DYNAMIC SYSTEM

Introduction to SQL

Database Normalization and Entity Relationship Model(self-paced)

SQL Operators

Working with SQL: Join, Tables, and Variables

Deep Dive into SQL Functions

Working with Subqueries

SQL Views, Functions, and Stored Procedures

Deep Dive into User-defined Functions

SQL Optimization and Performance

Advanced Topics

Managing Database Concurrency

Practice Session

Case Study

Writing comparison data between the past year and to present year concerning top products, ignoring the redundant/junk data, identifying the meaningful data, and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Introduction to R

R Packages

Sorting DataFrame

Matrices and Vectors

Reading Data from External Files

Generating Plots

Analysis of Variance (ANOVA)

Association Rule Mining

Regression in R

Analyzing Relationship with Regression

Advanced Regression

Logistic Regression

Advanced Logistic Regression

Database Connectivity with R

Integrating R with Hadoop

R Case Studies

Descriptive Statistics

The measure of central tendency, the measure of spread, five points summary, etc.

Probability

Probability Distributions, Probability in Business Analytics

Probability Distributions, Binomial distribution, Poisson distribution, Bayes’ Theorem, central limit theorem Inferential Statistics

Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.
Hypothesis Testing

Type-I error

Type-II error

Case Study This case study will cover the following concepts:

Building a statistical analysis model that uses quantification, representations, and experimental data

Reviewing, analyzing, and drawing conclusions from the data

Power BI Basics

Introduction to PowerBI, Use cases and BI Tools, Data Warehousing, Power BI components, Power BI Desktop, workflows and reports, and Data Extraction with Power BI.

SaaS Connectors, Working with Azure SQL database, Python, and R with Power BI

Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data, M Query, and Hierarchies in Power BI.

DAX

Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics

Slicers, filters, Drill Down Reports

Power BI Query, Q & A and Data Insights

Power BI Settings, Administration and Direct Connectivity

Case Study:

This case study will cover the following concepts:

Creating a dashboard to depict actionable insights in sales data

Reading the Data, Referencing in formulas , Name Range, and Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering Working with Charts in Excel, Pivot Table, Dashboards, Data, and File Security VBA Macros, Ranges, and Worksheets in VBA IF conditions, loops, Debugging, etc. Excel For Data Analytics

Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc. Data Visualization with Excel


Charts, Pie charts, Scatter, and bubble charts
Bar charts, Column charts, Line charts, Maps
Multiples: A set of charts with the same axes, Matrices, Cards, Tiles
Ensuring Data and File Security

Data and file security in Excel, protecting row, column, and cell, the different safeguarding techniques.
Learning about VBA macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros, In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing an action with Sub and ending Sub if condition not met. Statistics with Excel

ONE-TAILED TEST AND TWO-TAILED T-TEST, LINEAR REGRESSION,PERFORMING STATISTICAL ANALYSIS USING EXCEL, IMPLEMENTING LINEAR REGRESSION WITH EXCEL

Data Visualization Using Tableau
Introduction to Data Visualization
Introduction to Tableau
Basic Charts and Dashboard
Descriptive Statistics, Dimensions and Measures
Visual Analytics: Storytelling through Data
Dashboard Design & Principles
Advanced Design Components/ Principles: Enhancing the Power of Dashboards
Special Chart Types
Tableau to Analyze Data
Connect Tableau to a variety of dataset
Analyze, Blend, Join and Calculate Data
Tableau to Visualize Data
Visualize Data In the form of Various Charts, Plots and Maps
Data Hierarchies
Work with Data Blending in Tableau
Work with Parameters
Create Calculated Fields
Adding Filters and Quick Filters
Create Interactive Dashboards
Adding Actions to Dashboard
Case Study: Hands-on Using Tableau
Integrate Tableau with Google Sheets

Module 1: Introduction to Descriptive Analytics
- Overview of descriptive analytics: Definition, scope, and importance.
- Types of data: Categorical, ordinal, and numerical.
- The role of descriptive analytics in the data analytics lifecycle.
Module 2: Data Collection and Preparation
- Data sources: Primary vs. secondary data.
- Data cleaning and preprocessing: Handling missing data, outliers, and noise.
- Data transformation: Normalization, standardization, and scaling.
- Data preprocessing using Excel/Python/R.
Module 3: Descriptive Statistics
- Measures of central tendency: Mean, median, mode.
- Measures of dispersion: Range, variance, standard deviation.
- Skewness, kurtosis, and their implications.
- Calculating and interpreting descriptive statistics on a given dataset.
Module 4: Data Visualization Techniques
- Principles of data visualization: Clarity, accuracy, and efficiency.
- Common visualization tools: Bar charts, histograms, scatter plots, box plots.
- Advanced visualization: Heatmaps, treemaps, and time-series graphs.
- Creating visualizations using Excel/Tableau/Python (Matplotlib, Seaborn).
Module 5: Data Aggregation and Reporting
- Techniques for data aggregation: Summarizing large datasets.
- Pivot tables and cross-tabulations.
- Designing dashboards for data reporting.
- Building interactive dashboards using Excel/Power BI/Tableau.
Module 6: Pattern Recognition and Trend Analysis
- Identifying trends and patterns in data.
- Time series analysis: Detecting seasonality and trends.
- Case studies on trend analysis in various industries.
- Analyzing time-series data for trend identification.
Module 7: Applications of Descriptive Analytics
- Business: Sales analysis, customer segmentation, and market trends.
- Healthcare: Patient data analysis, disease prevalence.
- Social sciences: Demographic studies, survey analysis.
- Applying descriptive analytics techniques to industry-specific datasets.
- Final project: Comprehensive descriptive analysis of a real-world dataset.

Module 1: Data Collection and Cleaning
- Data types, Data collection methods, Data cleaning techniques
- Handling missing data, dealing with outliers, cleaning data with Pandas Module 2: Descriptive Statistics

-Measures of central tendency, measures of variability, skewness, and kurtosis
-Computing and interpreting descriptive statistics with Pandas
Module 3: Data Visualization Techniques
-Principles of data visualization, Types of plots (bar, histogram, boxplot, scatterplot)
-Creating and interpreting visualizations using Matplotlib and Seaborn
Module 4: Univariate Analysis
-Analyzing single variables, Frequency distributions, Density plots
-Conducting univariate analysis on different types of data
Module 5: Bivariate Analysis
-Analyzing relationships between two variables, Correlation, Cross-tabulation
-Scatterplots, heatmaps, and pairplots in Seaborn
Module 6: Multivariate Analysis
-Introduction to multivariate relationships, Principal Component Analysis (PCA)
-Conducting multivariate analysis and visualization using PCA and pairwise plots
Module 7: Handling Categorical Data
-Encoding categorical variables, Analyzing categorical data
-One-hot encoding, visualizing categorical data with bar plots and pie charts
Module 8: Advanced Visualization Techniques

-Interactive visualizations, using tools like Plotly and Dash
-Creating interactive dashboards and plots
Module 9: Case Studies and Project Presentations
-Reviewing case studies of EDA in real-world applications
-Students present their EDA projects based on a dataset of their choice

Module 1: Introduction to Diagnostic Analytics
- Overview of diagnostic analytics: Definition, scope, and importance.
- Differentiating between descriptive, predictive, and diagnostic analytics.
- The role of diagnostic analytics in decision-making processes.
Module 2: Data Collection and Preparation
- Data sources and types: Structured vs. unstructured data.
- Data cleaning, preprocessing, and transformation.
- Identifying relevant features for diagnostic analysis.
Module 3: Statistical Methods in Diagnostic Analytics
- Hypothesis testing and statistical inference.
- Regression analysis: Linear, logistic, and multiple regression.
- Analysis of variance (ANOVA) and its applications.
- Applying statistical methods to diagnose issues in datasets.
Module 4: Machine Learning for Diagnostic Analytics
- Supervised learning techniques: Decision trees, random forests, and support vector machines.
- Unsupervised learning techniques: Clustering, anomaly detection.
- Model evaluation and validation.
- Implementing machine learning algorithms to identify root causes.
Module 5: Root Cause Analysis Techniques
- Introduction to root cause analysis (RCA).
- Tools and techniques for RCA: Fishbone diagrams, 5 Whys, Pareto analysis.
- Case studies on RCA in various industries.
Module 6: Applications of Diagnostic Analytics
- Healthcare: Diagnostics in medical data and patient outcomes.
- Finance: Fraud detection, risk assessment.
- Manufacturing: Quality control and process optimization.
- Case studies on the use of diagnostic analytics in different sectors.
- Analyzing case studies and applying diagnostic analytics to industry-specific problems.

Module 1: Introduction to Predictive Data Analysis
- Overview of Predictive Analytics
- Predictive vs. Descriptive Analytics
- Applications and Use Cases in Industry
- Data Preparation and Feature Engineering
- Course Introduction and Overview
- Case Study: Predictive Analytics in Industry
Module 2: Fundamentals of Predictive Modelling
- Introduction to Predictive Modelling
- Types of Predictive Models (Regression, Classification, Time Series)
- Model Selection and Evaluation Criteria
- Cross-Validation and Model Tuning
- Hands-on Lab: Implementing Basic Predictive Models using Python/R
- Model Evaluation and Comparison
Module 3: Regression Analysis
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial and Interaction Terms
- Model Diagnostics and Improvement

Module 1: Introduction to Prescriptive Analytics
- What is Prescriptive Analytics?
- Introduction to the analytics spectrum: Descriptive, Diagnostic, Predictive, and Prescriptive.
- Key concepts: Optimization, simulation, decision-making.
- Applications of prescriptive analytics in different industries.
- The Role of Data in Prescriptive Analytics
- Data types and sources relevant to prescriptive analytics.
- The importance of high-quality data.
- Ethical considerations in prescriptive analytics.
Module 2: Introduction to Decision Science
- Fundamentals of Decision Science
- Understanding decision-making processes.
- Decision criteria and objectives.
- Trade-offs and multi-objective decision-making.
- Introduction to Optimization
- Basic concepts of optimization.
- Types of optimization problems: Linear, nonlinear, integer, and mixed-integer programming.
- Real-world examples of optimization in prescriptive analytics.
Module 3: Optimization Models and Techniques
- Linear and Integer Programming
- Linear Programming (LP)
- Formulating linear programming models.
- Solving LP problems using graphical and simplex methods.
- Sensitivity analysis and interpreting LP solutions.
- Integer Programming (IP)
- Introduction to integer programming.
- Formulating and solving IP problems.
- Applications of IP in resource allocation and scheduling.
Module 4: Advanced Optimization Techniques
- Nonlinear Programming (NLP)
- Understanding nonlinear problems.
- Techniques for solving NLP problems.
- Real-world applications of NLP.
- Stochastic Optimization
- Introduction to uncertainty in decision-making.
- Stochastic programming models.
- Solving and interpreting stochastic optimization problems.
Module 5: Simulation and Heuristics
- Simulation in Prescriptive Analytics
- Introduction to Simulation
- The role of simulation in decision-making.
- Monte Carlo simulation and its applications.
- Building simulation models to assess risk and uncertainty.
- Case Study: Simulation in Supply Chain Management
- Developing a simulation model for a supply chain problem.
- Analyzing the results to make informed decisions.
Module 6: Heuristic and Metaheuristic Approaches
- Heuristics for Optimization
- Introduction to heuristic methods: Greedy algorithms, local search.
- When to use heuristics in prescriptive analytics.
- Limitations and advantages of heuristic methods.
- Metaheuristic Techniques
- Overview of metaheuristic algorithms: Genetic algorithms, simulated annealing, ant colony optimization.
- Practical examples and applications of metaheuristics.
- Implementing metaheuristic algorithms in Python/R.
Module 7: Decision Support Systems (DSS) and Tools
- Building Decision Support Systems
- Introduction to Decision Support Systems
- Components and architecture of a DSS.
- Integrating prescriptive analytics into DSS.
- Case studies of DSS in various industries.
- Developing a Simple DSS
- Building a DSS using Excel or Google Sheets.
- Incorporating optimization models into a DSS.
- Hands-on practice: Developing a basic DSS for a business problem.
Module 8: Tools for Prescriptive Analytics
- Industry Tools Overview
- Introduction to tools like Gurobi, CPLEX, AMPL, and others.
- Overview of Python and R libraries for prescriptive analytics (e.g., PuLP, Pyomo, SciPy).
- Selecting the right tool for different types of problems.
- Hands-on: Using Prescriptive Analytics Tools
- Installing and setting up tools.
- Solving a real-world optimization problem using Gurobi/PuLP.
- Discussion on the results and decision-making insights.
Module 9: Applications and Case Studies
- Applications in Business and Industry
- Supply Chain Optimization
- Prescriptive analytics in supply chain management.
- Optimization of inventory, transportation, and production.
- Case study: Improving a supply chain using prescriptive analytics.
- Healthcare and Finance Applications
- Prescriptive analytics in healthcare: Treatment planning, resource allocation.
- Applications in finance: Portfolio optimization, risk management.
- Case study: Financial decision-making using prescriptive analytics.
Module 10: Real-World Case Studies
- Case Study Analysis
- In-depth analysis of a selected case study in a domain of choice (e.g., marketing, energy, logistics).
- Discussion on the approach, models used, and outcomes.
- Group activity: Developing a prescriptive analytics solution for a hypothetical business problem.
Module 12: The Future of Prescriptive Analytics
- Emerging Trends and Technologies
- AI and machine learning in prescriptive analytics.
- Real-time analytics and decision automation.
- The future role of prescriptive analytics in business strategy.
- Final Project Presentation
- Participants present their final projects.
- Peer review and instructor feedback.
- Course wrap-up and discussion on future learning paths.
- Final Assessment:
- A comprehensive project requiring participants to develop a prescriptive analytics solution for a real or simulated business problem.

Module 1: Introduction to Cognitive Science and Data Analytics
- Overview of cognitive science: Key concepts and theories.
- Introduction to data analytics: Basic concepts and tools.
- Cognitive data: Types, sources, and importance.
Module 2: Cognitive Models and Data Analytics
- Cognitive models: Types and their role in understanding cognition.
- Data-driven modeling approaches: Linear models, regression analysis.
- Case studies on cognitive models in psychology and neuroscience.
- Building a simple cognitive model using real-world data.
Module 3: Machine Learning for Cognitive Data
- Introduction to machine learning: Supervised vs. unsupervised learning.
- Applications of machine learning in cognitive data analysis.
- Feature selection and engineering for cognitive models.
- Implementing machine learning algorithms on cognitive datasets.
Module 4: Data Visualization in Cognitive Analytics
- Principles of effective data visualization.
- Tools and techniques for visualizing cognitive data.
- Interpretation and communication of results through visualization.
- Creating data visualizations using Python/R (Matplotlib, Seaborn, etc.).
Module 5: Advanced Cognitive Data Analytics
- Time series analysis for cognitive data.
- Network analysis in cognitive science.
- Predictive modeling and validation.
- Analyzing time series data from cognitive experiments.
Module 6: Applications in Psychology and Neuroscience
- Cognitive data analytics in psychological research.
- Neural data analysis and brain-computer interfaces.
- Case studies of cognitive analytics in real-world applications.
- Working on a dataset related to psychology/neuroscience.

Introduction to Machine Learning

1.Supervised, Unsupervised learning.
2.Introduction to scikit-learn, Keras, etc. Regression

1.Introduction classification problems, Identification of a regression problem, dependent and independent variables
2.How to train the model in a regression problem?
3.How to evaluate the model for a regression problem?
4.How to optimize the efficiency of the regression model?

Module 1: Overview of Looker Studio
- What is Looker Studio?
- Introduction to data visualization and reporting.
- Overview of Looker Studio’s interface and features.
- Understanding the role of Looker Studio in the data analytics process.
- Setting up an account and navigating the platform.
- Overview of the user interface: Menus, toolbars, and panels.
Module 2: Connecting to Data Sources
- Understanding Data Sources
- Overview of different data sources (Google Sheets, BigQuery, SQL databases, etc.).
- Connecting to a data source.
- Configuring and managing data sources.
- Hands-on Practice
- Connecting to a sample data source.
- Exploring data schemas and tables within Looker Studio.
Module 3: Building Basic Reports and Dashboards
- Report Basics
- Creating a new report.
- Adding charts, tables, and other visualizations.
- Customizing chart types and styles.
- Data Transformation
- Introduction to data fields and metrics.
- Creating calculated fields.
- Understanding aggregation and filtering data.
Module 4: Designing Interactive Dashboards
- Dashboard Design Principles
- Best practices for designing dashboards.
- Layout and formatting options.
- Using themes and templates.
- Adding Interactivity
- Using filters, date ranges, and controls.
- Linking charts for interactive dashboards.
- Hands-on: Creating a simple interactive dashboard.
Module 5: Data Blending and Joins
- Combining Data from Multiple Sources
- Introduction to data blending.
- Using joins to combine data tables.
- Practical examples of blending different data sources.
- Advanced Calculated Fields
- Creating complex calculated fields.
- Using functions and expressions.
Module 6: Advanced Charting Techniques
- Custom Visualizations
- Introduction to advanced chart types (e.g., geo maps, heat maps).
- Creating and customizing advanced visualizations.
- Conditional Formatting and Annotations
- Using conditional formatting to highlight key data points.
- Adding annotations and notes to charts and tables.
Module 7: Sharing Collaborating and Publishing
- Sharing Reports
- Setting sharing permissions.
- Collaborating on reports with team members.
- Embedding reports in websites and presentations.
- Exporting and Scheduling Reports
- Exporting reports to PDF or CSV.
- Scheduling automated report deliveries.
Module 8: Managing Data Sources and Reports
- Data Source Management
- Refreshing data sources.
- Managing and updating data connections.
- Report Management
- Version control and report history.
- Organizing reports in folders and workspaces.
Module 9: Customization and Optimization
- Customizing Report Appearance
- Creating and applying custom themes.
- Incorporating branding elements (logos, colors).
- Hands-on: Applying branding to a report.
- Optimizing Report Performance
- Best practices for performance optimization.
- Managing large datasets.
- Troubleshooting common performance issues.
Module 10: Case Studies and Final Project
- Review of Key Concepts
- Recap of major topics covered in the course.
- Q and A Module for any remaining questions.
- Case Studies
- Analysis of real-world examples.
- Discussion on how Looker Studio is used in different industries.
- Final Project
- Participants create a comprehensive report or dashboard using their data.
- Presentations and peer reviews.

Data Analytics using ChatGPT
Data Analytics using Gemini AI
Data Analytics using Calude
Data Analytics using Julius

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