Data Science Essentials with Python – ASOCIATIA „INVESTESTE PENTRU VIITORUL TAU” BUZAU

English Online Course

Python • Pandas • Matplotlib • Data Storytelling

Build a modern data science foundation with Python.

Data Science Essentials with Python is a fully interactive, project-based introduction to the data-science workflow. Learners move from their first Python statements to cleaning, analyzing, visualizing and storytelling with real-world datasets using industry-standard tools.

40–50 Hours 6 Chapters Project-Based Learning No Formal Prerequisites

Explore the 6-Chapter Curriculum Jump to the Course Button

Course Overview

From first Python steps to portfolio-ready notebooks

This course introduces the practical workflow of data science through hands-on, guided and open-ended projects. Learners gain code and data fluency from scratch, build confidence with visualizations, and finish with work they can share with instructors or employers.

The course is especially strong for beginners who want a modern, structured entry point into working with data using Python.

Who This Course Is For

Built for students, aspiring data scientists and modern professionals

A strong fit for aspiring data scientists, university and advanced high-school STEM or business students, and professionals who want a practical path into data work using Python-based tools.

Aspiring Data Scientists STEM Students Business Students High-School Learners Professionals

Python from scratch

Learn how to instantiate objects, update variables, and call methods and functions in Python as the foundation for modern data work.

Pandas and DataFrames

Read CSV files into DataFrames, use eval(), query(), groupby(), and perform basic left merges to prepare and analyze data more effectively.

Matplotlib and storytelling

Create bar charts, scatter plots and line plots, then communicate findings through clear, beginner-friendly data storytelling techniques.

What You Will Learn

Import Pandas and Matplotlib libraries into Python.
Read CSV files into DataFrames.
Perform core DataFrame moves including eval(), query() and groupby().
Combine multiple DataFrames with basic left merges.
Generate plots and charts using Matplotlib.
Apply best practices for readable bar charts, scatter plots and line plots.
Understand linear models and goodness of fit at an introductory level.
Form and test hypotheses based on exploratory data analysis.
Convey findings using basic data storytelling techniques.

Learning Experience

Fully interactive, project-based course design.
Integrated AI assistant to help guide learners.
Game-based learning and visual coding puzzles.
Interactive visual feedback throughout the course.
Available as instructor-led or self-paced delivery.
Recognition through a digital badge after completion.
Recommended next course: Introduction to Modern AI.

6-Chapter Curriculum

A practical journey from Python basics to data storytelling

The curriculum is organized into six themed chapters with assessments and projects that gradually build confidence in coding, cleaning, visualizing, modeling and explaining data.

A Game-Based Intro

Start with Python, Pandas, Matplotlib and the core moves for working with DataFrames.

Lessons include: Zero to Python in 60 Seconds, Meet the DataFrame, Matplotlib’s Pyplot, Core Pandas Moves

Coding for Answers

Practice core moves to build Python scripts that answer questions about datasets.

Sample projects: Skeletal Variation, Our World Connected, Art as Data

Data Cleaning

Learn basic techniques for preparing DataFrames for analysis in Python.

Sample projects: A Century of Top Songs, A Plant-Based Coffee Shop, Flight Delays

Data Visualization

Build charting confidence with Matplotlib and practical visualization best practices.

Sample projects: The Ocean’s Deep-Diving Animals, Is Granola Healthy?, The Internet and Dating

Data Modeling

Explore introductory linear modeling, linear regression and goodness-of-fit thinking.

Sample projects: Tusked Elephants, Lion Attacks, E-Bike Stopping Distances

Data Storytelling

Use analysis to test hypotheses and communicate trends while thinking about causation versus correlation.

Sample projects: Any Animal Except…, Warm Waters off Peru, The Cicadas are Coming

Requirements

Simple setup for getting started

A device with a modern web browser, preferably desktop or laptop.
An active internet connection.
Recent Chrome, Firefox, Edge or Safari versions work for the online course.
Extended projects can be completed in a local Python environment or in Google Colab.
Physical equipment is not required.

Prerequisites

Beginner-friendly entry point

There are no formal prerequisites for this course. Prior exposure to Python through Python Essentials 1 can still be helpful for learners who want a slightly easier start.

No Formal Prerequisites Helpful: Python Essentials 1 ASC Alignment Recommended Instructor Training Optional

FAQ

Common questions about the course

Do I need previous data science experience?

No. This course is designed as a beginner-friendly introduction. Prior Python exposure can help, but there are no formal prerequisites.

Which tools are covered in the course?

The course focuses on Python, Pandas, Matplotlib and beginner-level data science workflows built around DataFrames, charts, projects and storytelling.

How long does it take to complete?

The estimated time to completion is around 40 to 50 hours, combining self-paced content, instructor demos and project work.

What do learners receive at the end?

Learners complete guided and open-ended projects, take a final exam and can earn a digital badge.

Start Learning

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Data Science Essentials with Python