Data Science and Machine Learning Internship (Python Track) (BE)
Internship Overview
This internship is a highly structured, project-driven program designed to transition candidates from programming novices to capable data practitioners. Over the course of the internship, participants will immerse themselves in the Python data ecosystem. They will learn how to extract and clean messy data, conduct Exploratory Data Analysis (EDA) to find hidden trends, and apply statistical theories. The program culminates in building, evaluating, and tuning both supervised and unsupervised Machine Learning models to solve real-world business problems.
BE SEM VI-VII STUDENTS
Internship Objective
To equip aspiring data professionals with hands-on, industry-relevant experience by taking them through the complete data science lifecycle. The goal is to bridge the gap between theoretical academic concepts and practical application, enabling interns to transform raw data into actionable insights and robust predictive models
Brief Description
Module 1: Foundations & Data Wrangling
- Covered Syllabus: 1. Python Basics, 2. Working with Data, 3. NumPy, 4. Pandas
- Task Details: Interns will be provided with a raw, unstructured dataset (e.g., retail sales or web server logs in CSV/JSON formats).
- Deliverable: Write a Python script to ingest the files, handle missing/null values, utilize Pandas and NumPy to aggregate the data by specific metrics (like monthly sales or user demographics), and output a clean, structured dataset.
Module 2: Insights & Exploratory Data Analysis (EDA)
- Covered Syllabus: 5. Data Visualization, 6. Exploratory Data Analysis (EDA)
- Task Details: Using the cleaned data from Phase 1, interns will use Matplotlib and Seaborn to identify correlations, outliers, and distributions.
- Deliverable: Create an interactive EDA dashboard or a comprehensive Jupyter Notebook report presenting at least 5 key business insights through visualizations (Heatmaps, Scatter plots, Boxplots).
Module 3: Statistics & Data Preprocessing
- Covered Syllabus: 7. Data Preprocessing, 8. Probability & Statistics
- Task Details: Interns must prepare their data for machine learning by applying statistical tests to validate hypotheses (e.g., A/B testing on user behavior) and engineering new features.
- Deliverable: A fully processed dataset where categorical variables are encoded (One-Hot/Label), numerical features are scaled/normalized, and feature selection has been scientifically applied to isolate the most important variables.
Module 4: Machine Learning Modeling
Eligibility Criteria
Internship Outcome
By the end of this program, interns will achieve the following:
- Industry Readiness: Proficiency in the standard Python data stack (Pandas, NumPy, Matplotlib, Scikit-Learn).
- Practical Portfolio: A GitHub repository featuring end-to-end data projects (from data cleaning to machine learning deployment) to showcase to future employers.
- Analytical Mindset: The ability to look at raw data, ask the right statistical questions, and choose the correct algorithm to find the answers.
- Certification: A certificate of completion validating their hands-on experience in Applied Data Science.
