Python for Data Science – Introduction to Python for Data Science Overview/Description Target Audience Prerequisites Expected Duration Lesson Objectives Course Number Expertise Level Overview/Description
This course introduces the concepts of data science and provides a brief overview of the Python skills needed to work with data. In this course, you will learn about IPython components, Notebook, and the NumPy module. There's still more to come as the course guides you toward managing financial statistics with financial big data.
Target Audience
This path is targeted toward individuals wanting to expand their knowledge of Python while learning data science; IT specialists aspiring to learn a new skillset; statisticians; computer scientists; and IT analysts. Python knowledge is recommended.
Prerequisites
None
Expected Duration (hours)
2.5
Lesson Objectives Python for Data Science – Introduction to Python for Data Science
start the course
describe elements of data science and datasets with various modeling and prediction relationships
recognize the various pipelines in data science and the stages of the data science cycle
define and describe the various libraries and packages for data analysis
perform the key steps involved in installing Anaconda including all the necessary packages for this course
describe the various Python containers for data management
create lists, tuples, and dictionaries with Python to drive data
use Python list comprehensions to create lists
describe the IPython shell and shell commands
run the Jupyter Notebook and familiarize with the basics of its user interface
capture Python code output in Jupyter Notebook
run the Jupyter QT Console and familiarize with the basics of its user interface
use IPython to perform debugging and error management on Python code
basic access and usage of the NumPy package in a Python development environment
describe the various components of NumPy
describe ndarray object attributes
describe the various NumPy array operations applicable to data science
describe different ways of creating NumPy arrays
describe how Pandas library may be used to read and write various formats of data
use Pandas library to read data from a CSV file and write data out to a CSV file
use Python's standard JSON package to read JSON data
use the pandas library to generate and parse date values
perform data clean up by handling missing and erroneous data
download and load a sample dataset into Python from a URL
load a large dataset as smaller chunks by obtaining an iterator for the dataset
recognize the main concepts in data science using Python
Course Number: sd_pyds_a01_it_enus
Expertise Level
Intermediate