Python for Data Science Fundamentals
Course outline.
The Python for Data Science Fundamentals course is designed to introduce participants to the essential concepts and techniques of data science using Python. Through a combination of theory and hands-on projects, learners will acquire the skills needed to manipulate data, perform statistical analysis, visualize data, and build predictive models. This course is perfect for beginners and those looking to enter the field of data science with a strong foundation in Python programming.
Programme highlights.
Industry-led teaching
Live materials from practitioners working in the field today.
Hands-on exercises
You'll apply what you learn through structured workshops and case studies.
Mentor access
Personal contact with the instructor for questions and feedback.
UTE Certificate
A signed certificate of completion you can add to your CV.
What we'll cover.
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01
Introduction to Data Science and Python
- • Overview of Data Science Concepts
- • Introduction to Python and Its Ecosystem
- • Setting Up Your Environment (Jupyter Notebooks)
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02
Python Basics for Data Science
- • Data Types and Structures (Lists
- Tuples
- Sets
- Dictionaries)
- • Control Flow (Loops and Conditionals)
- • Functions and Modules
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03
Data Manipulation with Pandas
- • Introduction to Pandas Library • DataFrames and Series: Creation and Manipulation • Data Cleaning and Transformation Techniques
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04
Data Visualization with Matplotlib and Seaborn
- • Introduction to Data Visualization • Creating Basic Plots with Matplotlib • Advanced Visualization Techniques with Seaborn
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05
Statistical Analysis with Python
- • Descriptive Statistics and Data Summary • Inferential Statistics: Hypothesis Testing • Correlation and Regression Analysis
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06
Introduction to Machine Learning
• Basics of Machine Learning Concepts • Supervised vs. Unsupervised Learning • Building a Simple Predictive Model with Scikit-Learn