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

Window Functions

Common Table Expressions

Full Text Search

Query Profiling

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).

Generative AI for SQL
Query Generation: Turn natural language prompts into complex SQL queries instantly.
Performance Optimization: Receive suggestions for enhanced query efficiency.
Error Detection: Identify and resolve syntax errors effortlessly.
Data Insights: Analyze results and generate actionable recommendations.
Task Automation: Simplify tasks like creating tables, updating records, and joining datasets

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

Introduction to Probability and Statistics
Population,Sample, Types of Variables
Descriptive Statistics
The measure of central tendency, the measure of spread, five points summary
Correlation, covariance,Inter Quartile Range(IQR)
Probability Distributions, Probability in Data Analytics
Probability Distributions, Binomial distribution, Poisson distribution, Bayes Theorem, central limit theorem
Inferential Statistics
Confidence intervals,F-test, Z-test, t-test, ANOVA, chi-square test, etc.
Hypothesis Testing,Type-I error,Type-II error

Realtime Business Problem Case Studies on

Sales, Marketing & Retail Domain
1.Analyzing Customer Purchase Patterns Using Descriptive Statistics in Retail.
2.Evaluating the Effect of Advertising Spend on Sales: A Correlation and Regression Study.
3.Identifying Seasonality in Online Sales Using Time Series Analysis.
4.Customer Segmentation Using Cluster Analysis on Sales Data.
5.Measuring Impact of Price Changes on Product Demand Using Elasticity Models.
Banking & Finance Domain
6.Risk Analysis of Loan Defaults Using Probability Distributions.
7.Testing the Efficiency of a Portfolio Using Statistical Tools.
8.Time Series Forecasting of Stock Prices Using Moving Averages.
9.Comparing Returns of Mutual Funds Using ANOVA.
10.Regression Analysis of Economic Indicators and Market Trends.
Manufacturing & Quality Control Domain
11.Statistical Process Control in a Manufacturing Line: A Six Sigma Case Study.
12. Reducing Product Defects Using Hypothesis Testing in Quality Assurance.
13. Applying Pareto Analysis to Identify Key Causes of Downtime in Production.
14. Measuring Process Variability Using Standard Deviation and Control Charts.
15. Evaluating Supplier Performance Using Statistical Sampling.
HR & Business Operations Domain
16. Using Statistics to Analyze Employee Attrition Rates.
17. Hypothesis Testing to Assess the Impact of Training on Productivity.
18. Forecasting Workforce Needs Using Regression and Time Series.
19. Exploring Gender Pay Gap Through Inferential Statistics.
20. Employee Satisfaction Survey Analysis Using Measures of Central Tendency.
E-commerce & Digital Domain
21. Web Traffic Analysis Using Descriptive and Inferential Statistics.
22. A/B Testing to Evaluate Website Design Impact on Conversion Rate.
23. Correlation Between Delivery Time and Customer Satisfaction in E-Commerce.
Healthcare & Medical Research Domain
24. Analyzing Patient Waiting Time Using Measures of Central Tendency.
25. Survival Analysis of Patients Undergoing Different Treatment Protocols.
26. Correlation Between BMI and Blood Pressure in Adults.
27. Hypothesis Testing to Evaluate New Drug Effectiveness.
28. Predicting Hospital Readmission Using Logistic Regression.

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

Generative AI for PowerBI
Natural Language Queries: Ask plain English questions (e.g., "What were last quarter's total sales?") to generate visuals and reports.
Automated Insights: Detect trends, anomalies, and patterns instantly.
Data Transformation Assistance: Get Power Query suggestions tailored to your needs.
Dynamic Visualizations: Receive chart and layout recommendations for your datasets.
Enhanced Data Modeling: Create DAX formulas and relationships using natural language input.
Collaboration & Summarization: Summarize dashboards and data into stakeholder-friendly formats

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

Generative AI for MS Excel
Automation: Effortlessly create formulas, workflows, and visualizations.
Data Insights: Gain actionable insights and recommendations from AI tools.
Ease of Use: Simplify tasks with natural language or conversational commands.
Accuracy: Minimize errors with AI-assisted calculations and processing.
Collaboration: Seamlessly integrate with tools for better teamwork

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.

Module 1: Introduction & Environment Setup
Overview of Qlik products (Qlik Sense, QlikView)
Installation / cloud access setup
Qlik interface navigation & workspace basics
Data loading editor & hub
Module 2: Data Loading & Preparation
Connecting to data sources (Excel, CSV, SQL, APIs)
Qlik Data Model basics (associative model)
Data Load Script syntax & debugging
Data transformation: joins, concatenations, mapping
Data cleansing & filtering
Module 3: Data Modeling
Synthetic keys & circular references handling
Creating data model diagrams
Master items: measures, dimensions, variables
Hierarchies & drill-down groups Module 4: Data Visualization Basics
Creating sheets & dashboards
Common charts: bar, line, pie, combo, scatter
Tables, KPIs, and text objects
Sorting, filtering, and selections
Color coding & themes
Module 5: Advanced Visualization Techniques
Set Analysis for advanced filtering
Alternate states for comparative analysis
Conditional formatting & expressions
Custom visualizations & extensions
GeoAnalytics for map visualizations
Module 6: Interactivity & UX
Filters, slicers, and bookmarks
Sheet navigation & storytelling
Input boxes & variables for user interaction
Module 7: Publishing & Deployment
Exporting dashboards & data
Security rules & user access control
Qlik Sense Cloud / Qlik Server publishing
Performance optimization tips

Module 1: Introduction & Setup
Overview of Alteryx Designer, Server, and Gallery
Installing Alteryx Designer
Workspace navigation & basic terminology
Alteryx workflow concept
Module 2: Data Input & Output
Connecting to data sources (Excel, CSV, SQL, APIs)
Input Data tool & configuration
Output Data formats & publishing
File and database connection best practices
Module 3: Data Preparation
Select, Filter, Sort, and Sample tools
Data cleansing (remove nulls, trim, case change)
Formula tool for calculated fields
Multi-Field & Multi-Row formulas
Data parsing & transformation
Module 4: Data Blending & Joins
Join tool types (Inner, Left, Right)
Union tool for combining datasets
Append Fields & Find Replace tools
Fuzzy Matching for data deduplication
Module5:Advanced Data Transformation
Summarize tool for aggregations
Transpose & Cross Tab for pivoting
Grouping and aggregation techniques
Macros basics for repetitive tasks
Module 6:Analytics and Modeling
Data Investigation tools (Profile, Field Summary)
Predictive tools overview (Linear Regression, Decision Trees)
Time Series tools basics
Geospatial analytics (Spatial Match, Create Points)
Module 7: Automation & Workflow Optimization
Scheduling workflows
Batch and Iterative macros
Performance tuning & best practices
Module 8: Sharing & Deployment
Publishing workflows to Alteryx Gallery
Collaboration & version control
Integration with BI tools (Tableau, Power BI, Qlik)

Module I: Basics & Navigation
Introduction to Google Sheets interface
Creating, saving, and sharing sheets
Basic data entry & formatting (font, color, borders)
Sheet navigation (tabs, freezing rows/columns, zoom)
Module II: Data Management
Sorting & filtering data
Data validation (drop-downs, rules)
Conditional formatting
Find & replace
Module III: Formulas & Functions
Basic math functions: SUM, AVERAGE, MIN, MAX
Logical functions: IF, AND, OR
Text functions: CONCATENATE, SPLIT, LEN, TRIM
Lookup functions: VLOOKUP, HLOOKUP, INDEX, MATCH
Date & time functions: TODAY, NOW, DATEDIF
Module IV: Data Analysis
Pivot tables
Charts & graphs (bar, line, pie)
Sparklines
Explore & Quick Analysis tool
Module V: Collaboration & Automation
Sharing & permissions
Commenting & chat
Version history
Google Sheets add-ons & integrations
Introduction to Google Apps Script for automation
Module VI: Advanced Features
Array formulas
QUERY function basics
Import functions: IMPORTRANGE, IMPORTDATA, IMPORTXML
Data linking between sheets

Module 1: Introduction & Setup
Overview of KNIME Analytics Platform
Installation & workspace setup
Understanding workflows, nodes, and connections
Navigating the KNIME interface
Module 2: Data Access & Preparation
Importing data (CSV, Excel, databases, APIs)
Data filtering, sorting, and grouping
Missing value handling
Data type conversion & normalization
Data sampling
Module 3: Data Transformation
String & number manipulation
Aggregations & pivoting
Column splitting & concatenation
Join, union, and append operations
Rule-based transformations
Module 4: Data Visualization
Bar, line, scatter, and pie charts
Color management & formatting
Interactive views & dashboards
Module 5: Analytics & Machine Learning
Basic statistics & correlation
Regression & classification models
Clustering (k-means, hierarchical)
Model training, testing, and evaluation
Cross-validation
Module 6: Advanced Analytics
Text mining & sentiment analysis
Time series analysis
Image processing basics
Integration with Python, R, and SQL
Module 7: Automation & Deployment
Workflow automation & scheduling
Using loops & flow variables
KNIME Server basics
Exporting & sharing workflows

1. Introduction to OLAP
Definition and purpose of OLAP
OLAP vs OLTP
Characteristics of OLAP systems
Importance in Business Intelligence (BI) and Data Warehousing
2. OLAP Concepts
Multidimensional data models: Cubes, Dimensions, Hierarchies, Measures, Facts
OLAP operations: Slice, Dice, Drill-Down, Roll-Up, Pivot (Rotate)
Types of OLAP:
MOLAP (Multidimensional OLAP)
ROLAP (Relational OLAP)
HOLAP (Hybrid OLAP)
3. OLAP Architecture
Data Warehouse and OLAP server architecture
Star schema, Snowflake schema
Cube processing and storage
Aggregation and indexing techniques
4. OLAP Tools Overview
Comparison of popular OLAP tools
Criteria for tool selection
5. Hands-on with OLAP Tools
Microsoft SQL Server Analysis Services (SSAS)
Apache Kylin
Setup and cube creation
Querying with SQL
Pentaho Mondrian
Cube design and MDX querying
SAP BusinessObjects
Basic OLAP reporting
Power BI (OLAP features)
Connecting to OLAP cubes
Using PivotTables and slicers
6. Advanced OLAP Topics
Real-time OLAP
OLAP in Big Data and Cloud
Google BigQuery OLAP functions
AWS Redshift Spectrum
Performance tuning and optimization
Security and access control in OLAP
7. Case Studies and Projects
Design and implement an OLAP cube for sales analysis
Multidimensional analysis using Power BI connected to SSAS
Performance benchmarking of MOLAP vs ROLAP on a sample dataset

1. Introduction to Azure Data Analytics Ecosystem
Overview of Azure Data Factory and Azure Synapse Analytics
Basic cloud and Azure concepts (regions, resource groups, subscriptions)
2. Getting Started with Azure Data Factory (ADF)
Understanding pipelines, activities, datasets, and linked services
Creating your first simple pipeline (copy data from one storage to another)
Introduction to triggers (manual and scheduled)
Monitoring pipeline runs and understanding run history
3. Introduction to Azure Synapse Analytics
What is Synapse Analytics? Key components explained simply
Creating a Synapse workspace
Overview of dedicated and serverless SQL pools
Running simple queries in Synapse Studio
4. Basic Data Movement and Transformation
Copying data from Azure Blob Storage to Synapse using ADF
Introduction to data transformation basics with Mapping Data Flows
Simple transformations: filter, select, and aggregate data
5. Hands-on Mini Project
Build a pipeline to ingest sample data into Synapse
Run basic SQL queries on the ingested data
6. Monitoring and Troubleshooting Basics
How to track pipeline execution
Common errors and how to fix them

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

1. Sales Performance Dashboard using Power BI
2. Customer Churn Analysis using Python & MySQL
3. Retail Store Inventory Optimization
4. COVID-19 Data Visualization & Trend Analysis
5. HR Analytics: Employee Attrition Prediction
6. Web Traffic Analysis using Google Analytics & Tableau
7. E-Commerce Product Recommendation (Basic Collaborative Filtering)
8. Bank Loan Approval Data Analysis
9. Marketing Campaign Effectiveness using Excel & Power BI
10.Crime Data Mapping & Hotspot Detection
11.Real-Time Sales Analytics Pipeline using Kafka, Spark, and Elasticsearch
12.Fraud Detection in Financial Transactions using ML & Big Data
13.Customer Lifetime Value Prediction with Feature Engineering
14.Predictive Maintenance Analytics for Manufacturing Equipment
15.Social Media Sentiment Analysis using NLP & Cloud Tools
16.Dynamic Pricing Model for E-commerce
17.Telecom Network Outage Prediction using Time Series Forecasting
18.Supply Chain Optimization with Demand Forecasting
19.Geospatial Analytics for Delivery Route Optimization
20.Energy Consumption Forecasting using IoT Data
21.Enterprise Data Lakehouse Implementation with Snowflake + Databricks
22.Real-Time Fraud Detection System using Flink + Druid + Grafana
23.AI-Powered Root Cause Analysis Platform for Manufacturing Defects
24.Global Retail Demand Prediction with Multi-Market Data Integration
25.Healthcare Patient Risk Stratification using Multi-Modal Data
26.Generative AI-Powered Analytics Chatbot for Business Intelligence
27.Cross-Channel Marketing Attribution Model
28.Intelligent Traffic Management Analytics Platform (Edge + Cloud)
29.End-to-End Cloud Cost Optimization & Forecasting Dashboard
30.Data Governance & Compliance Analytics Platform for Enterprises