Advanced Python Development For Data Science
Course: PYTHONA
Duration: 4 Days
Level: II
On our website at:
http://www.verhoef-training.com/courses/PYTHONA.html
Course Summary
This course aims to extend and solidify your Python experience by exploring structural techniques and common Python APIs. You'll learn how to write OO and functional code, define and consume REST services and web sockets, implement multithreaded code, use popular Python data science libraries, implement Big Data solutions, and more.
Topics Covered In This Course
Recap Essential Python Features
- Language Fundamentals
- Functions
- Data Structures
- Defining and Using Packages
- Additional Techniques
Object-Oriented Programming
- Essential Concepts
- Defining and Using a Class
- Class-Wide Members
Additional Object-Oriented Techniques
- A Closer Look at Attributes
- Implementing Special Methods
- Inheritance
XML Processing
- XML Essentials
- Reading XML Data in Python
- Locating Content using XPath
- Updating XML Data in Python
- Using the Lxml Library
Functional Programming
- Functional Programming in Python
- Higher Order Functions
- Additional Techniques
Web Processing
- Python Web Servers
- Python Rest Services
- Python Web Sockets
Decorators
- Getting Started with Decorators
- Additional Decorator Techniques
- Parameterized Decorators
Asynchronous Processing in Python
- Getting Started with Asynchrony in Python
- Creating Tasks to Run in Different Threads
- Additional Task Techniques
Getting Started with Python Data Science and NumPy
- Introduction to Python Data Science
- NumPy Arrays
- Manipulating Array Elements
- Manipulating Array Shape
NumPy Techniques
- NumPy Universal Functions
- Aggregations
- Broadcasting
- Manipulating Arrays using Boolean Logic
- Additional Techniques
Getting Started with Pandas
- Introduction to Pandas
- Creating a Series
- Using a Series
- Creating a DataFrame
- Using a DataFrame
Pandas Techniques
- Universal Functions
- Merging and Joining Datasets
- A Closer Look at Joins
Working with Time Series Data
- Introduction to Time Series Data
- Indexing and Plotting Time Series Data
- Testing Data for Stationarity
- Making Data Stationary
- Forecasting Time Series Data
- Scaling Back the ARIMA Results
Introduction to Big Data
- Setting the Scene
- Introduction to Hadoop
- Hadoop Components
Getting Started with PySpark
- Introduction to Spark
- Spark Architecture
- Application Execution
- Using the Python Spark Shell
Using the PySpark API
- Essential Concepts
- Creating an RDD
- Working with RDDs
RDD Operations
- RDD Transformations
- RDD Transformations on Key-Value Pairs
- Actions
- Caching
- Spark Jobs - The Big Picture
What You Can Expect
Object-oriented Python programming
Functional Python programming
REST services and web sockets
Defining and using decorators
Asynchronous programming
Python data science techniques
Python Big Data and PySpark
Recommended Prerequisites
Approx. 6 months Python experience
Related Courses
Code |
Course Title |
Duration |
Level |
|
PYTHONDS |
Python Data Science |
3 Days |
I |
Details |
PYTH3F |
Python Programming |
5 Days |
I |
Details |
Every student attending a Verhoef Training class will receive
a certificate good for $100 toward their next public class taken
within a year.
You can also buy "Verhoef Vouchers" to get a discounted rate for a
single student in any of our public or web-based classes.
Contact your account manager or our sales office for details.