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.
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
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?
Skills Learners Will Gain
Program Learning Outcomes
By the end of the program, learners will be able to:
Target Job Roles
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 |
|---|---|---|
| ▸ Root Cause Analysis ▸ Probability Theory ▸ Sampling Techniques ▸ Hypothesis Testing ▸ Correlation & Causation ▸ Regression Analysis ▸ Anomaly Detection ▸ Comparative Analysis Techniques ▸ Data Interpretation and Insight Generation | ▸ Problem investigation labs ▸ Probability simulations ▸ Sampling exercises ▸ Statistical testing labs ▸ Correlation analysis ▸ Regression modeling ▸ Fraud detection case study | ▸ Excel ▸ Python ▸ SciPy ▸ pandas ▸ scikit-learn |
| Topics | Practical Work | Tools & Technologies |
|---|---|---|
| ▸ Introduction to Machine Learning ▸ Supervised Learning ▸ Regression Models ▸ Classification Techniques ▸ Unsupervised Learning ▸ Clustering Techniques ▸ Model Evaluation ▸ Hyperparameter Tuning ▸ Forecasting & Predictive Modeling | ▸ ML setup activities ▸ Prediction labs ▸ Forecasting exercises ▸ Classification tasks ▸ Pattern discovery labs ▸ Customer segmentation ▸ Accuracy evaluation ▸ Model optimization ▸ Demand forecasting project ; | ▸ scikit-learn ▸ Python ▸ GridSearchCV |
| Topics | Practical Work | Tools & Technologies |
|---|---|---|
| ▸ Decision-Making Models ▸ Optimization Fundamentals ▸ Discrete Optimization ▸ Nonlinear Optimization ▸ Simulation Techniques ▸ Risk-Based Analysis | ▸ Business scenario analysis ▸ Optimization exercises ▸ Scheduling activities ▸ Optimization modeling ▸ Monte Carlo simulation ▸ Risk assessment project | ▸ Excel ▸ Python ▸ PuLP |
| Topics | Practical Work | Tools & Technologies |
|---|---|---|
| ▸ ETL Fundamentals ▸ Data Extraction ▸ Web Scraping ▸ Data Transformation ▸ Data Mapping & Conversion ▸ ETL Tools ▸ Batch & Real-Time ETL ▸ Error Handling & Logging ▸ Automation ▸ Monitoring & Maintenance | ▸ Workflow mapping ▸ API and file extraction ▸ Scraping exercises ▸ Data cleaning pipelines ▸ Schema conversion ▸ Workflow development ▸ Streaming demonstrations ▸ Logging exercises ▸ ETL automation tasks ▸ Workflow monitoring | ▸ Python ▸ BeautifulSoup ▸ pandas ▸ Apache Airflow ▸ Kafka ▸ Cron ▸ Airflow |
| Topics | Practical Work | Tools & Technologies |
|---|---|---|
| ▸ Tableau Fundamentals ▸ Data Organization ▸ Charts & Dashboards ▸ Filters & Parameters ▸ Heat Maps, Treemaps & Pareto Charts ▸ Designing Interactive Dashboards ▸ Storytelling with Data ▸ Dashboard Publishing ▸ Visualization Best Practices | ▸ Dashboard setup ▸ Dataset preparation ▸ Visualization labs ▸ Interactive reports ▸ Advanced visualization tasks ▸ Storyboard presentation ▸ Dashboard deployment | ▸ Tableau |
| Topics | Practical Work | Tools & Technologies |
|---|---|---|
| ▸ Generative AI Basics ▸ AI in Analytics ▸ Neural Networks & GANs ▸ Transformers & LLMs ▸ Prompt Engineering ▸ Data Augmentation ▸ AI-Based Visualization ▸ AI in ETL ▸ AI Forecasting ▸ Future of AI Analytics | ▸ AI demonstrations ▸ AI workflow case studies ▸ AI model visualization ▸ Chatbot interaction labs ▸ Prompt creation exercises ▸ Synthetic data generation ▸ Visualization automation ▸ Intelligent transformation tasks ▸ Predictive analytics enhancement ▸ Industry trend analysis | ▸ ChatGPT ▸ Python ▸ ChatGPT ▸ TensorFlow ▸ Hugging Face ▸ OpenAI Playground ▸ Python ▸ Power BI AI ▸ Python ▸ AutoML ▸ Research Platforms |
| Topics | Practical Work | Tools & Technologies |
|---|---|---|
| ▸ Introduction to Data Ethics Legal & Regulatory Frameworks Privacy & Security Bias in Data Responsible Visualization Societal Implications | ▸ Ethics discussion sessions Compliance analysis Data protection exercises Bias detection labs Ethical dashboard redesign Group presentations | ▸ LMS GDPR Case Studies Security Tools Python Tableau Research Articles |
| Project Activities | Deliverables | Tools & Technologies |
|---|---|---|
| ▸ Industry-oriented project ▸ Data collection & preparation ▸ Analytics & visualization ▸ Predictive modeling ▸ Final presentation | ▸ Project proposal ▸ Dataset and ETL workflow ▸ Dashboard and reports ▸ ML models ▸ Report and presentation | ▸ Any relevant tools ▸ Python, SQL ▸ Power BI, Tableau ▸ scikit-learn ▸ PowerPoint |