Time-Series Analysis in Python Notebook
Budget: $2 – $8 AUD
I need a well-structured Jupyter notebook that walks from raw, time-stamped data all the way through cleaning, descriptive exploration, and clear visualisations. The dataset will arrive as a CSV and contains several months of sequential observations; I want to understand overall trends, seasonality, and any anomalies that stand out.
Please rely on mainstream Python tooling—pandas for wrangling, NumPy where helpful, and visual libraries such as Matplotlib, Seaborn, or Plotly for compelling charts. The notebook should be fully commented so that another analyst can rerun or adapt the workflow on new data without guessing at intermediate steps.
Deliverables:
• Cleaned, ready-for-analysis data file
• Jupyter notebook with step-by-step descriptive analysis and visuals
• Brief written summary of key findings (can be a final markdown cell)
Acceptance criteria: the notebook runs top-to-bottom without errors, every transformation is explained in plain language, and each visual is labeled clearly enough for a non-technical stakeholder to interpret.
Please rely on mainstream Python tooling—pandas for wrangling, NumPy where helpful, and visual libraries such as Matplotlib, Seaborn, or Plotly for compelling charts. The notebook should be fully commented so that another analyst can rerun or adapt the workflow on new data without guessing at intermediate steps.
Deliverables:
• Cleaned, ready-for-analysis data file
• Jupyter notebook with step-by-step descriptive analysis and visuals
• Brief written summary of key findings (can be a final markdown cell)
Acceptance criteria: the notebook runs top-to-bottom without errors, every transformation is explained in plain language, and each visual is labeled clearly enough for a non-technical stakeholder to interpret.
Related categories:
Python
Software Architecture
Statistics
Machine Learning (ML)
NumPy
Data Visualization
Data Analysis
Pandas