Introduction to Python for Data Analytics
This course introduces the Python language to students who want to use Python as a tool for their data science initiatives. The goal is to become proficient enough with the Python language to leverage powerful Data Science packages such as Pandas and matplotlib.
This is a comprehensive introduction to Python programming with a focus on understanding and using the Pandas library for storing data in DataFrames and plotting portions of the data with matplotlib. In addition to data visualization, you will learn how to use the Pandas library to import and filter data. Typical data science skills such as data interpretation and analysis will be addressed.
This course is suitable for: Data analysts, Data scientists, Data engineers, and Developers.
Students should have a basic proficiency in some programming language. Prerequisite language skills include understanding of datatypes, Boolean logic, control flow and basics of collections, such as arrays or hash tables. An understanding of using Excel for data manipulation is helpful.
Register 21 days before class start date and save 10%! Enter discount code EARLY10 during registration.
Register 21 days before class start date and save $250! Enter discount code EARLY250 during registration.
Getting Around in Python
· Using Python at the Command Line
· Running the Interactive Shell
· Using Jupyter Notebooks
Jupyter Notebook Basics
· Cell Types
· Edit and Command Mode
· Running cells
· Restarting the Kernel
· Exporting the Notebook
· Cell and Line Magics
· Comments, Indenting, print()
· Control Flow
· List and Set Type Comprehensions
· Comprehensions as Generator Expressions
Functions and Lambda Expressions
· Built-in Functions
· User-defined functions
· Anonymous in-line functions
· Importing and Selective Importing
· random and math Modules
Data Sources and Formats
· CSV, TSV
· Others: XML, YAML, Splunk
· Indexing and Slicing
· Masking and Broadcasting
· Why Pandas?
· Populating DataFrames
· Importing CSV, Excel, SQL Data
· DataFrame Columns and Cells
· DataFrame Retrieval
Pandas and Data Analysis
· Functions on DataFrames
· Using Lambdas
· Merging and Concatenating DataFrames
· Data Cleaning
· Data Analysis
· Aggregate Functions
· Plotting with matplotlib
· Enhancing Visualizations with seaborn
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.