AI-Powered Data Analytics Professional Program
AI-Powered Data Analytics Professional Program
AI-Powered Data Analytics Professional Program
Build industry-ready skills with practical training, expert guidance and career-focused learning.
Apply Now3 Months
120 Hours
Monday – Friday
Offline / Online
17 Modules
Course Overview
The AI-Powered Data Analytics Professional Program at Jigsaw TechnoHub is designed to help learners transform raw data into meaningful insights and business decisions using modern data analytics tools, programming techniques, business intelligence platforms, and Artificial Intelligence.
The program begins with data analytics fundamentals, Excel, statistics, and data visualization before progressing into SQL, Python, data preparation, Power BI, dashboards, and business intelligence. At the Professional level, learners explore AI-assisted analytics, predictive analytics, automation, advanced Power BI, and real-world business analytics projects.
Through practical assignments, datasets, dashboards, case studies, and industry-oriented projects, students learn how to collect, clean, analyse, visualize, interpret, and present data effectively.
By the end of the programme, students will:
- Understand the complete data analytics lifecycle
- Work with Excel and spreadsheet-based data
- Clean and prepare real-world datasets
- Write SQL queries to extract and analyse data
- Use Python for data analysis
- Apply statistics to business data
- Create interactive Power BI dashboards
- Perform exploratory data analysis
- Generate business insights from datasets
- Use AI tools to accelerate data analysis
- Automate repetitive analytics tasks
- Perform basic predictive analytics
- Build professional data analytics projects
- Create a job-ready analytics portfolio
COURSE MODULES
01 | Introduction to Data Analytics
This module introduces students to the fundamentals of data analytics and how organizations use data to make better decisions.
What You'll Learn
- Introduction to Data Analytics
- Data Analytics vs Data Science
- Data Analyst vs Data Scientist
- Types of Data
- Structured and Unstructured Data
- Data Analytics Lifecycle
- Data Collection
- Data Preparation
- Data Analysis
- Data Visualization
- Data Interpretation
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics – Introduction
- Prescriptive Analytics – Introduction
- Role of Data in Business Decision-Making
- Data Analytics Career Opportunities
02| Excel for Data Analytics
Students learn how to use Microsoft Excel to organize, analyse, clean, and visualize business data.
What You'll Learn
- Excel interface and data management
- Data entry and formatting
- Sorting and filtering
- Tables
- Basic and advanced formulas
- Relative and absolute references
- IF functions
- SUMIF and COUNTIF
- XLOOKUP / lookup functions
- Text functions
- Date and time functions
- Conditional formatting
- Data validation
- Removing duplicates
- Data cleaning in Excel
- Pivot Tables
- Pivot Charts
- Basic Excel dashboards
03| Statistics for Data Analytics
Students learn the statistical concepts required to understand datasets and communicate meaningful insights.
What You'll Learn
- Introduction to Statistics
- Population and Sample
- Mean
- Median
- Mode
- Range
- Variance
- Standard Deviation
- Percentiles
- Quartiles
- Probability fundamentals
- Distributions
- Correlation
- Outliers
- Sampling
- Interpreting statistical results
- Business applications of statistics
04| Data Visualization Fundamentals
Students learn how to transform numerical information into clear and meaningful visual insights.
What You'll Learn
- Introduction to Data Visualization
- Importance of Data Visualization
- Choosing the right chart
- Bar Charts
- Column Charts
- Line Charts
- Pie Charts
- Scatter Plots
- Histograms
- Area Charts
- Tables
- KPIs
- Data labels
- Chart formatting
- Visual hierarchy
- Dashboard design fundamentals
- Data storytelling fundamentals
05| Beginner Data Analytics Projects
Students apply the concepts learned throughout the Beginner level to real-world datasets.
Projects
- Sales Data Analysis
- Employee Data Analysis
- Student Performance Analysis
- Customer Data Analysis
Students will create:
- Clean datasets
- Excel reports
- Pivot-based analysis
- Basic dashboards
- Business insights
06 | SQL for Data Analytics
Students learn how to extract, filter, transform, and analyse data stored in relational databases.
What You'll Learn
- Introduction to Databases
- Relational Databases
- Tables and Relationships
- SQL Fundamentals
- SELECT statements
- WHERE conditions
- ORDER BY
- GROUP BY
- HAVING
- Aggregate Functions
- CASE statements
- Subqueries
- Joins
- INNER JOIN
- LEFT JOIN
- RIGHT JOIN
- UNION
- Common Table Expressions
- Window Functions – Introduction
- Data analysis using SQL
07| Python for Data Analytics
Students learn Python fundamentals and apply them to practical data analysis tasks.
What You'll Learn
- Introduction to Python
- Python development environment
- Variables and Data Types
- Operators
- Conditional Statements
- Loops
- Functions
- Lists and Dictionaries
- File Handling
- Exception Handling
- Introduction to Jupyter Notebook
- Working with CSV files
- Introduction to NumPy
- Introduction to Pandas
08| Data Cleaning & Exploratory Data Analysis
Students learn how to transform raw and inconsistent datasets into analysis-ready data.
What You'll Learn
- Understanding raw datasets
- Data quality
- Missing values
- Duplicate records
- Incorrect data types
- Outlier identification
- Data transformation
- Data standardization
- Data normalization
- Data filtering
- Data aggregation
- Data merging
- Data manipulation using Pandas
- Exploratory Data Analysis
- Identifying patterns and trends
- Generating analytical insights
09| Power BI Fundamentals
Students learn to use Power BI to connect, transform, analyse, and visualize business data.
What You'll Learn
- Introduction to Power BI
- Power BI Desktop
- Connecting data sources
- Excel data
- CSV data
- Database connections
- Data transformation
- Power Query
- Data cleaning
- Data relationships
- Data models
- Calculated columns
- Measures
- Basic DAX
- Charts and visuals
- Filters
- Slicers
- Interactive reports
- Dashboard fundamentals
10| Business Intelligence & Dashboard Design
Students learn how to convert business requirements into useful analytical dashboards.
What You'll Learn
- Introduction to Business Intelligence
- Business KPIs
- Understanding business requirements
- Identifying analytical questions
- Dashboard planning
- Data storytelling
- Interactive dashboards
- Drill-downs
- Drill-throughs
- Filters and slicers
- KPI cards
- Trend analysis
- Comparative analysis
- Dashboard usability
- Dashboard design principles
- Presenting business insights
11| Intermediate Data Analytics Projects
Students develop complete analytics projects using SQL, Python, Excel, and Power BI.
Projects
- Sales Performance Dashboard
- Customer Analysis Dashboard
- HR Analytics Dashboard
- E-commerce Analytics
- Financial Performance Dashboard
- Marketing Campaign Analysis
Each project should include:
Data Cleaning → Data Analysis → Visualization → Dashboard → Business Insights
12 | Advanced Power BI & DAX
Students learn advanced Power BI capabilities used to build professional business intelligence solutions.
What You'll Learn
- Advanced Power Query
- Data transformation techniques
- Advanced data modelling
- Star schema fundamentals
- Fact and dimension tables
- Relationships
- Calculated columns
- Measures
- DAX fundamentals
- DAX functions
- CALCULATE
- FILTER
- Time intelligence
- Date tables
- YTD, MTD and YoY analysis
- Advanced KPIs
- Dynamic dashboards
- Drill-through reports
- Power BI report optimization
13| AI-Powered Data Analytics
Students learn how AI can accelerate data analysis, insight generation, visualization, and reporting.
What You'll Learn
- Introduction to AI in Data Analytics
- AI-assisted data exploration
- AI-assisted data cleaning
- AI-generated SQL queries
- AI-assisted Python analysis
- AI-assisted formula generation
- AI-powered insight generation
- AI-assisted visualization
- AI-powered data storytelling
- Natural language data analysis
- AI-assisted report generation
- AI-powered anomaly detection
- AI-assisted business recommendations
- Using AI to improve analyst productivity
- Responsible use of AI in analytics
14| Predictive Analytics Fundamentals
Students are introduced to predictive techniques that help organizations forecast future trends and outcomes.
What You'll Learn
- Introduction to Predictive Analytics
- Predictive vs Descriptive Analytics
- Business Forecasting
- Trend Analysis
- Time Series Fundamentals
- Moving Averages
- Forecasting Techniques
- Regression Fundamentals
- Classification Fundamentals
- Customer Churn Prediction
- Sales Forecasting
- Demand Forecasting
- Risk Analysis
- Model evaluation fundamentals
- Interpreting predictive results
Tools Introduced
- Python
- Pandas
- Scikit-learn – Introduction
- Power BI
15| Data Automation & AI Workflows
Students learn how to automate repetitive analytics processes and create efficient AI-assisted workflows.
What You'll Learn
- Introduction to Data Automation
- Automated data collection
- Automated data cleaning
- Automated reporting
- Scheduled data refresh
- Automated dashboard updates
- AI-assisted reporting
- Automated insight generation
- Excel automation fundamentals
- Python automation
- API fundamentals
- Connecting data sources
- Workflow automation
- AI-powered reporting workflows
- Error handling
- Monitoring automated workflows
Tools Introduced
- Python
- Power Automate
- Make / Zapier – Introduction
- APIs
- AI tools
16| Advanced Analytics & Business Intelligence
Students learn how professional analysts use analytics to support strategic business decisions.
What You'll Learn
- Advanced business analytics
- Customer analytics
- Marketing analytics
- Sales analytics
- Financial analytics
- HR analytics
- Product analytics
- Operational analytics
- Customer segmentation
- Cohort analysis
- Funnel analysis
- Retention analysis
- Profitability analysis
- KPI frameworks
- Business performance analysis
- Data-driven decision-making
- Advanced data storytelling
- Presenting insights to stakeholders
17| Professional Data Analytics Capstone & Career Preparation
Students complete an end-to-end analytics project that combines data preparation, SQL, Python, Power BI, AI, and business intelligence.
Capstone Project Process
Business Problem → Data Collection → Data Cleaning → SQL Analysis → Exploratory Analysis → KPI Development → Power BI Dashboard → AI-Assisted Insights → Predictive Analysis → Business Recommendations → Final Presentation
Capstone Project Options
Students can develop projects such as:
- E-commerce Business Analytics
- Sales & Revenue Analytics
- Customer Churn Analytics
- Marketing Performance Analytics
- HR Workforce Analytics
- Financial Analytics
- Healthcare Analytics
- Retail Analytics
- Supply Chain Analytics
- Product Analytics
What Students Will Create
- Cleaned datasets
- SQL analysis
- Python analysis
- Interactive Power BI dashboard
- KPI report
- AI-generated analytical insights
- Business recommendations
- Professional analytics presentation
- Final capstone report
Software & Tools Covered
Throughout the Professional Program, students will gain practical experience with:
- Data Analyst
- Business Data Analyst
- Business Intelligence Analyst
- Junior Data Scientist
- Reporting Analyst
- MIS Analyst
- Power BI Developer – Entry Level
- BI Analyst
- Marketing Data Analyst
- Sales Data Analyst
- Financial Data Analyst
- HR Data Analyst
- Operations Data Analyst
- Product Data Analyst
- Data Visualization Analyst
- AI-Powered Data Analyst
- Analytics Consultant – Entry Level
Career Opportunities
After completing the Professional Program, learners can pursue roles such as:
- AI Engineer
- Machine Learning Engineer
- Data Scientist
- Data Analyst
- Deep Learning Engineer
- NLP Engineer
- Computer Vision Engineer
- AI Application Developer
- MLOps Engineer (Entry Level)
- Business Intelligence Analyst
- Python Developer (AI Focus)
- Generative AI Developer
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Course Cetificate
Certificate
The Cloud Computing & DevOps Professional Program at Jigsaw TechnoHub is designed to help learners build practical skills in cloud infrastructure, Linux, networking, virtualization, DevOps practices, automation, containerization, and cloud deployment.
- Work with Linux and cloud infrastructure
- Understand networking and virtualization
- Deploy applications on cloud platforms
- Use Git and GitHub for version control
- Build and manage Docker containers
- Create CI/CD pipelines
- Automate infrastructure deployment
- Work with Infrastructure as Code
Ready to Build Your Future?
Your journey from graduate to professional starts here.Join Jigsaw TechnoHub and gain the skills, confidence, and industry exposure needed to succeed in today's competitive job market.