Summer Internship - I

My Internship Experience: Data Analytics in Python

This summer, I had the opportunity to intern at Techmicra IT Solutions, Ahmedabad, where I worked on a Data Analytics in Python Internship.

The internship was a valuable experience that combined learning, problem-solving, and applying theoretical knowledge in real-world contexts. Over five weeks, I explored data analytics, machine learning, and full-stack development through two major projects.

Project 1: Wikipedia Data Scraper & Analyzer

The first project focused on web scraping and data analysis. I developed a Python-based tool that:

• Extracted tables from Wikipedia using the Requests and BeautifulSoup libraries.
• Cleaned and processed the data with Pandas and NumPy.
• Generated summary statistics and visualizations using Matplotlib and Seaborn.
• Allowed data export to CSV format for reuse in research or analytics.

This project highlighted the importance of data preprocessing and visualization while working with real-world datasets.

Project 2: Cryptocurrency Tracker & Forecast Dashboard

The second project was the development of a real-time cryptocurrency dashboard. Its main features included:

• Integration of the CoinGecko API for live cryptocurrency prices and historical data.
• Incorporation of the NewsData.io API for real-time cryptocurrency-related news.
• Implementation of Linear Regression using scikit-learn to forecast 7-day price trends.
• Development of a Flask-based web application with an interactive frontend using HTML, CSS, JavaScript, and Jinja2.
• Additional features such as theme toggling between light and dark modes, responsive design, and dynamically rendered charts.

This project provided end-to-end exposure to backend development, frontend design, API integration, and machine learning within a single system.

Key Learnings

Throughout the internship, I enhanced both technical and soft skills, including:

• Proficiency in Python programming and data wrangling.
• Experience with web scraping and API integration.
• Data visualization using Seaborn and Matplotlib.
• Application of machine learning techniques such as Linear Regression.
• Full-stack development using Flask, HTML, CSS, and JavaScript.
• Improved project management, documentation, and adaptability.

Conclusion

This internship was a transformative journey that taught me how to take an idea from data collection to a deployable predictive dashboard. It strengthened my confidence in applying data science concepts to practical problems and deepened my interest in building intelligent systems. The structured guidance and mentorship I received were instrumental in this learning process.

A special note of gratitude to my mentors Dr. Spoorthy V and Mr. Pallav Mamtora for their support and guidance throughout this journey.

Project repository: GitHub – Internship Project