Intermediate Python for Data Science | Explore NumPy, Pandas, SciKit Learn, SciPy, TensorFlow & More (TTPS4876)

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:

Objectives

This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you’ll explore:

• Master Numerical Operations with NumPy: Gain proficiency in handling large numerical data sets, performing array operations, and using vectorized computation for increased efficiency.
• Advanced Data Manipulation with Pandas: Acquire the ability to clean, filter, and manipulate complex data sets using Pandas, allowing for more insightful data analysis.
• Implementing Scientific Computing with SciPy: Learn to apply complex mathematical functions and algorithms in Python using SciPy, thereby broadening your toolbox for scientific computing tasks.
• Visualizing Data Using Matplotlib and Seaborn: Develop advanced data visualization skills for creating comprehensive, interpretable visual representations of complex data sets.
• Data Merging and Concatenation Techniques: Understand and implement advanced techniques to merge, join, and concatenate data sets effectively, enabling better data integrity and usefulness.
• Utilizing Pillow for Image Processing: Become proficient in basic image processing tasks like loading, transforming, and saving images using the Pillow library, thus widening the range of data types you can manipulate.
• Applying Machine Learning Models with scikit-learn: Understand the fundamentals of machine learning algorithms and how to implement them using scikit-learn for tasks such as classification, regression, and clustering.
• Developing Problem-Solving Skills for Real-world Applications: Cultivate the ability to apply the acquired technical skills to solve real-world problems, enhancing your capacity to contribute effectively to data-driven projects in a professional setting.

If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goal

Course Prerequisites

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

Take Before: Students should have incoming practical skills aligned with those in the course(s) below, or should have attended the following course(s) as a pre-requisite:

• TTPS4873     Fast Track to Python for Data Science and Machine Learning (3 days)
• TTPS4874     Applied Python for Data Science & Engineering (4 days)

Take Next: Some recommended options:

• TTML5503   AI / ML JumpStart | Introduction to AI, AI Programming & Machine Learning (3 days)
• TTML5506-P Machine Learning Essentials with Python (3 days)
• TTPS4879     Hands-on Predictive Analysis with Python (3 days)
• TTPS4880     Hands-On Practical Python for Data Wrangling & Transformation (3 days)
• TTPS4883     Forecasting, Behavioral Analysis, and What-If Scenarios with Python (3 days)
• TTAI2360     Applied AI: Building Recommendation Systems with Python (3 days)
• TTAI2361     Quick Start to Building AI-Driven Intelligent Web Applications (2 days)

We offer wide variety of additional follow-on courses and learning paths for leveraging Python for next-level development, data science and analytics, AI and machine learning, automation, testing, networking, security and more. Please see our Python Pro Training Suite & Learning Paths or our AI & Machine Learning Courses, Learning Journeys & Skills Roadmaps or inquire for recommendations based on your specific role and goals.

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.

Python Review (Optional)

• An overview of machine learning models
• The scikit-learn modules for different models
• Data representation in scikit-learn
• Supervised learning – classification and regression
• Unsupervised learning – clustering and dimensionality reduction
• Measuring prediction performance

Machine Learning with scikit-learn

• How to Install Pillow 
• How to Load and Display Images 
• How to Convert Images to NumPy Arrays and Back 
• How to Save Images to File
• How to Resize Images 
• How to Flip, Rotate, and Crop Images 
• Extensions

Using PIL/Pillow

• A crash course in Matplotlib
• Covariance and correlation
• Conditional probability
• Bayes' theorem

Visualization Using Matplotlib

• Introducing the data sets
• Concatenating data sets
• Missing values in concatenated DataFrames
• Left joins
• Inner joins
• Outer joins
• Merging on index labels
• Coding challenge

Merging, Joining and Concatenating

• Optimizing a data set for memory use
• Filtering by a single condition
• Filtering by multiple conditions
• Filtering by condition
• Dealing with duplicates
• Coding challenge

Filtering a DataFrame

• Overview of a DataFrame
• Similarities between Series and DataFrames
• Sorting by index
• Setting a new index
• Selecting columns and rows from a DataFrame
• Selecting rows from a DataFrame
• Extracting values from Series
• Renaming columns or rows
• Resetting an index

The DataFrame Object

• Data in the 21st century
• Introducing pandas
• A tour of pandas
• Summary

Next-Level Pandas

• Cluster
• Constants
• FFTpack
• Integrate
• Interpolate
• Linalg
• Ndimage
• Spatial

SciPy

• NumPy arrays
• Array functions
• Data processing using arrays
• Linear algebra with NumPy
• NumPy random numbers

NumPy Arrays and Vectorized Computation

• Why Python?
• Python syntax compared to other programming languages
• Python interpreter
• Strings
• Understanding lists
• Tuples and Sets
• Dictionaries
• Parsing command-line arguments
• Decision making
• Loops
• Iterators
• Generators
• Functions & Modules

Optional: Working with TensorFlow

• TensorFlow overview
• Keras
• Getting Started with TensorFlow

Course Materials

All applicable course software, digital courseware files or course notes, labs, data sets and solutions, live coaching support channels, CodeCoach.AI anytime tutor access, and rich extended learning and post training resources are provided for you in our “easy access, single source, no install required” online Learning Experience Platform (LXP), remote lab and content environment. Access periods vary by course.