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{ Udacity × Bertelsmann } Data Science Scholarship Project Showcase

Challenge Course

A Roadmap of Data Science Beginnings

Summer 2018 Expedition

Melissa

Simon

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Is every lesson of the Challenge Course* equally dense?

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HYPOTHESIS

Python and SQL lessons contain more content than they appear relative to the overall course.

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QUESTION

ASSUMPTIONS

  • Progression through course at consistent pace.
  • No significant prior subject matter knowledge assumed.
  • Scoring of difficulty relative to only activities within this course.
  • Scoring of activity duration assumes learner has a satisfactory understanding of the applied concepts.

* Bertelsmann Data Science Challenge Scholarship Course (2018)

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Data collected from the Course was analyzed in spreadsheets and Tableau.

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1496

DATA ROWS

OBJECTIVE MEASURES

video length

word count

SUBJECTIVE MEASURES

activity duration

activity difficulty level

AGGREGATION

various levels

sections

lessons

units*

time

CALCULATED

len(video)�+

COUNT(word)

+

{activity:duration}

* 4 units — meta: 1, 22, 33; statistics: 2-21; Python: 23-27; SQL: 28-32

METHOD

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Programming units are denser and each unit requires about ⅓ of the time.

GIVEN COURSE COMPOSITION (%)

ANALYZED COURSE COMPOSITION (%)

ANALYZED TIME PER CONTENT TYPE PER LESSON

4 units — meta: 1, 22, 33; statistics: 2-21; Python: 23-27; SQL: 28-32

FINDING

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Allocate more time for Python & SQL and do not rely on progress indicator.

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difficulty — level 1: low; level 2: moderate; level 3: high�based on experience and perception of Forum & Slack learner questions

RECOMMENDATIONS

TIME PER SECTION�grouped by units

  • INSTRUCTORS�adjust course progress indicator (%).�
  • COURSE PARTICIPANTS allocate more time for Python and SQL lessons.

MEAN DIFFICULTY�grouped by lessons

stats

m

Python

SQL

m

m

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Bertelsmann Data Science Challenge Scholarship Course

July 2018

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{ Udacity × Bertelsmann } Data Science Scholarship Project Showcase