UUY-DTS 2242 units200 LevelSecond Semester

Programming in Data Science

B.Sc. Data Science, University of Uyo

UUY-DTS 224 (Programming in Data Science) is a 2-unit, 200 Level course taught in the Second Semester at the University of Uyo, Akwa Ibom State, Nigeria.

The working Python data stack: NumPy, pandas, Matplotlib and scikit-learn. Where the mathematics of the other 200 Level courses becomes code you can run on a real dataset.

On Areté, UUY-DTS 224 comes with

  • 2 recommended textbooks
  • an AI tutor that has read this course’s outline and notes
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Course outline

  1. 01The scientific Python ecosystem; notebooks, environments and package management
  2. 02NumPy: ndarrays, dtypes, vectorisation, broadcasting, slicing and linear algebra routines
  3. 03pandas: Series and DataFrame, indexing, selection and filtering
  4. 04Data cleaning in pandas: missing values, type conversion, duplicates, string operations
  5. 05Reshaping and combining data: groupby, pivot, melt, merge and concat
  6. 06Time-series handling: datetime indexes, resampling and rolling windows
  7. 07Visualisation with Matplotlib and Seaborn
  8. 08An introduction to scikit-learn: the estimator API, fit/predict, pipelines
  9. 09Writing reusable code: functions, modules, virtual environments and version control with Git

Recommended textbooks

  • Python for Data Analysis — Wes McKinney

    3rd ed. — written by the creator of pandas; free at wesmckinney.com/book

  • Python Data Science Handbook — Jake VanderPlas

    Free at jakevdp.github.io; excellent on NumPy and scikit-learn

How to pass UUY-DTS 224

  • Learn groupby properly — the split-apply-combine pattern solves an astonishing share of real analysis tasks
  • Write vectorised NumPy/pandas code rather than Python loops; on a large dataset the difference is minutes versus hours
  • Keep every notebook in Git from day one. Reproducibility is a graded expectation by DTS 417
  • Recreate a chart you have seen in a news article using Matplotlib; matching a real design teaches more than following a tutorial

UUY-DTS 224 — questions and answers

What is UUY-DTS 224 at the University of Uyo?
UUY-DTS 224 (Programming in Data Science) is a 2-unit, 200 Level course taught in the Second Semester at the University of Uyo, Akwa Ibom State, Nigeria. The working Python data stack: NumPy, pandas, Matplotlib and scikit-learn. Where the mathematics of the other 200 Level courses becomes code you can run on a real dataset.
What topics are covered in UUY-DTS 224?
The UUY-DTS 224 outline covers 9 topics: The scientific Python ecosystem; notebooks, environments and package management; NumPy: ndarrays, dtypes, vectorisation, broadcasting, slicing and linear algebra routines; pandas: Series and DataFrame, indexing, selection and filtering; Data cleaning in pandas: missing values, type conversion, duplicates, string operations; Reshaping and combining data: groupby, pivot, melt, merge and concat; Time-series handling: datetime indexes, resampling and rolling windows; Visualisation with Matplotlib and Seaborn; An introduction to scikit-learn: the estimator API, fit/predict, pipelines; Writing reusable code: functions, modules, virtual environments and version control with Git.
What textbooks are recommended for UUY-DTS 224?
Recommended reading for UUY-DTS 224: Python for Data Analysis by Wes McKinney; Python Data Science Handbook by Jake VanderPlas.
How do I pass UUY-DTS 224?
Learn groupby properly — the split-apply-combine pattern solves an astonishing share of real analysis tasks Write vectorised NumPy/pandas code rather than Python loops; on a large dataset the difference is minutes versus hours Keep every notebook in Git from day one. Reproducibility is a graded expectation by DTS 417 Recreate a chart you have seen in a news article using Matplotlib; matching a real design teaches more than following a tutorial
Where can I find UUY-DTS 224 lecture notes and past questions?
Areté (https://www.aretecyb.tech/courses/uuy-dts-224) carries 2 recommended textbooks for UUY-DTS 224, plus an AI tutor that has read the outline. It is free for University of Uyo students — sign in with your email, no password.

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