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Fast Track to Python for Data Science
Description
Fast Track to Python for Data Science Introduction
Join us for the “Fast Track to Python for Data Science” course, a concentrated three-day program designed to quickly elevate your Python skills tailored specifically for data science.
This course provides an immersive experience, focusing on essential Python techniques for effective data handling and visualization. Engage in practical learning with exercises that cover key topics such as data manipulation, and visualization using Python’s most powerful libraries and tools.
During the course, you will gain hands-on experience with Python’s core libraries including iPython, Jupyter, and Zeppelin, which are pivotal in data analysis and visualization.
Learn to master flow control, work with sequences, manipulate arrays, and utilize dictionaries and file handling to broaden your understanding of Python’s application in data science.
This program ensures that by the conclusion, participants will have the skills and confidence to apply Python effectively in their data science projects. Our goal is to boost your capabilities in data science, preparing you for success in this rapidly advancing field.
Fast Track to Python for Data Science Course Objectives
- Master Python Data Handling: Learn effective techniques for data manipulation and processing.
- Develop Skills in Data Visualization: Gain proficiency in using Python to create insightful data visualizations.
- Explore Python Libraries: Deep dive into using essential Python libraries such as iPython, Jupyter, and Zeppelin for data analysis.
- Understand Core Python Concepts: Master foundational Python concepts including flow control, sequences, arrays, and dictionaries.
- Practical Application: Apply your learning in practical exercises designed to simulate real-world data science scenarios.
Prerequisites
- While there are no specific programming prerequisites, students should be comfortable working with files and folders and should not be afraid of the command line and basic scripting.
Audience
- This course is geared for data analysts, developers, engineers, or anyone tasked with utilizing Python for data analytics tasks.
Fast Track to Python for Data Science Outline
An Overview of Python
- Why Python?
- Python in the Shell
- Python in Web Notebooks (iPython, Jupyter, Zeppelin)
- Demo: Python, Notebooks, and Data Science
Getting Started
- Using variables
- Built-in functions
- Strings
- Numbers
- Converting among types
- Writing to the screen
- Command line parameters
- Running standalone scripts under Unix and Windows
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
Sorting
- The sorted() function
- Alternate keys
- Lambda functions
- Sorting collections
- Using operator.itemgetter()
- Reverse sorting
Errors and Exception Handling
- Syntax errors
- Exceptions
- Using try/catch/else/finally
- Handling multiple exceptions
- Ignoring exceptions
Essential Demos
- 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
numpy
- numpy basics
- Creating arrays
- Indexing and slicing
- Large number sets
- Transforming data
- Advanced tricks
Python and Data Science
- Data Science Essentials
- Working with Python in Data Science
Working with Pandas
- pandas overview
- Dataframes
- Reading and writing data
- Data alignment and reshaping
- Fancy indexing and slicing
- Merging and joining data sets
Working with matplotlib
- Creating a basic plot
- Commonly used plots
- Ad hoc data visualization
- Advanced usage
- Exporting images
$1995.00
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3 Days Course |