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COURSE DATES

on request
COURSE DURATION

6 weeks
LANGUAGE

English
LOCATION

Online
CERTIFICATE

TU Berlin Certificate of Professional Education (6 ETCS)
FORMAT

Online
LECTURER

Prof. Dr. Rand Kouatly
PRICE

3980 €

Recognized as Bildungszeit
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INTRODUCTION TO STATISTICS AND PROGRAMMING FOR DATA SCIENCE

The certificate course provides intensive instruction in the fundamentals of statistics and programming for data science. It covers the basics of descriptive and inferential statistics as well as the fundamentals of programming for data analysis. Participants learn how to use the R/Python language to analyse data and create data visualizations.

Learning goals

Upon completion of the course, participants will know how to import, export and manipulate data using R/Python packages and understand basic statistical concepts and techniques and how to calculate them in R/Python. They will be able to create and customize different types of plots and graphs using R visualization packages. They will be proficient in creating and customizing different types of plots and graphs using Excel as complementary tools for statistics and be able to use R/Python to perform simple statistical analysis, hypothesis testing and data exploration. They can independently perform data cleaning, data transformation and data preparation using R/Python and know techniques for data pre-processing, cleaning and preparation.

Content

Week 1: Preparation for the course - Software installation
  • What are R and RStudio? What are Python and Jupyter (Notebook)?
  • Installing R and RStudio / Python and Jupyter (Notebook)? on your personal computer
  • Errors, warnings and messages
  • Tips on learning to code
  • Package installation
  • Package loading
  • Testing and Hello World Program
Week 2: Introduction to Programming
  • Topic 1: Data Types
  • Topic 2: Basics Operations
  • Topic 3: Data Structure
  • Programming practice 1
Week 3: Overview of Statistics in Programming
  • Topic 4: Types of Data
  • Topic 5: Exploratory Data Analysis
  • Topic 6: Statistics Analytics Using Excel
  • Programming practice 2
Week 4: Data Manipulation and Cleaning
  • Topic 7: Data Frame operations
  • Topic 8: Input and Output using R/Python
  • Topic 9: Data Reshaping
  • Programming practice 3
Week 5: Data Exploration and Visualization
  • Topic 10: Missing data handling
  • Topic 11: Exploring and visualizing techniques
  • Topic 12: Visualizing data using R/Python
  • Programming practice 4
Week 6: Capstone Project
  • Applying the skills and knowledge learned throughout the course to a real-world data
  • Exam Project Requirement and specification
  • 4 hours open consultation (Groups) – Online only
The course starts with a self-learning phase in which the participants will prepare themselves for the content of the course and will be provided with the corresponding software. This will be followed by virtual classroom sessions and self-study phases. Each unit is rounded off by programming exercises.

Target group

This course is designed for professionals who want to take an in-depth look at data science and to learn more about fundamentals and concepts in this field.

This course is recognized as Bildungszeit according to paragraph § 10 (5) of the Berliner Bildungszeitgesetz (BiZeitG).

Prerequisites

  • English level B1 (according to the European Framework)
  • Prior knowledge of programming and statistics
  • Basics in mathematics
  • Understanding of standard Microsoft Office applications
  • Laptop/PC + headset with microphone

Dates

Currently no dates. If you are interested, please contact us.
LECTURER

Prof. Dr. Rand Kouatly
Prof. Dr. Rand Kouatly is an international academic leader, professor and researcher with more than 20 years of experience in higher education, educational technology and corporate knowledge management as well as in professional education for managers. He also has more than 10 years of international experience as a senior project manager and consultant in various technology, market and business sectors. He is currently the Program Director for Software Engineering at the University of Europe for Applied Sciences. From 2013-2016 he worked as a Researcher at Technische Universität Berlin and since 2016, has been teaching as a Guest Lecturer in Technische Universität Berlin programs in fields including Java, machine learning and Big Data. He is an international scholar and expert in telecommunication engineering and software development, frontend and backend development, artificial intelligence, deep learning, artificial neural networks, including the fields of pattern recognition, audio and speech processing and speech and speaker recognition, e-learning, project management and business consultancy. In addition, he is experienced in leading small companies, start-ups, projects and team leaders. He has also taken on multiple roles such as Dean and Vice Dean, Academic Researcher and Supervisor and study program creator in various public and private universities.
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