Study Guide

Objective

You understand the basics of data analytics in data engineering and machine learning.
You can use common data analytics environments and tools for machine learning purposes.
You learn to find insight in data by using explorative data analytics.
You learn methods on how to optimize dataset contents and distributions.
You know how to share your results and exercises via a version control system

Content

Data preparation and pre-processing
Exploratory Data Analysis (EDA): statistical, visual, and other common methods
Finding insight in datasets to optimize their structure
Use of data analytics environments and libraries/modules
Common data analytics tools regarding machine learning

Qualifications

Basics of Python programming, Basics of statistics

Assessment criteria, satisfactory (1)

You know the basics of data analytics in data engineering and machine learning.
You are able to apply basic data analytics techniques in data engineering and machine learning tasks.
You can share your results and exercises via a version control system.

Assessment criteria, good (3)

You understand the basics of data analytics in data engineering and machine learning.
You are able to apply a variety of data analytics techniques in data engineering and machine learning tasks with a suitable approach, regarding the given dataset at hand.
You can share your results and exercises via a version control system.

Assessment criteria, excellent (5)

You understand the basics of data analytics in data engineering and machine learning.
You are able to apply a variety of data analytics techniques in data engineering and machine learning tasks with a suitable approach, regarding the given dataset at hand.
You are able to study and apply advanced tools and approaches regarding exploratory data analytics and dataset optimization with your data
You can share your results and exercises via a version control system.