Applied Python for Data Science (TTPS4870)

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Course Features

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Duration

5 days

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Delivery Method

Online

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Available on

Lifetime Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

Course Description

Applied Python For Scientists and Engineers is a course specifically designed for engineers and scientists with little to no programming experience. The main goal of this course is to introduce Python as a tool for scientific and mathematical computing. The course begins by covering the basics of Python programming, ensuring that students have a solid foundation to build upon. Once the fundamentals are covered, the course then focuses on the most important Python modules essential for working with data. This includes modules for handling arrays, performing statistical analysis, and creating visualizations of data through plotting and graphing.

One significant aspect that may attract individuals to this course is the potential career opportunities it can offer. According to research, the average annual salary for a Python Data Scientist is $93,185. This indicates that there is a growing demand for professionals skilled in Python and data science.

In conclusion, Applied Python For Scientists and Engineers provides a comprehensive introduction to Python for individuals in the engineering and scientific fields. It equips students with the necessary skills to effectively work with data through various Python modules. With the increasing importance of data science in various industries, this course offers individuals an opportunity to enhance their career prospects as Python Data Scientists.

Course Overview

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International Faculty

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Post Course Interactions

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Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Students should be comfortable working with files and folders, and should not be afraid of the command line

Basic programming experience would be helpful

What You Will Learn

Create and run basic programs

Design and code modules and classes

Implement and run unit tests

Use benchmarks and profiling to speed up programs

Process XML and JSON

Manipulate arrays with numpy

Get a grasp of the diversity of subpackages that make up scipy

Use iPython notebooks for ad hoc calculations, plots, and what-if?

Manipulate images with PIL

Solve equations with sympy

Get introduced to advanced data science with Python including SciKit-Learn and PySpark

Target Students

While there are no specific programming prerequisites, basic programming experience would be helpful

Course Instructors

Tom Robertson

Data Science Enthusiast

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defi...
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