DATA SCIENCE

Module 1: Introduction to Data Science

• What is Data Science?
  • Definition and scope
  • Importance and applications of data science
• The Data Science Process
  • Steps in the data science workflow
  • CRISP-DM methodology
• Tools and Technologies
  • Overview of popular tools (Python, R, SQL)
  • Setting up your data science environment

Module 2: Data Collection and Data Cleaning

• Data Collection Techniques
  • Web scraping and APIs
  • Data from databases
  • Data formats (CSV, JSON, XML)
• Data Cleaning and Preprocessing
  • Handling missing values
  • Data transformation and normalization
  • Data quality assessment

Module 3: Exploratory Data Analysis (EDA)

• Introduction to EDA
  • Importance of EDA
  • Techniques for data exploration
• Descriptive Statistics
  • Measures of central tendency and variability
  • Data distributions and outliers
• Data Visualization
  • Visualization tools and libraries (Matplotlib, Seaborn)
  • Creating effective charts and plots

Module 4: Statistical Analysis

• Probability and Distributions
  • Basic probability concepts
  • Common probability distributions
• Hypothesis Testing
  • Null and alternative hypotheses
  • Type I and Type II errors
  • P-values and confidence intervals
• Regression Analysis
  • Linear regression
  • Multiple regression
  • Model evaluation metrics

Module 5: Machine Learning

• Introduction to Machine Learning
  • What is machine learning?
  • Supervised vs. unsupervised learning
• Supervised Learning Algorithms
  • Regression algorithms (Linear Regression, Decision Trees)
  • Classification algorithms (Logistic Regression, K-Nearest Neighbors, SVM)
• Unsupervised Learning Algorithms
  • Clustering algorithms (K-Means, Hierarchical Clustering)
  • Dimensionality reduction (PCA, t-SNE)
• Model Evaluation and Optimization
  • Cross-validation
  • Hyperparameter tuning
  • Model selection criteria

Module 6: Advanced Machine Learning

• Ensemble Methods
  • Bagging and boosting
  • Random Forests and Gradient Boosting Machines (GBM)
• Deep Learning
  • Introduction to neural networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
• Natural Language Processing (NLP)
  • Text preprocessing techniques
  • Sentiment analysis
  • Topic modeling

Module 7: Big Data Technologies

• Introduction to Big Data
  • Characteristics of big data (Volume, Velocity, Variety)
  • Big data ecosystem and tools
• Hadoop and Spark
  • Hadoop architecture and HDFS
  • Introduction to Apache Spark
  • Spark SQL and DataFrames
• Data Warehousing and ETL
  • ETL process
  • Data warehousing concepts
  • Tools for ETL (Talend, Apache Nifi)

Module 8: Data Visualization and Reporting

• Advanced Data Visualization
  • Interactive visualization tools (Tableau, Power BI)
  • Dashboards and storytelling
• Reporting and Communication
  • Creating comprehensive reports
  • Best practices for data presentation
  • Communicating insights effectively

Module 9: Capstone Project

• Project Planning and Execution
  • Defining the problem statement
  • Data collection and preparation
  • Model building and evaluation
• Final Presentation and Feedback
  • Presenting your findings
  • Receiving feedback from peers and instructors
  • Course completion and certification