Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods.

• Business Statistics
• MS Excel
• Mathematics Expertise

Module 1: Introduction to Business Analytics

• What is Business Analytics?

  • Definition and scope of Business Analytics.
  • The importance of Business Analytics in modern business decision-making.
  • The relationship between Business Analytics, Business Intelligence (BI), and Data Science.

• Types of Business Analytics

  • Descriptive Analytics: Understanding historical data and trends.
  • Diagnostic Analytics: Identifying the cause of past outcomes.
  • Predictive Analytics: Forecasting future outcomes using statistical models and machine learning.
  • Prescriptive Analytics: Recommending actions based on data insights.

• Key Concepts in Business Analytics

  • Data, Information, and Knowledge.
  • Business Performance Metrics and KPIs (Key Performance Indicators).
  • Data-Driven Decision Making.

Module 2: Data Collection and Data Sources

• Types of Data

  • Structured vs. Unstructured Data.
  • Primary vs. Secondary Data.
  • Internal vs. External Data.

• Data Sources

  • Internal Data Sources: CRM, ERP, financial databases.
  • External Data Sources: Market reports, social media, government databases.
  • Data Warehouses and Data Lakes.

• Data Collection Methods

  • Surveys, focus groups, interviews.
  • Web scraping, APIs, and real-time data collection.
  • Data extraction from various business systems.

Module 3: Data Preparation and Cleaning

• Data Preparation Process

  • Data extraction, transformation, and loading (ETL).
  • Handling missing data and outliers.
  • Data normalization, scaling, and encoding.

• Data Cleaning Techniques

  • Removing duplicates and handling inconsistencies.
  • Dealing with missing values: Imputation methods and removal strategies.
  • Data type conversions (date, categorical, numerical).

• Data Transformation

  • Aggregation and summarization of data.
  • Creating new features and data transformation (log transformation, binning).
  • Normalization and standardization of data.

Module 4: Exploratory Data Analysis (EDA)

• Purpose of EDA

  • Understanding data distributions, correlations, and relationships.
  • Visualizing data to identify patterns and trends.

• EDA Techniques

  • Descriptive statistics: Mean, median, mode, standard deviation, variance.
  • Visualizations: Histograms, scatter plots, box plots, and heatmaps.
  • Correlation analysis: Pearson and Spearman correlation.

• EDA Tools

  • Using Python libraries: Pandas, Matplotlib, Seaborn.
  • Using R libraries: dplyr, ggplot2.
  • Excel-based techniques for basic EDA.

Module 5: Predictive Analytics

• Introduction to Predictive Analytics

  • The role of predictive analytics in forecasting business outcomes.
  • The difference between supervised and unsupervised learning in predictive analytics.

• Key Predictive Modeling Techniques

  • Regression Analysis: Linear and logistic regression models.
  • Decision Trees: CART (Classification and Regression Trees).
  • Time Series Forecasting: ARIMA, Exponential Smoothing.
  • Machine Learning Algorithms: Random Forest, Support Vector Machines (SVM), Neural Networks.

• Model Evaluation and Validation

  • Cross-validation, train/test split.
  • Accuracy, precision, recall, F1-score, ROC curves, and AUC.
  • Model overfitting and underfitting.

Module 6: Prescriptive Analytics

• Overview of Prescriptive Analytics

  • What is prescriptive analytics and how does it support decision-making?
  • Optimization and simulation models in business analytics.

• Prescriptive Analytics Techniques

  • Linear Programming: Resource allocation and optimization problems.
  • Decision Support Systems (DSS) for solving complex business problems.
  • Simulation modeling: Monte Carlo simulation, System dynamics modeling.

• Applications of Prescriptive Analytics

  • Supply chain optimization.
  • Inventory management and logistics.
  • Pricing and revenue management.

Module 7: Business Analytics Tools and Technologies

• Spreadsheet Tools (Excel)

  • Advanced Excel functions: VLOOKUP, PivotTables, Solver, and Power Query.
  • Data visualization using Excel charts and graphs.
  • Excel for basic statistical analysis.

• BI Tools

  • Power BI: Data visualization, dashboards, and reporting.
  • Tableau: Interactive dashboards and data visualization.
  • QlikView: Data discovery and visualization.

• Programming Tools

  • Python: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn for data analysis and modeling.
  • R: Data manipulation and statistical analysis using packages like dplyr, ggplot2, and caret.

• Big Data Technologies

  • Apache Hadoop and Spark for processing large datasets.
  • Cloud-based tools: AWS, Azure, and Google Cloud for data storage and processing.

Module 8: Data Visualization

• Importance of Data Visualization in Business Analytics

  • The role of visualizations in communicating insights.
  • Choosing the right type of visualization for different data types and analysis.

• Visualization Techniques

  • Basic visualizations: Bar charts, line graphs, pie charts, histograms.
  • Advanced visualizations: Heatmaps, tree maps, scatter plots, waterfall charts.
  • Dashboards and reporting.

• Tools for Data Visualization

  • Power BI: Creating interactive reports and dashboards.
  • Tableau: Visual analytics and storytelling.
  • Google Data Studio: Free dashboarding and reporting tool.
  • Using Python libraries (Matplotlib, Seaborn) for custom visualizations.

Module 9: Data-Driven Decision Making

• Business Intelligence and Data-Driven Strategy
  • How to use Business Intelligence (BI) tools to make strategic decisions.
  • Implementing data-driven decision-making frameworks.
• Key Performance Indicators (KPIs) and Metrics
  • Defining KPIs to measure business performance.
  • Metrics for marketing, finance, sales, and operations.
• Creating Business Reports
  • Designing business reports and dashboards for executives.
  • Automating reports for real-time decision making.
  • Presentation techniques for stakeholders.

Module 10: Ethical Considerations in Business Analytics

• Data Privacy and Security

  • Understanding the importance of data security in analytics.
  • Ethical concerns around the use of personal data.

• Bias in Data and Models

  • Identifying and addressing biases in data collection and modeling.
  • Fairness and transparency in predictive analytics.

• Compliance and Regulations

  • GDPR and other data privacy regulations.
  • Compliance with industry-specific standards (e.g., HIPAA for healthcare).

Module 11: Real-World Business Analytics Applications

• Case Studies in Business Analytics

  • Case study 1: Customer segmentation and marketing analytics.
  • Case study 2: Financial forecasting and risk management.
  • Case study 3: Operations and supply chain optimization.

• Industry-Specific Applications

  • Retail: Demand forecasting, inventory optimization.
  • Finance: Fraud detection, investment analysis.
  • Healthcare: Patient outcomes, cost prediction.
  • Manufacturing: Predictive maintenance, production optimization.

40 Days (also available fast track course with short term duration)

• Flexible Schedules
• Live Online Training

• Training by highly experienced and certified professionals
• No slideshow (PPT) training, fully Hand-on training
• Interactive session with interview QA’s
• Real-time projects scenarios & Certification Help
• 24 X 7 Support