Intermediate Python for Data Science | Explore NumPy, Pandas, SciKit Learn, SciPy, TensorFlow & More

Course Objectives

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.  Working in a hands-on learning environment, guided by our expert team, attendees will learn how to:

• How to work with Python in a Data Science Context
• How to use NumPy, Pandas, and MatPlotLib
• How to create and process images with PIL
• How to visualize with Seaborn
• Key features of SciPy and Scikit Learn

• Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional python, data science, AI / machine learning and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

This course is geared for experienced data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks.  Attending students are required to have a background in basic Python development skills.

Take Before: Students should have attended or have incoming skills equivalent to those in the following courses:

• TTPS4873      Introduction to Python for Data Science / Jumpstart (3 days)
• TTPS4874     Applied Python for Data Science and Engineering (4 days)

Follow On Courses:  Our Python tracks include a wide variety of follow-on courses and learning paths for leveraging Python for next-level web development, data science / machine learning, networking, task automation, security and other topics. Please see the attached Python Training Suite list of courses, or inquire for recommendations based on your specific role and goals. Please see the Related Courses tab for course descriptions and recommendations.

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

1. Python Quick Refresher

• Python Language
• Essential Syntax
• Lists, Sets, Dictionaries, and Comprehensions
• Functions
• Classes, Modules, and imports
• Exceptions

2. iPython

• iPython basics
• Terminal and GUI shells
• Creating and using notebooks
• Saving and loading notebooks
• Ad hoc data visualization
• Web Notebooks (Jupyter)

3. numpy

• numpy basics
• Creating arrays
• Indexing and slicing
• Large number sets
• Transforming data
• Advanced tricks

4. scipy

• What can scipy do?
• Most useful functions
• Curve fitting
• Modeling
• Data visualization
• Statistics

5. A tour of scipy subpackages

• Clustering
• Physical and mathematical Constants
• FFTs
• Integral and differential solvers
• Interpolation and smoothing
• Input and Output
• Linear Algebra
• Image Processing
• Distance Regression
• Root-finding
• Signal Processing
• Sparse Matrices
• Spatial data and algorithms
• Statistical distributions and functions
• C/C++ Integration

6. pandas

• pandas overview
• Dataframes
• Reading and writing data
• Data alignment and reshaping
• Fancy indexing and slicing
• Merging and joining data sets

7. matplotlib

• Creating a basic plot
• Commonly used plots
• Ad hoc data visualization
• Advanced usage
• Exporting images

8. The Python Imaging Library (PIL)

• PIL overview
• Core image library
• Image processing
• Displaying images

9. seaborn

• Seaborn overview
• Bivariate and univariate plots
• Visualizing Linear Regressions
• Visualizing Data Matrices
• Working with Time Series data

10. SciKit-Learn Machine Learning Essentials

• SciKit overview
• SciKit-Learn overview
• Algorithms Overview
• Classification, Regression, Clustering, and Dimensionality Reduction
• SciKit Demo

11. Optional: Working with TensorFlow

• TensorFlow overview
• Keras
• Getting Started with TensorFlow

Course Materials

All course software (limited versions, for course use only), courseware files or course notes (as applicable), labs / data sets and solutions (as applicable) are provided for you in our “easy access / no install required” high-speed remote lab environment.  In most cases, we can also offer local (non-cloud) set up as an alternative. Either way, our dedicated live tech team works with every student to ensure everyone is set up with working access and ready to go prior to every course start date, ensuring a smooth delivery and great hands-on experience. All your coursework can be accessed or downloaded after class, so you never lose your work or materials. Please ask for details.