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SQL Analysis
Pizza Sales
SQLMySQLExcelPower BI
A SQL-based data analysis project on pizza sales data. Covers revenue trends, best-selling items, order patterns, and time-based performance using advanced SQL queries.
Market Intelligence
DATA
ANALYTICS
Real-time insights into the fastest growing tech sector of 2026
6 LPA
Average annual salary for Data Analytics professionals
SourceGlassdoor
1
27k+
Career opportunities related to Data Analytics
SourceLinkedin
2
25%
Compound annual growth rate of the Data Analytics market
SourceSimplilearn
3
Learning Experience
Industry-Oriented Data Analytics Training at DIGIT innovations
Embark on a transformative journey into Data Analytics at DIGIT innovations. Learn essential skills for managing, analyzing, and visualizing data using modern tools and methodologies.
Our expert instructors bring extensive real-world experience and personalized guidance, ensuring you not only understand the theory but also apply practical skills. Engage in hands-on projects, case studies, and mock assessments to prepare for a successful career in the dynamic field of data analytics.
Data Visualization
Learn Data Visualization using Tools like Power BI
Technical Skills
Build Skills in Excel, SQL & Python for Data Analysis
Real-World Projects
Projects You'll Analyze
Work on 3 real-world data analysis projects using SQL, Power BI, and Python turning raw datasets into actionable insights and visual dashboards that employers value.
Business Intelligence Dashboard
Sales Performance Dashboard
Power BIExcelSQLDAX
An interactive sales performance dashboard built in Power BI, visualizing KPIs, regional performance, product trends, and year-over-year growth analysis.
Exploratory Data Analysis
Titanic Dataset EDA
PythonPandasNumPyMatplotlibSeabornJupyter
An exploratory data analysis of the Titanic dataset using Python libraries. Includes data cleaning, feature engineering, survival analysis, and visual storytelling.
Course Details
Data Analytics Proficiency Path
Explore our Data Analytics curriculum, from Excel basics to advanced SQL, Python data analysis, visualization, and Power BI insights for real-world data mastery.
Excel
2 Modules
Excel
22 Topics
1
Introduction to Excel for Data Analysis
2
Overview of Excel interface
3
Basics of navigating and working with sheets
4
Introduction to cells, rows, columns, and ranges
5
Understanding basic functions (SUM, AVERAGE, COUNT)
6
Working with mathematical and statistical functions
7
Introduction to text functions for data manipulation
8
Advanced Formulas and Functions
9
Working with logical functions (IF, AND, OR)
10
Exploring lookup functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
11
Introduction to array formulas
12
Identifying and handling missing data
13
Removing duplicates and dealing with errors
14
Text-to-columns and data-splitting techniques
15
Formatting data for analysis
16
Creating basic charts and graphs
17
Tips for effective data presentation
18
Introduction to PivotTables for dynamic data analysis
19
Creating PivotCharts for visual insights
20
Customizing and formatting PivotTables and PivotCharts
21
Time-saving shortcuts and productivity hacks
22
Excel with AI
Advanced Excel
22 Topics
1
Mastering advanced Excel functions and formulas
2
Using nested IFs and complex logical expressions
3
Dynamic named ranges using OFFSET and INDIRECT
4
Creating interactive dashboards with form controls
5
Advanced data validation techniques
6
Leveraging conditional formatting for insights
7
Working with advanced lookup techniques (XLOOKUP, XMATCH)
8
Understanding and applying array formulas
9
Using dynamic arrays and new Excel functions
10
Advanced PivotTable techniques and slicers
11
Creating calculated fields and calculated items
12
Using Power Query for data transformation
13
Merging and appending queries in Power Query
14
Creating data models using Power Pivot
15
Working with relationships and DAX basics
16
Optimizing workbook performance
17
Automating tasks with macros and VBA intro
18
Customizing Excel interface with macro buttons
19
Collaborating with shared workbooks and co-authoring
20
Advanced charting techniques for data storytelling
21
Securing workbooks and protecting data
22
Using Excel for business intelligence applications
Excel
2 Modules
Excel
22 Topics
1
Introduction to Excel for Data Analysis
2
Overview of Excel interface
3
Basics of navigating and working with sheets
4
Introduction to cells, rows, columns, and ranges
5
Understanding basic functions (SUM, AVERAGE, COUNT)
6
Working with mathematical and statistical functions
7
Introduction to text functions for data manipulation
8
Advanced Formulas and Functions
9
Working with logical functions (IF, AND, OR)
10
Exploring lookup functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
11
Introduction to array formulas
12
Identifying and handling missing data
13
Removing duplicates and dealing with errors
14
Text-to-columns and data-splitting techniques
15
Formatting data for analysis
16
Creating basic charts and graphs
17
Tips for effective data presentation
18
Introduction to PivotTables for dynamic data analysis
19
Creating PivotCharts for visual insights
20
Customizing and formatting PivotTables and PivotCharts
21
Time-saving shortcuts and productivity hacks
22
Excel with AI
Advanced Excel
22 Topics
1
Mastering advanced Excel functions and formulas
2
Using nested IFs and complex logical expressions
3
Dynamic named ranges using OFFSET and INDIRECT
4
Creating interactive dashboards with form controls
5
Advanced data validation techniques
6
Leveraging conditional formatting for insights
7
Working with advanced lookup techniques (XLOOKUP, XMATCH)
8
Understanding and applying array formulas
9
Using dynamic arrays and new Excel functions
10
Advanced PivotTable techniques and slicers
11
Creating calculated fields and calculated items
12
Using Power Query for data transformation
13
Merging and appending queries in Power Query
14
Creating data models using Power Pivot
15
Working with relationships and DAX basics
16
Optimizing workbook performance
17
Automating tasks with macros and VBA intro
18
Customizing Excel interface with macro buttons
19
Collaborating with shared workbooks and co-authoring
20
Advanced charting techniques for data storytelling
21
Securing workbooks and protecting data
22
Using Excel for business intelligence applications
SQL
4 Modules
SQL
10 Topics
1
Introduction to SQL and Database Fundamentals
2
Overview of SQL and its applications
3
Introduction to Relational Databases
4
Basic SQL syntax and structure
5
Creating and modifying tables with CREATE and ALTER
6
Understanding data types and constraints
7
Retrieving Data with SELECT Statements
8
Basics of SELECT statements
9
Filtering data with WHERE clause
10
Sorting results with ORDER BY
Advanced SQL Techniques
8 Topics
1
Aggregation and Grouping
2
Understanding aggregate functions (SUM, AVG, COUNT)
3
Grouping data with GROUP BY
4
Working with complex WHERE conditions
5
Using operators (AND, OR, NOT, etc)
6
Window Functions and Analytic Queries
7
Introduction to window functions
8
Performing analytic queries with OVER clause
Advance SQL
8 Topics
1
Joins and Subqueries
2
Performing INNER and OUTER joins
3
Using subqueries for complex queries
4
Case Statements and CTE Queries
5
Understanding and using CASE statements in SQL
6
Applying CASE statements in data analysis scenarios
7
Introduction to Common Table Expressions
8
Using CTEs for recursive queries and data manipulation
More on SQL
7 Topics
1
Time-saving shortcuts and productivity hacks
2
Optimization of queries
3
Optimization of queries using AI
4
Interview based SQL queries
5
Working on live project
6
Working on industry-oriented data
7
Problem-solving using SQL on industrial data
SQL
4 Modules
SQL
10 Topics
1
Introduction to SQL and Database Fundamentals
2
Overview of SQL and its applications
3
Introduction to Relational Databases
4
Basic SQL syntax and structure
5
Creating and modifying tables with CREATE and ALTER
6
Understanding data types and constraints
7
Retrieving Data with SELECT Statements
8
Basics of SELECT statements
9
Filtering data with WHERE clause
10
Sorting results with ORDER BY
Advanced SQL Techniques
8 Topics
1
Aggregation and Grouping
2
Understanding aggregate functions (SUM, AVG, COUNT)
3
Grouping data with GROUP BY
4
Working with complex WHERE conditions
5
Using operators (AND, OR, NOT, etc)
6
Window Functions and Analytic Queries
7
Introduction to window functions
8
Performing analytic queries with OVER clause
Advance SQL
8 Topics
1
Joins and Subqueries
2
Performing INNER and OUTER joins
3
Using subqueries for complex queries
4
Case Statements and CTE Queries
5
Understanding and using CASE statements in SQL
6
Applying CASE statements in data analysis scenarios
7
Introduction to Common Table Expressions
8
Using CTEs for recursive queries and data manipulation
More on SQL
7 Topics
1
Time-saving shortcuts and productivity hacks
2
Optimization of queries
3
Optimization of queries using AI
4
Interview based SQL queries
5
Working on live project
6
Working on industry-oriented data
7
Problem-solving using SQL on industrial data
Python
5 Modules
Introduction to Python for Data Analysis
10 learning pointsIntroduction to Python and Jupyter NotebooksOverview of Python programming languageIntroduction to Jupyter Notebooks for data analysisVariables, data types, and basic operationsLists, tuples, and dictionariesInbuilt functionsData Manipulation with PythonConditional statements and loopsUser defined functionsFunctions such as map, filter, lambda
Exploring Data with Pandas & Matplotlib
7 learning pointsData Manipulation with PandasOverview of Pandas LibraryReading and writing data along with basic operations with PandasData Cleaning and Preprocessing with PandasHandling missing dataRemoving duplicates and dealing with outliersCleaning and adjustments in data
EDA & Data Visualization
7 learning pointsExploratory Data Analysis (EDA) with PandasDescriptive statistics and data summarizationGrouping and aggregating dataSQL-like operations in dataData Visualization with MatplotlibCreating basic plots (line plots, scatter plots, histograms)Customizing and styling visualizations
Data Analysis with NumPy
5 learning pointsIntroduction to NumPy for numerical operationsWorking with arrays and matricesAdvanced Data Visualization with SeabornCreating informative and aesthetically pleasing visualizationsPair plots, heatmaps, and advanced plotting techniques
Statistical Analysis
6 learning pointsStatistical Analysis with SciPyIntroduction to statistical tests and hypothesis testingImplementing statistical tests in PythonFinal Project and Case StudiesParticipants work on a real-world data analysis projectApplying learned Python skills to analyze and visualize data
Python
5 Modules
Introduction to Python for Data Analysis
10 learning pointsIntroduction to Python and Jupyter NotebooksOverview of Python programming languageIntroduction to Jupyter Notebooks for data analysisVariables, data types, and basic operationsLists, tuples, and dictionariesInbuilt functionsData Manipulation with PythonConditional statements and loopsUser defined functionsFunctions such as map, filter, lambda
Exploring Data with Pandas & Matplotlib
7 learning pointsData Manipulation with PandasOverview of Pandas LibraryReading and writing data along with basic operations with PandasData Cleaning and Preprocessing with PandasHandling missing dataRemoving duplicates and dealing with outliersCleaning and adjustments in data
EDA & Data Visualization
7 learning pointsExploratory Data Analysis (EDA) with PandasDescriptive statistics and data summarizationGrouping and aggregating dataSQL-like operations in dataData Visualization with MatplotlibCreating basic plots (line plots, scatter plots, histograms)Customizing and styling visualizations
Data Analysis with NumPy
5 learning pointsIntroduction to NumPy for numerical operationsWorking with arrays and matricesAdvanced Data Visualization with SeabornCreating informative and aesthetically pleasing visualizationsPair plots, heatmaps, and advanced plotting techniques
Statistical Analysis
6 learning pointsStatistical Analysis with SciPyIntroduction to statistical tests and hypothesis testingImplementing statistical tests in PythonFinal Project and Case StudiesParticipants work on a real-world data analysis projectApplying learned Python skills to analyze and visualize data
Power BI
3 Modules
Introduction, Case Studies & Power BI
7 Topics
Case Studies and Discussion & Power BI
Reviewing case studies of Python usage in data analysis
Q&A and discussions on best practices
Introduction to Power BI
Understanding the Power BI interface
Importing data from different sources
Transforming and shaping data within Power BI
Power BI
15 Topics
Data Modeling and Relationships in Power BI
Creating a data model in Power BI
Understanding relationships between tables
Implementing calculated columns and measures
Using DAX (Data Analysis Expressions) for advanced calculations
Visualizations and Interactivity
Creating common visualizations (bar charts, line charts, etc.)
Customizing visualizations for better insights
Adding interactivity to reports and dashboards
Implementing drill-through actions for detailed analysis
The Art of Storytelling with Data
Principles of Effective Data Storytelling
Importance of narrative in data presentations
Building a cohesive narrative in Power BI
Using bookmarks and storytelling features
Power BI for Real-Time Analytics and Advanced Features
9 Topics
Real-Time Dashboards
Setting up real-time data streaming in Power BI
Creating dashboards for live data monitoring
Advanced Features and Custom Visuals
Exploring custom visuals and visuals from the marketplace
Leveraging advanced features like forecasting and clustering
Case Studies and Discussion
Reviewing case studies of effective Power BI usage
Q&A and discussions on best practices in storytelling with data
Power BI
3 Modules
Introduction, Case Studies & Power BI
7 Topics
Case Studies and Discussion & Power BI
Reviewing case studies of Python usage in data analysis
Q&A and discussions on best practices
Introduction to Power BI
Understanding the Power BI interface
Importing data from different sources
Transforming and shaping data within Power BI
Power BI
15 Topics
Data Modeling and Relationships in Power BI
Creating a data model in Power BI
Understanding relationships between tables
Implementing calculated columns and measures
Using DAX (Data Analysis Expressions) for advanced calculations
Visualizations and Interactivity
Creating common visualizations (bar charts, line charts, etc.)
Customizing visualizations for better insights
Adding interactivity to reports and dashboards
Implementing drill-through actions for detailed analysis
The Art of Storytelling with Data
Principles of Effective Data Storytelling
Importance of narrative in data presentations
Building a cohesive narrative in Power BI
Using bookmarks and storytelling features
Power BI for Real-Time Analytics and Advanced Features
9 Topics
Real-Time Dashboards
Setting up real-time data streaming in Power BI
Creating dashboards for live data monitoring
Advanced Features and Custom Visuals
Exploring custom visuals and visuals from the marketplace
Leveraging advanced features like forecasting and clustering
Case Studies and Discussion
Reviewing case studies of effective Power BI usage
Q&A and discussions on best practices in storytelling with data
AI & GenAI
9 Modules
AI & Generative AI Fundamentals
4 TopicsWhat is AI, Machine Learning, Deep Learning, and Generative AI?
Simple analogies with real-world examples
Understanding where AI is used in daily life
Why Generative AI matters in modern apps and analytics workflows
How Large Language Models Work
4 TopicsHigh-level understanding of LLM training
Tokens, context window, and parameters
How models generate responses
Why LLM outputs can sometimes be inaccurate
Popular AI Models
4 TopicsGPT series and when to use it
Gemini strengths and use cases
Claude for reasoning and writing
Grok and Llama overview
Prompt Engineering Basics
5 TopicsZero-shot and Few-shot prompting
Chain-of-thought prompting basics
Role-playing and structured prompts
Output formatting with JSON
Temperature, top-p, and system prompts
Limitations, Ethics & Responsible AI
4 TopicsHallucinations and bias in AI
Context loss and response limitations
Ethical concerns in production apps
Cost awareness and token usage
AI APIs vs Open Source Models
4 TopicsDifference between AI APIs and open-source models
When to choose hosted APIs
When open-source models make sense
Basic introduction to AI app architecture
Excel, SQL & Python for AI Analytics
4 TopicsUsing AI to clean, summarize, and analyze spreadsheet data
Writing SQL queries with AI assistance
Using Python for data wrangling, statistical analysis, and automation
Generating insights from structured datasets
Data Preparation, RAG & Data Storage
4 TopicsSaving datasets, reports, and AI outputs for future analysis
Introduction to RAG and why it is useful for analytics
Storing documents and retrieving relevant parts
Basic input validation and prompt injection protection
Power BI Dashboards & AI Insights
5 TopicsCalling AI tools from analytics workflows
Building dashboard narratives and summary insights
Markdown rendering and loading states for AI-driven reporting
Creating business-ready dashboards with AI-assisted analysis
Publishing and sharing reports on Power BI Service or cloud platforms
AI & GenAI
9 Modules
AI & Generative AI Fundamentals
4 TopicsWhat is AI, Machine Learning, Deep Learning, and Generative AI?
Simple analogies with real-world examples
Understanding where AI is used in daily life
Why Generative AI matters in modern apps and analytics workflows
How Large Language Models Work
4 TopicsHigh-level understanding of LLM training
Tokens, context window, and parameters
How models generate responses
Why LLM outputs can sometimes be inaccurate
Popular AI Models
4 TopicsGPT series and when to use it
Gemini strengths and use cases
Claude for reasoning and writing
Grok and Llama overview
Prompt Engineering Basics
5 TopicsZero-shot and Few-shot prompting
Chain-of-thought prompting basics
Role-playing and structured prompts
Output formatting with JSON
Temperature, top-p, and system prompts
Limitations, Ethics & Responsible AI
4 TopicsHallucinations and bias in AI
Context loss and response limitations
Ethical concerns in production apps
Cost awareness and token usage
AI APIs vs Open Source Models
4 TopicsDifference between AI APIs and open-source models
When to choose hosted APIs
When open-source models make sense
Basic introduction to AI app architecture
Excel, SQL & Python for AI Analytics
4 TopicsUsing AI to clean, summarize, and analyze spreadsheet data
Writing SQL queries with AI assistance
Using Python for data wrangling, statistical analysis, and automation
Generating insights from structured datasets
Data Preparation, RAG & Data Storage
4 TopicsSaving datasets, reports, and AI outputs for future analysis
Introduction to RAG and why it is useful for analytics
Storing documents and retrieving relevant parts
Basic input validation and prompt injection protection
Power BI Dashboards & AI Insights
5 TopicsCalling AI tools from analytics workflows
Building dashboard narratives and summary insights
Markdown rendering and loading states for AI-driven reporting
Creating business-ready dashboards with AI-assisted analysis
Publishing and sharing reports on Power BI Service or cloud platforms
Your Enrollment Journey
Track Your Progress Toward Enrollment
Follow these four milestones to transform your career. From registration to your first class, we've made it seamless.
1
Registration
Register online, provide details, help admissions understand your career goals.
25%
2
Test
Submit application, then an assessment ensures eligibility and program aptitude.
50%
3
Offer Applicable
After review, eligible candidates get scholarship offers and confirmation email.
75%
4
Fee Payment
Review your admission offer, promptly pay fee to confirm enrollment.
100%
Industry Mentors
Mentor Community
Our mentor community at DIGIT Innovations believes in the power of sharing. We partner with experienced professionals from leading companies to guide you through real projects, career advice, and hiring-ready skills.
Verified Certification
Advance Your Career with Our Data Analytics Certification
Global Industry Recognition
Our certifications are well-regarded in the software industry, providing valuable opportunities for career advancement
Practical Skill Validation
Our certifications validate your skills through practical application, equipping you for the workforce and demonstrating your expertise to employers.
Career Advancement
Achieve new heights in your professional journey with our certifications.
What Our Students Say
Shared Experiences from Our Students
Global Impact
Companies Hiring
DIGIT innovations Alumni
Our alumni work with leading companies worldwide, delivering real-world impact.
Upcoming Sessions
Join Our Upcoming
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Secure your spot in the next batch. Limited seats available. Enroll now and start your learning journey with expert-led live training.
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FAQ
Frequently Asked Questions
Everything you need to know about the course. Find answers to common queries regarding enrollment, curriculum, and career paths.
Talk to usNo prior experience is required, although familiarity with basic IT concepts and coding can be beneficial. Our course caters to beginners as well as those looking to enhance their existing skills.
Absolutely! We provide career support services including resume building, mock tests, mock interviews, and job placement assistance to help you kickstart your career in Data Analytics.
Yes, our course includes hands-on projects and practical assignments that simulate real-world scenarios. You'll have the opportunity to apply your skills and work on actual Data Analytics tasks and cloud configurations.
Yes, our online platform provides access to course materials, lectures, and resources, allowing you to study at your own pace.
Enrolling is easy! Simply visit our website or contact our admissions team for more information on course schedules, fees, and enrollment procedures.
This course equips you with the skills and knowledge needed to excel in Data Analytics, a field that is in high demand. With comprehensive training in data wrangling, statistical analysis, visualization, and more, you will be well-prepared for a variety of roles in the industry.