_db_id | title |
---|---|
105 |
Datascience short course 1 |
This course is to be completed in 8 weeks at maximum 10 hours per week.
Learners doing this course should first do the "Intro to Tilde for non-coder bootcampers" course.
- {{< contentlink path="medium-touch-course-common/0-how-this-course-works" >}}
- {{< contentlink path="data-science/foundational-short-course/1-what-is-data-science" >}}
- {{< contentlink path="data-science/foundational-short-course/2-the-data-science-method" >}}
- {{< contentlink path="data-science/foundational-short-course/3-why-python-and-git" >}}
- {{< contentlink path="topics/git/git-summary">}}
- {{< contentlink path="topics/git/setting-up-git" >}}
- {{< contentlink path="topics/git/git-introduction" >}}
- {{< contentlink path="coding_aptitude_assessment/introduction_to_github" >}}
- {{< contentlink path="projects/tilde/basic-repo-card-tutorial" flavour="" >}}
- {{< contentlink path="data-science/foundational-short-course/4-what-is-data-science-questions" flavour="" >}}
- {{< contentlink path="topics/data-science-specific/intro-to-python-data-camp" >}}
The first group session should be an ice-breaker. The learners should be introduced to the staff who will be taking part in the course in different ways. Any setup problems should be addressed.
Conceptual session: This should be based on previous conceptual questions covered in:
- {{< contentlink path="data-science/foundational-short-course/4-what-is-data-science-questions" flavour="" >}}
If the learners are struggling with finishing the questions on time then this can be a hot-seat session focused on helping the learners overcome any technical difficulties people are struggling with.
Learners will need to know about the basics of Python, lists, loops, iterating, if statements, and functions to be able to apply these skills in even the most basic data-wrangling projects. The entire solo-learn basic course will be useful.
- {{< contentlink path="topics/solo-learn/python/intro-to-python/6-functions-project" flavour="">}}
- {{< contentlink path="environment-setup/python-on-computer" >}}
- {{< contentlink path="coding_aptitude_assessment/coding_challenges/task_1" flavour="python" >}}
- {{< contentlink path="coding_aptitude_assessment/coding_challenges/task_2" flavour="python" optional="1" >}}
- {{< contentlink path="coding_aptitude_assessment/coding_challenges/task_3" flavour="python" optional="1" >}}
- {{< contentlink path="coding_aptitude_assessment/coding_challenges/task_4" flavour="python" optional="1" >}}
- {{< contentlink path="coding_aptitude_assessment/coding_challenges/task_5" flavour="python" >}}
- {{< contentlink path="coding_aptitude_assessment/coding_challenges/task_6" flavour="python" optional="1" >}}
- {{< contentlink path="coding_aptitude_assessment/coding_challenges/task_7" flavour="python" >}}
- {{< contentlink path="coding_aptitude_assessment/coding_challenges/task_8" flavour="python" optional="1" >}}
- {{< contentlink path="coding_aptitude_assessment/coding_challenges/task_9" flavour="python" optional="1" >}}
- {{< contentlink path="coding_aptitude_assessment/coding_challenges/task_10" flavour="python" optional="1" >}}
How to learn to code. Assessing your own knowledge and each others.
Hotseat
- {{< contentlink path="data-science/foundational-short-course/5-statistics-role-in-datascience" >}}
- {{< contentlink path="data-science/foundational-short-course/6-data-types" >}}
- {{< contentlink path="data-science/foundational-short-course/7-decriptive-statistics" >}}
- {{< contentlink path="data-science/foundational-short-course/8-measures-of-central-tendency" >}}
- {{< contentlink path="data-science/foundational-short-course/9-measures-of-central-tendency-in-python" >}}
- {{< contentlink path="data-science/foundational-short-course/10-measures-of-central-tendency-questions" >}}
- {{< contentlink path="data-science/foundational-short-course/11-measures-of-central-tendancy-project" flavour="python" >}}
- {{< contentlink path="data-science/foundational-short-course/12-measures-of-dispersion" >}}
- {{< contentlink path="data-science/foundational-short-course/13-measures-of-dispersion-questions" flavour="" >}}
- {{< contentlink path="data-science/foundational-short-course/14-measures-of-dispersion-project" flavour="python" >}}
- {{< contentlink path="data-science/foundational-short-course/15-frequency-distribution" >}}
- {{< contentlink path="data-science/foundational-short-course/16-frequency-distribution-questions" flavour="" >}}
- {{< contentlink path="data-science/foundational-short-course/17-intro-to-numpy" >}}
- {{< contentlink path="data-science/foundational-short-course/18-positional-measures" >}}
- {{< contentlink path="data-science/foundational-short-course/19-positional-measures-questions" flavour="" >}}
- {{< contentlink path="data-science/foundational-short-course/20-skewness-and-kurtosis" >}}
- {{< contentlink path="data-science/foundational-short-course/21-skewness-and-kurtosis-questions" flavour="" >}}
- {{< contentlink path="data-science/foundational-short-course/22-probability" >}}
- {{< contentlink path="data-science/foundational-short-course/23-probability-questions" flavour="">}}
Projects:
- implement statistical functions in Python from scratch. Eg calculate the mean etc
Intro to Pandas
- calculate statistical things using dataframes
-
Load and display data in different ways
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no real wrangling required at this point, focus on drawing stuff and knowing what graphs are useful
-
simple graph projects. Maybe a datacamp thing
Something that uses data wrangling and visuals. Do we have such a thing?
- {{< contentlink path="projects/data-science-specific/data-wrangling" flavour="python" >}}
If the breathers are not necessary or if learners get to the end of the course early, we'll do this: