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.

DEMO LECTURE

Topic: Numpy

Module: Data Analysis Packages

Instructor: Hasan Aljabbouli

Descrpition: NYC Data Science Academy’s Instructor Hasan Aljabbouli walks through a lecture on numpy array.

SYLLABUS

Unit 1 – List Manipulation

– Simple values and expressions
– Defining functions, using ordinary syntax and lambda syntax
– Lists
– Built-in functions and sub-scripting
– 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

INSTRUCTOR

Hasan Aljabbouli

Hasan Aljabbouli is an Assistant Professor in Computer Science. He obtained his Master’s and Doctorate in Artificial Intelligence from Cardiff University in the United Kingdom and his Bachelor’s in Engineering in Information Technology from Homs University. He worked for different universities and has published many scholastic materials in Data Mining and Machine Learning and its applications. In addition to his academic experience, Hasan received two patents and earned relevant experiences participating in various technical projects.

hasan
SESSION SCHEDULE

January Session – Jan 12 – Feb 11, 2021
Tuesdays & Thursdays | 7:00-9:00pm

– January 12, 2021
– January 14, 2021
– January 19, 2021
– January 21, 2021
– January 26, 2021
– January 28, 2021
– February 2, 2021
– February 4, 2021
– February 9, 2021
– February 11, 2021

Early Registration Price: $1510.50

March Session – Mar 2 – Apr 1, 2021
Tuesdays & Thursdays | 7:00-9:00pm

– March 2, 2021
– March 4, 2021
– March 9, 2021
– March 11, 2021
– March 16, 2021
– March 18, 2021
– March 23, 2021
– March 25, 2021
– March 30, 2021
– April 1, 2021

Early Registration Price: $1510.50