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Fast Track to Python for Data Science | Introduction to Python for Data Science
Duration
3
Course
TTPS4873
Available Formats
Classroom Training, Online Training
Course Description
Overview
Fast Track to Python for Data Science is a three-day, hands-on course that introduces data analysts and business analysts to the Python programming language, as it’s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice.Students will explore basic Python syntax and concepts applicable to using Python to work with data. The course begins with quick introduction to Python, with demonstrations of both script-based and web notebook-based Python, and then dives into the essentials of Python necessary to a data scientist. The tail end of the course explores a quick integration of these skills with key Data Science libraries including NumPy, Pandas and Matplotlib. Students will explore the concepts and work with large data sets in a workshop style lab. This class is hands-on and includes basic programming labs that introduce students to basic Python syntax and concepts applicable to using Python to work with data, AI and machine learning basics.
Students will explore basic Python syntax and concepts applicable to using Python to work with data. The course begins with quick introduction to Python, with demonstrations of both script-based and web notebook-based Python, and then dives into the essentials of Python necessary to a data scientist. The tail end of the course explores a quick integration of these skills with key Data Science libraries including NumPy, Pandas and Matplotlib. Students will explore the concepts and work with large data sets in a workshop style lab.
This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students will learn to leverage core Python scripting for data science skills using the most current and efficient skills and techniques.
Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore:
- 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
Objectives
Audience
This introductory-level course is geared for data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks.
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. This is for attendees new to Python.
Topics
An Overview of Python
- Why Python?
- Python in the Shell
- Python in Web Notebooks (iPython, Jupyter, Zeppelin)
- Demo: Python, Notebooks, and Data Science
- Using variables
- Builtin functions
- Strings
- Numbers
- Converting among types
- Writing to the screen
- Command line parameters
- Running standalone scripts under Unix and Windows
- About flow control
- White space
- Conditional expressions
- Relational and Boolean operators
- While loops
- Alternate loop exits
- 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 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
- Defining functions
- Parameters
- Global and local scope
- Nested functions
- Returning values
- The sorted() function
- Alternate keys
- Lambda functions
- Sorting collections
- Using operator.itemgetter()
- Reverse sorting
- Syntax errors
- Exceptions
- Using try/catch/else/finally
- Handling multiple exceptions
- Ignoring exceptions
- Importing Modules
- Classes
- Regular Expressions
- Math functions
- The string module
- Working with dates and times
- Translating timestamps
- Parsing dates from text
- Formatting dates
- Calendar data
- numpy basics
- Creating arrays
- Indexing and slicing
- Large number sets
- Transforming data
- Advanced tricks
- Data Science Essentials
- Working with Python in Data Science
- pandas overview
- Dataframes
- Reading and writing data
- Data alignment and reshaping
- Fancy indexing and slicing
- Merging and joining data sets
- Creating a basic plot
- Commonly used plots
- Ad hoc data visualization
- Advanced usage
- Exporting images
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Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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