Junior Data Scientist

Objective: In this week-long task, you are required to collect a publicly available dataset related to a topic of your choice (e.g., retail, healthcare, finance, etc.), and perform thorough data cleaning and preprocessing. The goal is to gain practical experience in managing and refining raw data for further analysis. This exercise is crucial for building a strong foundation in the data science process.

Expected Deliverables: A comprehensive report in PDF format along with the cleaned dataset submitted as a CSV file. The report should detail the methods and steps taken during data cleaning, including handling missing values, outliers, and data type conversions.

Key Steps:

• Locate a relevant publicly available dataset that is of adequate size and complexity.
• Perform initial data exploration to understand the structure of the dataset.
• Identify missing, inconsistent, or abnormal values. Document these issues and decide on appropriate corrective measures (such as imputation, deletion, or correction).
• Apply data type conversions, normalization, or standardization as required.
• Record your methodology and rationale in a well-structured report.

Evaluation Criteria:

• Thoroughness in identifying and handling data anomalies.
• Clarity and depth of the process explanation in the report.
• Quality of the documentation detailing the preprocessing steps.
• Correctness and usability of the final cleaned dataset.

This task is designed to push you to practice hands-on data preprocessing—a vital step before any exploratory or predictive analysis. Careful adherence to the outlined steps and thoughtful documentation of your work is essential. The successful completion of this task will equip you with the practical skills needed for the subsequent stages of the internship and reinforce the importance of data quality in building robust data science pipelines.

Objective: The aim of this task is to conduct a comprehensive Exploratory Data Analysis (EDA) on a publicly available dataset of your choosing. You will uncover hidden patterns, identify trends, and discover potential relationships between variables. This task is designed not only to sharpen your analytical skills but also to make you comfortable with statistical summaries, hypothesis testing, and visualization techniques essential for data science.

Expected Deliverables: A detailed EDA report in PDF format and a notebook file (Jupyter Notebook or equivalent) containing all the code you executed during the analysis. The report should synthesize your findings with appropriate visuals and commentary.

Key Steps:

• Select an interesting dataset from publicly available resources.
• Utilize descriptive statistics to summarize the dataset, including measures of central tendency and dispersion.
• Create visualizations (such as histograms, scatter plots, box plots, and heat maps) to highlight key aspects of the data.
• Perform initial hypothesis testing if applicable to better understand potential relationships between variables.
• Provide clear annotations and inferences from the visualizations and statistical tests.

Evaluation Criteria:

• Depth of analysis and selection of appropriate statistical measures.
• Quality and clarity of visual representations.
• Insightfulness in drawing conclusions from the data.
• Overall quality of the documented code and reproducibility of your analysis.

This EDA task is crucial for understanding the dynamics of any dataset. Your work will lay the foundation for further data modeling and predictive tasks. Fully explore and discuss the findings along with any assumptions made during the process. The comprehensive details in your report will enable others to understand the methodology and replicate your analysis independently.

Objective: In this week’s assignment, you are required to develop a statistical model based on a publicly available dataset that you find interesting. The focus is on hypothesis formulation, model fitting, and parameter estimation. You will apply regression analysis or other relevant statistical tools to explore the relationships between variables and confirm or refute your hypotheses.

Expected Deliverables: A final deliverable consisting of a PDF report and an associated code file (Jupyter Notebook or equivalent). The report should detail your hypothesis, methodology, results, and interpretation of the model’s performance and significance.

Key Steps:

• Select a dataset with sufficient complexity to analyze relationships between key variables.
• Formulate a clear research question and corresponding hypotheses based on the dataset.
• Prepare the dataset for modeling by ensuring that it is clean and conforms to the assumptions required (e.g., normality, multicollinearity checks).
• Apply a suitable statistical model (e.g., linear regression, logistic regression) and evaluate its performance through methods such as p-values, R-squared metrics, and residual analysis.
• Interpret the results in the context of your original hypothesis, discussing potential limitations and future improvements.

Evaluation Criteria:

• Clarity in objective definition and hypothesis formulation.
• Appropriate selection and execution of the statistical model.
• Depth of analysis provided through rigorous testing and diagnostics.
• Quality of interpretations and insights drawn from the model's performance.

This task will challenge you to apply statistical theory practically by constructing and evaluating models. Providing clear reasoning behind every step and creating reproducible code will be essential in demonstrating your understanding of the underlying statistical concepts. Your detailed documentation will be pivotal in showcasing not only the technical details but also your ability to communicate complex analytical findings effectively.

Objective: During Week 4, your goal is to build and evaluate a machine learning model using a publicly available dataset. This task aims to simulate a real-world scenario where you have to choose an appropriate algorithm, optimize model parameters, and provide a robust evaluation of its performance. The focus is on hands-on coding, model selection, cross-validation, and performance metrics interpretation.

Expected Deliverables: A deliverable package consisting of a well-documented code file (Jupyter Notebook or equivalent) that includes all your work on model development, and a PDF report detailing your methodology, hyperparameter tuning process, evaluation results, and interpretations of the model’s predictive power.

Key Steps:

• Select a publicly available dataset that supports classification or regression analysis.
• Preprocess and split the data into training and testing sets.
• Choose an appropriate machine learning algorithm (e.g., decision trees, support vector machines, random forest, or regression models) and justify your choice.
• Implement cross-validation and hyperparameter tuning to optimize the model.
• Evaluate model performance using relevant metrics (e.g., accuracy, precision, recall for classification; MAE, MSE, or RMSE for regression) and discuss the model’s strengths and weaknesses.

Evaluation Criteria:

• Appropriateness and clarity in algorithm selection and justification.
• Rigorous approach to model optimization using cross-validation and hyperparameter tuning.
• Accuracy and detail in the evaluation metrics presented.
• Quality of the report analysis and clarity in documenting each step.

This task will enable you to integrate both coding and analytical skills in a machine learning context. The ability to clearly document and explain each stage—from data preprocessing to model evaluation—is paramount. Your deliverables should reflect a strong commitment to producing reproducible and well-analyzed scientific work, thus preparing you for real-life data science challenges.

Objective: In this task, you will focus on transforming complex data insights into clear, effective visual representations. The objective is to create a series of visualizations that communicate key findings from a dataset of your choice. Emphasis will be not only on technical proficiency with visualization libraries but also on the narrative that links the visual data to an insightful analysis.

Expected Deliverables: Submit a portfolio that includes a series of high-quality visualizations (exported as images or within a slide deck) along with a written narrative report in PDF format. The narrative should explain the insights derived from each visualization, the choice of visual technique, and its relevance to the dataset’s broader context.

Key Steps:

• Choose a publicly accessible dataset relevant to topics such as trends, patterns, or peculiarities that can be visualized effectively.
• Plan out a storyboard or outline describing how each visualization contributes to the overall narrative.
• Create multiple visualizations using tools such as Matplotlib, Seaborn, Plotly, or similar libraries. Consider incorporating interactive visualizations if appropriate.
• Annotate your visuals with titles, labels, legends, and appropriate styling to enhance readability and impact.
• Synthesize your findings into a coherent narrative that ties together the various visual elements into a meaningful story.

Evaluation Criteria:

• Visual quality and clarity of each visualization.
• The strength and persuasiveness of the narrative linking visual insights to the dataset.
• Creativity in visualization techniques and overall presentation.
• Technical accuracy in the data processed to generate the visual outputs.

This visualization task is designed to assess your ability to effectively communicate analysis results. Visual storytelling is a key skill for data scientists, especially when conveying complex information to diverse audiences. By focusing on both the aesthetic and analytical dimensions of data visualization, you will develop a holistic approach to presenting data-driven insights that are both compelling and accurate.

Objective: In the final week of your internship, you will integrate and apply all the skills developed over the previous tasks to a comprehensive data science project. This project requires you to go through the full data science pipeline: from data collection and cleaning, through exploratory analysis, statistical modeling, machine learning, and data visualization. The final deliverable is a complete project that addresses a real-world problem through systematic analysis and clear documentation.

Expected Deliverables: Submit a main project report in PDF format, which should encompass an executive summary, methodology, analysis, results, and conclusions. Additionally, provide all supporting files including code files (e.g., Jupyter Notebooks), visualization outputs, and the final dataset used. The deliverables should be well-organized, and the report should be structured in a way that it stands as a professional example of a data science project.

Key Steps:

• Select a challenging, yet manageable, publicly available dataset. Define the real-world problem you will address.
• Use data preprocessing methods to ensure that the data is clean and ready for analysis.
• Conduct exploratory data analysis to uncover underlying patterns, trends, and relationships.
• Develop statistical models and apply machine learning algorithms to provide predictive or descriptive insights.
• Create visualizations to communicate findings effectively as part of the comprehensive report.
• Discuss the implications of your findings, any limitations of your analysis, and suggestions for further work.

Evaluation Criteria:

• Coherence and depth in addressing the data science problem.
• Integration of multiple techniques and methodologies into a unified project.
• Clarity, professionalism, and thoroughness of the final report.
• Reproducibility of the entire project, including the quality and organization of the codebase.

This final project is intended to emulate the complete workflow of a data science professional. It challenges you to not only apply technical skills but also to synthesize and communicate your work effectively. The integrative nature of this task is designed to prepare you for real-world scenarios where a comprehensive and well-documented approach is critical. Adequate planning, execution, and detailed reporting will be essential to demonstrate your readiness for a professional role as a data scientist.