Master Data-Driven Decision Making
With Business Analytics and Intelligence Training

Business Growth & Stability Factors - Key Drivers of Business Growth (Market Demand, Innovation, Scalability, Branding, Operational Efficiency), Factors Affecting Business Stability (Economic Conditions, Competition, Regulatory Policies, Customer Retention), The Role of Data Analytics in Sustaining Business Growth & Stability; Competitive Market Analysis - Understanding Market Positioning & Competitive Advantages, Competitor Benchmarking Using Data Analytics, Techniques for Competitive Analysis (Benchmarking, Price Sensitivity, Sentiment Analysis, Trend Analysis); Case Study: How Amazon Uses Analytics to Dominate the E-Commerce Market; Identifying Key Business Performance Indicators (KPIs) - What are KPIs & Why are They Important?, Types of KPIs - Financial KPIs, Operational KPIs, Marketing KPIs, Customer KPIs, Employee KPIs; Selecting the Right KPIs for Business Goals, How to Measure and Track KPIs Using Power BI & Python?, Building Data-Driven KPI Dashboards for Decision-Making; Business Metrics & their Impact on Decision-Making - Understanding the Difference Between KPIs and Metrics, Common Business Metrics Across Industries: Retail Industry, Finance & Banking, Healthcare; Aligning Business Metrics with Business Objectives, Real-World Example: How Netflix Uses Data-Driven Metrics for Business Growth.

Analyzing Past Business Trends - What is Descriptive Analytics?, Use Cases of Descriptive Analytics: Identifying Revenue Growth Trends, Customer Buying Behavior Analysis, Performance Monitoring; Tools for Descriptive Analytics (Power BI, Tableau, SQL, Excel); Identifying Reasons for Past Performance - What is Diagnostic Analytics?, Techniques Used in Diagnostic Analytics (Root Cause Analysis, Correlation Analysis, Anomaly Detection; Forecasting Business Trends - What is Predictive Analytics?, Popular Predictive Models in Business Analytics - Regression Models, Time Series Forecasting (ARIMA, Exponential Smoothing), Machine Learning Models (Decision Trees, Random Forest, Neural Networks), Real-Life Example: How Starbucks Predicts Store Performance Using Data Analytics; What is Prescriptive Analytics?, How Businesses Use Prescriptive Analytics for Decision-Making, Tools & Techniques Used in Prescriptive Analytics (AI & Optimization Algorithms), Case Study: How Uber Uses Prescriptive Analytics to Optimize Ride Pricing.

SWOT Analysis (Strengths, Weaknesses, Opportunities, Threats), Understanding SWOT Analysis & Its Application in Business; Case Study: How Apple Uses SWOT Analysis to Make Strategic Decisions; Porter’s Five Forces Analysis - Competitive Rivalry (Industry Competition), Threat of New Entrants (Barriers to Entry), Bargaining Power of Suppliers & Customers, Threat of Substitute Products; Case Study: How Tesla Uses Porter’s Five Forces for Business Strategy; PESTLE Analysis (Political, Economic, Social, Technological, Legal, Environmental), Impact of External Environmental Factors on Business Strategy; Case Study: How COVID-19 Affected Global Businesses (PESTLE Analysis); Market Research & Competitor Analysis - Using Data to Understand Consumer Trends & Competitor Movements, Market Sizing & Demand Forecasting Using Data Analytics; Real-World Example: How Netflix Uses Data-Driven Market Research to Expand Globally.

Introduction to Internal Data Sources (Sales & Transaction Data, Customer Relationship Management (CRM) Data, Marketing & Website Analytics, Employee Performance & HR Data, Inventory & Supply Chain Data); Introduction to External Data Sources (Market Research Reports, Social Media & Online Reviews, Competitor & Industry Benchmarks, Government & Publicly Available Data, Third-Party API Data), Pros & Cons of Internal vs. External Data for Business Analytics, How Businesses Combine Internal & External Data for Better Decision-Making; Data Collection Methods - Surveys & Questionnaires for Business Research (Designing Effective Business Surveys, Customer Feedback Collection, Employee Satisfaction & HR Surveys), CRM Data Extraction & Processing (Understanding CRM Systems (Salesforce, HubSpot, Zoho CRM), Extracting Customer Data from CRM for Business Insights), Web Scraping for Business Intelligence (Scraping Competitor Pricing & Product Data, Extracting Customer Reviews & Sentiment Analysis, Legal & Ethical Considerations in Web Scraping, Hands-on Web Scraping Using Python (BeautifulSoup, Scrapy)), Using APIs for Business Data Collection (API Basics & Authentication (REST, JSON), Extracting Real-Time Market & Stock Data Using APIs, Working with APIs from Google, Twitter, OpenWeather, etc.); Structured Data (Databases, Excel Sheets, CSV, SQL); Unstructured Data (Emails, PDFs, Images, Videos, Social Media Posts), How Businesses Handle Structured vs. Unstructured Data?, Big Data Technologies for Managing Unstructured Data (Hadoop, Spark, NoSQL).

Introduction to SQL & MySQL, Why is SQL Important for Business Analytics?, Relational Databases vs. NoSQL Databases, Setting Up MySQL for Business Data Storage, Understanding Tables, Relationships, and Schemas; Writing SQL Queries to Extract Business Data - SELECT, WHERE, ORDER BY, GROUP BY, HAVING, Joining Data from Multiple Tables (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN); Using Subqueries & Common Table Expressions (CTEs) for Business Analysis; Extracting Time-Based Data (YEAR, MONTH, WEEK, DATE_FORMAT); Data Aggregation & Filtering for Business Reports - Summarizing Business Data Using SQL Aggregation Functions (SUM, AVG, COUNT, MIN, MAX), Filtering & Segmenting Business Data for Reporting, Creating Customer Segmentation Using SQL Queries, Analyzing Revenue Trends Using SQL Window Functions (RANK, DENSE_RANK, ROW_NUMBER, PARTITION BY); Handling Large Business Databases Efficiently - Indexing for Faster Query Performance, Normalization vs. Denormalization for Business Analytics, Optimizing SQL Queries for Large Datasets, Best Practices for Data Storage & Retrieval.

Installing Python, Jupyter Notebook, and Required Libraries, Setting Up Python for Business Analytics (Installing Anaconda for Business Data Analysis, Setting Up Jupyter Notebook & VS Code for Data Analytics, Using Google Colab for Cloud-Based Analysis), Essential Python Libraries for Business Analytics (Pandas – Data Manipulation & Analysis, NumPy – Numerical Computation, Matplotlib & Seaborn – Data Visualization, Scipy – Statistical & Financial Analysis, Scikit-learn – Machine Learning for Predictive Analytics, Plotly & Streamlit – Interactive Dashboards & Reports); Understanding Data Structures in Python for Business Analysis - Python Lists, Tuples, and Dictionaries for Business Data Handling, Working with Pandas DataFrames & NumPy Arrays, Understanding DataFrames vs. SQL Tables vs. Excel Sheets.

Data Cleaning Techniques, Identifying & Handling Missing Data - Imputation Methods (Mean, Median, Mode, Interpolation), Dropping Missing Values vs. Filling Missing Values; Removing Duplicates & Fixing Inconsistent Data - Handling Data Entry Errors, Identifying Outliers in Business Data; Formatting Business Data in Excel for Analysis - Cleaning Raw Business Data Using Excel Functions (TEXT, CONCATENATE, TRIM, LEFT/RIGHT, MID, SUBSTITUTE), Using Pivot Tables for Quick Business Insights, Automating Data Cleaning Using Macros & VBA; Automating Data Cleaning Tasks Using Python - Using Pandas for Business Data Cleaning (Handling Missing Data in Pandas (fillna(), dropna()), Removing Duplicates (drop_duplicates()), Standardizing & Transforming Data (apply(), map())), Using NumPy for Handling Large Business Datasets, Applying Regular Expressions (RegEx) for Data Standardization, Exporting Cleaned Data to Excel & Databases.

Understanding Business Trends Using Data - Using Summary Statistics to Understand Business Performance (Measures of Central Tendency (Mean, Median, Mode), Measures of Spread (Variance, Standard Deviation, IQR)), Using Time Series Data for Business Trend Analysis (Identifying Seasonal Patterns in Sales Data, Detecting Anomalies in Business Performance); Identifying Revenue Trends, Market Gaps & Customer Preferences - Analyzing Sales Performance Over Time, Identifying Best-Selling Products & High-Value Customers, Understanding Customer Behavior Using Transaction Data, Analyzing Customer Sentiments Using NLP (Natural Language Processing); Segmenting Data for Effective Business Decision-Making - Customer Segmentation Based on Spending Behavior (Using K-Means Clustering for Customer Segmentation, RFM (Recency, Frequency, Monetary) Analysis for Customer Value Ranking), Market Segmentation Using Business Data, Churn Prediction: Identifying Customers Likely to Leave the Business; Data Visualization for Business Intelligence - Creating Business Growth Charts Using Matplotlib & Seaborn, Building Revenue & Customer Behavior Dashboards in Power BI, Using Advanced Business Graphs: Heatmaps, Correlation Matrix, Boxplots, Violin Plots.

Loading Business Data in Python, Reading Business Data from Different Sources (CSV, Excel, SQL Databases, APIs, Extracting Data from Web (Web Scraping & API Integration)), Saving Processed Data for Reporting & BI Dashboards (Writing Cleaned Data to CSV, Excel, SQL Databases, Automating Data Storage in Cloud Services); Cleaning & Transforming Business Data - Handling Missing Data (Dropping vs. Imputing Missing Values (Mean, Median, Mode), Forward & Backward Fill for Time Series Data, Using Scikit-learn’s SimpleImputer for Automation), Handling Duplicates & Outliers (Detecting & Removing Duplicates in Business Data, Identifying Outliers Using Z-Scores & IQR (Boxplots), Business Impact of Outliers (Pricing Errors, Fraudulent Transactions)), Transforming Business Data (Normalization & Standardization for Business Metrics, Encoding Categorical Data for Business Modeling, Data Binning for Customer Segmentation); Aggregating Business Data for Insights - Grouping & Aggregation in Pandas (Grouping Sales & Revenue by Region, Category, & Period, Applying SUM, COUNT, AVG, MIN, MAX Functions for Reports, Calculating Customer Lifetime Value (CLV)), Pivot Tables in Pandas for Business Intelligence (Creating Pivot Tables for Sales Reports, Analyzing Monthly & Quarterly Trends in Revenue, Filtering & Sorting Business Data Dynamically).

Creating Business Growth Charts Using Matplotlib & Seaborn, Bar Charts for Sales & Revenue Trends, Line Charts for Market Growth & Business Stability, Pie Charts for Market Share & Revenue Contribution, Heatmaps for Analyzing Customer Behavior Across Segments, Histograms for Demand Forecasting & Inventory Planning; Understanding Business Performance Trends Using Data Visualization - Using Seaborn to Identify Patterns in Business Data, Boxplots for Profitability & Financial Risk Analysis, Pairplots & Correlation Heatmaps for Identifying Business Drivers, Using KDE Plots for Customer Distribution & Spending Patterns; Identifying High-Value Customers Using Visualization - Customer Segmentation with Clustering & Data Visualization (Identifying High-Value vs. Low-Value Customers Using Scatter Plots, Analyzing Customer Purchasing Behavior with Line & Boxplots, Using Treemaps to Visualize Customer Lifetime Value (CLV)), Real-World Example (How Amazon Uses Data Visualization to Track Customer Buying Behavior, How Netflix Uses Data Analytics to Improve Customer Retention); Interactive Visualizations & Dashboards for Business Reporting - Using Plotly for Interactive Business Reports, Building Dynamic Business Dashboards with Streamlit, Deploying Business Reports to the Web for Stakeholders.

Credit Risk Prediction Using Statistical Models, Understanding Credit Risk & Loan Default Probability, Applying Logistic Regression for Credit Scoring, Using Decision Trees to Classify High-Risk vs. Low-Risk Customers, Evaluating Model Performance Using Confusion Matrix & AUC-ROC Curve; Identifying Fraudulent Transactions Using Anomaly Detection, What is Anomaly Detection in Business Finance?, Techniques for Fraud Detection (Isolation Forest Algorithm, Local Outlier Factor (LOF), Autoencoders for Fraud Detection), Real-World Example: How Banks Use Machine Learning for Fraud Prevention, Time Series Analysis for Revenue & Market Prediction, Using ARIMA, Exponential Smoothing & Moving Averages for Sales Forecasting, Seasonality & Trend Analysis for Revenue Growth, Case Study: Forecasting Sales for E-Commerce Businesses.

What is Business Intelligence?, Role of BI in Data-Driven Decision Making, Power BI vs. Tableau vs. Google Data Studio; Installing and Setting Up Power BI Desktop, Understanding Power BI Interface & Key Components, Introduction to Power BI Service & Power BI Mobile; Connecting Power BI to Different Data Sources - Importing Data from Excel, CSV, and JSON, Connecting Power BI to SQL Databases, Using APIs to Fetch Real-Time Data, Integrating Power BI with Cloud Platforms (AWS Redshift); Data Transformation in Power BI (Power Query Editor) - Cleaning and Preparing Data for Analysis, Handling Missing Values & Data Formatting, Merging & Appending Queries, Data Modeling & Creating Relationships.

Designing Interactive Reports in Power BI - Creating & Customizing Reports, Adding Filters, Slicers & Drill-Throughs, Using Bookmarks & Buttons for Enhanced Interactivity; Building KPI Dashboards for Decision-Making - Understanding KPIs (Key Performance Indicators), Creating KPI Visuals & Performance Metrics, Business Use Cases (Sales Performance Dashboards, Financial Reports & Forecasting, Customer Retention & Churn Analysis); Using Power BI Visuals for Business Analysis - Bar, Pie, and Line Charts, Maps & Geo-Visualization, Waterfall & Funnel Charts, Advanced Visuals: Decomposition Tree, Gauge Chart, Key Influencer; DAX (Data Analysis Expressions) for Business Analytics - Introduction to DAX Formulas, Creating Calculated Columns & Measures, Implementing Time Intelligence Functions.

Row-Level Security (RLS) in Power BI - Setting up User-Based Security, Implementing Dynamic RLS, Role-Based Access Control; Automated Data Refresh & Scheduled Reports - Setting Up Data Refresh in Power BI Service, Configuring Automatic Report Updates, Managing Data Gateways for On-Premise Connectivity; Publishing Reports & Sharing Insights - Publishing Dashboards to Power BI Service, Embedding Reports in Websites & Applications, Sharing Insights with Business Teams, Power BI Collaboration: Workspaces & Apps; Power BI Integration with Other Tools - Power Automate for Workflow Automation, Connecting Power BI with Python & R for Advanced Analytics, Exporting Reports to PDF, PowerPoint, and Excel; Business Dashboarding with Python (Plotly & Streamlit) - Why Use Python for BI & Dashboarding?, Overview of Python Libraries: Plotly, Dash, Streamlit; Creating Interactive Business Dashboards in Python - Setting Up a Streamlit Web Application, Building Interactive Charts with Plotly (Bar, Line, Pie, Scatter), Filtering and Manipulating Data in Real-Time; Adding User Input Elements (Dropdowns, Sliders, Date Pickers), Implementing Live Data Updates & API Integration.

Introduction to Forecasting in Business Analytics, What is Predictive Analytics & Why is it Important?, Understanding the Role of Regression & Time Series in Business Predictions; Linear Regression for Revenue Prediction - Understanding the Basics of Regression Analysis, Implementing Simple & Multiple Linear Regression, Predicting Business Revenue from Sales & Market Trends, Evaluating Model Performance (R² Score, MSE, RMSE), Case Study: Predicting Monthly Sales Revenue; Time Series Forecasting for Business Trends - Introduction to Time Series Data, ARIMA (AutoRegressive Integrated Moving Average) Model, Exponential Smoothing Methods (Holt-Winters), Seasonal Decomposition of Time Series (STL), Implementing Time Series Forecasting Using Python (statsmodels, prophet), Case Study: Sales Trend Forecasting & Demand Prediction; Moving Averages & Exponential Smoothing for Business Forecasting - Understanding Moving Averages (Simple, Weighted, Cumulative), Implementing Exponential Smoothing for Trend Detection, Forecasting Stock Prices & Market Trends, Case Study: Forecasting Seasonal Demand for an E-Commerce Business.

Customer Segmentation for Business Growth - Understanding Customer Behavior Using Data, Techniques for Market Segmentation (Demographic, Behavioral, Psychographic); Using Clustering (K-Means) for Customer Segmentation - Introduction to Clustering & Unsupervised Learning, Implementing K-Means Clustering for Customer Segmentation, Finding the Optimal Number of Clusters Using the Elbow Method, Visualizing Clusters Using PCA & t-SNE, Case Study: Segmenting E-Commerce Customers Based on Purchase Behavior; Identifying High-Value Customers for Business Growth - Introduction to RFM Analysis (Recency, Frequency, Monetary), Identifying VIP Customers Using Data Analytics, Case Study: Loyalty Program Optimization for Customer Retention; Predicting Customer Churn & Retention Strategies - Understanding Customer Churn & Why It Happens, Logistic Regression for Churn Prediction, Random Forest & Gradient Boosting for Churn Prediction, Business Strategies for Customer Retention (Personalized Offers, Discounts, Loyalty Programs), Case Study: Predicting Customer Churn in a Subscription-Based Business.

A/B Testing for Marketing Strategies - Introduction to A/B Testing (Control & Treatment Groups), Designing and Implementing Effective A/B Tests, Statistical Significance (p-values, Confidence Intervals), Case Study: Optimizing Landing Page Conversions Through A/B Testing; Optimizing Advertising Budget Using Data Science - ROI Analysis for Ad Campaigns (Cost vs. Conversion), Multi-Touch Attribution Modeling for Marketing Channels, Predicting Customer Lifetime Value (CLV), Case Study: Allocating Ad Budgets for Maximum ROI in Digital Marketing - Sentiment Analysis for Customer Feedback & Brand Management, Introduction to NLP for Marketing Analytics, Analyzing Customer Reviews Using Sentiment Analysis, Monitoring Social Media Trends for Business Growth, Case Study: Using Twitter Data for Brand Reputation Management.

Demand Forecasting for Inventory Planning - Understanding Demand Forecasting Models, Implementing Time Series Forecasting for Inventory Management, Using Machine Learning (Random Forest or XGBoost) for Demand Prediction, Case Study: Predicting Product Demand in Retail Supply Chains; Identifying Supply Chain Bottlenecks Using Data - Analyzing Supply Chain Efficiency Using Analytics, Root Cause Analysis for Delays & Disruptions, Optimizing Logistics & Warehouse Management Using AI, Case Study: Reducing Supply Chain Costs Through Predictive Analytics, Automating Inventory Replenishment Using Predictive Models - Building Predictive Inventory Management Systems, Understanding Just-in-Time (JIT) Inventory Techniques, Case Study: Optimizing Stock Levels in an E-Commerce Business.

Introduction to Market Research with Data Analytics - Role of Data Analytics in Market Research, Using BI Tools (Power BI & Python) for Market Insights, Key Market Metrics: Customer Demographics, Buying Patterns, Brand Perception, Analyzing Competitor Strengths & Weaknesses Using Data - Data Sources for Competitor Analysis (Social Media, Web Scraping, Financial Reports, Customer Reviews, Industry Reports); Power BI & Python for Competitor Intelligence - Creating Competitor Dashboards in Power BI, Web Scraping Competitor Data (Using BeautifulSoup & Scrapy), Sentiment Analysis for Competitor Review Analysis (NLTK & TextBlob), Case Study: Analyzing Competitor Market Share with Power BI; Building Competitor Benchmarking Dashboards - Sales Performance Comparison, Pricing Analysis Across Competitors, Customer Satisfaction & Review Analysis, Market Positioning (Strengths vs. Weaknesses Mapping); Identifying Business Expansion Opportunities, Finding New Market Trends with Power BI - Using DAX Measures & AI Insights, Analyzing Geospatial Data (Power BI Map Visuals, Python Geopandas); Predicting Market Demand for Expansion - Time Series Forecasting (ARIMA, Prophet) for Market Demand Prediction, Identifying Emerging Industry Trends with Google Trends API, Case Study: Forecasting Market Trends for Expansion Strategies; Competitive Pricing & Customer Segmentation - Using Clustering (K-Means, DBSCAN) for Market Segmentation, Finding High-Value Market Segments for Targeted Growth.

Identifying Revenue Leakage Using Data, Understanding Revenue Leakage in Businesses, Using Power BI for Financial Performance Monitoring - Analyzing Profit & Loss Statements, Creating Revenue Leakage Dashboards; Using Python for Revenue Leakage Detection - Detecting Irregular Transactions Using Anomaly Detection (Isolation Forest, DBSCAN), Building a Revenue Forecasting Model with Power BI & Python, Case Study: Detecting Revenue Leakage in a Subscription-Based Business; Optimizing Pricing Strategies Based on Market Trends - Dynamic Pricing with Business Intelligence, Price Elasticity Analysis Using Regression Models, Building Price Optimization Models Using Power BI & Python; Using Power BI for Pricing Optimization - Competitive Pricing Dashboards, Analyzing Price Sensitivity Across Market Segments; Building AI-Based Pricing Models with Python - Using Machine Learning (XGBoost, Decision Trees) to Predict Optimal Prices, Customer Willingness-to-Pay (WTP) Analysis, Case Study: Optimizing E-Commerce Pricing Strategies; Using Sentiment Analysis to Evaluate Pricing Perception - Extracting Customer Sentiments from Reviews & Social Media, Visualizing Pricing Impact on Customer Satisfaction with Power BI.

Employee Performance Analysis, Key Workforce KPIs for Performance Evaluation; Creating an HR Dashboard in Power BI - Employee Productivity Metrics, Performance Evaluation Heatmaps, Salary & Compensation Analysis; Predicting Employee Productivity Using Python - Regression Models for Productivity Prediction, Case Study: Forecasting Employee Performance Using Historical Data; Attrition Prediction & Workforce Planning - Understanding Employee Turnover & Attrition; Building an Employee Attrition Dashboard in Power BI - Using DAX for Attrition Analysis, Tracking Engagement & Retention Metrics; Using Python for Predicting Employee Attrition - Implementing Classification Models (Logistic Regression, Decision Trees, Random Forest, XGBoost) for Attrition Prediction, Identifying Factors Impacting Attrition (Work Culture, Salary, Work-Life Balance), Case Study: Employee Attrition Prediction for HR Strategy; Optimizing Workforce Planning with Predictive Analytics - Demand & Supply Forecasting for Hiring Needs, Using Time Series Forecasting for Workforce Planning, Scenario Planning Using Predictive Models.