Welcome to the Data Science Fundamentals Course! This course is designed for beginners with no prior programming or data science knowledge. Over two weeks, you'll learn essential Python programming concepts and basic data science techniques.

Course Overview

Duration: 2 weeks
Schedule: 3 days per week, 2 hours per day
Mode: Online, instructor-led sessions
Min. participants: 3

Price:

• 1-2 participants: price on request

• 3–5: participants: 300 EUR per person

• 5-8: participants: 275 EUR per person

• 8+: participants 250 EUR per person

Week 1: Core Programming Concepts with Python

Day 1: Introduction to Python and Basic Data Types

Goal: Equip learners with the fundamentals of Python syntax and key data types.
Topics:

• What is Data Science? Brief overview and relevance.

• Python setup (IDEs like Jupyter Notebooks).

• Variables and basic data types (integers, floats, strings, booleans). Input/output operations.

• Activities:

  • Simple exercises (storing your name in a variable, basic calculations).

Day 2: Lists and Dictionaries

Goal: Introduce core data structures and their practical use.
Topics:

• Lists: Creating, accessing, modifying.

• Dictionaries: Keys and values, accessing and updating.

• Common operations (e.g., loops through lists, dictionary lookups).

• Activities:

  • Create a contact book using a dictionary.

  • Manipulate lists of numbers and strings.

Day 3: Loops and Control Flow

Goal: Enable learners to control the flow of their programs.
Topics:

• Statements: if, elif, else.

• Loops: for and while.

• Combining loops and conditionals for basic data processing.

• Activities:

  • Writing a simple program that prints all even numbers in a list.

  • Basic menu program using if/else.

Week 2: Introduction to Data Science Concepts

Day 4: Functions

Goal: Teach learners how to write reusable code.
Topics:

• Defining and calling functions.

• Parameters, arguments, and return values.

• Scope and best practices.

• Activities:

  • Write a function to calculate the average of a list of numbers.

  • Practice modularizing simple programs.

Day 5: Data Analysis with Pandas

Goal: Introduce data manipulation using a fundamental library.
Topics:

• Introduction to Pandas.

• Loading data from CSV files.

• DataFrames: Accessing, filtering, and modifying data.

• Activities:

  • Load a sample dataset (e.g., a small dataset of student grades).

  • Perform basic data analysis (e.g., filtering by grade, calculating averages).

Day 6: Visualization and Wrapping Up

Goal: Provide a glimpse of data visualization and summarize key takeaways.
Topics:

• Introduction to Matplotlib.

• Basic plots (line, bar, scatter).

• Course summary and next steps in data science learning.

• Activities:

  • Create a basic bar chart of a dataset (e.g., grades by category).

  • Discuss how to pursue further data science topics (e.g., machine learning, SQL).

Data Science Fundamentals