Course Content

Course Duration: 12 weeks (3 hours per session, 2 sessions per week)

Week 1-2: Introduction to Data Analytics

• Understanding the role and importance of data analytics in various industries
• Introduction to the data analytics process: Define, Collect, Clean, Analyze, Interpret, and Visualize (DCCAIV)
• Overview of data analytics tools and technologies
• Setting up the analytical environment: Installing necessary software (Python, Jupyter, etc.)
  
  

Week 3-4: Data Wrangling and Cleaning

• Exploring different data types: Numerical, Categorical, Textual
• Data cleaning techniques: Handling missing values, duplicate data, and outliers
• Introduction to data transformation and feature engineering
• Hands-on: Cleaning and preparing real-world datasets
  
  

Week 5-6: Exploratory Data Analysis (EDA)

• Importance of EDA in understanding data patterns and relationships
• Data visualization techniques: Matplotlib, Seaborn, Plotly
• Descriptive statistics and data summarization
• EDA for different types of data: Univariate, Bivariate, Multivariate analysis
  
  

Week 7-8: Statistical Analysis for Decision Making

• Probability distributions and inferential statistics
• Hypothesis testing: t-tests, ANOVA, chi-squared tests
• Introduction to regression analysis
• Applying statistical concepts to real-world scenarios
  
  

Week 9-10: Machine Learning Fundamentals

• Introduction to machine learning and its applications in data analytics
• Supervised vs. unsupervised learning
• Regression analysis: Linear regression, logistic regression
• Classification algorithms: Decision trees, random forests, support vector machines
  
  

Week 11-12: Advanced Topics in Data Analytics

• Clustering techniques: K-means, hierarchical clustering
• Dimensionality reduction: Principal Component Analysis (PCA)
• Time series analysis and forecasting
• Introduction to natural language processing (NLP) for text data analysis
  
  

Week 13-14: Data Visualization and Communication

• Principles of effective data visualization
• Data visualization libraries: Tableau, Power BI
• Creating interactive dashboards and reports
• Telling a compelling data-driven story
  
  

Week 15-16: Capstone Project

• Applying learned concepts to a real-world data analytics project
• Project selection, data acquisition, and preparation
• Exploratory data analysis and feature selection
• Model building, evaluation, and interpretation
• Creating a comprehensive project report and presentation
  
  

Week 17-18: Ethical and Legal Considerations

• Privacy and data protection regulations (GDPR, HIPAA, etc.)
• Ethical considerations in data collection and analysis
• Bias and fairness in machine learning
• Ensuring responsible and transparent data analytics practices
  
  

Week 19-20: Future Trends in Data Analytics

• Introduction to big data and cloud computing
• AI and machine learning advancements
• Automation in data analytics: AutoML
• Emerging trends and opportunities in the field
  
  

Week 21-22: Career Development in Data Analytics

• Building a strong data analytics portfolio
• Job roles and career paths in data analytics
• Interview preparation and resume building
• Networking and continuous learning strategies
  
  

Week 23-24: Case Studies and Industry Applications

• Analyzing real-world case studies from various industries
• Healthcare analytics, financial analytics, marketing analytics, etc.
• Guest lectures from industry experts
• Identifying data analytics opportunities in different domains