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Python for Data Science

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Turn raw data into real insight using Python. This course covers everything from core programming concepts to data wrangling with Pandas and building visualizations with Matplotlib and Seaborn. It’s built for people who want practical, job-relevant skills — not just theory.

What you'll learn

  • Write Python code for data manipulation and analysis
  • Load, clean, and explore datasets with Pandas
  • Build charts and visualizations with Matplotlib and Seaborn
  • Identify trends and patterns in real-world data
  • Structure a basic data analysis workflow

Requirements

  • Install Python 3.x and Jupyter Notebook before starting (guide provided)
  • No prior programming experience strictly required, but helpful
  • A computer with at least 4GB RAM recommended

Who this course is for

  • Aspiring data analysts and data scientists
  • Business professionals who work with data regularly
  • Python beginners interested in data careers

Materials included

  • Sample datasets (CSV format)
  • Jupyter Notebook exercise files
  • Python cheat sheet (PDF)

Course Information

Intermediate

Categories:

Course Instructor

Marco Rossi Marco Rossi Instructor

Software engineer turned teacher. Web development and data science courses at Northlight; office hours every other Wednesday.

 

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Events

Python office hours with Marco Rossi
November 4 @ 4:00 pm – 5:00 pm

Topic 1: Python Basics

Lesson 1: Data Types, Variables & Operators
Lesson 1 of 3 within section Topic 1: Python Basics.
0
Lesson 2: Loops, Functions & File Handling
Lesson 2 of 3 within section Topic 1: Python Basics.
0
Python & Pandas Essentials
Lesson 3 of 3 within section Topic 1: Python Basics.
0
Has Quiz

Topic 2: Data Analysis with Pandas

Lesson 1: Loading & Exploring Datasets
Lesson 1 of 3 within section Topic 2: Data Analysis with Pandas.
0
Python for Data Science – Knowledge Check
Lesson 2 of 3 within section Topic 2: Data Analysis with Pandas.
0
Has Quiz
Lesson 2: Filtering, Grouping & Aggregating Data
Lesson 3 of 3 within section Topic 2: Data Analysis with Pandas.
0

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