Introduction to Python for Data science
Intro
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.
Syllabus
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? |
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| 2 | Control Structures | What are conditionals? For and While loops Functions |
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| 3 | Data Structures | Lists Dictionaries Sets and Tuples List comprehension User inputs |
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| 4 | Modules and Packages | Python standard libraries External libraries Numerical computing with numpy |
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| 5 | Object Oriented Programming | Classes Instantiate objects Advanced OOP |
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| 6 | File Input / Output | Files and streams Reading and writing text files CSV files JSON files Databases |
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| 7 | MPL Visualization | Graphics in python | ||
| 8 | Pandas Library I | Introduction to pandas DataFrame and Series |
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| 9 | Pandas Library II | Load data in pandas Data cleaning Data exploration Data visualization |
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| 10 | Pandas Library III | Time series | ||
| 11 | Pandas Library IV | Fun with pandas |
Saeed Varasteh Yazdi
Assistant Professor, Machine Learning Researcher