1. Effective Big Data Management for business analytic and decision

Provide operational-level employees with a foundational understanding of business analytics and intelligence concepts, tools, and techniques. Participants will learn how to effectively manage and analyze data to derive insights that drive business decisions and improvements.

Software requirements

• Python 3

Introduction to Business Analytics, Business Intelligence and Big Data

• Basic understanding of business analytics, business intelligence and big data
• 6Vs in big data
  • Volume
  • Velocity
  • Variety
  • Veracity
  • Value
  • Variability
• Overview of data sources, types, and formats of Data
  • Database
  • File system
  • External system

Data Management Fundamentals

• Fundamentals of data management
• Basic of Data pipeline
  • Data collection
  • Data cleansing
  • Data transformation
  • Data storage
• Fundamentals of data collection, storage, and organization
• Data cleansing and advanced data management techniques
• Overview of data integration and transformation processes
• Tools for Data management and Data pipeline
  • Apache Airflow
  • Apache NiFi
  • dbt (data build tool)
  • Apache Doris
• Workshop
  • Manage large data
  • Cleansing data with tools and programming
  • Create a data pipeline with Apache Airflow
  • Programming with Python

Data Analysis Techniques

• Techniques for data exploration and analysis (Exploratory Data Analysis)
• Statistical Analysis for data analysis
  • Descriptive statistics
  • Inferential statistics
• Practical application using data analysis tools and software
  • Microsoft Power BI
  • Tableau
• Workshop
  • Create a data pipeline with Apache Airflow
  • Programming with Python

Business Intelligence and Visualization

• Importance of business intelligence in decision support
• Concepts of data visualization and best practices
• Practical exercises in creating dashboards and interactive reports
• Tools for Data Visualization
  • Microsoft Power BI
  • Tableau
  • Apache Superset
  • Google Looker
  • Pentaho
• Workshop
  • Visualization from data to get more insight
  • Programming with Python

Final project

• Application of business analytics and intelligence concepts
• Real-world business problem solving
• Presentation of findings and strategic recommendations

2. Business Analytics and Intelligence

Course Objectives:

• Develop proficiency in data analysis techniques and tools.
• Understand the role of data in driving business decisions and strategy.
• Learn how to interpret and communicate insights derived from data analysis.
• Gain practical experience through hands-on exercises and case studies.
• Apply business analytics principles to identify and capitalize on business opportunities.

Data Analysis Fundamentals

• Exploratory Data Analysis: Techniques for Understanding Data Patterns and Trends
• Statistical Analysis Basics
  • Descriptive Statistics
  • Inferential Statistics
• Visualization Techniques: Creating Compelling Data Visualizations
  • Histograms
  • Scatter Plots
  • Box Plots
  • Heat Maps
  • Line Charts
• Correlation Analysis: Identifying Relationships Between Variables
• Multivariate Analysis: Analyzing Multiple Variables Simultaneously
• Handling Missing Data and Outliers
• Outlier Detection and Treatment Techniques
• Time Series Analysis: Analyzing Temporal Data Patterns
• Workshop: Analyzing Real-World Data Sets
  • Read data source
  • Clean and preprocess data
  • Perform exploratory data analysis
  • Generate insights and recommendations
  • Present findings in a clear and concise manner

Advanced Analytics Techniques

• Predictive Analytics: Forecasting Future Trends and Outcomes
• Machine Learning Essentials: Introduction to Algorithms and Models
• Practical Applications of Advanced Analytics in Business
• Workshop
  • Building Predictive Models
  • Evaluating Model Performance
  • Interpreting Model Results
  • Applying Predictive Analytics to Real-World Business Problems

Strategic Data Interpretation

• Understanding Stakeholder Information Needs
• Data Storytelling Techniques for Impactful Communication
• Techniques in strategic data interpretation
  • Trend Analysis
  • Comparative Analysis
  • Segmentation Analysis
  • Correlation and Causation Analysis
  • Scenario and Sensitivity Analysis
  • Predictive Analytics
  • Cost-Benefit Analysis
  • SWOT Analysis (Strengths, Weaknesses, Opportunities, Threats)
  • Competitive Analysis
• Case Studies: Analyzing Effective Reports in Decision-Making
• Workshop
  • Creating Data-Driven Presentations
  • Communicating Insights to Non-Technical Audiences

Business Intelligence and Reporting

• Introduction to Business Intelligence Tools and Platforms
  • Microsoft Power BI
  • Tableau
  • Apache Superset
  • Google Looker
  • Pentaho
• Creating Interactive Dashboards and Reports for Data Visualization
• Case Studies: Using Business Intelligence for Strategic Decision-Making
• Workshop
  • Building Interactive Dashboards
  • Creating Custom Reports
  • Sharing Insights with Stakeholders

Applied Business Analytics and Final project

• Integrating Analytics into Business Strategy and Operations
• Identifying and Capitalizing on Business Opportunities through Data Analysis
• Final Project: Applying Business Analytics Skills to Solve a Real Business Problem