Strengthen advanced data skills through 21 independent programming challenges: 15 centered on Pandas and six that extend into scikit-learn. You will implement solutions from requirements and examples for cleaning, selection, transformation, aggregation, time series, file IO, DataFrame combination, visualization, and machine learning workflows.
The collection contains one Beginner challenge and twenty Intermediate challenges. Scenarios use sales, finance, customer, weather, movie, and other compact datasets, then progress to built-in machine learning datasets for classification, regression, clustering, model validation, and scoring. This is an assessment-oriented course for applied practice, not a step-by-step Pandas introduction.
What You Will Learn
- Clean missing, duplicate, inconsistent, date, and string data and engineer new features
- Filter and update tables with MultiIndex accessors,
where,query, Boolean reductions, and custom functions - Reindex, transform, group, normalize, resample, shift, and aggregate Series and DataFrames
- Compare yearly datasets and combine tables through concatenation, merging, and index-based joins
- Import and export CSV and JSON data and create analytical charts from prepared results
- Train and evaluate k-nearest neighbors, decision tree, and logistic classification models
- Build linear, Ridge, and Lasso regression workflows with feature selection and cross-validation
- Compare K-means, hierarchical, and DBSCAN clustering and analyze validation curves and scoring metrics
Who This Course Is For
This course is for learners with established Pandas fundamentals who want demanding, varied implementation practice and an introduction to using tabular data in scikit-learn workflows. It is best for self-directed learners comfortable interpreting specifications, debugging starter code, and choosing suitable data operations without a full walkthrough.
Prerequisites: You should be comfortable with Python functions and common Pandas operations such as selection, filtering, grouping, missing-value handling, and file loading. Basic statistics and introductory machine learning concepts are strongly recommended for the six scikit-learn challenges.
Learning environment: All 21 challenges run in an Ubuntu 22.04 WebIDE with starter Python files, local data, and task verification. Visualization tasks use Matplotlib or related plotting tools, while machine learning tasks use bundled scikit-learn datasets and local files; no external service account is required.
Frequently Asked Questions
Is this course only about Pandas?
No. Fifteen challenges focus on Pandas, but six explicitly use scikit-learn for k-NN, decision trees, linear regression, clustering, validation curves, and classification or regression scoring. Choose this course only if that machine learning extension is relevant to your goals.
Is it suitable for a complete beginner?
No. Although the course is cataloged at Beginner level, twenty of its 21 challenges are marked Intermediate. The tasks assume that you can select, transform, group, and debug Pandas data with limited guidance.
Are the challenges guided, and must I complete them in order?
Each challenge provides requirements, examples, starter files, and checks, but not a complete solution walkthrough. The 21 scenarios are independent and can be selected by topic; within a multi-step scenario, complete the steps in order when later work uses earlier outputs.
Do I need external datasets or cloud accounts?
No. Pandas challenges use local CSV or JSON files and generated tables, while the machine learning challenges use local files or datasets bundled with scikit-learn. All work stays in the provided environment.
