TTPS4876: Intermediate Python for Data Science | Explore NumPy, Pandas, SciKit Learn, SciPy, TensorFlow & More
About this Course
Next-Level (Intermediate) Python for Data Science and /or Machine Learning is a five-day hands-on course designed for Python enthusiasts looking to expand their data science and machine learning skills. Whether you’re already familiar with Python basics or have dabbled in some coding, this course will take you further, focusing on practical applications of popular libraries like pandas, NumPy, and Scikit-Learn. By the end, you’ll be ready to tackle intermediate data science tasks with confidence.
You’ll start by diving deep into pandas, exploring its powerful DataFrame and Series structures to clean, filter, and manipulate data with ease. Then, you’ll shift gears into the world of NumPy, learning to perform efficient numerical computations, a crucial skill for any data scientist. The course also introduces you to text data processing and teaches you how to visualize your results with Matplotlib, making your data easy to understand and present.
In the final stretch, you’ll get hands-on with machine learning using Scikit-Learn. You’ll learn to build simple models, train them on data, and evaluate their performance, giving you a solid foundation in the machine learning workflow. This course offers a comprehensive and approachable way to level up your Python skills and apply them to real-world data science problems.
Audience Profile
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.
At Course Completion
This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you will explore:
Mastering pandas Operations: Learn to navigate, manipulate, and explore data using pandas DataFrames and Series, improving your ability to handle diverse datasets.
Enhancing Numerical Computation with NumPy: Gain proficiency in performing efficient numerical operations using NumPy arrays, essential for data-driven calculations.
Working with Series Data in pandas: Understand how to create, manipulate, and apply methods to pandas Series for effective data slicing and mathematical operations.
Filtering and Exploring DataFrames: Develop the ability to filter, conditionally select, and efficiently explore data within pandas DataFrames, even when working with large datasets.
Processing and Analyzing Text Data: Learn to handle, clean, and analyze text data using pandas, preparing it for visualization or machine learning applications.
Applying Machine Learning with Scikit-Learn: Build, evaluate, and apply simple machine learning models using Scikit-Learn to address basic predictive tasks and gain insights from data.
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 goals.
Outline
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. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation.
MODULE 1: Getting Started
1. Introduction to pandas
Overview of pandas library
Installation and setup
Understanding the importance of pandas in data science
2. A Whirlwind Tour of pandas
Exploring basic operations in pandas
Introduction to DataFrames and Series
Overview of essential pandas functionalities
MODULE 2: The Python Ecosystem
3. Python Crash Course
Python basics: Variables, data types, and control flow
Functions and modules in Python
Introduction to object-oriented programming in Python
4. NumPy Crash Course
Understanding NumPy arrays
Basic operations with NumPy
Utilizing NumPy for numerical computing
MODULE 3: The Series
5. The Series Object
Introduction to pandas Series
Creating and manipulating Series objects
Understanding indexing and slicing in Series
6. Series Methods
Applying methods on Series
Handling missing data in Series
Performing mathematical operations on Series
MODULE 4: The DataFrame
7. The DataFrame Object
Understanding the structure of DataFrames
Creating DataFrames from various data sources
Exploring data in DataFrames
8. Filtering a DataFrame
Techniques for filtering data in DataFrames
Applying conditions to DataFrames
Handling large datasets with efficient filtering
MODULE 5: Working with Text Data
9. Working with Text Data
Introduction to text data in pandas
String operations and methods in pandas
Handling and cleaning text data
MODULE 6: Working with AI and Visuals
10. Working with Matplotlib and PIL
Basics of Matplotlib for data visualization
Creating plots and charts
Introduction to the Python Imaging Library (PIL) for image processing
11. Machine Learning with Scikit-Learn
Introduction to machine learning concepts
Applying Scikit-Learn for basic machine learning tasks
Building and evaluating simple machine learning models
Prerequisites
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)
TTPS4820 Mastering Python Programming Boot Camp
TTPS4824 Python Essentials for Networking & Systems Administration
TTPS4873 Fast Track to Python for Data Science and/or Machine Learning
TTPS4874 Applied Python for Data Science and Engineering
TTPS4883 Forecasting, Behavioral Analysis, and What-If Scenarios with Python