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Python Primer for Data Science & Machine Learning
Description
Python Primer for Data Science & Machine Learning Introduction
Dive into the world of data science and machine learning with our “Python Primer for Data Science & Machine Learning” course. This course is the perfect starting point for data analysts, business analysts, technical managers, and anyone eager to master Python, a pivotal language in today’s data-driven industries. Our curriculum is crafted to provide a practical and foundational understanding of Python, particularly focusing on its application in data science projects.
Participants will begin their journey with an engaging overview of Python, exploring both script-based and interactive web notebooks to appreciate Python’s flexibility and power in different contexts. The course then progresses to hands-on sessions where you will learn to utilize essential Data Science libraries like NumPy and Pandas. These skills are fundamental for any budding data scientist and will serve as the building blocks for more advanced data manipulation and analysis techniques.
Expect to emerge from this course with a robust basic knowledge of Python tailored for data science applications. You’ll be well-prepared to implement your skills in real-world projects or to advance further into specialized studies. For those who wish to dive deeper post-course, our “JumpStart to Python for Data Science” offers an extended curriculum with intensive lab sessions, perfect for enhancing your understanding and practical capabilities in Python.
Python Primer for Data Science & Machine Learning Objectives
By the end of this course, learners will understand (to a basic level):
- How to work with Python interactively in web notebooks
- The essentials of Python scripting
- Key concepts necessary to enter the world of Data Science via Python
Prerequisites
- No prior programming experience is required.
Audience
- This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments.
- No prior programming experience is required.
Python Primer for Data Science & Machine Learning Outline
Python Quick View
- Why Python?
- Python in the Shell
- Python in Web Notebooks (iPython, Jupyter, Zeppelin)
- Exploring Python, Notebooks, and Data Science
Getting Started
- Using variables
- Builtin functions
- Strings
- Numbers
- Converting among types
- Writing to the screen
- Command line parameters
Flow Control
- About flow control
- White space
- Conditional expressions
- Relational and Boolean operators
- While loops
- Alternate loop exits
Sequences, Arrays, Dictionaries and Sets
- About sequences
- Lists and list methods
- Tuples
- Indexing and slicing
- Iterating through a sequence
- Sequence functions, keywords, and operators
- List comprehensions
- Generator Expressions
- Nested sequences
- Working with Dictionaries
- Working with Sets
Working with files
- File overview
- Opening a text file
- Reading a text file
- Writing to a text file
- Reading and writing raw (binary) data
Functions
- Defining functions
- Parameters
- Global and local scope
- Nested functions
- Returning values
Essential Demos
- Sorting
- Exceptions
- Importing Modules
- Classes
- Regular Expressions
The standard library
- Math functions
- The string module
Dates and times
- Working with dates and times
- Translating timestamps
- Parsing dates from text
- Formatting dates
- Calendar data
Python and Data Science
- Data Science Essentials
- Pandas Overview
- NumPy Overview
- SciKit Overview
- MatPlotLib Overview
- Working with Python in Data Science
$1795.00
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2 Days Course |