Intermediate Python in Data Science | Hands-on Numpy, Pandas & 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

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)

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