Tasks and Duties
Objective
This task is designed for students to develop a comprehensive strategic blueprint for analyzing telecom data. As a Telecom Data Science Analyst, understanding the industry’s data landscape is critical. You will plan a project that identifies relevant data sources, outlines data acquisition techniques, and formulates an initial hypothesis for potential analysis. The focus should be on designing a thoughtful strategy using techniques taught in your Data Science with Python course.
Expected Deliverables
- A detailed DOC file containing a strategic plan, including a clear objective, hypothesis, and roadmap.
- An outline of the possible data sources that could be publicly available and methods of data collection.
- A description of potential challenges and how to address them.
Key Steps
- Research and identify public telecom datasets and resources.
- Develop a detailed outline of the data acquisition process and establish your analysis objectives.
- Design a strategic blueprint that includes timeline, tools, and techniques (e.g., Python libraries).
- Discuss potential issues such as data quality and privacy concerns, and propose initial ideas to mitigate them.
- Draft the DOC file presenting your strategy in a structured manner with clear sections.
Evaluation Criteria
The deliverable will be assessed on clarity of the strategic plan, relevance of chosen methodologies, practical use of Python-based techniques, and thoroughness in addressing potential challenges. Your ability to articulate a structured, decisive plan that integrates both theoretical concepts and practical steps will be critical.
This task should require approximately 30 to 35 hours of effort, as you research, plan, and document the strategy in a well-organized DOC file.
Objective
The goal of this task is to apply Python programming skills to perform data wrangling, cleaning, and visualization using techniques from your Data Science with Python course. As a Telecom Data Science Analyst, you need to prepare raw telecom datasets for further analysis. Although no specific dataset is provided, you should simulate a workflow using publicly available examples, hypothetical scenarios, or generated data. This task focuses on the execution phase where coding expertise transforms raw information into insightful visual outputs.
Expected Deliverables
- A DOC file that documents your entire process in a step-by-step manner.
- Descriptions and screenshots of Python code snippets for data cleaning and visualization (e.g., using Pandas, Matplotlib, or Seaborn).
- An explanation of the reasoning behind your approach and interpretation of the visualized data.
Key Steps
- Outline the general structure of a typical telecom dataset and identify common data quality issues.
- Develop a Python script for cleaning the data, focusing on standard operations like handling missing values, data type conversion, and normalization.
- Create visualizations that depict key aspects of the data such as usage trends, network performance metrics, or customer segments.
- Document and annotate your code in the DOC file, detailing each main step and its purpose.
- Provide a reflective discussion on the challenges faced and alternative solutions attempted.
Evaluation Criteria
The submission will be evaluated on the clarity and thoroughness of your documentation, the logical flow of your Python code, and the meaningfulness of your visualizations. Demonstrate practical execution and reflective analysis by showing how your Python-based approach is applicable to telecom data scenarios.
This task is expected to take 30 to 35 hours to complete.
Objective
In this task, your primary goal is to design a predictive model to assess customer churn in the telecom industry. As a Telecom Data Science Analyst, predictive analytics is a key responsibility. This exercise involves utilizing machine learning techniques learned in your Python course to build, evaluate, and optimize a model that predicts the likelihood of customer churn based on simulated or publicly available data characteristics.
Expected Deliverables
- A DOC file that elaborates your modeling approach, from hypothesis formulation to model validation and evaluation.
- A structured explanation of the data preprocessing steps, feature selection, model choice, and evaluation metrics.
- Visual aids such as charts or tables to communicate model performance and findings effectively.
Key Steps
- Conceptualize a scenario around telecom customer churn and identify relevant predictors.
- Outline data preprocessing techniques including data cleaning, normalization, and feature engineering using Python libraries such as Pandas and Scikit-Learn.
- Choose an appropriate machine learning model (e.g., logistic regression, decision trees) and justify your selection based on the scenario.
- Describe the model training process, cross-validation, and evaluation metrics such as accuracy, precision, and recall.
- Document all processes in the DOC file and include pseudocode or code snippets as needed to illustrate your approach.
Evaluation Criteria
The DOC file will be assessed on the feasibility of your predictive approach, the robustness of the machine learning techniques used, and clarity in explaining each step of the process. Emphasis will be placed on the reasoning behind model selection and the interpretation of results, linking back to telecom industry challenges.
This task should require approximately 30 to 35 hours of dedicated work.
Objective
The final task is centered on evaluating analysis outcomes, deriving actionable insights, and preparing a comprehensive report that encapsulates the previous weeks’ activities. As a Telecom Data Science Analyst, effective communication of data-driven insights is crucial for informed decision-making. In this assignment, you are required to synthesize your strategic planning, data wrangling, and predictive modeling efforts into a coherent narrative that highlights key findings, proposes actionable recommendations, and outlines future analysis paths.
Expected Deliverables
- A DOC file that includes a detailed final report summarizing your analysis process and results.
- Sections discussing insights from data cleaning, visualization, predictive modeling, and how these insights can drive telecom strategies.
- Visual elements like charts, graphs, and summary tables that effectively communicate your findings.
Key Steps
- Review and collate the outcomes from your prior tasks, emphasizing key milestones and findings.
- Develop a structured report that includes an executive summary, analysis methodology, results, insights, and recommendations.
- Incorporate visual aids and Python-generated outputs to support your conclusions.
- Discuss the evaluation of the techniques used, any limitations encountered, and a proposal for further investigation.
- Ensure the DOC file is logically organized with clear headings, subheadings, and a consistent narrative flow.
Evaluation Criteria
Your final report will be evaluated on its clarity, depth of insight, logical sequencing, and the practical relevance of the recommendations provided. Special attention will be given to the integration of previous tasks’ work, and how well you translate technical findings into strategic business insights.
This comprehensive task is designed to take approximately 30 to 35 hours, ensuring you have adequate time to reflect and detail every aspect of the evaluation and reporting process.