Data Analytics

1. Introduction to Data Analytics

• What is Data Analytics?
  • Definition and importance in decision-making
  • Types of analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
• Applications of Data Analytics
  • Real-world examples (business, healthcare, sports, etc.)
• Overview of the Data Analytics Process
  • Data collection, cleaning, analysis, and visualization

2. Fundamentals of Data

• Types of Data
  • Structured vs. Unstructured data
  • Qualitative vs. Quantitative data
• Data Collection Techniques
  • Surveys, sensors, web scraping, APIs, etc.
• Data Storage
  • Databases (SQL, NoSQL), cloud storage, data warehouses

3. Data Cleaning and Preprocessing

• Importance of Data Cleaning
  • Handling missing data, duplicates, and errors
• Techniques for Data Cleaning
  • Removing outliers, data imputation, normalization
• Data Transformation
  • Scaling, encoding categorical variables, handling time series data

4. Exploratory Data Analysis (EDA)

• What is EDA?
  • The importance of understanding your data before analysis
• Statistical Summary
  • Measures of central tendency (mean, median, mode)
  • Measures of dispersion (variance, standard deviation)
• Data Visualization
  • Types of plots: Histograms, Box plots, Scatter plots, Heatmaps
• Identifying Patterns and Relationships
  • Correlation analysis, covariance

5. Statistical Methods for Data Analytics

• Probability Theory
  • Basic probability concepts
  • Probability distributions (normal, binomial, Poisson)
• Statistical Hypothesis Testing
  • Null and alternative hypotheses
  • p-values, t-tests, chi-square tests
• Confidence Intervals and Significance
• Regression Analysis
  • Linear regression
  • Multiple regression
• ANOVA (Analysis of Variance)

6. Data Modeling and Machine Learning

• Introduction to Machine Learning
  • Overview of supervised and unsupervised learning
• Supervised Learning Algorithms
  • Regression models: Linear, Logistic, and Polynomial Regression
  • Classification models: Decision Trees, Random Forests, K-Nearest Neighbors (KNN)
• Unsupervised Learning Algorithms
  • Clustering: K-Means, Hierarchical Clustering, DBSCAN
  • Principal Component Analysis (PCA)
• Model Evaluation
  • Accuracy, precision, recall, F1-score, ROC-AUC

7. Time Series Analysis

• What is Time Series Data?
  • Characteristics of time series data
• Decomposition of Time Series
  • Trend, seasonal, and residual components
• Forecasting Techniques
  • ARIMA, Exponential Smoothing
• Evaluating Forecasting Models

8. Data Visualization and Reporting

• Importance of Data Visualization
  • Communicating insights effectively
• Tools for Data Visualization
  • Matplotlib, Seaborn, Tableau, Power BI
• Creating Interactive Dashboards
  • Using tools like Power BI, Tableau, or Python libraries (Dash, Streamlit)
• Best Practices in Visualization
  • Choosing the right charts, color schemes, and layout

9. Big Data and Advanced Analytics

• Big Data Technologies
  • Hadoop, Spark, and NoSQL databases
• Cloud-based Analytics
  • Google Cloud, AWS, Microsoft Azure
• Advanced Analytics Techniques
  • Text mining, Natural Language Processing (NLP), and sentiment analysis
  • Image and video analytics

10. Ethical Considerations in Data Analytics

• Ethics in Data Handling
  • Data privacy and security
  • Avoiding bias in data and models
• GDPR and Other Legal Guidelines
  • Data protection regulations and compliance
• Responsible Use of Data Analytics

11. Real-World Applications and Case Studies

• Business Analytics
  • Customer segmentation, market basket analysis, sales forecasting
• Healthcare Analytics
  • Predictive models for patient outcomes, clinical trials analysis
• Finance Analytics
  • Credit scoring, fraud detection
• Sports Analytics
  • Player performance analysis, team strategies

12. Tools and Software for Data Analytics

• Programming Languages
  • Python, R
• Data Analytics Libraries and Packages
  • Pandas, NumPy, Scikit-learn, TensorFlow, Keras
• Database Querying
  • SQL and NoSQL queries
• Data Visualization Tools
  • Power BI, Tableau, Matplotlib, Plotly

13. Capstone Project

• Project-based Learning
  • Apply the concepts learned in the course to a real-world data analytics project
• Data Collection, Cleaning, Analysis, and Visualization
• Model Building and Evaluation
• Presentation and Reporting

Course Delivery Format

• Lectures
  • Online video tutorials or live classes
• Hands-on Labs
  • Practical exercises using data analysis tools and software
• Assignments
  • Problem-solving exercises, quizzes, and case studies
• Final Project
  • End-to-end data analytics project

By the end of the course, students should be able to analyze complex datasets, apply statistical and machine learning techniques, visualize insights, and effectively communicate findings.