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Applied Python for Data Science & Engineering (TTPS4874)

Name: Applied Python for Data Science & Engineering (TTPS4874)
Price: 1795.00 EUR
Availability: InStock
Rating: 4.6 (5878 reviews)

Applied Python for Data Science & Engineering®is a hands-on introductory-level course that provides you with a ramp-up to using Python for scientific and mathematical computing. Working in a hands-on learning environment, you'll learn basic Python scripting skills and concepts, as well as the most important Python modules for working with...

4.6 out of 5 rating
Last updated : 23/07/2026
English

Available as Instructor Led Training, Live Online & In Person at your Offices or Ours.

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Download Overview

Duration:

32.00 hours

26.0 CPD hours

Overview:

Description:

Geared for scientists and engineers with limited practical programming background or experience, Applied Python for Data Science & Engineering is a hands-on introductory-level course that provides you with a ramp-up to using Python for scientific and mathematical computing. Working in a hands-on learning environment, you'll learn basic Python scripting skills and concepts, as well as the most important Python modules for working with data, from arrays, to statistics, to plotting results.
Throughout the course, guided by our expert instructor, you'll gain a robust skill set that will equip you to make data-driven
decisions and elevate operational efficiencies within your organization. You'll explore data manipulation with Pandas,
advanced data visualization using Matplotlib, and numerical analysis with NumPy. You'll also delve into best practices for error and exception handling, modular programming techniques, and automated workflow development, equipping you with the skill set to enhance both the effectiveness and efficiency of your data-driven projects.

Course Outline:

The Python Environment
About Python
Starting Python
Using the interpreter
Running a Python script
Python scripts on Unix/Windows
Using the Spyder editor
Getting Started
Using variables
Builtin functions
Strings
Numbers
Converting among types
Writing to the screen
String formatting
Command line parameters
Flow Control
About flow control
White space
Conditional expressions (if,else)
Relational and Boolean operators
While loops
Alternate loop exits
Array Types
About sequences
Lists
Tuples
Indexing and slicing
Iterating through a sequence
Using enumerate()
Functions for all sequences
Keywords and operators for all sequences
The range() function
Nested sequences
List comprehensions
Generator expressions
Working with files
File overview
Opening a text file
Reading a text file
Writing to a text file
Raw (binary) data
Dictionaries and Sets
Creating dictionaries
Iterating through a dictionary
Creating sets
Working with sets
Functions, modules, and packages
Four types of function parameters
Four levels of name scoping
Single/multi dispatch
Relative imports
Using __init__ effectively
Documentation best practices
Errors and Exception Handling
Syntax errors
Exceptions
Using try/catch/else/finally
Handling multiple exceptions
Ignoring exceptions
Using the Standard Library
The sys module
Launching external programs
Walking directory trees
Grabbing web pages
Sending e-mail
Paths, directories, and filenames
Dates and times
Zipped archives
Pythonic Programming
The Zen of Python
Common idioms
Named tuples
Useful types from collections
Sorting
Lambda functions
List comprehensions
Generator expressions
String formatting
Introduction to Python Classes
Defining classes
Constructors
Instance methods and data
Attributes
Inheritance
Multiple inheritance
Developer tools
Program development
Comments
pylint
Customizing pylint
Using pyreverse
The unittest module
Fixtures
Skipping tests
Making a suite of tests
Automated test discovery
The Python debugger
Starting debug mode
Stepping through a program
Setting breakpoints
Profiling
Benchmarking
Excel spreadsheets
The openpyxl module
Reading an existing spreadsheet
Creating a spreadsheet from scratch
Modifying an existing spreadsheet
Setting Styles
Serializing Data
Using ElementTree
Creating a new XML document
Parsing XML
Finding by tags and XPath
Parsing JSON into Python
Parsing Python into JSON
Working with CSV
iPython and Jupyter
iPython features
Using Jupyter notebooks
Benchmarking
External Commands
Cells
Sharing Notebooks
Introduction to NumPy
NumPy basics
Creating arrays
Shapes
Stacking
Indexing and slicing
Array creation shortcuts
Matrices
Data Types
Brief intro to SciPy
What is SciPy
The Python science ecosystem
How to use SciPy
Getting Help
SciPy subpackages
Intro to Pandas
Pandas overview & architecture
Series
Dataframes
Reading and writing data
Data alignment and reshaping
Basic indexing
Broadcasting
Removing Entries
Timeseries
Reading Data
Introduction to Matplotlib
Overal architecture
Plot terminology
Kinds of plots
Creating plots
Exporting plots
Using Matplotlib in Jupyter
What else can you do

What You'll Learn:

Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore:
- Core Python Proficiency: By the close of the course, participants will have a firm grasp on the foundational elements of Python, such as variables, data types, and flow control, empowering them to write scripts and build simple programs with confidence.
- Analytical Problem-Solving: Utilizing libraries such as NumPy and SciPy, students will develop the ability to perform
complex mathematical operations and statistical analyses, significantly amplifying their analytical capabilities for tasks such as data modeling or optimization problems.
- Data Manipulation Mastery: By the end of the course, participants will be proficient in employing Pandas to clean,
transform, and analyze data sets, enabling them to make data-driven decisions effectively.
- Automated Workflow Development: Students will acquire the ability to construct automated scripts using Python's
Standard Library, optimizing repetitive tasks and thereby enhancing operational efficiency in their organizations.
- Advanced Data Visualization: Upon course completion, learners will be equipped to utilize Matplotlib and other Python libraries to craft intricate visual representations of data, facilitating clearer and more impactful reporting and
presentations.
- Error-Resilient Coding: Attendees will learn best practices for implementing robust error and exception handling
techniques, leading to the creation of more stable and secure Python applications.
- Modular Programming Proficiency: By mastering Python functions, modules, and packages, students will be adept at developing modular and maintainable code, a key skill for scalability and collaborative programming projects.

Available Delivery Options for Applied Python for Data Science & Engineering (TTPS4874)

Live Instructor Led Classroom Online (Live Online)
Traditional Instructor Led Classroom training (TILT/ILT)
Delivery at your offices in United Kingdom
Private dedicated course for your staff

How many CPD hours does Applied Python for Data Science & Engineering (TTPS4874) provide?

The 4 day Applied Python for Data Science & Engineering (TTPS4874) course gives you up to 26.0 CPD hours/structured learning hours.

Who is the correct audience for Applied Python for Data Science & Engineering (TTPS4874)?

This introductory-level course is geared for technical professionals new to Python. Roles include data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks. Familiarity with basic
scripting skills is recommended, as this course does not teach general scripting basics.

Do you provide training for Applied Python for Data Science & Engineering (TTPS4874)?

Yes, we provide corporate training, dedicated training and closed classes for Applied Python for Data Science & Engineering (TTPS4874). This can take place anywhere in United Kingdom including London, Manchester, Birmingham, Edinburgh or live online.

What is the duration of Applied Python for Data Science & Engineering (TTPS4874)?

The Applied Python for Data Science & Engineering (TTPS4874) training takes place over 4 day(s), with each day lasting approximately 32.00 hours including breaks.

What other terms do people search for?

Popular related searches include: Python; Data Science; Data Anlaysis; Machine Learning

Why are Nexus Human the best provider for Applied Python for Data Science & Engineering (TTPS4874)?

Nexus Human are recognised as one of the best training companies. Our trainers have won awards including Small Firms Best Trainer, national training partner of the year for Ireland, and global top 30 instructor awards in 2012, 2019 and 2021.

Is there a discount code for Applied Python for Data Science & Engineering (TTPS4874)?

Yes, the discount code PENPAL5 is currently available for Applied Python for Data Science & Engineering (TTPS4874) training. Only one discount code can be used per booking.

DURATION 1 Day

No upcoming local classes scheduled.

02 Sep 26

09:00 – 17:00

Virtual

€1,795.00

16 Dec 26