Data Science

Introduction to Data Science

    1. Overview of Data Science
    2. The Data Science Process
    3. Tools and Software for Data Science

Programming for Data Science

    1. Python Programming
      • Introduction to Python
      • Variables and Data Types
      • Control Structures
      • Functions and Modules
      • Object-Oriented Programming
    2. R Programming
      • Basics of R
      • Data Structures in R
      • Functions in R
      • Statistical Modeling with R

Mathematics and Statistics

    1. Linear Algebra
      • Vectors and Matrices
      • Eigenvalues and Eigenvectors
    2. Calculus
      • Differentiation and Integration
      • Multivariable Calculus
    3. Probability and Statistics
      • Descriptive Statistics
      • Probability Distributions
      • Hypothesis Testing
      • Statistical Inference

Data Handling and Processing

    1. Data Collection
      • Web Scraping
      • APIs
    2. Data Cleaning
      • Handling Missing Data
      • Data Transformation
      • Data Normalization
    3. Data Manipulation
      • Pandas
      • Numpy

Data Visualization

    1. Introduction to Data Visualization
    2. Visualization Tools
      • Matplotlib
      • Seaborn
      • Plotly
    3. Principles of Effective Visualization
    4. Creating Interactive Visualizations

Databases and SQL

    1. Introduction to Databases
    2. SQL Basics
      • CRUD Operations
      • Joins and Subqueries
    3. Advanced SQL
      • Window Functions
      • CTEs and Recursive Queries
    4. NoSQL Databases
      • MongoDB
      • Cassandra

Machine Learning

    1. Introduction to Machine Learning
    2. Supervised Learning
      • Linear Regression
      • Logistic Regression
      • Decision Trees
      • Support Vector Machines
      • Ensemble Methods
    3. Unsupervised Learning
      • K-Means Clustering
      • Hierarchical Clustering
      • Principal Component Analysis
    4. Reinforcement Learning
    5. Deep Learning
      • Neural Networks
      • Convolutional Neural Networks
      • Recurrent Neural Networks
    6. Model Evaluation and Validation
      • Cross-Validation
      • Hyperparameter Tuning
      • Performance Metrics

Advanced Topics

    1. Natural Language Processing
      • Text Processing
      • Sentiment Analysis
      • Topic Modeling
    2. Time Series Analysis
      • ARIMA Models
      • Exponential Smoothing
    3. Big Data Technologies
      • Hadoop
      • Spark
    4. Cloud Computing for Data Science
      • AWS
      • Google Cloud
      • Azure

Practical Applications

    1. Case Studies in Data Science
    2. Data Science Projects
    3. Ethical Considerations in Data Science

Capstone Project

    1. Project Proposal
    2. Data Collection and Preprocessing
    3. Model Building and Evaluation
    4. Final Presentation and Reporting

Supplementary Skills

    1. Git and Version Control
    2. Software Engineering Practices for Data Scientists
    3. Communication Skills for Data Scientists
    4. Networking and Professional Development in Data Science