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

Elevate your data science expertise with Intermediate Python for Data Science, perfect for analysts and developers ready to harness the power of libraries like NumPy, Pandas, and Scikit-Learn for efficient data manipulation and machine learning techniques.

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Essential Skills Gained

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Design robust data manipulation techniques using pandas DataFrames and Series for complex datasets.

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Implement numerical computations efficiently with NumPy to enhance data processing capabilities.

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Understand the application of machine learning models with Scikit-Learn for predictive analytics.

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Explore text data processing and visualization with Matplotlib for impactful data presentation.

Format

  • Instructor-led
  • 5 days with lectures and hands-on labs.

Audience

  • Experienced Data Analysts
  • Developers
  • Engineers
  • Python Enthusiasts

Description

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.

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Upcoming Course Dates

July 7-11, 2025

10:00 AM - 6:00 PM

Virtual: Online - US/Eastern

Guaranteed to Run

Enroll

$2695

September 29, 2025 - October 03, 2025

10:00 AM - 6:00 PM

Virtual: Online - US/Eastern

Enroll

$2695

September 29, 2025 - October 03, 2025

10:00 AM - 6:00 PM

Virtual: Online - US/Eastern

Enroll

$2695

Course Outline

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MODULE 1: Getting Started

  1. Introduction to pandas

  2. Overview of pandas library

  3. Installation and setup

  4. Understanding the importance of pandas in data science

  5. A Whirlwind Tour of pandas

  6. Exploring basic operations in pandas

  7. Introduction to DataFrames and Series

  8. Overview of essential pandas functionalities

MODULE 2: The Python Ecosystem

  1. Python Crash Course

  2. Python basics: Variables, data types, and control flow

  3. Functions and modules in Python

  4. Introduction to object-oriented programming in Python

  5. NumPy Crash Course

  6. Understanding NumPy arrays

  7. Basic operations with NumPy

  8. Utilizing NumPy for numerical computing

MODULE 3: The Series

  1. The Series Object

  2. Introduction to pandas Series

  3. Creating and manipulating Series objects

  4. Understanding indexing and slicing in Series

  5. Series Methods

  6. Applying methods on Series

  7. Handling missing data in Series

  8. Performing mathematical operations on Series

MODULE 4: The DataFrame

  1. The DataFrame Object

  2. Understanding the structure of DataFrames

  3. Creating DataFrames from various data sources

  4. Exploring data in DataFrames

  5. Filtering a DataFrame

  6. Techniques for filtering data in DataFrames

  7. Applying conditions to DataFrames

  8. Handling large datasets with efficient filtering

MODULE 5: Working with Text Data

  1. Working with Text Data

  2. Introduction to text data in pandas

  3. String operations and methods in pandas

  4. Handling and cleaning text data

MODULE 6: Working with AI and Visuals

  1. Working with Matplotlib and PIL

  2. Basics of Matplotlib for data visualization

  3. Creating plots and charts

  4. Introduction to the Python Imaging Library (PIL) for image processing

  5. Machine Learning with Scikit-Learn

  6. Introduction to machine learning concepts

  7. Applying Scikit-Learn for basic machine learning tasks

  8. Building and evaluating simple machine learning models

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Let us tailor the course to your needs at no extra cost.