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EVENT DETAILS |
Course Overview
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 & string manipulation, control structures, simple data analysis packages, & introduce modules for downloading data from the web.
* Tuition paid for part-time courses can be applied to the Data Science Bootcamp if admitted within 9 months.
Instructors
Tony Schultz
Tony Schultz
Tony received his Ph.D. in Physics from the City University of New York & has taught at Sarah Lawrence College over the past decade. Tony specializes in developing machine learning & pattern recognition algorithms for processing motion capture data. He is passionate about teaching scientific computing & studying deep structures in human motion.
Alexander Baransky
Alexander Baransky
Alex received his degree in Environmental Biology from Columbia University. He has experience with multiple computer languages including Python, R, & SQL. As an engineer at heart & biologist through training, Alex is passionate about animal behavior & finding innovative ways to use data science in the field of biology.
This Introductory Python class is designed 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 & string manipulation, control structures, simple data analysis packages, & introduce modules for downloading data from the web.
This Introductory Python class runs over four weeks, with five hours of class per week (split into 2 hour evening classes). Classes will be given in a lab setting, with student exercises mixed with lectures. Students should bring a laptop to class. There will be a modest amount of homework after each class. Due to the focused nature of this course, there will be no individual class projects but the instructors will be available to help students who are applying Python to their own work outside of class.
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 & final project (where applicable). Students who complete 80% of the homework & attend a minimum of 85% of all classes are eligible for the certificate of completion.
Syllabus
Unit 1: List manipulation
Simple values & expressions
Defining functions, using ordinary syntax & lambda syntax
Lists
Built-in functions & subscripting
Nested lists
Functional operators: map & filter
List Comprehensions
Multiple-list operations: map & zip
Functional operators: reduce
Unit 2: Strings & 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 & nested if statements
Conditional expressions in lambda functions
While loops
break & continue
Unit 4: Data Analysis Packages
NumPy
Ndarray
Subscripting & slicing
Operations
Pandas
Data Structure
Data Manipulation
Grouping & Aggregation
Preparation - How to set up Python environment
[IMPORTANT] In the class we will use Python 3. If you are following this video to set up Python environment, please make sure you download the Python 3.X version starting from 1 min 23 s in the video.
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