Tasks and Duties
Objective
This week, your challenge is to establish a comprehensive data acquisition and strategy plan in the context of the telecom sector. You will design a plan that outlines how to identify, gather, and manage publicly available telecom-related data that could be used for innovative data science projects with Python. The goal is to demonstrate thorough knowledge of data sourcing techniques, data governance, and strategic planning necessary in the telecom industry.
Expected Deliverables
- A DOC file containing a detailed strategy plan.
- An introduction outlining the importance of data in the telecom industry.
- Identification of at least 3 publicly available data sources relevant to telecom.
- A data governance and quality assurance plan for handling such data.
Key Steps to Complete the Task
- Research and Identify Data Sources: Use your course learnings to identify publicly available data sources, such as government open data portals and research repositories.
- Strategy Formulation: Develop a step-by-step plan to acquire, validate, and pre-process the data. Create a flow diagram where appropriate.
- Governance and Quality Checks: Explain how you would ensure data quality and abide by ethical standards in data usage.
- Documentation: Document the entire strategy using clear sections, bullet points, and illustrations if needed. Your DOC file should be well-organized and professional.
Evaluation Criteria
Your submission will be evaluated on the clarity, depth, and creativity of your strategy, adherence to best practices from your Data Science with Python course, and your ability to accurately present a comprehensive plan. The work should reflect an understanding of industry-specific challenges and should be between 200 and 300 words. Be sure to include relevant details, clarity in structure, and logical flow of information.
Objective
This task requires you to design a comprehensive plan for cleaning and pre-processing telecom-related data using Python. You are required to focus on managing real-world challenges such as handling missing values, outliers, and data inconsistencies, which often occur with publicly available telecom datasets. The task will help you merge your Data Science with Python skills with practical data wrangling techniques applied in the telecom sector context.
Expected Deliverables
- A DOC file outlining your approach to data cleaning and preprocessing.
- A step-by-step methodology detailing techniques for missing data imputation, outlier detection, and normalization.
- Clean data visualization or descriptive statistics plans that could be implemented later.
Key Steps to Complete the Task
- Define the Data Issues: Describe potential issues you might encounter in telecom data such as incomplete records, duplication, or inconsistent formats.
- Methodology Description: Provide a detailed explanation of the processes and Python libraries you would use (such as Pandas, NumPy, and Matplotlib) for cleaning and pre-processing data.
- Sample Strategies and Pseudocode: Include sample strategies or pseudocode to show how you might implement these cleaning methods.
- Documentation: Provide detailed explanations and rationales for each step in your strategy, including how these methods support deeper data analysis.
Evaluation Criteria
Your DOC submission should demonstrate both practical and theoretical understanding of data cleaning methods tailored to telecom data. Clarity, thoroughness, and alignment with best practices taught in the Data Science with Python course will be considered. The document should be structured logically, referenced properly, and should be more than 200 words in description.
Objective
This week’s task requires you to develop an in-depth plan for conducting an Exploratory Data Analysis (EDA) on a dataset representative of telecom usage data. You will design a strategy that outlines how to utilize Python libraries for data visualization and EDA, drawing insights from trends, anomalies, and patterns within the data. The focus is on combining statistical analysis with visualization techniques to uncover hidden insights in telecom data.
Expected Deliverables
- A DOC file containing your EDA strategy document.
- A clear outline that includes the selection of visualization tools (e.g., Matplotlib, Seaborn) and statistical methods.
- A section defining key metrics and trends that might be relevant for telecom data performance evaluation.
Key Steps to Complete the Task
- Define the Analytical Objectives: Identify what key trends, patterns, and correlations you expect to uncover in telecom data.
- Outline Your Methodology: Describe the step-by-step process including data summarization, visualization of distribution, correlation analysis, and outlier detection.
- Select Tools and Techniques: Specify which Python libraries you would use, and why these are appropriate for telecom data.
- Interpretation Strategies: Discuss how the insights from EDA could influence decision-making in telecom services.
Evaluation Criteria
Your submission should be detailed, logically structured, and integrate both theoretical and practical perspectives on EDA. It must stay within the context of telecom data and demonstrate a clear plan that is executable using Python. You will be assessed on the depth of analysis, clarity of presentation, and the ability to connect EDA insights to potential industry decisions. Ensure your document contains more than 200 words and is well-organized.
Objective
This week, you are tasked with creating a robust plan to develop and implement machine learning models that address key problems in the telecom sector. The objective is to design a strategy detailing the steps for building predictive models using Python. You should outline how to handle data segmentation, feature engineering, model selection, evaluation metrics, and deployment considerations specific to telecom insights. This task emphasizes applying your coursework knowledge in Data Science with Python to real-world telecom scenarios.
Expected Deliverables
- A DOC file that presents a complete model development plan.
- A detailed explanation of the problem statement, such as churn prediction or network optimization.
- An outline of the data preparation, model training, validation, and testing strategies.
- Documentation of the planned use of Python libraries like Scikit-Learn and TensorFlow (if applicable).
Key Steps to Complete the Task
- Problem Definition: Articulate the telecom problem that the model will address.
- Data Preparation: Describe your strategy for feature engineering and data splitting, indicating why these steps are critical.
- Model Selection and Evaluation: Detail the candidate models, evaluation criteria (accuracy, precision, recall, F1-score, etc.), and cross-validation methods.
- Implementation Outline: Provide pseudocode or a workflow diagram to illustrate the process.
Evaluation Criteria
Your DOC submission will be evaluated based on how well it integrates concept and practice. The document should clearly detail every step of the machine learning pipeline, from data preparation to evaluation. The clarity, depth, and logical structure of your document are essential, and your plan should be technically feasible and tailored to the telecom sector. The overall description must exceed 200 words, demonstrating an insightful understanding of model development challenges and strategies within the telecom context.
Objective
The final week focuses on compiling a comprehensive evaluation report that summarizes your previous tasks and outlines a presentation strategy. In this task, you will design a final document that not only evaluates the outcomes of your data innovation efforts but also provides an actionable plan for presenting your findings. Emphasis should be on synthesizing data acquisition, cleaning, analysis, and machine learning outcomes to deliver actionable insights in the telecom domain using Python.
Expected Deliverables
- A DOC file presenting an overall evaluation report.
- A summary of methodologies used in earlier tasks.
- An evaluation of successes, limitations, and potential improvements.
- A detailed presentation framework, including slide structure, key message points, and visual aids you would use, such as charts or graphs.
Key Steps to Complete the Task
- Review and Synthesize: Consolidate the work from previous weeks into a cohesive narrative that reflects on data acquisition, data cleaning, EDA, and machine learning model building.
- Evaluation and Reflection: Critically assess the processes and outcomes. Elaborate on lessons learned, technical challenges faced, and strategies for overcoming them.
- Presentation Strategy: Create a detailed plan for a professional presentation, including an outline for slide decks, potential visualizations, and speaking points to effectively communicate your findings to stakeholders.
- Future Recommendations: Include a section discussing how your strategy could be adapted or scaled in a real-world telecom environment.
Evaluation Criteria
Your final DOC document should be detailed and narrative-driven, showing a clear connection between theoretical approaches and practical execution. It should provide a logical sequence that ties in all the tasks from your internship and guide the viewer through your analytical journey. The document must be rich in detail, with clear headings, bullet points, and structured sections, and must contain more than 200 words. You will be evaluated on the comprehensiveness of the report, clarity of presentation strategy, and the quality of reflection and future recommendations. The final submission represents your complete internship experience and should demonstrate the convergence of data science techniques and telecom industry insights.