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LMS

Diploma in Data Analytics & Engineering

Diploma Program

The Diploma in Data Analytics & Engineering is a comprehensive industry-focused program designed to equip learners with practical skills in data analytics, business intelligence, data engineering, machine learning, visualization, Generative AI, and ethical data practices. The program combines theoretical foundations with extensive hands-on learning using modern tools and technologies. Learners will gain the ability to collect, process, analyze, visualize, and interpret data for strategic business decision-making across multiple industries.

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Duration

500+ hours

9 Months @ 15 hours per week
OR
8 weeks @ 25 hours per week

Session Days

Weekdays
Weekends

Course Delivery

Classroom
Live Remote

Target Audience

Students, Professionals

Level

Foundation to Intermediate

Cost

PKR 20,000 per month USD 499

* Installments options are available

Program Features

  • Delivered by experienced & certified instructors
  • Extensive coverage of Data Analytics, Engineering, AI, and Visualization
  • Exposure to modern ML & AI frameworks and APIs.
  • Hands-on experience with data visualization tools
  • Focus on Problem Solving & Critical Thinking
  • STEM based Approach
  • Skill based Curriculum
  • Project-based Learning
  • Focus on practical hands-on with real-world example cases
  • Focus on business intelligence and decision-making
  • Generative AI integration within analytics workflows
  • Data engineering and ETL pipeline development
  • Instructor-led sessions and guided labs
  • Mentorship from industry professionals
  • Mock examinations and quizzes
  • Very high student satisfaction
  • Certificate of Specialization
Data Analytics and Data Engineering are among the fastest-growing domains globally due to the increasing reliance on data-driven decision-making across industries. Organizations require professionals who can manage, process, visualize, and analyze large datasets to improve operational efficiency and business performance. This program prepares learners with in-demand analytical and technical skills required in business intelligence, artificial intelligence, cloud analytics, and digital transformation initiatives, making graduates highly valuable in modern workplaces.

Why to Study This Program?

AI-powered Tools widely used in Academia

High global demand for data analytics professionals

Increase employability in Data Analyticsroles

Future-proof your career

Career transition into data science and AI roles

Develop skills highly valued by employers

Learn modern visualization and BI tools

Improveu0026nbsp; productivity with AI tools

Gain programming and database management expertise

Enhance analytical and problem-solving abilities

Prepare for roles in analytics and engineering domains

Learn ethical and responsible use of data technologies

Skills Learners Will Gain

  • Data visualization and storytelling
  • Statistical data analysis
  • Python programming for analytics
  • Exploratory data analysis (EDA)
  • Dashboard design and development
  • Python programming for analytics
  • Dashboard design and development
  • SQL database querying and management
  • Data preprocessing and cleaning
  • Probability modeling
  • Predictive and prescriptive analytics
  • ETL workflow development
  • Web scraping and data extraction
  • Business intelligence reporting
  • Generative AI applications in analytics
  • Prompt engineering techniques
  • Machine learning model development
  • Model evaluation and optimization
  • Hyperparameter tuning
  • Ethical and secure data handling
  • Working on AI Projects

Program Learning Outcomes

By the end of the program, learners will be able to:

  • Apply data analytics techniques to solve business and organizational problems.
  • Perform data acquisition, transformation, cleaning, and preparation using analytical tools.
  • Develop Python-based analytical and machine learning solutions.
  • Design and manage relational databases using SQL
  • Create interactive dashboards and visual reports using Power BI and Tableau.
  • Build ETL workflows and data engineering pipelines.
  • Apply Generative AI tools and prompt engineering techniques in analytics workflows
  • Demonstrate ethical, secure, and responsible use of data and AI technologies.

Target Job Roles

  • Data Scientist
  • Data Analyst
  • Business Intelligence Analyst
  • Software Engineer
  • AI & ML Engineer
  • Database Administrators
  • Researcher in AI/ML
  • Business Intelligence Professionals

Curriculum

Topics Practical Work Tools & Technologies
▸ Introduction to Data Analytics ▸ Role of Data Analyst ▸ Introduction to Business Analytics ▸ Excel for Analytics ▸ Conditional Formatting & Functions ▸ Analysis with Pivot Tables ▸ Statistical Data Analysis ▸ Dashboard Creation ▸ Introduction to Power BI ▸ Data Visualization Principles ▸ Analytics lifecycle activity ▸ Case study analysis ▸ Business KPI exercises ▸ Spreadsheet reporting labs ▸ Formula-based assignments ▸ Sales data reporting ▸ Statistical calculations ▸ Dashboard development ▸ Data import and modeling ▸ Visualization redesign tasks ▸ Google Sheets ▸ Power BI ▸ MS Excel
Topics Practical Work Tools & Technologies
▸ Python Introduction ▸ Data Types & Operators ▸ Conditional Statements ▸ Loops ▸ Functions ▸ Error Handling ▸ File Handling ▸ NumPy Fundamentals ▸ Working with pandas ▸ Visualization ▸ Python setup and coding exercises ▸ Operator practice labs ▸ Decision-based programs ▸ Iterative programming exercises ▸ Modular coding activities ▸ Exception handling labs ▸ CSV and text processing ▸ Numerical computation labs ▸ Dataset manipulation ▸ Plotting exercises ▸ Python ▸ VS Code ▸ NumPy ▸ pandas ▸ Matplotlib
Topics Practical Work Tools & Technologies
▸ Python Analytics Packages ▸ Text and Categorical Data ▸ Statistical Functions ▸ Hypothesis Testing ▸ Exploratory Data Analysis ▸ Data Visualization ▸ Package installation and usage ▸ Sentiment analysis mini lab ▸ Statistical exercises ▸ A/B testing simulation ▸ EDA case study ▸ Analytical dashboards ; ▸ pandas ▸ NumPy ▸ Python ▸ SciPy ▸ Matplotlib ▸ Seaborn
Topics Practical Work Tools & Technologies
▸ Fundamental SQL Statements ▸ Database Backup & Restore ▸ Filtering & Ordering ▸ Joins ▸ Subqueries ▸ Views & Indexes ▸ Query writing labs ▸ Backup simulations ▸ Data retrieval exercises ▸ Multi-table reporting ▸ Nested query labs ▸ Query optimization tasks ▸ MySQL ▸ SQL Server ▸ PostgreSQL
Topics Practical Work Tools & Technologies
▸ Data Collection ▸ Data Preparation ▸ Statistical Summaries ▸ Exploratory Data Analysis (EDA) ▸ Descriptive Statistics ▸ Measures of Central Tendency ▸ Measures of Dispersion ▸ Percentiles and Quartiles ▸ Trend Analysis ▸ Dashboard Reporting ▸ Communicating Insights through Visuals ▸ Dataset acquisition activities ▸ Cleaning exercises ▸ Reporting labs ▸ Visualization and summaries ▸ Sales trend analysis ▸ KPI dashboard creation ▸ Excel ▸ Python ▸ pandas ▸ Power BI ▸ Tableau
Topics Practical Work Tools & Technologies
▸ AI ▸ AI  ▸ Explore  ▸ Demonstrate  ▸ ChatGPT ▸ Google Gemini
Topics Practical Work Tools & Technologies
▸ AI ▸ AI  ▸ Explore  ▸ Demonstrate  ▸ ChatGPT ▸ Google Gemini
Topics Practical Work Tools & Technologies
▸ AI ▸ AI  ▸ Explore  ▸ Demonstrate  ▸ ChatGPT ▸ Google Gemini
Topics Practical Work Tools & Technologies
▸ AI ▸ AI  ▸ Explore  ▸ Demonstrate  ▸ ChatGPT ▸ Google Gemini
Topics Practical Work Tools & Technologies
▸ AI ▸ AI  ▸ Explore  ▸ Demonstrate  ▸ ChatGPT ▸ Google Gemini
Topics Practical Work Tools & Technologies
▸ AI ▸ AI  ▸ Explore  ▸ Demonstrate  ▸ ChatGPT ▸ Google Gemini
Topics Practical Work Tools & Technologies
▸ AI ▸ AI  ▸ Explore  ▸ Demonstrate  ▸ ChatGPT ▸ Google Gemini

Project Work

▸ Problem identification

▸ Data collection & preprocessing

▸ Model selection & training

▸ Evaluation & optimization

▸ Deployment-ready prototype

Tools

▸ Python, scikit-learn, TensorFlow

▸ Jupyter Notebook

▸ GitHub for version control

Deliverables

▸ Working AI model

▸ Project report

▸ Presentation & demo

Bootcamp Types

Weekend Intensive
  • Session Days:
    3 Full Days
  • Session Length:
    8 hours per day
  • Course Duration:
    3 Days
  • Total Contact Hours:
    24 hours
Weekday Regular
  • Session Days:
    Every Monday, Tuesday, Wednesday
  • Session Length:
    2 hours per day
  • Course Duration:
    40 weeks
  • Total Contact Hours:
    24 hours
Weekend Regular
  • Session Days:
    Every Saturday, Sunday
  • Session Length:
    3 hours per day
  • Course Duration:
    4 weeks
  • Total Contact Hours:
    24 hours

Upcoming Bootcamps

  • Bootcamp ID: A
  • Bootcamp Type: Weekend Regular
  • Class Days: Saturday, Sunday
  • Timing: 9:00am - 12:00pm UTC
  • Start Date: Mar 21, 2026
  • End Date: Apr 26, 2026
  • Registration Early Bird: Mar 6, 2026
  • Registration Deadline: Mar 15, 2026
  • Location: Online Live Class
  • Training Fee (Early Bird): USD 99
  • Training Fee (Regular): USD 99
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