Specialization Certificate in Data Science
Specialization Certificate 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 30,000 per month USD 99 per month
* 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 |
|---|---|---|
| ▸ 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 |
|---|---|---|
| ▸ 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 |
| 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 |