Statements
• Functions and Lambda Expressions
• Working with NumPy Arrays
• Pandas for DataFrames and Data Handling

Module 8: Feature Engineering and Model Evaluation

• Feature Scaling and Normalization
• Handling Categorical and Numerical Data
• Train-Test Split and Cross-Validation
• Overfitting and Underfitting in Machine Learning Models

Module 9: Capstone Project

• Real-world dataset analysis
• Hands-on application of concepts learned
• Model Training and Evaluation
• Presentation of findings