Introduction to Python

The Introduction to Python course is designed for beginners with no prior programming experience. Participants will learn the fundamentals of Python, including syntax, data types, and control structures, while gaining hands-on experience with Jupyter Notebook and .py files. The course also introduces GUI creation within Jupyter Notebook. By the end, students will have the skills to write Python programs and develop simple applications. Python is widely used across industries for automation, web development, data science, and machine learning. This live, instructor-led course provides a structured, practical approach to learning Python, making it accessible for both new programmers and those transitioning from other languages. Note: Available in three full-day or five partial-day sessions. Check the schedule for options.

Course Details

Price:

$1,795.00

Days:

Location:

Virtual

Course Overview

Participants will develop a strong foundation in Python by learning core concepts such as syntax, data types, control flow, and key data structures. They will apply Python to real-world use cases, including automation, web scraping, GUI development, and game design, allowing them to build a portfolio of practical projects. The course also covers advanced Python features such as object-oriented programming (OOP), decorators, generators, and modules, enabling students to write more efficient and scalable code. Additionally, they will gain hands-on experience with industry-standard tools, including Jupyter Notebooks, Python libraries, and integrated development environments (IDEs), to write, debug, and deploy Python applications professionally.

1. Introduction to Python for Data Science

Overview of Python and its role in data science
Setting up Python environments (Anaconda, Jupyter Notebooks)
Writing and running Python scripts

2. Working with Jupyter Notebooks

Introduction to Jupyter Notebooks
Markdown and code cells
Running, saving, and sharing notebooks

3. Numerical Computing with NumPy

Understanding arrays and their advantages
Creating and manipulating NumPy arrays
Mathematical operations and broadcasting

4. Data Manipulation with Pandas

Understanding Series and DataFrames
Importing and exploring datasets
Filtering, sorting, and transforming data

5. Data Input and Output (I/O)

Reading and writing Excel files
Working with CSV files
Connecting and querying SQL databases

6. Converting Datasets to Pandas DataFrames

Transforming structured and unstructured data
Importing datasets from APIs and web sources

7. Advanced Data Handling

Altering specific data using custom functions
Handling missing data – filling, dropping, and imputing values
Aggregating data using group operations

8. Data Visualization with Matplotlib

Creating fully customizable plots
Implementing custom figures and axis
Adding labels, legends, and annotations

9. Statistical Data Visualization with Seaborn

Creating scatter plots
Generating distribution plots
Visualizing summary statistics with box plots

10. Hands-on Projects and Real-World Applications

Data analysis case studies
End-to-end data science project
Best practices for working with large datasets

Class Dates & Times


01/05/2026 - 01/07/2026 Virtual 09:00:00 to 17:00:00 EST Enroll Now $1,795.00
02/04/2026 - 02/06/2026 Virtual 09:00:00 to 17:00:00 EST Enroll Now $1,795.00
03/02/2026 - 03/04/2026 Virtual 09:00:00 to 17:00:00 EST Enroll Now $1,795.00
04/01/2026 - 04/03/2026 Virtual 09:00:00 to 17:00:00 EST Enroll Now $1,795.00
05/04/2026 - 05/06/2026 Virtual 09:00:00 to 17:00:00 EST Enroll Now $1,795.00
06/15/2026 - 06/17/2026 Virtual 09:00:00 to 17:00:00 EST Enroll Now $1,795.00
07/13/2026 - 07/15/2026 Virtual 09:00:00 to 17:00:00 EST Enroll Now $1,795.00
10/27/2025 - 10/29/2025 Virtual 09:00:00 to 17:00:00 EST Enroll Now $1,795.00
11/17/2025 - 11/19/2025 Virtual 09:00:00 to 17:00:00 EST Enroll Now $1,795.00
12/15/2025 - 12/17/2025 Virtual 09:00:00 to 17:00:00 EST Enroll Now $1,795.00

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