1. Introduction to Data Analytics

• Overview of data analytics and its role in business
• Types of analytics: Descriptive, Predictive, Prescriptive
• Understanding data structures and types
• The data analytics workflow: Data collection, cleaning, analysis, and visualization

2. Data Collection and Preprocessing

• Methods of data collection (surveys, sensors, databases, etc.)
• Data cleaning: Handling missing values, duplicates, and outliers
• Data transformation techniques: Normalization, standardization
• Introduction to data wrangling using tools like Python or Excel

3. Exploratory Data Analysis (EDA)

• Understanding the importance of EDA in data analysis
• Using descriptive statistics to summarize data
• Visualizing data using charts and plots (histograms, scatter plots, box plots)
• Identifying patterns, trends, and correlations in data

4. Statistical Analysis for Data Analytics

• Basics of probability theory
• Descriptive statistics (mean, median, mode, standard deviation)
• Inferential statistics: Hypothesis testing, confidence intervals, p-values
• Statistical tests (t-tests, chi-square tests, ANOVA)
• Correlation and regression analysis

5. Data Visualization and Reporting

• Principles of effective data visualization
• Creating charts and graphs using tools like Excel, Tableau, or Power BI
• Building interactive dashboards and reports
• Storytelling with data: Communicating insights effectively
• Data visualization best practices (choosing the right chart type, color theory, etc.)

6. Introduction to Databases and SQL

• Understanding relational databases and SQL
• Writing SQL queries to extract, filter, and aggregate data
• Joining tables and working with subqueries
• Data manipulation using SQL (INSERT, UPDATE, DELETE)
• Introduction to NoSQL databases (MongoDB, Cassandra)

7. Predictive Analytics and Machine Learning

• Introduction to machine learning concepts
• Supervised learning: Regression (linear, logistic) and classification (decision trees, random forests)
• Unsupervised learning: Clustering (k-means, hierarchical)
• Model evaluation: Accuracy, precision, recall, F1 score
• Hands-on with machine learning libraries (Scikit-learn, TensorFlow)

8. Time Series Analysis and Forecasting

• Basics of time series data and analysis
• Trend, seasonality, and noise components in time series
• Forecasting models: ARIMA, Exponential Smoothing, Holt-Winters method
• Evaluating forecasting models: RMSE, MAE, MAPE

9. Advanced Analytics Techniques

• Working with big data: Introduction to Hadoop and Spark
• Predictive modeling using advanced techniques (Random Forest, SVM)
• Natural Language Processing (NLP) for text analysis
• Deep learning and neural networks for complex data problems

10. Big Data and Cloud Analytics

• Introduction to Big Data concepts: Volume, Variety, Velocity
• Big data tools and frameworks: Hadoop, Spark, MapReduce
• Cloud platforms for data analysis: AWS, Google Cloud, Microsoft Azure
• Working with cloud-based data storage and analytics tools

11. Ethics and Data Privacy

• Ethical considerations in data analytics
• Data privacy regulations (GDPR, CCPA)
• Bias and fairness in data analysis and algorithms
• Security concerns in handling sensitive data

12. Data Analytics Tools and Software

• Introduction to data analysis tools: Python, R, Excel
• Working with Python libraries: Pandas, NumPy, Matplotlib, Seaborn
• Introduction to R programming for data analysis
• Data manipulation and analysis in Excel (Pivot Tables, Power Query)

13. Business Applications of Data Analytics

• Using data analytics in marketing: Customer segmentation, A/B testing
• Financial analytics: Predicting trends, budgeting, and forecasting
• Operations analytics: Inventory management, supply chain optimization
• Healthcare analytics: Patient data analysis, predictive healthcare models

14. Capstone Project/Case Studies

• Applying skills to solve real-world data problems
• Data cleaning, analysis, and visualization on a real dataset
• Building predictive models and presenting findings
• Case study presentations and peer reviews