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Introductory

Introductory Python

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Introductory

Introductory Python

This is a class for computer-literate people with no programming background who wish to learn basic Python programming. The course is aimed at those who want to learn “data wrangling” – manipulating downloaded files to make them amenable to analysis. We concentrate on language basics such as list and string manipulation, control structures, simple data analysis packages, and introduce modules for downloading data from the web.

* Tuition paid for part-time courses can be applied to the Data Science Bootcamps if admitted within 9 months.

Course Overview

This course is an introduction to data analysis with the Python programming language, and is aimed at beginners. We introduce how to work with different data structure in Python. We covered the most popular modules, including Numpy, Scipy, Pandas, matplotlib, and Seaborn, to do data analytics and visualization. We use ipython notebook to demonstrate the results of codes and change codes interactively during the class.

Prerequisites

If you have good knowledge of basic data types (e.g. string, numeric), data structures (e.g. list, tuple, dictionary) and are familiar with concepts of list comprehension and for/while loop, you are good to go with the Python for Data Analysis and Visualization course.

Certificate

Certificates are awarded at the end of the program at the satisfactory completion of the course. Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.

Syllabus

Unit 1 – List manipulation

  • Simple values and expressions
  • Defining functions, using ordinary syntax and lambda syntax
  • Lists
    • Built-in functions and subscripting
    • Nested lists
  • Functional operators: map and filter
  • List Comprehensions
  • Multiple-list operations: map and zip
  • Functional operators: reduce

Unit 2 – Strings and simple I/O

  • Characters
  • Strings as lists of characters
  • Built-in string operations
  • Input files as lists of strings
  • Print statement
  • Reading data from the web
    • Using the requests package
    • String-based web scraping (e.g. handling csv files)

Unit 3- Control structures

  • Statements vs. expressions
  • For loops
    • Variables in for loops
  • if statements
    • Simple and nested if statements
    • Conditional expressions in lambda functions
  • While loops
    • break and continue

Unit 4 – Data Analysis Packages

  • NumPy
    • Ndarray
    • Subscripting and slicing
    • Operations
  • Pandas
    • Data Structure
    • Data Manipulation
    • Grouping and Aggregation
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