The Data Analytics with DAX, Python, SQL, and Business Intelligence Tools Training Course provides participants with practical knowledge and skills for collecting, preparing, analyzing, modeling, and visualizing data to support informed business decision-making.

The course combines key data analytics technologies and techniques, including Python, SQL, DAX (Data Analysis Expressions), Power BI, data modeling, data transformation, statistical analysis, and visualization. Participants will learn how to work with structured datasets, clean and transform data, perform exploratory and statistical analysis, build analytical data models, create DAX calculations, and develop interactive dashboards and reports.

The training is designed as a general, vendor-relevant data analytics program suitable for professionals who need an end-to-end understanding of modern analytics workflows, from raw data preparation through analysis and business intelligence reporting.

 

 

Duration 5 Days – 35 hrs.

 

Objectives

• Understand the fundamentals of data analytics and the modern analytics lifecycle.
• Identify different types, structures, and sources of data.
• Prepare, clean, transform, and validate datasets for analysis.
• Use SQL to retrieve, filter, aggregate, and analyze relational data.
• Use Python for data manipulation and analytical tasks.
• Work with Python libraries such as Pandas, NumPy, and Matplotlib.
• Perform exploratory data analysis (EDA).
• Apply descriptive statistical techniques to business datasets.
• Understand dimensional data modeling concepts.
• Build relationships between tables using fact and dimension structures.
• Understand DAX fundamentals and evaluation concepts.
• Create calculated columns, measures, and analytical calculations using DAX.
• Apply time intelligence and business calculations.
• Build data models and reports using Power BI.
• Create effective charts, dashboards, KPIs, and interactive reports.
• Combine SQL, Python, DAX, and BI techniques in an end-to-end analytics workflow.
• Translate analytical findings into meaningful business insights.

 

Target Audience

• Data Analysts
• Business Analysts
• Reporting Analysts
• Business Intelligence Analysts
• MIS and Reporting Professionals
• Junior Data Scientists
• Database Professionals
• IT Professionals
• Finance Analysts
• Operations Analysts
• Marketing Analysts
• Business Intelligence Developers
• Power BI Users and Developers
• Professionals responsible for dashboards and management reports
• Professionals transitioning into data analytics roles
• Managers and decision-makers who work extensively with business data

 

Prerequisites

• Basic computer literacy and familiarity with spreadsheets.
• Basic understanding of business data and reporting concepts.
• Basic Microsoft Excel knowledge is recommended.
• Basic SQL or programming knowledge is helpful but not mandatory.
• Basic understanding of databases is advantageous.
• No advanced Python or DAX programming experience is required.

 

 

Course Outline

Day 1 – Data Analytics Fundamentals and Data Preparation

Module 1: Introduction to Data Analytics

• What is data analytics?
• Role of analytics in business decision-making
• Descriptive, diagnostic, predictive, and prescriptive analytics
• Structured, semi-structured, and unstructured data
• Data sources and data formats
• Modern data analytics ecosystem
• Overview of the analytics lifecycle
• From raw data to actionable insight

Module 2: Understanding and Preparing Data

• Understanding datasets and data structures
• Numerical, categorical, date, and text data
• Data quality concepts
• Missing and incomplete data
• Duplicate records
• Incorrect and inconsistent values
• Data standardization
• Handling null values
• Detecting data quality issues
• Data validation techniques

Module 3: Data Transformation and ETL Concepts

• Introduction to ETL and ELT
• Extracting data from different sources
• Data transformation concepts
• Filtering and sorting
• Splitting and merging columns
• Changing data types
• Combining datasets
• Aggregation and grouping
• Preparing analytics-ready datasets

Module 4: Introduction to Data Modeling

• Data modeling fundamentals
• Tables, rows, columns, and keys
• Primary and foreign keys
• Relationships between datasets
• Transactional versus analytical models
• Introduction to dimensional modeling
• Fact tables and dimension tables
• Introduction to star schemas

 

Day 2 – SQL for Data Analytics

Module 5: SQL Fundamentals for Analysts

• Introduction to relational databases
• Database tables and relationships
• SQL query structure
• SELECT statements
• Selecting specific columns
• Column aliases
• DISTINCT values
• Filtering with WHERE
• Comparison and logical operators
• Sorting with ORDER BY

Module 6: SQL Data Analysis Techniques

• Aggregate functions
• COUNT, SUM, AVG, MIN, and MAX
• GROUP BY
• HAVING
• Conditional logic with CASE
• Working with text data
• Working with dates
• Handling NULL values
• Calculated fields

Module 7: Combining and Analyzing Multiple Tables

• Understanding table relationships
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• FULL JOIN concepts
• Joining multiple tables
• Subqueries
• Common Table Expressions (CTEs)
• Introduction to window functions
• Ranking and analytical calculations

Module 8: SQL for Business Analysis

• Customer analysis
• Sales and revenue analysis
• Product performance analysis
• Trend analysis
• Period-over-period comparisons
• Identifying top and bottom performers
• Creating reusable analytical queries
• Preparing SQL datasets for BI and Python analysis

 

Day 3 – Python for Data Analytics

Module 9: Python Fundamentals for Data Analysts

• Introduction to Python for analytics
• Python environment and notebooks
• Variables and data types
• Lists, tuples, dictionaries, and sets
• Operators and expressions
• Conditional statements
• Loops
• Functions
• Working with external data files

Module 10: Data Analysis with NumPy and Pandas

• Introduction to NumPy
• Arrays and numerical operations
• Introduction to Pandas
• Series and DataFrames
• Importing CSV and Excel datasets
• Inspecting datasets
• Selecting rows and columns
• Filtering data
• Sorting data
• Creating calculated columns
• Grouping and aggregation

Module 11: Data Cleaning and Transformation with Python

• Identifying missing values
• Handling missing data
• Removing duplicates
• Correcting data types
• String manipulation
• Date and time manipulation
• Combining DataFrames
• Merge and join operations
• Pivoting and reshaping data
• Preparing datasets for analysis

Module 12: Exploratory Data Analysis and Statistics

• Introduction to exploratory data analysis
• Summary statistics
• Mean, median, mode, and percentiles
• Variance and standard deviation
• Distribution analysis
• Identifying outliers
• Correlation analysis
• Segment and group analysis
• Identifying trends and patterns
• Translating findings into business insights

Module 13: Data Visualization with Python

• Principles of analytical visualization
• Introduction to Matplotlib
• Line charts
• Bar charts
• Histograms
• Scatter plots
• Distribution visualization
• Choosing appropriate visualizations
• Presenting analytical findings

 

Day 4 – DAX and Analytical Data Modeling

Module 14: Data Modeling for Business Intelligence

• Analytical data model concepts
• Star schema design
• Fact and dimension tables
• Granularity and data grain
• Relationships and cardinality
• One-to-many relationships
• Filter direction
• Date dimensions
• Building efficient analytical models
• Data model best practices

Module 15: Introduction to DAX

• What is DAX?
• DAX in Power BI and analytical models
• DAX syntax and expressions
• Calculated columns versus measures
• Implicit versus explicit measures
• Basic aggregation functions
• SUM
• AVERAGE
• COUNT and COUNTROWS
• DISTINCTCOUNT
• MIN and MAX
• DIVIDE

Module 16: DAX Evaluation and Filter Context

• Understanding row context
• Understanding filter context
• Context transition
• CALCULATE
• FILTER
• ALL
• VALUES
• SELECTEDVALUE
• RELATED and RELATEDTABLE
• Conditional calculations
• Building reusable business measures

Module 17: DAX Time Intelligence

• Working with date tables
• Date relationships
• Year-to-date calculations
• Month-to-date calculations
• Quarter-to-date calculations
• Previous-period analysis
• Year-over-year comparisons
• Period growth calculations
• Running totals
• Moving and rolling calculations

 

Day 5 – Power BI, Visualization, and End-to-End Analytics

Module 18: Power BI for Data Analytics

• Introduction to Power BI
• Power BI Desktop workflow
• Connecting to data sources
• Importing and transforming data
• Power Query overview
• Building the data model
• Creating relationships
• Creating measures and KPIs
• Organizing analytical models

Module 19: Data Visualization and Dashboard Design

• Principles of effective data visualization
• Selecting appropriate chart types
• Tables and matrices
• Bar and column charts
• Line and trend charts
• KPI and card visuals
• Slicers and filters
• Drill-down and drill-through concepts
• Interactive report design
• Dashboard layout and usability
• Avoiding misleading visualizations

Module 20: Business Analytics and KPI Development

• Defining business metrics
• KPI design principles
• Revenue and profitability metrics
• Growth metrics
• Operational performance indicators
• Customer metrics
• Variance analysis
• Actual versus target analysis
• Trend and comparative analysis
• Translating business requirements into analytical measures

Module 21: Integrating SQL, Python, DAX, and Power BI

• Role of each technology in the analytics workflow
• Using SQL for data extraction
• Using Python for cleaning and advanced analysis
• Creating analytical data models
• Using DAX for business calculations
• Using Power BI for visualization and reporting
• Choosing the appropriate tool for different analytical requirements
• Building maintainable analytics workflows

Module 22: End-to-End Data Analytics Case Study

• Understanding the business requirement
• Identifying relevant data sources
• Extracting and preparing data
• Cleaning and transforming datasets
• Performing exploratory analysis
• Designing the analytical data model
• Developing DAX measures and KPIs
• Creating interactive visualizations
• Building a management dashboard
• Identifying trends, patterns, and anomalies
• Developing data-driven business insights
• Presenting analytical findings and recommendations