Superport Lab – Standard AI & Data Science Live Training

Superport Lab – Standard AI & Data Science Live Training

$49.00

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Superport Lab – Standard Program

Work on Live UK Government Projects – While You Learn

About the Program

The Standard Program is the most popular track for learners who want structured training, mentorship, and community-driven growth.

Program Description

This program offers everything in Basic plus recordings, mentorship, guided projects, and a certificate. Learners benefit from a mix of live learning, peer collaboration, and career development sessions.

Who Can Take This Program

  • Students in college looking for structured learning
  • Working professionals who want recordings for flexible study
  • Learners aiming to add projects and certification to their portfolio

What’s Included

  • Live workshops & labs
  • Weekly group mentorship sessions
  • WhatsApp/Telegram community
  • Assignments & project-based learning
  • Career guidance sessions (resume prep, interview tips)
  • Graded certificate upon completion
  • Lifetime access to recordings

Course Modules / Curriculum

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.

Module 7: Power BI

  • 7.1 Power BI introduction
  • 7.2 Power BI concepts
  • 7.3 Power BI dashboard building.
  • 7.4 Power BI project building and advanced concept

Module 8: Techniques

8.1 Advanced Techniques:

  • Ensemble Methods: Random Forests and Gradient Boosting (XGBoost).
  • Unsupervised Learning: K-Means Clustering for customer segmentation.
  • Introduction to Feature Engineering.

8.2 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.

8.3 Introduction to Deployment:

  • Learn how to build a simple, interactive web app for your model using Streamlit.

Additional Benefits

  • Monthly industry expert masterclasses
  • Mini-hackathons & coding challenges
  • One code/project review per month
  • Discounted upgrade option to Premium

Certification & Outcomes
Earn a graded Standard Certificate. Outcomes include:

  • Improved technical & practical skills
  • Resume-ready projects
  • Interview preparation

The ambitious beginner ready to master the essentials.

Build a rock-solid foundation in ai & data science with our immersive, live training program. This is the perfect first step into the world of data, designed to give you core, in-demand skills quickly and effectively.

  • Live Instructor-Led Training: Learn directly from industry experts in interactive, 4-week sessions where you can ask questions and get instant feedback.
  • Complete Class Recordings: Never miss a concept. Revisit any lesson, anytime, with lifetime access to all course materials.
  • Official Certificate of Completion: Earn a verifiable credential to showcase your new skills on your CV and LinkedIn profile

About the Program

Superport-IT is a UK-based tech company engaged in UK Government projects through departments like Home Office, Civil Service, and the Department for Transport.

To meet growing talent demands, we are hiring freshers Data Scientists and Analysts and conducting a 3-month live training program to prepare learners for these roles. This isn’t just a course  it’s a launchpad into real-world impact. We offer up to 6LPA if you are selected in the hiring process after the training.

 What You’ll Learn

  • Python for Data Science
  • Data Analysis & Visualization
  • Real-time Project Handling
  • Communication for Data Professionals
  • Working with UK Govt. Data Sets
  • Final Project & Interview Prep

 Why Choose Us?

  • Live Training by Inhouse Data Scientist
  • Hands-on Projects from actual UK government case studiesJob Interview Consideration after training
  • Internship Certificate & Portfolio Development
  •  Guided Career Support & CV Building

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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.

Module 7: Power BI

7.1 Power BI introduction

7.2 Power BI concepts

7.3 Power BI dashboard building.

7.4 Power BI project building and advanced concept

 
Module 8: Techniques
8.1 Advanced Techniques:
  • Ensemble Methods: Random Forests and Gradient Boosting (XGBoost).
  • Unsupervised Learning: K-Means Clustering for customer segmentation.
  • Introduction to Feature Engineering.
8.2 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.
8.3 Introduction to Deployment:
  • Learn how to build a simple, interactive web app for your model using Streamlit.