Intermediate
🎓 Certificate
DATA ANALYTICS WITH AI
Master Data Analytics with AI using Excel, Python, SQL, Pandas and Power BI. Learn AI-assisted data cleaning, analysis, visualization, dashboard development and real-world projects.
★
4.5
(0+ reviews)
0+ Learners
60+ Hours
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About this Course
The Data Analytics with AI Intermediate Master Program helps learners develop practical analytical skills and use AI for data-driven decision-making.
Students will learn the complete analytics workflow, including data cleaning, analysis, visualization and reporting using Excel, Python, Pandas, SQL and Power BI.
The course covers AI-assisted prompt engineering, formula generation, coding, SQL queries, data interpretation and dashboard development, with an emphasis on practical assignments and responsible AI usage.
Through real-world projects in sales, student performance, HR analytics and customer churn, students will gain hands-on experience, build a project portfolio and learn to deliver meaningful business insights.
What you'll learn
Understand Data Analytics and Artificial Intelligence fundamentals
Apply Prompt Engineering for data analysis
Perform Data Cleaning and Analysis using Excel
Develop Python-based Data Analysis solutions
Manipulate data using NumPy and Pandas
Write SQL queries for data extraction and analysis
Create visualizations using Matplotlib and Seaborn
Develop interactive Power BI dashboards using DAX
Apply AI tools for coding, debugging and insight generation
Complete real-world analytics projects and build a professional portfolio
Available Batches
Choose a batch that fits your schedule and budget
🔔 UPCOMING
BATCH - 1
₹20000.00
₹399.00
98% OFF
Mode
🌐 Online
Language
Telugu
Start Date
10 Nov 2026
Days
MON - SAT
30 slots left out of 30
0 filled
No spam. We'll contact you to confirm.
Course Curriculum
- Introduction to Data Analytics
- What is Data Analytics?
- Importance of Data Analytics in Business
- Analytics vs Data Science vs Artificial Intelligence
- Types of Analytics
- Data Analytics Lifecycle
- Types of Data: Structured, Semi-Structured and Unstructured
- Data Collection, Data Cleaning, Data Analysis and Reporting
- Data Analyst Roles and Responsibilities
- Real-World Applications of Data Analytics
- Tools Used in Data Analytics
- Artificial Intelligence Fundamentals
- Introduction to Artificial Intelligence
- AI vs Machine Learning vs Deep Learning
- Introduction to Generative AI
- Prompt Engineering for Data Analytics
- Introduction to Microsoft Excel
- Excel Interface and Ribbon
- Workbooks and Worksheets
- Rows, Columns, Cells and Cell References
- Data Types and Data Entry
- Formatting and Number Formats
- Autofill and Flash Fill
- Sorting and Filtering
- Conditional Formatting
- Data Validation
- Excel Functions
- Lookup Functions
- Data Cleaning and Analysis
- AI with Excel
- Introduction to Python
- Python Installation and Development Environment
- Google Colab and VS Code
- Variables and Naming Rules
- Data Types and Type Conversion
- Input and Output Operations
- Operators
- Conditional Statements
- For and While Loops
- Break, Continue and Pass
- Strings and String Methods
- Lists and List Operations
- Tuples
- Sets
- Dictionaries
- Functions and Parameters
- Return Statements
- AI-Assisted Python Programming
- Introduction to NumPy
- Creating NumPy Arrays
- Array Dimensions and Shapes
- Array Indexing and Slicing
- Reshaping Arrays
- Array Operations
- Mathematical Operations
- Statistical Functions
- Random Number Generation
- Filtering Arrays
- Introduction to Pandas
- Series and DataFrames
- Reading CSV Files
- Reading Excel Files
- Writing CSV and Excel Files
- Inspecting Data Using head(), tail(), info() and describe()
- Selecting Columns and Rows
- loc and iloc
- Filtering and Sorting Data
- Handling Missing Values
- Removing Duplicate Records
- Replacing and Renaming Values
- Changing Data Types
- GroupBy and Aggregation
- Merge and Join
- Concatenate
- AI-Assisted Data Cleaning and Analysis
- Introduction to Databases and SQL
- Introduction to MySQL
- Database and Table Creation
- SQL Data Types
- Constraints
- INSERT, SELECT, UPDATE and DELETE
- WHERE & HAVING Clause
- Aggregate Functions
- GOURP BY
- JOINS
- AI for SQL Analysis
- Introduction to Matplotlib
- Figure and Axes Fundamentals
- Figure Size using figsize
- Titles, Labels and Legends
- Charts, Plots, Histograms
- Customizing and Saving Charts
- Introduction to Seaborn
- Count Plot, Bar Plot, Scatter Plots, Pair Plots, Violin Plots
- Histograms and Distribution Plots
- Heatmaps
- AI for Visualization and Insights
- Introduction to Power BI
- Power BI Desktop
- Power Query
- Power BI Workflow
- Connecting Excel and CSV Files
- Importing Data from Databases
- Data Transformation Using Power Query
- Removing Duplicates and Handling Missing Values
- Loading Data into the Model
- DAX
- Power BI Visualizations and Dashboards
- AI-Assisted Power BI
- Excel Sales Analysis Dashboard
- Student Performance Analysis
- Customer and Sales Analysis
- HR Analytics Dashboard
- Customer Churn Analysis
- IPL Data Analysis
Facilities Provided
🎥
Live Classes
📹
Recorded Classes
🎙️
Mock Interviews
🧭
Career Guidance
📄
Study Materials
💻
Coding Exam
🎓
Certificate
Frequently Asked Questions
This course teaches data analytics using Excel, Python, NumPy, Pandas, SQL and Power BI, along with AI tools for data cleaning, coding, analysis and dashboard development.
The course duration is 2 months, with practical training, assignments and real-world projects.
Students, graduates, working professionals and anyone interested in developing data analytics skills can enroll. Basic computer knowledge is helpful.
No advanced programming experience is required. Python fundamentals are covered before progressing to data analysis.
The course covers Microsoft Excel, Python, NumPy, Pandas, MySQL, Matplotlib, Seaborn, Power BI and AI tools such as ChatGPT and Google Gemini.
Students learn to use AI to generate formulas, write and debug code, create SQL queries, assist with data cleaning, interpret results and plan dashboards.
Yes. Students practise with business datasets and projects such as Sales Analysis, Student Performance Analysis, HR Analytics and Customer Churn Analysis.
Yes. Students learn data transformation, data modeling, DAX measures, KPIs, interactive visualizations and dashboard development.
Students who meet the institute's course completion requirements can receive a course completion certificate, subject to the institute's certification policy.
Yes. The course develops practical skills in data analysis, SQL, Python, Excel and Power BI, along with projects and interview preparation to support your job search.
Yes. A laptop or desktop computer is recommended for practising programming, SQL, Excel and Power BI. Power BI Desktop requires a compatible Windows environment.
Yes. Students learn how to write effective AI prompts for formulas, code generation, SQL queries, data analysis and dashboard planning.
Quick Info
- LevelIntermediate
- Duration60+ hours
- Learners0+
- Rating★ 4.5
- Certificate✅ Yes
- Internship❌ No
- Placement❌ No