A data project lifecycle has many phases, rather than being just an isolated analysis in a single tool. In this project you will experience doing an analysis using both Python and SQL to obtain the final result, by exploring each tool's behavior.
Pick up a dataset in our common datasets repos and break your work into big steps:
- Pick a topic and choose a dataset on that topic. Build around 10 Business questions to answer about this topic.
- Try to build the questions before knowing everything about the data
- If not possible, do step 2. first
- Data Analysis: Understand your dataset and create a report (word document) about it
- Data Exploration and Business Understanding:
- Import your dataset into SQL
- Answer your Business questions with SQL Queries
- Bonus points if you augment your data with data your obtain through WebScrapping
- Bonus points if you include visualizations from Python and/or Tableau in the final presentation
- Python Code: Provide well-documented Python code that conducts the analysis and SQL upload.
- SQL text file (.sql) well commented document with all the queries answering the Business questions
- Short Presentation: Structure the presentation in the following way:
- Intro Slides: introduce the problem and the datasets
- Data cleaning and assumptions
- Business questions and SQL query (1 slide per question with a print screen of the query and the answer is enough)
- PDF Document with notes you might want to share
A data project lifecycle has many phases, rather than being just an isolated analysis in a single tool. In this project you will experience doing an analysis using both Python and SQL to obtain the final result, by exploring each tool's behavior.
Football Top 5 Leagues Project – Master Summary Guide Single reference document aligned with the Ultimate Master Notebook. It consolidates the workflow, feature engineering, research questions, EDA strategy, SQL validation and project deliverables. Project Workflow • Load & inspect dataset • Data quality checks • Data cleaning • Feature engineering • Feature validation • Export cleaned_football_data.csv • EDA (univariate & bivariate) • SQL validation • Research question conclusions Shared Engineered Features Feature Purpose RQ match_result Match outcome 1-4 goal_difference Winning margin 1,3 total_goals Total goals 1 total_xg Total xG 1 xg_difference Expected goals difference 1 points_difference League points difference 3 recent_form_difference Last 4 matches form 2 average_home/away_team_ratinAgverage team quality 4 rating_difference Rating comparison 4 Final Research Questions RQ1 – Is Expected Goals (xG) a good predictor of actual goals? Main variables: home_xg, away_xg, total_xg, total_goals, goal_difference RQ2 – Does recent team form influence match outcomes? Main variables: recent_form_difference, match_result RQ3 – Do teams with more current points consistently achieve better match results? Main variables: points_difference, match_result, goal_difference, xg_difference RQ4 – How do team player ratings and formations influence performance? Main variables: rating_difference, formations Recommended Analysis per Research Question • Univariate: describe(), histograms, boxplots. • Bivariate: scatterplots, boxplots by category, correlations. • SQL: AVG, COUNT, GROUP BY to validate EDA. • Presentation: Objective → Hypothesis → Visual Findings → Conclusions. Deliverables • Ultimate Master Notebook • cleaned_football_data.csv • EDA charts • SQL queries • Final presentation • README Summary The project uses one shared cleaning strategy, one engineered dataset and four complementary research questions. Each question combines EDA, SQL validation and written conclusions to build a coherent analytical narrative.
- Python Code: Provide well-documented Python code that conducts the analysis and SQL upload.
- SQL text file (.sql) well commented document with all the queries answering the Business questions
- Short Presentation: Structure the presentation in the following way:
- Intro Slides: introduce the problem and the datasets
- Data cleaning and assumptions
- Business questions and SQL query (1 slide per question with a print screen of the query and the answer is enough)
- PDF Document with notes you might want to share
https://www.kaggle.com/datasets/leonidkiselev/football-matches-statistics