The first week involved onboarding sessions and setting up the necessary tools and environments. I familiarized myself with the company's datasets and the specific goals of the internship.
Week 2-3: Python and Data Libraries Immersion
I quickly delved into Python programming and focused on essential data science libraries such as NumPy, Pandas, and Scikit-learn. The tasks included data cleaning, basic exploratory data analysis, and understanding fundamental machine learning algorithms.
Week 4: Data Visualization and Geographic Analysis
In the final week, I concentrated on data visualization using Matplotlib and Seaborn, creating visual representations of insights from the datasets. Additionally, I explored geographical data analysis, working on generating simple maps using Folium and choropleth techniques.
Project and Presentation:
A significant part of the internship was dedicated to a small project. I applied my skills to analyze a specific dataset, draw insights, and present findings. The short duration required focused efforts, quick learning, and efficient application of the acquired knowledge.
Key Takeaways:
- Rapid immersion into Python and essential data science libraries.
- Basic proficiency in data cleaning, preprocessing, and exploratory data analysis.
- Introduction to data visualization techniques using Matplotlib and Seaborn.
- Initial exposure to geographical data analysis and mapping with Folium.
Overall Reflection:
Despite the brief duration, the internship provided valuable exposure to key aspects of data analytics. It emphasized the importance of quick learning, adaptability, and efficient utilization of tools in a fast-paced environment. The experience laid a foundation for further exploration and development in the field of data analytics.