Superport DataSprint 1.0: Your First Machine Learning Challenge

Superport DataSprint 1.0: Your First Machine Learning Challenge

₹50.00

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Ready to move from theory to practice? Join Superport DataSprint 1.0, an exciting online machine learning hackathon where your skills can earn you serious rewards. This is your chance to tackle a real-world showcase your talent, and compete for the top spot.

Compete for exciting cash prizes:

🥇 First Prize: A grand prize of ₹5,000 + Placement in Superport-IT as Data Scientist with Salary up to 6 LPA

🥈 Second Prize: An impressive ₹2,000

🥉 Third Prize: A respectable ₹500

Beyond the prizes, you will gain hands-on experience by building a project for your portfolio and get your code reviewed by industry experts from Superport-IT. All participants with valid submissions will also receive a Certificate of Participation.

This event is open to all students, professionals, and learners with a foundational knowledge of Python. Don’t miss this opportunity to learn, build, and win!

Rs 50 is a nominal entry fee to avoid spam users.

Note: Please provide valid mail address, the invoice will be sent to same email. Please check your spam folder if you are not able to find in your primary folder. It might take some time to receive the invoice. Thank you for your understanding.

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Unleash Your Data Science Skills. Build. Compete. Win

Superport DataSprint 1.0 is Live!

Join our thrilling Hackathon Challenge and put your data skills to the test!

Showcase your talent
Win ₹5,000 + Placement Opportunity
Last Date to Register: 16th August 2025

Enroll Now
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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.

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.
Module 8: Career Accelerator Workshop (Exclusive to the ₹58,000 Program)

Objective: Polish your professional profile and prepare for the job market.

8.1 Building Your Professional Brand:
  • Crafting a high-impact, keyword-optimized CV/Resume.
  • Optimizing your LinkedIn profile to attract recruiters.
8.2 Acing the Interview:
  • Technical Interview Preparation (SQL, Python, ML Theory).
  • Live Mock Interviews with personalized feedback.
8.3 Internship & Placement:
  • Onboarding for your guaranteed internship.
  • Workshops on abroad assistance (SOP/LOR writing) and job application strategies.