Who This Unit Is For
Best for students ready to analyze datasets, create portfolio-style notebooks, prepare for AI/ML work, or strengthen Python beyond beginner programming.
Python Programming Unit 6
Data Science With Python
Analyze tables, clean data, and explain patterns with NumPy and pandas.
This unit introduces students to data science workflows in Python. Students practice NumPy arrays, vectorized operations, broadcasting, descriptive statistics, random data, pandas DataFrames, Series, indexing, filtering, grouping, joins, missing values, reshaping, data cleaning, feature creation, exploratory data analysis, reproducible notebooks, and reading CSV, Excel, JSON, and web data.
Learning Goals
- Use NumPy arrays for numeric operations and simple simulations.
- Load tabular data into pandas DataFrames and inspect structure.
- Filter, group, join, reshape, and clean realistic datasets.
- Create simple features that make analysis clearer.
- Write reproducible notebooks with readable explanations between code cells.
Key Concepts
What students practice in this unit
Data science turns Python into a tool for asking better questions. Students learn to move from raw tables to summaries, comparisons, and cautious findings without pretending the data says more than it does.
NumPy arrays
Students see why vectorized operations are clearer and faster than many manual loops for numeric data.
DataFrames and Series
Students learn how rows, columns, indexes, and column types shape analysis choices.
Cleaning and missing values
Students decide whether to remove, fill, flag, or investigate missing data.
Exploratory analysis
Students ask questions, summarize columns, compare groups, and explain what the data can and cannot prove.
Practice
Exercises and mini-project ideas
These are public practice prompts students can use to strengthen the unit without exposing the full internal lesson sequence.
Practice Exercises
- Analyze anonymous student survey data by grade level or activity type.
- Summarize sports statistics with averages, maximums, and grouped totals.
- Explore weather data by month and identify unusual values.
- Build a school club participation dashboard table.
- Analyze fictional social engagement data by post type and day.
- Clean missing values and explain what choice was made.
Mini-Project Ideas
- Student survey analysis notebook with written findings.
- Weather dataset exploration with cleaning notes and summary tables.
- Club participation dashboard using grouped pandas results.
Common Student Mistakes
- Ignoring missing values and trusting a summary too quickly.
- Mixing text and numbers in the same column without cleaning.
- Changing a DataFrame in one cell and forgetting the earlier notebook state.
- Confusing correlation with proof that one thing caused another.
- Using a chart before checking whether the underlying data is clean.
Challenge Extension
Students create two versions of an analysis: one before cleaning and one after cleaning, then explain which results changed and why.
How This Prepares the Next Step
Students are ready to choose the right chart type, communicate findings visually, and build dashboards that make analysis easier to understand.
Related Code Scholars Paths
Ready to practice?
Build Python skills with a guided plan.
Students can use this page for review, then work with Code Scholars on targeted exercises, debugging support, projects, and next-step planning.