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# <a href="https://colab.research.google.com/github/pds2122/course/blob/main/nbs/01_Introduction.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

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# <div class='bar_title'></div>
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# *Practical Data Science*
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# # Introduction
#
# <NAME> / <NAME><br>
# Chair of Information Systems and Business Analytics
#
# Winter Semester 21/22

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# <h1>Table of Contents<span class="tocSkip"></span></h1>
# <div class="toc"><ul class="toc-item"><li><span><a href="#Chair-of-Information-Systems-and-Business-Analytics" data-toc-modified-id="Chair-of-Information-Systems-and-Business-Analytics-1"><span class="toc-item-num">1&nbsp;&nbsp;</span>Chair of Information Systems and Business Analytics</a></span><ul class="toc-item"><li><span><a href="#Team" data-toc-modified-id="Team-1.1"><span class="toc-item-num">1.1&nbsp;&nbsp;</span>Team</a></span></li><li><span><a href="#Research-Areas" data-toc-modified-id="Research-Areas-1.2"><span class="toc-item-num">1.2&nbsp;&nbsp;</span>Research Areas</a></span></li><li><span><a href="#Master-Teaching-Program" data-toc-modified-id="Master-Teaching-Program-1.3"><span class="toc-item-num">1.3&nbsp;&nbsp;</span>Master Teaching Program</a></span></li></ul></li><li><span><a href="#Course-Organization" data-toc-modified-id="Course-Organization-2"><span class="toc-item-num">2&nbsp;&nbsp;</span>Course Organization</a></span><ul class="toc-item"><li><span><a href="#Learning-Objectives" data-toc-modified-id="Learning-Objectives-2.1"><span class="toc-item-num">2.1&nbsp;&nbsp;</span>Learning Objectives</a></span></li><li><span><a href="#Tentative-Schedule-of-Topics" data-toc-modified-id="Tentative-Schedule-of-Topics-2.2"><span class="toc-item-num">2.2&nbsp;&nbsp;</span>Tentative Schedule of Topics</a></span></li><li><span><a href="#Programming-Assignments" data-toc-modified-id="Programming-Assignments-2.3"><span class="toc-item-num">2.3&nbsp;&nbsp;</span>Programming Assignments</a></span></li></ul></li><li><span><a href="#Programming-Language-(Python)" data-toc-modified-id="Programming-Language-(Python)-3"><span class="toc-item-num">3&nbsp;&nbsp;</span>Programming Language (Python)</a></span></li></ul></div>

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# ## Chair of Information Systems and Business Analytics

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# ### Team

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# <img src="https://github.com/NikoStein/pds_data/raw/main/images/01/team.png" style="width:80%">

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# ### Research Areas

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# <img src="https://github.com/NikoStein/pds_data/raw/main/images/01/research.png" style="width:80%">

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# ### Master Teaching Program

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# <img src="https://github.com/NikoStein/pds_data/raw/main/images/01/courses_master.png" style="width:80%">

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# ## Course Organization

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#  -  Classes
#     - Lecture: 	Thursday 10:15 – 11:45
#     - Tutorial: After Lecture 
#  -  Self-study and Tutorial
#     - Work on (bi-)weekly assignments and programming examples
#     - Worksheet submissions are mandatory

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# -  WueCampus      
#     - Forum and information
# - Github
#     - Slides and worksheets     
#     - Submission of solutions  

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# -  **Examination**: Graded capstone group project     
#     - Work on a real-world problem set
#     - Groups of 3 students

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# ### Learning Objectives

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# * The foundations, **frameworks and applications** of the emerging field of data science
# * Design, implement, and evaluate the core algorithms underlying an **end-to-end data science workflow**, including data import, analysis, and presentation of information
# * Leverage the **Python** application programming interface (API) ecosystem and data infrastructure that supports data acquisition, storage, retrieval and analysis
# * The application of a data-based **analytical approach** to identify and solve problems
# * Implementation and execution skills for **data-driven business analytics**

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# ### Tentative Schedule of Topics

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# 1. Introduction, Jupyter Notebooks, Version Control (**today**)
# 1. Descriptive Analytics
# 1. Basic Machine Learning
# 1. Feature Engineering
# 1. Deep Learning on Tabular Data
# 1. Deep Learning for Computer Vision
# 1. Deep Learning for Natural Language Processing

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# ### Programming Assignments

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# 0. Initial Assignment (**this week**, not graded)
# 1. Descriptive Analytics
# 1. Machine Learning and Feature Engineering
# 1. Deep Learning on Tabular Data
# 1. Deep Learning for Computer Vision
# 1. Deep Learning for Computer Vision II

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# ## Programming Language (Python)

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# __Python is <s>becoming</s> the world’s most popular coding language__
#
# For the first time in more than 20 years we have a new leader of the pack: the Python programming language
#
# <img src="https://github.com/NikoStein/pds_data/raw/main/images/01/tiobe_2021.png" style="width:50%">
#
# Source: [Tiobe](https://www.tiobe.com/tiobe-index/)

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# **Why Python?**
#
# While there are several great languages for data analysis (such as R, Julia, Scala, ...) we decided to use python for this class because of the following reasons:
#
# * Excellent interactive shell and a large collection of open source packages
# * Very simple syntax
# * Simple enough for things to happen quickly and powerful enough to allow the implementation of the most complex ideas
# * Several highly optimized machine learning libraries that you can drag and drop into your code even if you know a minimum of Python
# * Good visualization capabilities

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