Introduction to Data Analytics (Powepoint)

Module 1: Introduction to Data Analytics

1. What is Data Analytics?
   • Definition and scope
   • Importance in business decision-making
2. Types of Data Analytics
   • Descriptive
   • Diagnostic
   • Predictive
   • Prescriptive
3. The Data Analytics Process
   • Data collection
   • Data cleaning
   • Data analysis
   • Data interpretation and presentation
4. Data Analytics vs. Business Intelligence
5. Real-world Applications of Data Analytics

Module 2: Data Collection and Preparation

1. Data Sources
   • Internal vs external data
   • Structured vs unstructured data
2. Data Collection Methods
   • Surveys, web scraping, sensors, databases
3. Data Quality and Cleaning
   • Handling missing data
   • Outlier detection and treatment
   • Normalization and scaling
4. Data Formats and Storage
   • CSV, JSON, XML, and database systems
   • Data warehousing and cloud storage
5. Introduction to ETL (Extract, Transform, Load) Process

Module 3: Data Analysis Techniques

1. Descriptive Statistics
   • Mean, median, mode, variance, and standard deviation
   • Data visualization (charts, histograms)
2. Data Visualization Tools
   • Introduction to tools like Tableau, Power BI, Google Data Studio
   • Creating effective dashboards
3. Exploratory Data Analysis (EDA)
   • Identifying patterns, correlations, and trends
   • Data summarization and aggregation
4. Data Filtering and Transformation
   • Grouping, sorting, and pivoting data
   • SQL and Excel operations for data manipulation

Module 4: Advanced Analytics Techniques

1. Regression Analysis
   • Simple linear regression
   • Multiple regression
2. Classification Models
   • Decision trees, Random forests, Support Vector Machines (SVM)
3. Clustering Techniques
   • K-means clustering
   • Hierarchical clustering
4. Time Series Analysis
   • Forecasting models (ARIMA, Exponential smoothing)
   • Trend and seasonality detection
5. Text Analytics
   • Natural Language Processing (NLP)
   • Sentiment analysis

Module 5: Tools for Data Analytics

1. Excel for Data Analytics
   • Functions, pivot tables, charts, and macros
2. SQL for Data Analytics
   • Writing queries, joins, aggregations
   • Working with relational databases
3. Programming with Python
   • Libraries: Pandas, NumPy, Matplotlib, Scikit-learn
   • Data manipulation and visualization
4. Programming with R
   • Basic operations, data frames, ggplot2
   • Statistical analysis with R
5. Big Data Tools
   • Hadoop and Spark
   • Introduction to data lakes

Module 6: Machine Learning for Data Analytics

1. Introduction to Machine Learning
   • Supervised vs unsupervised learning
   • Common algorithms (linear regression, decision trees, k-NN)
2. Building Predictive Models
   • Training and testing data
   • Model evaluation (accuracy, precision, recall, F1 score)
3. Feature Selection and Engineering
   • Selecting the right features for modeling
   • Data transformation techniques
4. Model Optimization
   • Hyperparameter tuning
   • Cross-validation and overfitting prevention
5. Introduction to Deep Learning (Optional)
   • Neural networks and applications in analytics

Module 7: Data Visualization and Reporting

1. Importance of Data Visualization
   • Visual storytelling in data analytics
2. Creating Dashboards and Reports
   • Best practices for designing effective dashboards
   • Real-time data reporting
3. Data Storytelling Techniques
   • How to communicate insights clearly and effectively
4. Common Data Visualization Pitfalls
   • Misleading visuals and how to avoid them

Module 8: Case Studies and Real-world Applications

1. Industry-Specific Analytics Applications
   • Retail: Customer segmentation, sales forecasting
   • Healthcare: Predictive modeling, patient analytics
   • Finance: Risk modeling, fraud detection
   • Marketing: Campaign analysis, A/B testing
2. Group Projects
   • Analyze datasets from real-world business scenarios
   • Present findings using data visualizations and storytelling techniques

Module 9: Ethical and Legal Aspects of Data Analytics

1. Data Privacy and Security
   • GDPR, CCPA, and other data protection regulations
   • Ethical use of data
2. Bias in Data
   • Identifying and reducing bias in data analytics
3. Data Governance
   • Ensuring accuracy, consistency, and security of data
4. Ethical Dilemmas in Analytics
   • How to handle sensitive or confidential data responsibly

Module 10: Future Trends in Data Analytics

1. Artificial Intelligence in Analytics
   • AI-driven analytics and automation
2. Big Data and Real-time Analytics
   • Processing and analyzing large data sets in real time
3. Data Analytics in the Cloud
   • Cloud-based analytics platforms
4. The Future of Predictive and Prescriptive Analytics
   • Emerging technologies and trends