INTRODUCTION TO STATISTICS AND PROGRAMMING FOR DATA SCIENCE

Name: INTRODUCTION TO STATISTICS AND PROGRAMMING FOR DATA SCIENCE
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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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