6 students

Python Programming – Advanced Level

Python for Data Analysis

  • Starting Date: May 9th, 2021
  • Day/Time: Sundays, 11:00-12:45 pm EDT
  • Length: 8 weeks (16 hours)
  • Instructor:  Dr. Azar Tolouee

Important Notes: 

  • “Certificate of Completion” will be awarded to you, if you complete at least 70% of the course.
  • The full price of the course is $80. 
  • With early bird registration before May 2nd, you pay only $60.

Course Description

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring
many different types of data including images and videos. You will learn how to prepare data for analysis,
perform statistical analysis, create meaningful data visualizations, predict future trends from data, and
more. We cover a wide variety of topics, including:

  • Overview of Python Libraries for Data Scientists
  • Reading Data; Selecting and Filtering the Data; Data manipulation, sorting, grouping, rearranging
  • Plotting the data
  • Descriptive statistics

Data Analysis with Python will be delivered through lecture and assignments. It includes following parts:
Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with datasets. We will
introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and
visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will
use some of its machine learning algorithms to build smart models and make cool predictions.

Course Outline

Python libraries for data analysis:
– NumPy: introduces objects for multidimensional arrays and matrices, as well as
functions that allow to easily perform advanced mathematical and statistical operations
on those objects.
– SciPy: collection of algorithms for linear algebra, differential equations, numerical
integration, optimization, statistics and more.
– Pandas: adds data structures and tools designed to work with table-like data.
– SciKit-Learn: provides machine learning algorithms: classification, regression, clustering,
model validation etc.

Python visualization libraries:
– Matplotlib: python 2D plotting library which produces publication quality figures in a
variety of hardcopy formats.
– Seaborn: provides high level interface for drawing attractive statistical graphics.

Pygame: Focuses on creating games with Pygame. Creating a game window, rect objects, images,
responding to keyboard and mouse input, groups, detecting collisions between game elements,
and rendering text.

Django: Focuses on creating web apps with Django. Installing Django and starting a project,
working with models, building a home page, using templates, using data, and making user



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