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Business Intelligence

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Business Intelligence

Introduction to Python & Basic Statistics

Course Overview

This 3-day comprehensive course introduces participants to Python programming and basic statistics. Designed for beginners, the course focuses on Python fundamentals, data manipulation, visualization, and statistical analysis techniques. By the end of this program, participants will gain hands-on experience with tools like NumPy, Pandas, Matplotlib, and Seaborn to analyze and interpret data effectively.

Course Objectives

By the end of this course, participants will:

Understand the basics of Python programming and its applications in data analysis.
Write and execute Python scripts for programming tasks.
Use Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization.
Perform descriptive statistics and interpret key measures.
Apply fundamental concepts of probability and hypothesis testing.
Analyze relationships between variables using correlation and regression techniques.

Learning Outcomes

Participants will be able to:

Set up a Python programming environment (Anaconda, Jupyter Notebook).
Write Python scripts using loops, functions, and data structures.
Manipulate datasets with Pandas and perform operations using NumPy.
Visualize data using Matplotlib and Seaborn.
Apply statistical concepts like central tendency, variance, and probability distributions.
Conduct hypothesis testing and explore correlation and regression in Python.

Who Should Attend

This course is ideal for:

Beginners with no prior programming experience.
Students and professionals interested in data analysis.
Individuals transitioning to roles involving data-driven decision-making.

Prerequisites

No prior programming experience required.
Basic understanding of high school-level mathematics.

System Requirements

Software: Python (Anaconda Distribution recommended), Jupyter Notebook, or any Python IDE.
Hardware: Laptop or computer with internet access.

Why Choose This Course

100% HRDC Claimable
Step-by-step practical introduction to Python programming and statistical analysis.
Hands-on labs and assignments with real-world datasets.
Tools and techniques to perform data manipulation, visualization, and statistical analysis.

Key Topics

Day 1: Introduction to Python Programming (7 Hours)

1. Introduction to Python

Overview of Python and its applications.
Setting up Python (Anaconda, Jupyter Notebook).
Understanding basic syntax, variables, and data types.

2. Control Structures

Conditional statements: if, else, elif.
Loops: for and while loops.
Using break and continue statements.

3. Functions and Modules

Defining and calling functions.
Function parameters and return values.
Importing and using modules (e.g., math, random).

4. Data Structures

Lists, tuples, dictionaries, and sets.
List comprehensions for efficient operations.
Basic operations and manipulations.

Day 2: Data Manipulation and Visualization (7 Hours)

1. Introduction to NumPy

Creating and manipulating arrays.
Indexing, slicing, and reshaping arrays.
Performing mathematical operations with NumPy.

2. Data Manipulation with Pandas

Introduction to Pandas DataFrames.
Reading and writing data: CSV and Excel files.
Data cleaning and preprocessing:
- Handling missing data.
- Removing duplicates.

3. Data Visualization with Matplotlib

Creating basic plots: line plots, bar charts, and scatter plots.
Customizing visualizations: adding labels, titles, and legends.
Saving and exporting visualizations.

4. Introduction to Seaborn

Overview of Seaborn for statistical data visualization.
Creating advanced plots:
- Pair plots, histograms, and box plots.

Day 3: Basic Statistics with Python (7 Hours)

1. Descriptive Statistics

Measures of central tendency: mean, median, and mode.
Measures of dispersion: range, variance, and standard deviation.
Using Pandas and NumPy for statistical calculations.

2. Probability and Distributions

Basic probability concepts.
Introduction to probability distributions:
- Normal distribution and binomial distribution.
Visualizing distributions with Seaborn.

3. Hypothesis Testing

Formulating null and alternative hypotheses.
Types of statistical tests:
- t-tests and chi-square tests.
Performing hypothesis testing using scipy.stats.

4. Correlation and Regression

Understanding correlation between variables.
Introduction to simple linear regression.
Visualizing and calculating correlations using Python.

Assignments and Projects

Day 1 Assignment: Write a Python script using loops and functions to manipulate a list of numbers.
Day 2 Project: Load, clean, and visualize a dataset using Pandas, Matplotlib, and Seaborn.
Day 3 Assignment: Conduct a statistical analysis on a sample dataset, including hypothesis testing and regression analysis.

Teaching Methodology

Instructor-Led Sessions: Live coding demonstrations and interactive lectures.
Hands-On Labs: Practical exercises to reinforce learning.
Group Discussions: Interactive Q&A sessions.
Assignments and Projects: Practical tasks to apply concepts to real-world data.

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Business Intelligence

Preview Course

Business Intelligence

Introduction to Business Intelligence and Data Analysis

Course Overview

This 2-day introductory course provides participants with a foundational understanding of Business Intelligence (BI) and Data Analysis. The course covers essential concepts, tools, and techniques to collect, process, analyze, and visualize data for data-driven decision-making. With hands-on experience using BI tools like Power BI or Tableau, participants will learn how to generate meaningful insights that drive business performance.

Course Objectives

By the end of this course, participants will:

Understand the key concepts and significance of Business Intelligence (BI) and Data Analysis.
Learn the BI process, including data collection, processing, analysis, and visualization.
Explore popular BI tools like Microsoft Power BI and Tableau.
Apply data visualization principles to present insights effectively.
Use BI techniques to make informed, data-driven decisions.

Learning Outcomes

Participants will be able to:

Define the role of Business Intelligence in improving business strategy and operations.
Work with structured and unstructured data from different sources.
Analyze datasets using BI tools and generate actionable insights.
Design effective interactive dashboards and visualizations.
Solve real-world business problems through data analysis techniques.

Who Should Attend

This course is ideal for:

Business Professionals interested in leveraging data for better decisions.
Aspiring Data Analysts and Business Analysts.
Managers and Decision-Makers seeking insights into BI tools and techniques.

Prerequisites

Basic understanding of business operations and terminology.
Familiarity with Microsoft Excel or similar spreadsheet tools.
No prior experience with BI tools is required.

Why Choose This Course

100% HRDC Claimable
Hands-on training using popular BI tools (e.g., Power BI, Tableau).
Practical examples and real-world scenarios for better learning outcomes.
Step-by-step guidance on data visualization and analysis techniques.

Lab Setup

Computers with internet access and pre-installed BI tools (Power BI, Tableau).
Sample datasets for hands-on exercises.
Projector for live demonstrations and group activities.

Key Topics

Day 1: Introduction to Business Intelligence

Module 1: Overview of Business Intelligence (1 Hour)

What is Business Intelligence (BI)?
Importance and evolution of BI in businesses.
Key components of the BI process.

Module 2: Data and Its Role in BI (1.5 Hours)

Understanding different types of data:
- Structured, unstructured, and semi-structured data.
Data sources and importance of data quality.
Basics of data warehousing.

Module 3: BI Tools and Technologies (1.5 Hours)

Overview of popular BI tools:
- Microsoft Power BI, Tableau, and QlikView.
Introduction to data integration tools.
Choosing the right BI tool for business needs.

Module 4: Data Analysis Fundamentals (1.5 Hours)

Basics of data analysis and its types:
- Descriptive, predictive, and prescriptive analysis.
Key statistical concepts for data analysis.

Module 5: Hands-On with BI Tools: Getting Started (1.5 Hours)

Introduction to a selected BI tool (e.g., Power BI).
Importing datasets and creating simple visualizations.
Exploring the BI tool interface and key functionalities.

Day 2: Data Analysis and Visualization

Module 6: Advanced Data Analysis Techniques (1.5 Hours)

Data modeling and creating data relationships.
Using pivot tables and charts for exploring data.
Identifying trends, patterns, and insights.

Module 7: Data Visualization Best Practices (1.5 Hours)

Principles of effective data visualization.
Choosing the right chart types for specific data.
Avoiding common visualization mistakes.

Module 8: Creating Interactive Dashboards (2 Hours)

Designing dashboards that tell a story.
Adding interactivity: filters, slicers, and drill-through elements.
Best practices for designing effective dashboards.

Module 9: Case Study and Group Activity (1.5 Hours)

Real-world business scenario analysis.
Group Activity: Create and present a BI report using hands-on learning.
Group discussion: Extracting insights and supporting decision-making.

Module 10: Course Review and Q&A (0.5 Hours)

Recap of key learnings.
Open Q&A session for addressing doubts.
Recommendations for further learning and skill development.

Teaching Methodology

Instructor-Led Sessions: Interactive presentations and live demonstrations.
Hands-On Labs: Practical exercises using BI tools.
Group Activities: Collaborative tasks for real-world scenarios.
Case Studies: Examples from industries like finance, marketing, and retail.

Target Industries

Finance and Banking
Marketing and Sales
Retail and E-commerce
Healthcare and Manufacturing
Education and Government Services

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Business Intelligence

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Business Intelligence

Data Visualization with Power BI

Course Overview

This 3-day hands-on course provides a comprehensive understanding of Microsoft Power BI for data visualization, reporting, and analysis. Participants will learn to create interactive dashboards, design advanced visuals, and build effective data models. With a focus on Power Query, DAX calculations, and visualization best practices, this course empowers attendees to transform raw data into impactful, decision-driven insights.

Course Objectives

By the end of this course, participants will:

Understand the Power BI ecosystem, including Power BI Desktop, Service, and Mobile.
Connect, clean, and transform data using Power Query Editor.
Design and manage data models with relationships and DAX formulas.
Create and customize interactive reports and dashboards with advanced visuals.
Utilize AI-powered visuals for pattern recognition and data analysis.
Deploy and share Power BI reports securely across organizations.
Implement row-level security and ensure data privacy compliance.

Learning Outcomes

Participants will be able to:

Connect to various data sources (Excel, SQL, Web) and shape data for reporting.
Design optimized data models with calculated measures using DAX.
Build interactive dashboards with advanced visuals such as waterfall charts, AI visuals, and custom visuals.
Automate data refresh schedules and monitor report usage.
Implement security protocols, including row-level security and role-based access.
Develop and present a real-world dashboard project using Power BI.

Who Should Attend

This course is ideal for:

Data Analysts
Business Analysts
IT Professionals
Decision Makers
Professionals interested in learning data visualization tools.

Prerequisites

Basic understanding of data analysis concepts.
Familiarity with tools like Microsoft Excel is beneficial but not required.

Why Choose This Course

100% HRDC Claimable
Hands-on practical learning with real-world case studies.
Step-by-step guidance on creating interactive dashboards and advanced visuals.
Practical exposure to DAX formulas, AI-powered tools, and deployment processes.

Lab Setup

Power BI Desktop installed on participant machines.
Sample datasets for practical exercises (provided during the course).
Internet access for publishing reports and connecting to online sources.

Key Topics

Day 1: Introduction and Data Preparation

Module 1: Introduction to Power BI (1 Hour)

Understanding Power BI components: Desktop, Service, and Mobile.
Power BI workflow and installation of Power BI Desktop.

Module 2: Data Connections and Transformation (3 Hours)

Connecting to various data sources: Excel, SQL Server, Web, and APIs.
Using Power Query Editor for:
- Cleaning and transforming data.
- Merging, appending, filtering, and pivoting data.
Best practices for data loading and refresh.

Module 3: Data Modeling and Relationships (3 Hours)

Creating relationships between tables.
Introduction to DAX (Data Analysis Expressions) for:
- Calculated columns and measures.
- Sorting and categorizing data.

Day 2: Report Building and Advanced Visualizations

Module 4: Creating Basic Visuals (2 Hours)

Overview of visualization types:
- Tables, charts, maps, and KPIs.
Customizing visuals with formatting, sorting, and filtering options.
Using slicers and filters for interactivity.

Module 5: Advanced Visualizations and Custom Visuals (3 Hours)

Advanced visuals:
- Waterfall charts, scatter charts, and gauge charts.
Using AI-powered visuals:
- Q&A Visuals, Key Influencers, and Decomposition Tree.
Adding custom visuals from the Power BI marketplace.
Best practices for choosing and designing visuals.

Module 6: Designing Interactive Reports and Dashboards (2 Hours)

Report layout and navigation.
Adding interactivity with bookmarks, buttons, and drill-through.
Building and managing dashboards in Power BI Service.

Day 3: Advanced Techniques and Deployment

Module 7: DAX for Data Analysis (2 Hours)

DAX basics: creating calculated columns and measures.
Using Time Intelligence functions: YTD, QTD, and MTD.
Advanced DAX:
- Iterators, dynamic filtering, and row context.

Module 8: Publishing and Sharing Reports (2 Hours)

Publishing reports to Power BI Service.
Scheduling data refresh and managing datasets.
Best practices for collaboration and report sharing.

Module 9: Security and Administration (1 Hour)

Implementing row-level security (RLS) for controlled access.
Data privacy, compliance, and role-based permissions.
Monitoring report usage and performance using the Admin Portal.

Module 10: Final Project and Presentation (2 Hours)

End-to-end project: Build an interactive dashboard from scratch.
Present the dashboard and receive peer feedback.
Course summary and Q&A.

Teaching Methodology

Interactive Lectures: Live demonstrations of Power BI tools and techniques.
Hands-On Labs: Practical exercises for real-world problem-solving.
Case Studies: Industry-specific scenarios for applying Power BI.
Group Discussions: Collaborative learning and knowledge sharing.
Final Project: Develop and present a complete Power BI dashboard.

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Business Intelligence

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Business Intelligence

Data Scraping and Data Mining with Python

Course Overview

This comprehensive 3-day course introduces participants to data scraping and data mining using Python. The course focuses on collecting, processing, and analyzing data from online sources using tools like Beautiful Soup, Scrapy, and Selenium. It also dives into data mining techniques such as clustering, classification, and association rules. By the end of the course, participants will be equipped to extract meaningful insights for business, research, and technology applications.

Course Objectives

By the end of this course, participants will:

Understand the fundamentals of data scraping and data mining.
Use Python libraries like Beautiful Soup, Scrapy, and Selenium for web scraping.
Process and clean data for analysis using Pandas and NumPy.
Apply data mining techniques including clustering, classification, and association rules.
Automate data extraction and analysis workflows with Python.
Implement practical, real-world data scraping and mining applications.

Learning Outcomes

Participants will be able to:

Collect data from websites using web scraping tools and frameworks.
Handle dynamic web content with Selenium.
Perform data cleaning, preprocessing, and transformation for analysis.
Apply machine learning techniques for clustering and classification using Scikit-learn.
Use the Apriori algorithm for market basket analysis and pattern mining.
Develop a complete data scraping and mining workflow to derive actionable insights.

Who Should Attend

This course is ideal for:

Data Analysts and Data Scientists
Software Developers
Researchers requiring automated data extraction
Professionals interested in automating data collection and analysis

Prerequisites

Basic knowledge of Python programming.
Familiarity with HTML and web page structures.
Basic understanding of data analysis concepts.

System Requirements

Python environment: Anaconda or similar with Jupyter Notebook.
Installed Python libraries: Beautiful Soup, Scrapy, Selenium, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.
Internet access for real-time data scraping exercises.

Why Choose This Course

100% HRDC Claimable
Hands-on training with real-world web scraping and data mining tools.
Comprehensive practice with tools like Scrapy, Selenium, and Beautiful Soup.
Practical project on end-to-end data scraping and data mining workflows.

Key Topics

Day 1: Introduction to Data Scraping

Module 1: Introduction to Data Scraping

Overview of data scraping and its applications.
Ethical considerations and legal implications.

Module 2: Web Scraping Basics

Understanding HTML, DOM, and web page structures.
Tools and libraries for web scraping in Python.

Module 3: Beautiful Soup for Web Scraping

Introduction to Beautiful Soup.
Navigating and parsing HTML:
- Tags, attributes, and text extraction.
Hands-On Lab: Extracting structured data from web pages.

Day 2: Advanced Web Scraping Techniques

Module 4: Scrapy for Large-Scale Scraping

Introduction to the Scrapy framework.
Setting up a Scrapy project.
Working with Spiders, pipelines, and items.
Handling pagination and complex data extraction.
Hands-On Lab: Scraping large datasets using Scrapy.

Module 5: Selenium for Dynamic Content

Introduction to Selenium.
Automating browser actions for JavaScript-rendered content.
Interacting with dynamic elements: forms, buttons, and dropdowns.
Hands-On Lab: Scraping dynamic web content.

Module 6: Data Cleaning and Preparation

Data cleaning techniques:
- Handling missing data and duplicates.
Data transformation and preprocessing with Pandas.
Hands-On Lab: Preparing scraped data for analysis.

Day 3: Introduction to Data Mining

Module 7: Introduction to Data Mining

Overview of data mining concepts and processes.
Common data mining tasks and their real-world applications.

Module 8: Clustering and Classification

Clustering techniques: K-means and hierarchical clustering.
Classification methods: Decision Trees and Logistic Regression.
Implementation using Scikit-learn.
Hands-On Lab: Applying clustering and classification techniques.

Module 9: Association Rules and Pattern Mining

Market Basket Analysis and the Apriori algorithm.
Implementation in Python.
Hands-On Lab: Extracting association rules from datasets.

Module 10: Project and Case Study

Real-world project: End-to-end data scraping and mining workflow.
Applying learned concepts to solve practical problems.
Presenting findings and insights.

Teaching Methodology

Interactive Lectures: Step-by-step demonstrations of tools and concepts.
Hands-On Labs: Practical coding exercises with real-world examples.
Case Studies: Solving real-world problems with data scraping and mining techniques.
Assessments: Quizzes, coding challenges, and a final project presentation.

Target Industries

Technology
Finance
Marketing and E-Commerce
Research and Academia
Data-Driven Enterprises

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Business Intelligence

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Business Intelligence

Analyzing Data with Microsoft Power BI

Course Overview

This comprehensive 2-day course provides participants with the essential skills to model, visualize, and analyze data using Microsoft Power BI. The course emphasizes connecting to data sources, transforming and optimizing data, and designing interactive reports and dashboards for actionable insights. Participants will also learn best practices for managing datasets, implementing security standards, and deploying reports for effective distribution.

Course Objectives

By the end of this course, participants will:

Understand the roles of a data analyst and Power BI capabilities.
Connect and retrieve data from various relational and non-relational data sources.
Clean, transform, and optimize data for modeling.
Develop robust data models with relationships, hierarchies, and DAX calculations.
Design interactive reports and dashboards to visualize insights effectively.
Explore advanced AI visuals and analytics tools for pattern identification.
Manage datasets, parameters, and refresh schedules in Power BI.

Learning Outcomes

Participants will be able to:

Retrieve and connect to diverse data sources for analysis.
Transform raw data into structured formats using Power Query.
Create optimized data models for performance and scalability.
Use DAX (Data Analysis Expressions) to perform advanced calculations.
Design and enhance interactive reports with actionable KPIs.
Develop and optimize compelling dashboards for real-time insights.
Implement AI-powered visuals for trend analysis and pattern recognition.
Manage datasets, refresh options, and project deployments within Power BI.

Who Should Attend

This course is ideal for:

Data Analysts
Business Analysts
Finance Professionals
IT Professionals
Decision Makers requiring insights from data.

Prerequisites

Understanding of core data concepts.
Familiarity with relational and non-relational data in the cloud.
Basic knowledge of data analysis and visualization concepts.

Why Choose This Course

100% HRDC Claimable
Hands-on training in Microsoft Power BI with real-world examples.
Practical skills to build and deploy interactive dashboards and reports.
Focus on advanced AI visuals and analytics techniques for deeper insights.

Key Topics

Module 1: Get Started with Microsoft Data Analytics

Roles of data analysts.
Overview of the Power BI portfolio.
Getting started with Power BI tools and interfaces.

Module 2: Prepare Data in Power BI

Identifying and connecting to various data sources.
Optimizing data performance for analysis.
Resolving common data errors and ensuring data integrity.

Module 3: Clean, Transform, and Load Data in Power BI

Data profiling techniques for understanding data quality.
Cleaning and shaping data using Power Query.
Enhancing data structure for analysis.

Module 4: Design a Data Model in Power BI

Introduction to data modeling concepts.
Creating relationships between tables.
Managing dimensions and hierarchies for scalability.

Module 5: Create Model Calculations using DAX in Power BI

Introduction to Data Analysis Expressions (DAX).
Building calculated fields and measures.
Performing advanced DAX calculations.
Real-time dashboards with DAX integration.

Module 6: Optimize Model Performance in Power BI

Techniques to optimize data models for enterprise-level performance.
Optimizing DirectQuery models for efficiency.

Module 7: Create Reports in Power BI

Fundamentals of report design and layout.
Adding interactive elements for usability.
Enhancing reports with visuals, slicers, and charts.

Module 8: Create Dashboards in Power BI

Designing and customizing dashboards for insights.
Implementing real-time dashboards with dynamic KPIs.
Enhancing dashboards for accessibility and usability.

Module 9: Identify Patterns and Trends in Power BI

Exploring advanced analytics features.
Using AI-powered visuals to identify patterns and trends.
Gaining insights through automated data analysis tools.

Module 10: Manage Files and Datasets in Power BI

Managing datasets and parameters.
Setting up dataset refresh options.
Best practices for managing file sharing and deployment.

Teaching Methodology

Interactive Lectures: Comprehensive explanations with live demos.
Hands-On Labs: Step-by-step practical exercises using Power BI.
Real-World Examples: Data analysis and dashboard development scenarios.
Q&A and Discussions: Address participant queries and challenges.

Target Outcomes

By the end of this workshop, participants will:

Master data retrieval, transformation, and modeling using Power BI.
Build interactive reports and dashboards to deliver actionable insights.
Implement advanced DAX and AI tools for in-depth analysis.
Manage and deploy Power BI files and datasets for ongoing projects.

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Business Intelligence

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Business Intelligence

Advanced Excel for Business Intelligence

Course Overview

This comprehensive 2-day course is tailored to equip participants with advanced Microsoft Excel skills for Business Intelligence (BI) applications. The course focuses on advanced data analysis, data transformation, and dynamic visualization using Excel tools like Power Query, Power Pivot, and advanced charting techniques. Participants will also gain practical knowledge in automating workflows using VBA macros and integrating Excel with external BI tools, enabling efficient data-driven decision-making.

Course Objectives

By the end of this course, participants will:

Master advanced Excel functions for complex data analysis.
Use Power Query to clean, transform, and combine data from multiple sources.
Build and manage data models using Power Pivot and DAX calculations.
Design interactive dashboards using advanced charts and visualization tools.
Automate repetitive tasks and reporting with VBA macros.
Integrate Excel with external BI tools like Power BI and SQL databases.

Learning Outcomes

Participants will be able to:

Perform complex data lookups and advanced calculations using INDEX, MATCH, XLOOKUP, and DAX.
Clean and transform datasets efficiently with Power Query.
Build advanced data models and KPIs using Power Pivot.
Design visually appealing and interactive dynamic dashboards.
Automate tasks with Macros and VBA scripting.
Connect Excel to other BI tools and external data sources for broader analysis.

Who Should Attend

This course is ideal for:

Business Analysts
Data Analysts
Finance Professionals
Operations Managers
Anyone seeking to advance their Excel skills for BI applications.

Prerequisites

Basic to intermediate knowledge of Microsoft Excel.
Familiarity with basic data analysis concepts.

System Requirements

Latest version of Excel installed (with Power Query and Power Pivot enabled).
Sample datasets for hands-on practice (provided during the course).

Why Choose This Course

100% HRDC Claimable
Hands-on training with Excel's advanced BI tools like Power Query and Power Pivot.
Practical, real-world examples for building dynamic dashboards and automating tasks.
Focused on data-driven decision-making with interactive visualizations.

Key Topics

Day 1: Advanced Data Analysis and Modeling

Module 1: Advanced Functions and Formulas (2 Hours)

Overview of Excel's advanced functions.
Using INDEX, MATCH, and XLOOKUP for complex lookups.
Conditional functions: SUMIFS, COUNTIFS, and IFERROR.
Introduction to array formulas and dynamic arrays.
Formula auditing and nested functions.

Module 2: Data Cleaning and Transformation with Power Query (2 Hours)

Introduction to Power Query and its BI capabilities.
Importing data from multiple sources (Excel, CSV, databases).
Data cleaning techniques:
- Removing duplicates.
- Splitting columns.
- Handling missing data.
Transforming data:
- Merging and appending tables.
- Creating custom functions and calculated columns.

Module 3: Data Modeling with Power Pivot (3 Hours)

Overview of Power Pivot and its role in BI.
Creating relationships between tables and managing data models.
Introduction to DAX (Data Analysis Expressions):
- Calculated columns and measures.
Implementing KPIs and creating data hierarchies.
Hands-On Lab: Building and analyzing a data model with Power Pivot.

Day 2: Advanced Visualization, Automation, and Integration

Module 4: Advanced Data Visualization Techniques (3 Hours)

Creating advanced charts:
- Waterfall charts, Pareto charts, and Gantt charts.
Building dynamic dashboards with:
- Slicers, timelines, and interactive elements.
Applying conditional formatting to enhance visualization.
Best practices for effective dashboard design.
Hands-On Lab: Designing an interactive Excel dashboard.

Module 5: Automation with Macros and VBA (2 Hours)

Introduction to Macros and VBA for Excel automation.
Recording, editing, and running Macros.
Writing simple VBA scripts for automating repetitive tasks.
Error handling and debugging VBA code.
Automating report generation and data manipulation workflows.

Module 6: Integrating Excel with BI Tools and Data Sources (2 Hours)

Connecting Excel with external data sources:
- SQL databases, CSV files, and APIs.
Introduction to Excel-Power BI integration.
Importing and exporting data between Excel and Power BI.
Hands-On Lab: Solving a real-world BI problem using Excel integration.

Assessment and Q&A (30 Minutes)

Hands-On Project: Building a mini-dashboard using skills learned during the course.
Q&A session to clarify concepts and address specific participant queries.

Teaching Methodology

Instructor-Led Training: Step-by-step guidance with live demonstrations.
Hands-On Exercises: Practical application of Excel tools and techniques.
Case Studies: Real-world examples and BI scenarios.
Interactive Discussions: Q&A and collaborative learning.

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Business Intelligence

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Business Intelligence

Advanced Data Analysis with Pivot Tables and Dynamic Dashboards

Course Overview

This 2-day hands-on course equips participants with advanced skills in Microsoft Excel for data analysis, reporting, and dashboard creation. By leveraging tools like Pivot Tables, Advanced Charting, and Dynamic Dashboards, attendees will gain practical techniques to analyze large datasets, build interactive reports, and present insights for decision-making.

This course emphasizes creating interactive, user-friendly dashboards to visualize KPIs effectively, helping businesses extract meaningful insights from their data.

Course Objectives

By the end of this course, participants will:

Understand dashboard design principles and common pitfalls.
Create and manage Pivot Tables for advanced data summarization.
Apply grouping, sorting, and filtering techniques for data segmentation.
Perform complex calculations within Pivot Tables.
Use Excel charts, sparklines, and conditional formatting for effective data visualization.
Master advanced charting techniques like step charts, waterfall charts, and performance charts.
Utilize Excel formulas and functions to create dynamic, interactive dashboards.
Apply form controls to build fully interactive Excel dashboards.

Learning Outcomes

Participants will be able to:

Build advanced Pivot Tables for data analysis and dynamic reporting.
Create interactive charts to visualize data trends and KPIs.
Use conditional formatting and sparklines to highlight insights.
Leverage advanced Excel functions to enhance calculations and automation.
Develop interactive dashboards with dynamic charts and form controls.

Who Should Attend

This course is ideal for:

Business Analysts
Data Analysts
Finance Professionals
Sales Professionals
Managers and Decision Makers
Any Excel user who wants to take their data analysis and reporting skills to the next level.

Prerequisites

Basic to intermediate knowledge of Microsoft Excel.
Ability to build simple workbooks, charts, and use formulas.

Why Choose This Course

100% HRDC Claimable
Step-by-step guidance to create interactive dashboards and advanced data reports.
Hands-on exercises to master Pivot Tables and dynamic Excel techniques.
Real-world examples for effective KPI visualization and business reporting.

Key Topics

Module 1: Dashboard Design Principles

What is a dashboard?
Purpose and benefits of dashboards.
Understanding dashboard design principles.
Common mistakes when building dashboards.

Module 2: Creating a Pivot Table

Preparing data for Pivot Table reporting.
How to create a basic Pivot Table.
Using the Recommended Pivot Table feature.
Adding interactivity with Slicers.
Managing data source changes and updates.
Saving time with new Pivot Table tools.

Module 3: Grouping, Sorting, and Filtering Pivot Data

Automatically grouping dates.
Using the Pivot Table Fields List.
Sorting and filtering data in Pivot Tables.
Using the Filters Area for targeted insights.

Module 4: Performing Calculations in Pivot Tables

Introducing calculated fields and calculated items.
Creating calculated fields for custom metrics.
Managing rules and troubleshooting calculation issues.

Module 5: Data Visualization – Picking the Right Display

The anatomy of an Excel chart.
Displaying trends with charts.
Using a secondary axis for multi-series data.
Combining two chart types for better visualization.
Adding icons and images to dashboards.

Module 6: Using Sparklines and Conditional Formatting

Creating sparklines (line, column, win/loss).
Formatting sparklines for visual emphasis.
Applying conditional formatting:
- Data bars, color scales, and icon sets.
Enhancing reports with symbols and visual indicators.

Module 7: Advanced Charting Techniques

Step charts for showing stepwise trends.
Actual vs. Budget (Target) charts – Floating Markers.
Band charts to visualize performance against target ranges.
Conditional colors in column charts.
Frequency distribution charts.
Waterfall charts for financial analysis.

Module 8: Named Ranges and Excel Tables

Naming cells and ranges for efficiency.
Creating and managing Excel Tables.
Using structured references in formulas.
Refreshing tables with new data.

Module 9: Excel Advanced Formulas and Functions

Combining functions for advanced calculations:
- IF, Nested IFs, IFS, AND, OR, and IFERROR.
Aggregating data: SUMIF, COUNTIF, AVERAGEIF.
Using VLOOKUP, MATCH, and INDEX for data lookups.
Dynamic formulas with INDEX and MATCH combinations.
Using TEXT, CONCATENATE, and string functions for reporting.

Module 10: Building Interactive Charts and Dashboard Controls

Adding interactivity with Form Controls:
- Buttons, Combo Box, Check Box, Spin Button, List Box, Option Button, Scroll Bar.
Creating dynamic named ranges with OFFSET, COUNTA, and INDEX.
Using the INDIRECT function for dynamic data manipulation.
Setting up dynamic data validation lists for user inputs.

Teaching Methodology

Interactive Lectures: Step-by-step explanations of concepts and tools.
Hands-On Labs: Practical exercises to build Pivot Tables, charts, and dashboards.
Real-World Examples: Using sales and financial datasets to create actionable insights.
Group Discussions and Q&A: Interactive sessions to clarify doubts and reinforce learning.

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