Welcome to the Data Science Internship! This program is designed to enhance your understanding of data science concepts through practical tasks that encourage hands-on experience. You will engage with various projects to apply foundational knowledge and explore real-world applications. By completing these tasks, you’ll gain valuable insights into data analysis, visualization, and machine learning. Remember to complete at least two tasks and share your work on social media!
Notice: Complete a minimum of 2 tasks from the tasks listed below.
Task 1: Data Collection
Problem Statement:
Gather data from a public dataset or an API for analysis.
Steps to Complete:
- Choose a dataset from a reliable source (e.g., Kaggle, UCI Machine Learning Repository).
- If using an API, fetch data using requests or a suitable library.
- Save the dataset in a structured format (CSV, JSON).
Tools/Datasets/Platforms:
- Python, Pandas, requests (for APIs).
You can use any tool/platform or dataset.
How to Submit:
- Record a video demonstrating your data collection process.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Task 2: Data Cleaning
Problem Statement:
Prepare a dataset for analysis by cleaning and preprocessing it.
Steps to Complete:
- Load the dataset using Pandas.
- Identify and handle missing values.
- Normalize or standardize the data if necessary.
- Save the cleaned dataset to a new file.
Tools/Datasets/Platforms:
- Python, Pandas, Jupyter Notebook.
You can use any tool/platform or dataset.
How to Submit:
- Record a video showcasing your data cleaning steps.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Task 3: Exploratory Data Analysis (EDA)
Problem Statement:
Perform exploratory data analysis on a chosen dataset.
Steps to Complete:
- Load the cleaned dataset.
- Generate descriptive statistics and visualizations.
- Identify patterns, trends, and anomalies in the data.
Tools/Datasets/Platforms:
- Python, Pandas, Matplotlib, Seaborn.
You can use any tool/platform or dataset.
How to Submit:
- Record a video of your exploratory data analysis.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Task 4: Data Visualization
Problem Statement:
Create informative visualizations for data presentation.
Steps to Complete:
- Choose a dataset you previously analyzed.
- Use Matplotlib and Seaborn to create visualizations.
- Explain the insights gained from your visualizations.
Tools/Datasets/Platforms:
- Python, Matplotlib, Seaborn.
You can use any tool/platform or dataset.
How to Submit:
- Record a video showcasing your data visualizations.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Task 5: Feature Engineering
Problem Statement:
Apply feature engineering techniques to enhance model performance.
Steps to Complete:
- Select a dataset for your project.
- Create new features based on existing data.
- Evaluate the impact of the new features on a model.
Tools/Datasets/Platforms:
- Python, Pandas, Scikit-learn.
You can use any tool/platform or dataset.
How to Submit:
- Record a video detailing your feature engineering process.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Task 6: Statistical Analysis
Problem Statement:
Conduct statistical analysis on a dataset.
Steps to Complete:
- Load the dataset into your environment.
- Perform hypothesis testing or correlation analysis.
- Interpret and present your findings.
Tools/Datasets/Platforms:
- Python, SciPy, Pandas.
You can use any tool/platform or dataset.
How to Submit:
- Record a video explaining your statistical analysis.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Task 7: Machine Learning Model Development
Problem Statement:
Develop a machine learning model for prediction.
Steps to Complete:
- Choose a suitable dataset.
- Split the dataset into training and testing sets.
- Train a machine learning model using Scikit-learn.
- Evaluate the model’s performance.
Tools/Datasets/Platforms:
- Python, Scikit-learn, Jupyter Notebook.
You can use any tool/platform or dataset.
How to Submit:
- Record a video of your model training and evaluation.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Task 8: Model Evaluation Metrics
Problem Statement:
Calculate and interpret evaluation metrics for a machine learning model.
Steps to Complete:
- Train a model (e.g., logistic regression).
- Use the model to make predictions on the test set.
- Calculate accuracy, precision, recall, and F1-score.
- Discuss the significance of each metric.
Tools/Datasets/Platforms:
- Python, Scikit-learn.
You can use any tool/platform or dataset.
How to Submit:
- Record a video explaining your model evaluation metrics.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Task 9: Natural Language Processing (NLP)
Problem Statement:
Perform basic NLP tasks on a text dataset.
Steps to Complete:
- Choose a text dataset (e.g., tweets or reviews).
- Clean and preprocess the text data.
- Implement a simple NLP model (e.g., sentiment analysis).
Tools/Datasets/Platforms:
- Python, NLTK or SpaCy.
You can use any tool/platform or dataset.
How to Submit:
- Record a video demonstrating your NLP tasks.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Task 10: Data Science Dashboard
Problem Statement:
Create a simple data science dashboard to present findings.
Steps to Complete:
- Use Plotly Dash or Streamlit to create the dashboard.
- Incorporate visualizations and insights from previous tasks.
- Ensure the dashboard is user-friendly and interactive.
Tools/Datasets/Platforms:
- Python, Plotly Dash/Streamlit.
You can use any tool/platform or dataset.
How to Submit:
- Record a video walkthrough of your dashboard.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Task 11: Data Science Case Study
Problem Statement:
Conduct a case study on a specific data science problem.
Steps to Complete:
- Identify a data science challenge (e.g., predicting housing prices).
- Apply your knowledge from previous tasks to analyze the problem.
- Document your process and findings in a report.
Tools/Datasets/Platforms:
- Python, Jupyter Notebook.
You can use any tool/platform or dataset.
How to Submit:
- Record a video summarizing your case study.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Task 12: Capstone Project
Problem Statement:
Design and implement a comprehensive data science project.
Steps to Complete:
- Choose a real-world problem to solve using data science.
- Apply all stages from data collection to model deployment.
- Present your findings and insights.
Tools/Datasets/Platforms:
- Python, any data science libraries.
You can use any tool/platform or dataset.
How to Submit:
- Record a video presentation of your capstone project.
- Upload the video to LinkedIn or YouTube.
- Tag the following in your post:
- Submit the video link in the submission form.
Conclusion
This internship aims to enrich your experience in data science through hands-on tasks and projects. Make sure to engage with your mentors and peers as you work through each task, and enjoy the learning process!
Thank you!