Introduction to Python for Data science

Introduction to Python for Data science


This course provides a beginner introduction to Python. The objective of this course is to get students started with Python as the programming language and give them a taste of how to start working with data in Python using Pandas library.

We will be using Jupyter Notebook and Visual Studio Code environments.

We will cover the most commonly used Python libraries (e.g. numpy, seaborn, matplotlib) for advanced projects, including data visualization, machine learning, and in general. data science.


This is the planed schedule for the whole course, but of course changes are possible due to unforeseen reasons.
The mapping between lectures and course sessions is not one to one. Meaning that we might work on several lectures in one course session or working on one lecture for several sessions.

Lecture # Title Topics Notebooks Externals
0 Introduction About this course
What is programming?
Problem solving (Algorithms)
Why python?
Anaconda python installation
Setting up Python
1 Variables and Data Types What are variables?
What is a data type?
2 Control Structures What are conditionals?
For and While loops
3 Data Structures Lists
Sets and Tuples
List comprehension
User inputs
4 Modules and Packages Python standard libraries
External libraries
Numerical computing with numpy
5 Object Oriented Programming Classes
Instantiate objects
Advanced OOP
6 File Input / Output Files and streams
Reading and writing text files
CSV files
JSON files
7 MPL Visualization Graphics in python
8 Pandas Library I Introduction to pandas
DataFrame and Series
9 Pandas Library II Load data in pandas
Data cleaning
Data exploration
Data visualization
10 Pandas Library III Time series
11 Pandas Library IV Fun with pandas
Saeed Varasteh Yazdi

Saeed Varasteh Yazdi

Assistant Professor, Machine Learning Researcher

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