This course provides students with a foundational understanding of the Python programming language. Following an introductory exposition to the importance of Python for data science-related programming tasks, students will be acquainted with fundamental programming concepts like variables, data types, and statements. Building on this basis, the important notion of a function is explained and errors, exception handling, and logging are explicated. The course concludes with an overview of the most widely-used library packages for data science.

1. Introduction
   1. Why Python?
   2. Obtaining and installing Python
   3. The Python interpreter , IPython, and Jupyter
2. Variables and Data Types
   1. Variables and value assignment
   2. Numbers
   3. Strings
   4. Collections
   5. Files
3. Statements
   1. Assignment, expressions, and print
   2. Conditional statements
   3. Loops
   4. Iterators and comprehensions
4. Functions
   1. Function declaration
   2. Scope
   3. Arguments
5. Errors and Exceptions
   1. Errors
   2. Exception handling
   3. Logs
6. Modules and Packages
   1. Usage
   2. Namespaces
   3. Documentation
   4. Popular data science packages

This course builds upon basic knowledge of Python programming (Introduction to Programming with Python, DLBDSIPWP) and is concerned with the exposition of advanced Python programming concepts. To this end, important notions of object-oriented programming like classes and objects and pertaining design principles are outlined. Starting from an in-depth discussion of advanced features of Python functions, functional programming concepts and their implementation in Python are conveyed.

• This course provides students with a thorough introduction to important notions and concepts from the domain of object-oriented programming such as classes, objects, abstraction, encapsulation, inheritance, polymorphism, composition, and delegation. Additionally, the functional programming paradigm and pertaining ideas like functions as first class objects, decorators, pure functions, immutability and higher order functions are conveyed. Pursuant to the portfolio course type, the aforementioned concepts and ideas are explored by hands-on programming projects.

The goal of data science can be summarized as the extraction of insights (hence, value) from data. It is self-evident that this objective cannot be successfully achieved based on unreliable and untrustworthy data. This course aims at establishing the notion of data quality and the pertinent methods for data quality management. Furthermore, techniques for acquiring data as well as formatting and tidying data in order to make it suitable for subsequent analytical treatment are covered.

1. Data Quality
   1. Introduction to data quality
   2. Data quality dimensions and issue types
2. Data Quality Management
   1. Data governance and stewardship
   2. Activities and processes
3. Data Acquisition
   1. Web scraping
   2. Data APIs
4. Working with Common Data Formats
   1. Text-based formats (CSV, XML, JSON)
   2. Binary formats (HDF 5, Parquet, Arrow)
5. Tidy Data
   1. Structuring
   2. Cleansing
   3. Enrichment

Obtaining an overview of the salient characteristics of a data set is one of the core activities at the outset of any data analysis endeavour. The corresponding activities, methods, and techniques are grouped under the term “exploratory data analysis”. During exploratory data analysis, gaining insight into a given data set is often aided by the application of suitable visualization techniques. The utility of visualization, however, does not end at this stage; it is also crucial for communicating analytical outcomes.

This course first introduces a set of approaches, tools, and techniques that are useful for exploring data sets. It then takes a thorough look at the subject area of visualization, which is presented in detail by an exposition arc that spans from first principles of visualization to practical implementation to insights into the communication of data science results and findings.

1. Exploratory Data Analysis
   1. Location and variability
   2. Further exploration of data distribution
   3. Covariance and correlation
2. Data Visualization Principles
   1. Coordinates and axes
   2. Color spaces
   3. Graph types
3. Data Visualization Practice
   1. Amounts, proportions, associations, and distributions
   2. Time series and trends
   3. Geo-spatial data
4. Visualization in Python – Matplotlib and Seaborn
   1. Introduction to PyPlot, Matplotlib, and Seaborn
   2. Basic plots
   3. Geo-spatial plots
5. Communicating Data Science
   1. Unclutter, focus, and capture attention
   2. Lessons from design
   3. Principles of storytelling with data