E-Courses (DataCamp)

About the curriculum

Here you will find the e-course curriculum. The e-courses are provided by DataCamp.

DataCamp e-courses consist of a combination of video lectures and hands-on exercises, where you will have to solve small coding problems.

We strongly advice participants to complete as much as the mandatory course curriculum as possible. A lot of the same methods will be covered during the course but getting a feel for how python programming works before-hand allows you to get even more out of the PhD course.

If you are participating in the course, you should have received a DataCamp invite via the e-mail you registered with (make sure to check your spam filter).

Please contact Kristian Gade Kjelmann (kgk@adm.aau.dk), if you have not received an invite.

Mandatory preparation for the PhD course (~13.5 hours)

Introduction to python (4 hours)

https://www.datacamp.com/courses/intro-to-python-for-data-science

Entire course

Intermediate python for data science (2.5 hours)

https://www.datacamp.com/courses/intermediate-python-for-data-science

Chapter 2: Dictionaries and pandas
Chapter 3: Logic, control flow and filtering
Chapter 4: Loops

Pandas foundations (2 hours)

https://www.datacamp.com/courses/pandas-foundations

Chapter 1: Data ingestion & inspection
Chapter 2: Exploratory data analysis

Python data science toolbox (3 hours)

https://www.datacamp.com/courses/python-data-science-toolbox-part-1

Entire course

Project: Dr. Semmelweis and the Discovery of Handwashing (2 hours)

https://www.datacamp.com/projects/20

Further preparation for the course (~17 hours)

These courses are not mandatory but will prepare you further for the course.

Importing Data in Python (Part 1) (2 hours)

https://www.datacamp.com/courses/importing-data-in-python-part-1

Chapter 1: Introduction and flat files
Chapter 2: Importing data from other file types

Cleaning Data in Python (4 hours)

https://www.datacamp.com/courses/cleaning-data-in-python

Entire course

Data Visualization with Seaborn (3 hours)

https://www.datacamp.com/courses/data-visualization-with-seaborn

Chapter 1: Seaborn Introduction
Chapter 2: Customizing Searborn Plots
Chapter 3: Additional Plot Types

Regular Expressions in Python (2 hours)

https://www.datacamp.com/courses/regular-expressions-in-python

Chapter 1: Basic Concepts of String Manipulation
Chapter 3: Regular Expressions for Pattern Matching
Chapter 4: Advanced Regular Expression Concepts

Statistical thinking in python part 1 (3 hours)

https://www.datacamp.com/courses/statistical-thinking-in-python-part-1

Entire course

Statistical thinking in python part 2 (3 hours)

https://learn.datacamp.com/courses/statistical-thinking-in-python-part-2

Entire course

Supplementary courses for Monday (Nov. 23rd)

Supplementary courses for Tuesday (Nov. 24th)

Unsupervised Learning in Python

https://learn.datacamp.com/courses/unsupervised-learning-in-python

Supervised Learning with scikit-learn

https://learn.datacamp.com/courses/supervised-learning-with-scikit-learn

Supplementary courses for Wednesday (Nov. 25th)

Introduction to Network Analysis in Python

https://learn.datacamp.com/courses/introduction-to-network-analysis-in-python

Comment from Daniel Hain: “They [the network analysis courses on DataCamp] are nice to get an overall overview but I would not spent too much time on them. I find them a bit clunky and over-engineered.”