Your AI & Data Science Journey Starts Here

Work on live UK Government–grade projects through Superport Lab—or start with our In‑house Free Training and get job‑ready first.

Apply to Superport Lab

For candidates already trained in Data Science/AI or React.js & Node.js

Enroll in Free Training *($42 admin fee for infrastructure cost and co-ordination)

For beginners or upskillers who need end‑to‑end training

Apply to Superport Lab
Enroll in Free Training

Who We Are

Superport‑IT is a UK‑based tech company delivering enterprise solutions with UK public‑sector standards. We run two pathways:

Superport Lab (Live Projects)

contribute to real solutions alongside our engineers.

Superport Training

an end‑to‑end, mentor‑led program that prepares you for Lab work and junior roles.

Choose Your Path

We offer two powerful programs designed for your specific career goals, whether you’re starting out or aiming for a top job.

Superport Lab (Live Project Work)

Best For

Candidates already trained in Data Science & AI or React.js & Node.js and highly competent in applying these skills in live environment

What you’ll do

Build and ship production‑grade features for enterprise clients

Follow our engineering playbook (Git, CI/CD, code reviews, API standards)

Pair with senior engineers and a Data Scientist on scoped tasks

Get portfolio artifacts: PRs, tickets, demo videos, and deploy links

Tracks

Data Science & AI Track

Python, pandas, scikit‑learn, TensorFlow/PyTorch, FastAPI, MLOps basics (experiments, model registry, monitoring).

React.js & Node.js Track

React + Vite, Node/Express, REST APIs, authentication, Postgres/Mongo, testing, Docker.

Eligibility

Prior training or equivalent experience in the chosen track

Clear coding fundamentals (we run a short assessment)

Outcome

Real‑world project experience, Supervisor feedback, reference letter (on strong performance), and an interview pathway for open roles at Superport‑IT.

Apply in Superport Lab

Not trained? No problem.

Enroll to our in‑house free Training, then move into Superport Lab.

How it works

Apply online

Join the cohort

Build mini‑projects

Capstone

Transition to Superport Lab

Superport Training (If you’re not trained yet)

What you get

End‑to‑end curriculum (foundations → tools → projects → capstone)

Guidance from our in‑house Data Scientist/Engineers

Interview prep (portfolio, mock interviews)

Certificate of completion

Program fee

Training is provided at no tuition cost.

We only charge a $42 administrative fee to cover onboarding, LMS access, cohort coordination, and support.

No hidden charges.

Pace & Mode

6–8 weeks, online live + recordings

8 hours/week recommended

Outcome

You’ll be job‑ready for junior roles and eligible to enter Superport Lab to gain live‑project experience.

Enroll for Training ($42 admin only)
New Lab work Enrollments are Open Now

Sept 10th 2025

Limited Seats Available

Enroll Now

Your AI & Data Science,
Journey Starts Here

 Our Live AI & Data Science Lab work is your direct path to entering this high-growth field. Work along with our Data Scientist on a day-to-day basis and develop AI models. If you are not trained in Data Science, your application for internal training will be selected after written test, and we cannot guarantee that you will be selected for our training program. You can find more training details below.

Explore Course

What You’ll Build

DS/AI: data pipeline, feature store stub, model training script, evaluation report, FastAPI model service, monitoring notebook

Dev: secure auth flow, CRUD API, dashboard in React, tested endpoints, Dockerized deploy

Selection Process

  • 1. Application form
  • 2. Check Code
  • 3. Quick call
  • 4. Onborading Process

What Our Learners Say

Real stories. Real success.

At Superport-IT, our AI & Data Science training has transformed careers across the globe. From fresh graduates to working professionals, our learners have gained practical skills, cracked interviews, and secured roles at Superport-IT and leading companies worldwide.

Michael Carter (San Francisco, USA)

The AI & Data Science program at Superport-IT gave me hands-on exposure to real projects. I landed a role as a Machine Learning Engineer within three months of completion.

Jessica Lee (New York, USA)

Superport-IT’s training bridged the gap between theory and practice. I was hired at a top FinTech startup, and I still use the case studies from the course every day

Rohit Sharma (Bengaluru, India)


Superport-IT’s AI training was excellent. I joined their team as a Data Science Intern and quickly got promoted to Analyst

Ananya Verma (Hyderabad, India)


From being a fresher to landing a role at a top MNC, my journey started at Superport-IT. Their placement support is unmatched.

Priya Singh (Delhi, India)


I completed my AI & Data Science program and immediately got placed at Superport-IT. The environment here encourages growth and innovation.

Daniel Clark (Liverpool, UK)


The trainers are industry experts. Their guidance prepared me for tough interviews, and now I’m employed as a Machine Learning Engineer.

Ryan Walker (Texas, USA)


Superport-IT’s program was a game changer for me. From a marketing professional, I transitioned into an AI Specialist role at a leading tech firm.

Sophia Turner (Edinburgh, UK)

I highly recommend Superport-IT. Their AI program not only gave me technical skills but also confidence. I now work at Superport-IT as a Data Engineer.

Vikram Joshi (Mumbai, India)


The course gave me practical skills, not just theory. Today, I’m working on exciting AI projects at Superport-IT itself.

Meera Nandakumar (Trivandrum, India)

Superport-IT’s AI training transformed my career path. I’m now a Data Scientist with a multinational company and loving the journey.

About the Program

Superport-IT is a UK-based tech company delivering live UK Government projects in collaboration with departments such as the Home Office, Civil Service, Department for Transport, and the Government Digital Service.

To meet the growing demand for skilled talent, we run the AI & Data Science Lab & Recruitment Program, offering two specialised tracks:
• Data Science Track – For candidates already trained in Data Science and AI.
• Development Track – For candidates already trained in React.js and Node.js.

Through our Lab & Recruitment Program, you will:
• Work day-to-day with experienced professionals.
• Get hands-on with real-world projects from the UK Government.
• Build a portfolio that proves your skills in a competitive job market.

How does it work?

we’re here to Answer all your questions

Yes, there is no tuition. We only charge a $42 administrative fee to cover coordination and platform costs.

We don’t guarantee jobs. We do provide real project experience, a strong portfolio, interview prep, and priority consideration for roles at Superport‑IT.

Yes—if you are already trained in DS/AI or React & Node and clear our assessment.

Training: completion certificate. Lab: performance‑based reference and documented contributions (PRs, tickets, demos).

Yes. Live sessions + recordings; weekly targets; async support.

No. You cannot apply for support lab while you are studying

Scroll to Top
close menu icon

Data Science Foundation Track

Course Curriculum: Your Journey from Novice to Data Scientist

Our curriculum is meticulously designed to take you on a step-by-step journey through the world of data science. Each module builds upon the last, blending core theory with practical, hands-on projects to ensure you don’t just learn—you learn by doing.

Module 1: Kickstart & Python Fundamentals (Week 1-2)

Objective: Build the foundational launchpad for your data science journey.

1.1 Introduction to Data Science:
  • What is Data Science, AI, and Machine Learning?
  • Roles & Responsibilities: Data Analyst vs. Data Scientist vs. ML Engineer.
  • The Data Science Lifecycle: From Business Problem to Deployed Solution.
1.2 Python Programming Essentials:
  • Setting up Your Environment: Anaconda, Jupyter Notebooks & VS Code.
  • Python Basics: Variables, Data Types, Operators, and Control Flow (Loops & Conditionals).
  • Core Data Structures: Lists, Tuples, Dictionaries, and Sets.
  • Functions and Object-Oriented Programming (OOP) Concepts.
  • Project 1: Create a simple command-line application (e.g., a calculator or a text-based game) to solidify Python programming logic.

Module 2: Data Analysis with NumPy & Pandas (Week 3-4)

Objective: Learn to manipulate, clean, and analyze complex datasets with industry-standard libraries.

2.1 Numerical Computing with NumPy:
  • Introduction to NumPy Arrays and their advantages over Python Lists.
  • Array Indexing, Slicing, and Mathematical Operations.
  • Statistical functions and Linear Algebra basics.
2.2 Data Manipulation with Pandas:
  • Introduction to Pandas Series and DataFrames.
  • Importing Data: Reading from CSV, Excel, and other file formats.
  • Data Cleaning: Handling Missing Values, Duplicates, and Inconsistent Data.
  • Indexing, Filtering, and Sorting DataFrames (iloc, loc).
  • Grouping and Aggregation with groupby().
  • Project 2: Take a messy, real-world dataset (e.g., sales data) and perform a full data cleaning and initial analysis to uncover key insights.
Module 3: Database Management with SQL (Week 5)

Objective: Master the art of querying databases to extract exactly the data you need.

3.1 Relational Database Fundamentals:
  • Understanding Tables, Primary Keys, and Foreign Keys.
3.2 Essential SQL Queries:
  • SELECT, FROM, WHERE for data retrieval.
  • GROUP BY, HAVING, ORDER BY for data aggregation and sorting.
3.3 Advanced SQL:
  • Joining multiple tables (INNER, LEFT, OUTER JOINs).
  • Subqueries and Common Table Expressions (CTEs).
  • Project 3: Answer complex business questions by writing SQL queries against a sample relational database (e.g., an e-commerce store database).
Module 4: Data Visualization & Storytelling (Week 6)

Objective: Transform raw data into compelling visual stories that drive business decisions.

4.1 Principles of Effective Visualization:
  • Choosing the right chart for your data.
  • The art of storytelling with data.
4.2 Visualization with Matplotlib & Seaborn:
  • Creating basic plots with Matplotlib (Line, Bar, Scatter).
  • Building advanced statistical plots with Seaborn (Heatmaps, Box Plots, Violin Plots).
  • Customizing plots for professional reports.
4.3 Interactive Dashboards:
  • Introduction to interactive plotting with libraries like Plotly.
  • Project 4: Create a multi-chart dashboard to present the findings from your Project 2 dataset, telling a clear story about the insights you discovered.
Module 5: Essential Statistics & Probability (Week 7)

Objective: Understand the statistical concepts that form the backbone of all data science models.

5.1 Descriptive Statistics:
  • Measures of Central Tendency (Mean, Median, Mode).
  • Measures of Dispersion (Variance, Standard Deviation).
5.2 Probability Distributions:
  • Understanding Normal, Binomial, and Poisson distributions.
5.3 Inferential Statistics:
  • Hypothesis Testing, P-values, and Confidence Intervals.
  • Introduction to A/B Testing for business decision-making.
  • Project 5: Analyze the results of a sample A/B test to determine if a change to a website resulted in a statistically significant improvement.
Module 6: Machine Learning Fundamentals (Week 8-9)

Objective: Build and evaluate your first predictive models.

6.1 Introduction to Machine Learning:
  • Supervised vs. Unsupervised vs. Reinforcement Learning.
  • The Train-Test Split and Model Evaluation Metrics.
6.2 Regression Models:
  • Linear Regression: Predicting continuous values (e.g., house prices).
6.3 Classification Models:
  • Logistic Regression: Predicting binary outcomes (e.g., customer churn).
  • K-Nearest Neighbors (KNN) and Decision Trees.
  • Project 6: Build two models: one to predict house prices from a real estate dataset and another to predict customer churn from a telecom dataset.
  • Module 7: Advanced Machine Learning & Capstone Project (Week 10-12)

Objective: Tackle complex problems with advanced algorithms and complete a portfolio-worthy project.

7.1 Advanced Techniques:
  • Ensemble Methods: Random Forests and Gradient Boosting (XGBoost).
  • Unsupervised Learning: K-Means Clustering for customer segmentation.
  • Introduction to Feature Engineering.
7.2 Capstone Project:
  • Choose from a selection of real-world business problems.
  • Apply the entire data science lifecycle: Data Acquisition, Cleaning, EDA, Modeling, and Interpretation.
  • Present your findings and methodology in a final report.
7.3 Introduction to Deployment:
  • Learn how to build a simple, interactive web app for your model using Streamlit.