Data Visualization

Course Objective:

• Learn Data Visualization from beginner to professional level.
• Build a strong foundation in data analysis, statistics and visualization concepts.
• Learn Excel, SQL, Python and modern data visualization tools.
• Master Power BI and Tableau for professional dashboards and reporting.
• Learn data cleaning, transformation and exploratory data analysis.
• Understand charts, graphs, dashboards, KPIs and data storytelling.
• Learn advanced visualization techniques and interactive dashboards.
• Understand business intelligence and real-world reporting workflows.
• Build professional Data Visualization projects for portfolio and interviews.

1

Introduction to Data Visualization

• What is Data Visualization?
• Importance of Data Visualization.
• Data Visualization in Business.
• Data Visualization in Data Science.
• Data Visualization in Analytics.
• Types of Data.
• Structured Data.
• Unstructured Data.
• Qualitative Data.
• Quantitative Data.
• Discrete Data.
• Continuous Data.
• Data Visualization Workflow.

2

Data Analytics Fundamentals

• What is Data Analytics?
• Descriptive Analytics.
• Diagnostic Analytics.
• Predictive Analytics.
• Prescriptive Analytics.
• Business Intelligence.
• Data-Driven Decision Making.
• Analytics Lifecycle.
• Data Collection.
• Data Processing.
• Data Analysis.
• Data Visualization.
• Business Reporting.

3

Statistics Fundamentals

• Introduction to Statistics.
• Population and Sample.
• Mean.
• Median.
• Mode.
• Range.
• Variance.
• Standard Deviation.
• Percentiles.
• Quartiles.
• Interquartile Range.
• Correlation.
• Distribution Basics.
• Normal Distribution.

4

Excel Fundamentals

• Introduction to Microsoft Excel.
• Workbook and Worksheets.
• Rows and Columns.
• Cells and Ranges.
• Data Entry.
• Formatting.
• Sorting.
• Filtering.
• Tables.
• Basic Formulas.
• SUM.
• AVERAGE.
• COUNT.
• MIN and MAX.

5

Advanced Excel

• IF Function.
• IFS Function.
• SUMIF.
• SUMIFS.
• COUNTIF.
• COUNTIFS.
• AVERAGEIF.
• VLOOKUP.
• HLOOKUP.
• XLOOKUP.
• INDEX and MATCH.
• Text Functions.
• Date Functions.
• Conditional Formatting.
• Data Validation.

6

Excel Data Visualization

• Excel Charts.
• Column Charts.
• Bar Charts.
• Line Charts.
• Pie Charts.
• Area Charts.
• Scatter Charts.
• Combo Charts.
• Pivot Charts.
• Pivot Tables.
• Slicers.
• Interactive Excel Dashboards.
• KPI Reports.

7

SQL Fundamentals

• Introduction to SQL.
• Databases.
• Tables.
• Rows and Columns.
• SELECT.
• WHERE.
• ORDER BY.
• GROUP BY.
• HAVING.
• Aggregate Functions.
• COUNT.
• SUM.
• AVG.
• MIN and MAX.

8

Advanced SQL for Visualization

• INNER JOIN.
• LEFT JOIN.
• RIGHT JOIN.
• FULL JOIN.
• Self Join.
• Subqueries.
• CTE.
• CASE Statements.
• Window Functions.
• ROW_NUMBER.
• RANK.
• DENSE_RANK.
• LEAD and LAG.
• SQL Data Preparation.

9

Data Cleaning

• What is Data Cleaning?
• Missing Values.
• Duplicate Data.
• Incorrect Data Types.
• Invalid Values.
• Outliers.
• Data Standardization.
• Data Formatting.
• Removing Duplicates.
• Handling Null Values.
• Data Quality Checks.
• Data Validation.

10

Python for Data Visualization

• Python Introduction.
• Variables.
• Data Types.
• Lists.
• Tuples.
• Dictionaries.
• Sets.
• Conditional Statements.
• Loops.
• Functions.
• Lambda Functions.
• File Handling.
• Exception Handling.

11

NumPy

• Introduction to NumPy.
• NumPy Arrays.
• Array Creation.
• Array Indexing.
• Array Slicing.
• Array Operations.
• Mathematical Operations.
• Statistical Operations.
• Reshaping Arrays.
• Sorting.
• Filtering.
• Random Data Generation.

12

Pandas

• Introduction to Pandas.
• Series.
• DataFrame.
• Reading CSV Files.
• Reading Excel Files.
• Reading JSON Files.
• Data Selection.
• Filtering Data.
• Sorting Data.
• Grouping Data.
• Aggregation.
• Merge.
• Join.
• Concatenation.
• Missing Data Handling.

13

Matplotlib

• Introduction to Matplotlib.
• Figure and Axes.
• Line Charts.
• Bar Charts.
• Histograms.
• Scatter Plots.
• Pie Charts.
• Area Charts.
• Labels.
• Titles.
• Legends.
• Annotations.
• Subplots.

14

Seaborn

• Introduction to Seaborn.
• Statistical Visualization.
• Distribution Plots.
• Box Plots.
• Violin Plots.
• Heatmaps.
• Pair Plots.
• Count Plots.
• Regression Plots.
• Category Plots.
• Customizing Visualizations.

15

Data Visualization Principles

• Visual Perception.
• Choosing the Right Chart.
• Data-to-Ink Ratio.
• Chart Clarity.
• Color Selection.
• Typography.
• Labels.
• Annotations.
• Visual Hierarchy.
• Dashboard Layout.
• Avoiding Misleading Charts.
• Accessibility.

16

Charts & Graphs

• Bar Charts.
• Column Charts.
• Line Charts.
• Area Charts.
• Pie Charts.
• Donut Charts.
• Scatter Plots.
• Bubble Charts.
• Histogram.
• Box Plot.
• Heatmap.
• Treemap.
• Funnel Chart.
• Waterfall Chart.
• Combo Chart.

17

Power BI Fundamentals

• Introduction to Power BI.
• Power BI Desktop.
• Power BI Service.
• Power BI Interface.
• Importing Data.
• Excel Data.
• CSV Data.
• SQL Server Data.
• Web Data.
• Data Sources.
• Power BI Reports.
• Visualizations.
• Filters.

18

Power Query

• Introduction to Power Query.
• Data Import.
• Data Transformation.
• Removing Columns.
• Renaming Columns.
• Changing Data Types.
• Filtering Rows.
• Replacing Values.
• Splitting Columns.
• Merge Queries.
• Append Queries.
• Conditional Columns.
• Custom Columns.
• Data Cleaning.

19

DAX

• Introduction to DAX.
• DAX Syntax.
• Calculated Columns.
• Measures.
• SUM.
• AVERAGE.
• COUNT.
• CALCULATE.
• FILTER.
• ALL.
• VALUES.
• SUMX.
• AVERAGEX.
• Time Intelligence.
• YTD.
• MTD.

20

Power BI Data Modeling

• Data Modeling Concepts.
• Tables.
• Relationships.
• Primary Keys.
• Foreign Keys.
• One-to-One Relationships.
• One-to-Many Relationships.
• Many-to-Many Relationships.
• Star Schema.
• Snowflake Schema.
• Fact Tables.
• Dimension Tables.
• Model Optimization.

21

Power BI Dashboards

• Dashboard Design.
• Interactive Reports.
• KPI Cards.
• Charts.
• Tables.
• Matrix Visuals.
• Slicers.
• Drill Down.
• Drill Through.
• Bookmarks.
• Tooltips.
• Page Navigation.
• Interactive Dashboards.

22

Advanced Power BI

• Advanced DAX.
• Complex Measures.
• Time Intelligence.
• Dynamic Titles.
• Dynamic Measures.
• What-If Parameters.
• Field Parameters.
• Row-Level Security.
• Incremental Refresh.
• Performance Analyzer.
• Report Optimization.
• Power BI Service.

23

Tableau Fundamentals

• Introduction to Tableau.
• Tableau Desktop.
• Tableau Public.
• Connecting Data.
• Dimensions.
• Measures.
• Filters.
• Rows and Columns.
• Marks Card.
• Charts.
• Dashboards.
• Worksheets.

24

Advanced Tableau

• Calculated Fields.
• Table Calculations.
• Parameters.
• Sets.
• Groups.
• Hierarchies.
• LOD Expressions.
• FIXED.
• INCLUDE.
• EXCLUDE.
• Dashboard Actions.
• Filters and Parameters.
• Advanced Dashboards.

25

Business Intelligence

• What is Business Intelligence?
• BI Architecture.
• Data Sources.
• ETL Process.
• Data Warehouse.
• Data Mart.
• OLTP.
• OLAP.
• Business Reporting.
• KPIs.
• Business Metrics.
• Management Dashboards.

26

Exploratory Data Analysis

• What is EDA?
• Understanding the Dataset.
• Data Profiling.
• Summary Statistics.
• Distribution Analysis.
• Correlation Analysis.
• Outlier Detection.
• Trend Analysis.
• Pattern Detection.
• Feature Analysis.
• Visual Exploration.
• Business Insights.

27

Advanced Data Visualization

• Advanced Chart Selection.
• Multi-Dimensional Visualization.
• Interactive Visualizations.
• Geographical Visualization.
• Maps.
• Heatmaps.
• Time Series Visualization.
• Hierarchical Visualization.
• Network Visualization.
• Custom Visualizations.
• Dashboard Interactivity.

28

Data Storytelling

• What is Data Storytelling?
• Storytelling with Data.
• Understanding the Audience.
• Business Questions.
• Finding Key Insights.
• Building a Narrative.
• Visual Hierarchy.
• Highlighting Important Data.
• Annotations.
• Executive Presentations.
• Insight-Based Reporting.

29

Dashboard Design

• Dashboard Planning.
• Dashboard Layout.
• Grid Design.
• Visual Hierarchy.
• KPI Placement.
• Chart Selection.
• Color Theory.
• Typography.
• Interactive Filters.
• Responsive Dashboards.
• Executive Dashboards.
• Mobile Dashboards.

30

Data Visualization with Python

• Python Visualization Workflow.
• Pandas Visualization.
• Matplotlib.
• Seaborn.
• Interactive Charts.
• Plotly.
• Dashboards with Plotly.
• Time Series Charts.
• Geographical Visualization.
• Interactive Filters.
• Python Dashboard Projects.

31

Advanced Analytics Visualization

• Trend Analysis.
• Variance Analysis.
• Contribution Analysis.
• Cohort Analysis.
• Funnel Analysis.
• Retention Analysis.
• Customer Segmentation.
• Sales Analysis.
• Financial Analysis.
• Marketing Analytics.
• Operational Analytics.

32

Data Visualization Automation

• Automated Reporting.
• Scheduled Reports.
• Python Automation.
• Excel Automation.
• Power BI Refresh.
• Data Refresh Pipelines.
• API Data Sources.
• Automated Dashboards.
• Email Reports.
• Reporting Workflows.

33

Cloud & Modern Data Platforms

• Cloud Data Fundamentals.
• Azure Data Services.
• AWS Data Services.
• Cloud Databases.
• Data Warehouses.
• Snowflake.
• BigQuery.
• Azure Synapse Concepts.
• Cloud Data Sources.
• Connecting BI Tools to Cloud Data.

34

Professional Data Visualization

• Enterprise Dashboards.
• Production Reporting.
• Advanced KPI Frameworks.
• Dashboard Governance.
• Data Security.
• Row-Level Security.
• Performance Optimization.
• Report Optimization.
• Data Refresh Management.
• Business User Requirements.
• Executive Reporting.
• Enterprise BI Practices.

35

Real-World Data Visualization Projects

• Sales Dashboard.
• Automobile Sales Dashboard.
• Finance Dashboard.
• HR Analytics Dashboard.
• Employee Performance Dashboard.
• Customer Analytics Dashboard.
• E-Commerce Dashboard.
• Marketing Analytics Dashboard.
• Healthcare Dashboard.
• Banking Dashboard.
• Inventory Dashboard.
• Supply Chain Dashboard.
• Financial Performance Dashboard.
• Executive Business Dashboard.
• End-to-End Data Analytics Project.

36

Data Visualization Interview Preparation

• Data Visualization Interview Questions.
• Excel Interview Questions.
• SQL Interview Questions.
• Python Interview Questions.
• Power BI Interview Questions.
• Tableau Interview Questions.
• DAX Interview Questions.
• Data Modeling Questions.
• Dashboard Design Questions.
• Business Intelligence Questions.
• Scenario-Based Questions.
• Case Study Questions.
• Portfolio-Based Questions.
• Practical Visualization Tasks.

Skills You'll Gain:

• Strong understanding of Data Visualization and Data Analytics.
• Ability to analyze and interpret business data.
• Strong knowledge of Microsoft Excel.
• Strong SQL querying and data preparation skills.
• Knowledge of Python for data analysis and visualization.
• Knowledge of NumPy and Pandas.
• Ability to create charts using Matplotlib, Seaborn and Plotly.
• Strong Power BI dashboard development skills.
• Knowledge of Power Query and DAX.
• Understanding of Power BI data modeling.
• Knowledge of Tableau and advanced visualization techniques.
• Ability to create interactive dashboards and reports.
• Understanding of KPIs and business metrics.
• Ability to perform Exploratory Data Analysis.
• Knowledge of Data Storytelling and dashboard design.
• Understanding of Business Intelligence and data warehousing.
• Ability to build professional real-world Data Visualization projects.
• Preparation for Data Analyst, BI Analyst, Data Visualization Developer and Business Intelligence career paths.

Duration:

Typically ranges from 24 to 32 weeks, depending on the course intensity, practice, projects and learning format.

Certification:

Earn a certificate of completion that can be added to your resume, LinkedIn profile and professional portfolio.