Unit 775: Comprehensive Data Analytics

1. Introduction to Data Analytics

• Definition and scope of data analytics
• Importance in decision-making processes
• Applications across industries (healthcare, finance, marketing, etc.)
• Types of data analytics: Descriptive, Inferential, Predictive, Prescriptive

2. Data Collection and Preparation

2.1 Data Collection Methods

• Sources of data: databases, web, sensors, social media, surveys
• Data acquisition techniques

2.2 Data Cleaning and Preprocessing

• Handling missing values and outliers
• Data transformation and normalization
• Ensuring data quality and consistency

3. Exploratory Data Analysis (EDA)

• Techniques for summarizing and visualizing data
• Identifying patterns, trends, and outliers
• Use of statistical measures: mean, median, mode, variance, standard deviation
• Tools for EDA (e.g., pandas, matplotlib, seaborn)

4. Data Visualization Techniques

• Principles of effective visualization
• Types of visualizations: bar charts, histograms, scatter plots, heatmaps, dashboards
• Visualization tools: Tableau, Power BI, matplotlib, ggplot2
• Storytelling with data

5. Descriptive and Inferential Analytics

5.1 Descriptive Analytics

• Summarizing data characteristics
• Measures of central tendency and dispersion

5.2 Inferential Statistics

• Hypothesis testing
• Confidence intervals
• Correlation and regression analysis
• Significance testing

6. Predictive Analytics

• Introduction to predictive modeling
• Regression analysis and time series forecasting
• Building and evaluating predictive models
• Applying predictions to real-world scenarios

7. Prescriptive Analytics

• Definition and role in decision-making
• Optimization techniques
• Recommender systems and scenario analysis

8. Machine Learning Fundamentals

8.1 Overview of Machine Learning

• Supervised vs unsupervised learning
• Common algorithms: regression, classification, clustering

8.2 Model Evaluation Techniques

• Cross-validation
• Performance metrics: accuracy, precision, recall, F1-score

9. Advanced Machine Learning Techniques

• Text mining and sentiment analysis
  • Natural Language Processing (NLP) basics
  • Sentiment extraction from social media and reviews
• Data mining techniques
  • Association analysis, clustering, classification algorithms

10. Time Series Analysis

• Characteristics of time-dependent data
• Decomposition of time series: trend, seasonality, noise
• Forecasting methods and applications

11. Big Data Analytics

• Introduction to big data concepts
• Technologies and tools: Hadoop, Spark
• Challenges in handling large datasets
• Distributed computing frameworks

12. Data Ethics and Privacy

• Ethical considerations in data collection and analysis
• Data privacy laws and regulations (e.g., GDPR)
• Bias, fairness, and responsible data use
• Security practices and data governance

13. Real-world Case Studies

• Industry-specific examples (finance, healthcare, marketing, etc.)
• Problem-solving using data analytics techniques
• Lessons learned and best practices