Data Science with AI
The Data Science with AI Training by Internshala was a 6-week intensive online program designed to provide a hands-on introduction to the world of data science.
This course covered the fundamentals of Python, statistics, data visualization, predictive modeling, and machine learning, offering a balance between theory and practical application.
Throughout the training, I engaged in interactive video tutorials, assignments, and quizzes that reinforced conceptual understanding through practice-based learning. The program also included real-world projects, enabling the application of analytical concepts to solve business problems using data-driven approaches.
In the final assessment, I scored 87% marks.
Category
Data Science
Internship At
Internshala Trainings
Course Duration
6 Weeks / 42 Days
Course Dates
4th May 20′ to 15th June 20′
Final Score
87%
Verification Links
Key Learning Modules
The training was divided into multiple structured modules, covering the full lifecycle of data science workflows:
Python for Data Science – Variables, data types, loops, and functions.
Statistics & Data Preprocessing – Descriptive analytics, probability, data cleaning, and transformation.
Exploratory Data Analysis – Data visualization and correlation insights using libraries like Matplotlib and Seaborn.
Predictive Modeling & Machine Learning – Building models with Linear Regression, Decision Trees, and Random Forests.
Project Implementation – Applying end-to-end data workflows using real-world datasets.
Final Project
For the capstone project, I worked on a real-world classification problem for a retail banking client. The objective was to predict whether a customer would subscribe to a term deposit plan based on demographic, financial, and campaign-related features.
Using the datasets provided (train.csv and test.csv), I built and evaluated machine learning models to identify clients most likely to convert during telemarketing campaigns — helping optimize marketing spend and outreach efficiency.
Key Highlights:
Objective: Predict customer subscription likelihood to a term deposit product.
Tech Stack: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Jupyter Notebook.
Techniques Used: Data preprocessing, encoding categorical features, feature scaling, train-test split, logistic regression, random forest classification, and model evaluation via accuracy metric.
Outcome: Achieved a high prediction accuracy and identified key predictive features like contact duration, campaign frequency, and previous campaign outcome.
Key Learning Outcomes
- Acquired foundational knowledge of data science workflows and machine learning pipelines.
- Applied Python programming and data visualization for practical analysis.
- Built end-to-end predictive and forecasting models using real-world datasets.
- Strengthened understanding of statistical reasoning and data-driven decision-making.
Conclusion
The 6 Weeks Data Science Training by Internshala Trainings laid the foundation for my technical journey in data analytics.
It helped me understand how to apply programming, statistical, and analytical concepts to real-world datasets, forming the basis for my later work in automation, audit analytics, and data-driven cybersecurity assessments.