What this course is about
A twelve-month data science and analytics roadmap built around real datasets, business insights, ML models, dashboards, and a capstone portfolio.
Master Python, statistics, analytics, machine learning, dashboards, and portfolio-ready data projects.
Master Data Science and Analytics with hands-on projects, expert guidance, and a curriculum designed for real-world success.
Interactive sessions with expert instructors
Hands-on experience with industry projects
Create impressive projects for your portfolio
Personal mentorship and career counseling
Industry-recognized certification
Skills that employers are looking for
Latest tools and technologies
Professional resume and interview prep
Course overview
A twelve-month data science and analytics roadmap built around real datasets, business insights, ML models, dashboards, and a capstone portfolio.
A twelve-month data science and analytics roadmap built around real datasets, business insights, ML models, dashboards, and a capstone portfolio.
Students who want structured, practical, portfolio-focused training from SystemGrid Academy.
Build and present a portfolio-ready data science and analytics capstone with review checkpoints.
Tech stack glow map
Each tool is connected to a build step, not listed as decoration.
Python
Used inside Data Science and Analytics projects with guided practice and review checkpoints.
NumPy
Numerical arrays, feature matrices, and fast calculations behind ML workflows.
Pandas
Clean messy CSV/Excel data and prepare datasets for model training.
Matplotlib
Used inside Data Science and Analytics projects with guided practice and review checkpoints.
Seaborn
Used inside Data Science and Analytics projects with guided practice and review checkpoints.
Scikit-learn
Used inside Data Science and Analytics projects with guided practice and review checkpoints.
SQL
Used inside Data Science and Analytics projects with guided practice and review checkpoints.
Power BI
Used inside Data Science and Analytics projects with guided practice and review checkpoints.
Jupyter Notebook
Used inside Data Science and Analytics projects with guided practice and review checkpoints.
GitHub
Used inside Data Science and Analytics projects with guided practice and review checkpoints.
Curriculum Journey
Pick a quarter from the fixed roadmap and review only that stage, so the page stays focused and every topic explains its real purpose.
Quarter 1
Python programming, statistics thinking, and clean dataset preparation
Duration
3 Months
Students learn what Python fundamentals is used for, where it fits in Data Science and Analytics, and then practice it through guided tasks inside Python, Statistics, and Data Foundations.
Step 1
Organize code, notes, charts, and findings in one analysis notebook for review and portfolio presentation.
Step 2
Use NumPy arrays for fast numerical calculations, feature matrices, vector operations, and the math layer behind machine-learning datasets.
Step 3
Clean CSV/Excel data, fix missing values, group records, reshape tables, and prepare real datasets before model training.
Step 4
Students learn what Data types and structures is used for, where it fits in Data Science and Analytics, and then practice it through guided tasks inside Python, Statistics, and Data Foundations.
Step 5
Understand mean, median, spread, distribution, and what they reveal about real datasets.
Step 6
Students learn what Probability basics is used for, where it fits in Data Science and Analytics, and then practice it through guided tasks inside Python, Statistics, and Data Foundations.
Step 7
Explore the complete curriculum with hierarchical topic organization and concept breakdown.
Learning outcomes
Finish with practical evidence of your progress and a clear next step for work, freelancing, or further learning.
Build strong fundamentals before moving into professional workflows.
Practice each quarter through structured tasks and guided review checkpoints.
Finish projects that clearly show what you can create and explain.
Learn tools, communication habits, and delivery patterns used in professional environments.
Start with monthly payments, save with quarterly plans, or get the biggest discount with full-course payment.
Optional referral code preview. Final checkout always uses backend calculation.
Fee summary
Gross amount
Rs 5,000
Plan discount
Rs 0
Referral discount
Rs 0
Total savings
Rs 0
Your student portal access activates only after secure backend payment verification.
Score 80% or above in your quarterly test and unlock 50% off for the next quarter after approval.
Your friend gets PKR 500 off their first payment. You earn PKR 1,000 wallet credit after their payment is verified.
Admissions
Applying does not lock you into a batch. Our team will first help you confirm the right course, schedule, mode, and fee plan.
Course FAQ
Need something more specific? Submit the form below and our admissions team will guide you.
Yes. SystemGrid Academy focuses on live online classes with structured support and practical project guidance.
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