Data Analysis and Business Intelligence
Data Analysis and Business Intelligence
$5500.00
📊 Data Analysis and Business Intelligence: 5-Day Professional Training Course
Course Duration: 5 Days (40 Hours Total)
Level: Beginner to Intermediate
Format: Instructor-Led with Hands-On Labs
🎯 Course Overview
This intensive Data Analysis and Business Intelligence training equips participants with essential skills to transform raw data into actionable insights. Learn industry-standard BI tools, statistical analysis techniques, data visualization best practices, and strategic decision-making frameworks used by leading organizations worldwide.
Who Should Attend:
Business Analysts & Data Analysts
Marketing & Sales Professionals
Operations Managers
Financial Analysts
IT Professionals transitioning to BI roles
Entrepreneurs & Business Owners
Prerequisites:
Basic computer literacy
Familiarity with Microsoft Excel
Understanding of basic business concepts
📅 DAY 1: Foundations of Data Analysis & Business Intelligence
Learning Objectives:
Understand the data analysis lifecycle and BI ecosystem
Distinguish between descriptive, diagnostic, predictive, and prescriptive analytics
Master data types, structures, and quality assessment
Set up your analytics environment
Morning Session (9:00 AM - 12:30 PM)
Module 1.1: Introduction to Data Analysis
What is Data Analysis? Definition and business value
Data-driven decision making vs. intuition-based approaches
The analytics maturity model for organizations
Career paths in data analytics and business intelligence
Activity: Case study analysis - Data-driven business transformation
Module 1.2: Business Intelligence Fundamentals
BI architecture and components (ETL, data warehouses, OLAP)
Difference between databases, data warehouses, and data lakes
Key BI concepts: dimensions, measures, KPIs, and metrics
Modern BI stack overview (tools and technologies)
Afternoon Session (1:30 PM - 5:00 PM)
Module 1.3: Data Types and Structures
Structured vs. semi-structured vs. unstructured data
Quantitative and qualitative data analysis
Data formats: CSV, JSON, XML, databases
Understanding metadata and data dictionaries
Lab Exercise: Exploring different data formats and structures
Module 1.4: Data Quality Management
The six dimensions of data quality (accuracy, completeness, consistency, timeliness, validity, uniqueness)
Common data quality issues and their business impact
Data profiling and quality assessment techniques
Introduction to data cleaning concepts
Hands-On: Data quality assessment using Excel
Module 1.5: Analytics Environment Setup
Installing and configuring essential tools
Introduction to Excel Power Query and Power Pivot
Overview of SQL basics for data extraction
Lab: Setting up your analytics workspace
Day 1 Deliverables:
Data quality assessment checklist
Personal analytics toolkit setup
📅 DAY 2: Data Collection, Preparation & SQL Fundamentals
Learning Objectives:
Master data collection methods and sources
Perform comprehensive data cleaning and transformation
Write SQL queries for data extraction and analysis
Apply data wrangling techniques for analysis-ready datasets
Morning Session (9:00 AM - 12:30 PM)
Module 2.1: Data Collection Strategies
Primary vs. secondary data sources
Internal data sources (CRM, ERP, databases, logs)
External data sources (APIs, web scraping, public datasets)
Survey design and sampling techniques
Data acquisition best practices and compliance (GDPR, privacy)
Activity: Identifying relevant data sources for business scenarios
Module 2.2: SQL for Business Intelligence
Database fundamentals and relational data models
SQL SELECT statements: filtering, sorting, and aggregating
JOIN operations (INNER, LEFT, RIGHT, FULL)
Subqueries and nested queries
GROUP BY and aggregate functions (SUM, AVG, COUNT, MIN, MAX)
Hands-On Lab: Writing SQL queries for business reporting (2 hours)
Afternoon Session (1:30 PM - 5:00 PM)
Module 2.3: Data Cleaning and Preparation
Identifying and handling missing values (deletion, imputation methods)
Detecting and treating outliers (IQR, Z-score methods)
Removing duplicates and standardizing formats
Data validation and error correction
Lab Exercise: Cleaning a messy sales dataset using Excel and Power Query
Module 2.4: Data Transformation Techniques
Normalization and standardization
Feature engineering basics
Data type conversions and formatting
Pivot tables and unpivoting data
Creating calculated fields and measures
Date and time data manipulation
Hands-On: Transforming transactional data for analysis
Module 2.5: ETL Processes
Extract, Transform, Load (ETL) workflow
Introduction to ETL tools and automation
Data pipeline concepts
Demo: Building a simple ETL process
Day 2 Deliverables:
SQL query library for common business questions
Cleaned and transformed dataset ready for analysis
📅 DAY 3: Statistical Analysis & Exploratory Data Analysis (EDA)
Learning Objectives:
Apply statistical methods to business problems
Conduct comprehensive exploratory data analysis
Identify patterns, trends, and correlations in data
Use descriptive and inferential statistics for insights
Morning Session (9:00 AM - 12:30 PM)
Module 3.1: Descriptive Statistics
Measures of central tendency (mean, median, mode)
Measures of dispersion (range, variance, standard deviation)
Percentiles and quartiles (box plots)
Distribution shapes (skewness and kurtosis)
Lab: Calculating and interpreting descriptive statistics
Module 3.2: Exploratory Data Analysis (EDA)
EDA methodology and best practices
Univariate analysis techniques
Bivariate and multivariate analysis
Identifying data distributions and patterns
Hypothesis generation through EDA
Hands-On: Comprehensive EDA on a business dataset (sales, marketing, or operations)
Module 3.3: Correlation and Causation
Correlation analysis (Pearson, Spearman)
Understanding correlation matrices
Correlation vs. causation: critical thinking
Identifying spurious correlations
Activity: Correlation analysis exercise with interpretation
Afternoon Session (1:30 PM - 5:00 PM)
Module 3.4: Inferential Statistics for Business
Sampling distributions and the Central Limit Theorem
Confidence intervals and margin of error
Hypothesis testing fundamentals (null and alternative hypotheses)
T-tests for comparing means (one-sample, two-sample, paired)
Chi-square tests for categorical data
P-values and statistical significance
Lab Exercise: A/B testing analysis for marketing campaigns
Module 3.5: Trend Analysis and Forecasting Basics
Time series data characteristics
Trend, seasonality, and cyclical patterns
Moving averages and smoothing techniques
Introduction to forecasting methods
Hands-On: Analyzing sales trends over time
Module 3.6: Segmentation and Classification
Customer segmentation techniques
RFM analysis (Recency, Frequency, Monetary)
Basic classification concepts
Case Study: Market segmentation project
Day 3 Deliverables:
Comprehensive EDA report with statistical insights
Hypothesis testing results and recommendations
📅 DAY 4: Data Visualization & Dashboard Design
Learning Objectives:
Master data visualization principles and best practices
Create effective charts and graphs for different data types
Design interactive business dashboards
Tell compelling stories with data
Morning Session (9:00 AM - 12:30 PM)
Module 4.1: Data Visualization Principles
The science of visual perception and cognition
Choosing the right chart type for your data and message
Color theory and effective use of color in visualization
Design principles: contrast, alignment, repetition, proximity
Common visualization mistakes and how to avoid them
Accessibility considerations in data visualization
Activity: Chart type selection workshop
Module 4.2: Chart Types and Use Cases
Comparison charts: Bar charts, column charts, bullet charts
Composition charts: Pie charts, stacked bars, treemaps, waterfall charts
Distribution charts: Histograms, box plots, violin plots
Relationship charts: Scatter plots, bubble charts, heat maps
Trend charts: Line charts, area charts, slope graphs
Geospatial visualizations: Maps and choropleth maps
Lab: Creating a visualization library with examples
Afternoon Session (1:30 PM - 5:00 PM)
Module 4.3: Business Intelligence Tools - Power BI
Power BI Desktop interface and components
Connecting to data sources and data modeling
Creating relationships and DAX basics
Building visualizations and reports
Filters, slicers, and interactive elements
Hands-On Lab: Building your first Power BI report (2 hours)
Module 4.4: Dashboard Design Best Practices
Dashboard vs. report: understanding the difference
Information hierarchy and layout design
KPI selection and scorecard design
Real-time vs. static dashboards
Mobile-responsive dashboard considerations
Demo: Analyzing effective and ineffective dashboards
Module 4.5: Data Storytelling
The narrative arc in data presentations
Context, insight, and action framework
Building compelling executive summaries
Tailoring visualizations to different audiences
Workshop: Create a data story presentation
Day 4 Deliverables:
Interactive business dashboard in Power BI
Data storytelling presentation on business insights
📅 DAY 5: Advanced BI Concepts & Capstone Project
Learning Objectives:
Implement predictive analytics basics
Design KPI frameworks and balanced scorecards
Understand self-service BI and data governance
Complete an end-to-end BI project
Morning Session (9:00 AM - 12:30 PM)
Module 5.1: Key Performance Indicators (KPIs)
SMART criteria for KPI development
Leading vs. lagging indicators
Creating balanced scorecards
KPI trees and driver-based planning
Industry-specific KPIs (sales, marketing, finance, operations)
Workshop: Developing a KPI framework for your organization
Module 5.2: Predictive Analytics Introduction
Predictive vs. descriptive analytics
Regression analysis fundamentals (linear regression)
Classification basics (decision trees concept)
Customer churn prediction concepts
Sales forecasting techniques
Demo: Simple predictive model walkthrough
Module 5.3: Data Governance and Ethics
Data governance frameworks
Data privacy and security considerations
Ethical considerations in analytics (bias, fairness, transparency)
Documentation and metadata management
Self-service BI governance
Afternoon Session (1:30 PM - 5:00 PM)
Module 5.4: Capstone Project
Participants work on a comprehensive business intelligence project that includes:
Project Requirements:
Define business problem and objectives
Identify and collect relevant data sources
Clean and prepare data for analysis
Perform exploratory data analysis
Apply statistical analysis techniques
Create visualizations and an interactive dashboard
Develop insights and recommendations
Present findings to the class
Project Options:
Sales performance analysis and forecasting
Customer behavior analysis and segmentation
Marketing campaign effectiveness
Operational efficiency optimization
Financial performance dashboard
Deliverables:
Comprehensive analytical report
Interactive dashboard
10-minute presentation with Q&A
Module 5.5: Next Steps and Continuous Learning
Advanced BI topics to explore (machine learning, big data)
Industry certifications (Microsoft, Tableau, Google Analytics)
Building your data portfolio
Staying current with BI trends
Community resources and networking
Day 5 Deliverables:
Complete capstone project with presentation
Personal learning roadmap
Course completion certificate
🛠️ Tools and Technologies Covered
Primary Tools:
Microsoft Excel (Power Query, Power Pivot, Advanced Functions)
Power BI Desktop (Data modeling, DAX, Visualizations)
SQL (MySQL/PostgreSQL for queries and data extraction)
Additional Tools Introduced:
Tableau (overview and comparison)
Google Data Studio/Looker Studio
Python libraries (pandas, matplotlib) - introduction only
R Studio (basic statistical analysis)
📚 Course Materials Included
Comprehensive course manual (250+ pages)
Hands-on lab workbooks with datasets
SQL query reference guide
Data visualization best practices guide
KPI development templates
Dashboard design templates
Certificate of completion
30-day post-course support and Q&A access
🎓 Learning Outcomes
By the end of this course, participants will be able to:
✅ Design and implement complete business intelligence solutions
✅ Extract, clean, and transform data from multiple sources
✅ Perform statistical analysis to uncover business insights
✅ Create compelling data visualizations and interactive dashboards
✅ Develop KPI frameworks aligned with business objectives
✅ Present data-driven recommendations to stakeholders
✅ Apply best practices in data governance and quality management
✅ Use industry-standard BI tools (Power BI, SQL, Excel)
