Python Data Science for Beginners

Airaax

• Python Data Science for Beginners 2:11:00

  • 1: Module 1 Introduction to Data Science and Python 10:00
  • 2: Module 2 Setting Up Python, Jupyter Notebook and the Development Environment 09:00
  • 3: Module 3 Python Fundamentals, Variables and Data Types 09:00
  • 4: Module 4 Conditional Statements, Loops, Functions and Error Handling 16:00
  • 5: Module 5 Working with Lists, Tuples, Dictionaries and Sets 10:00
  • 6: Module 6 Introduction to NumPy and Numerical Data Processing 09:00
  • 7: Module 7 Data Analysis and Manipulation with Pandas 09:00
  • 8: Module 8 Importing, Cleaning and Preparing Real-World Data 15:00
  • 9: Module 9 Data Visualisation with Matplotlib and Seaborn 11:00
  • 10: Module 10 Exploratory Data Analysis and Statistical Fundamentals 11:00
  • 11: Module 11 Introduction to Machine Learning with Scikit-Learn 09:00
  • 12: Module 12 Practical Data Science Project, Model Evaluation and Final Assessment 12:00
  • 13: Python Data Science – Knowledge Assessment 01:00

Data science combines programming, statistics and analytical thinking to turn raw information into useful findings. Python is widely used in this field because its libraries support data preparation, exploration, visualisation and introductory machine learning.

This course provides a structured introduction for learners with little or no programming experience. You will practise writing Python code, working with datasets, preparing information for analysis and interpreting visual results.

Module 1: Introduction to Data Science and Python
Discover what data science involves, how Python supports analytical work and where data-driven methods are used across different industries.

Module 2: Setting Up Python, Jupyter Notebook and the Development Environment
Prepare your workspace by exploring Python installation, Jupyter Notebook and the tools needed to write, run and organise code.

Module 3: Python Fundamentals, Variables and Data Types
Learn the basic building blocks of Python, including variables, strings, numbers, Boolean values and common data operations.

Module 4: Conditional Statements, Loops, Functions and Error Handling
Develop programming logic using conditions, repetition and reusable functions. Explore how to identify and handle common coding errors.

Module 5: Working with Lists, Tuples, Dictionaries and Sets
Understand Python's core data structures and how to store, organise, access and manipulate collections of information.

Module 6: Introduction to NumPy and Numerical Data Processing
Explore NumPy arrays, numerical operations and efficient methods for working with structured numerical data.

Module 7: Data Analysis and Manipulation with Pandas
Learn how to use Pandas Series and DataFrames to inspect, filter, sort, transform and summarise datasets.

Module 8: Importing, Cleaning and Preparing Real-World Data
Discover how to load data from common file formats, identify missing or inconsistent values and prepare datasets for reliable analysis.

Module 9: Data Visualisation with Matplotlib and Seaborn
Create charts and visual representations to communicate patterns, comparisons and relationships within datasets.

Module 10: Exploratory Data Analysis and Statistical Fundamentals
Explore descriptive statistics, distributions, relationships and summary measures to understand datasets before building models.

Module 11: Introduction to Machine Learning with Scikit-Learn
Discover introductory machine learning concepts, including features, target variables, training and testing data, and basic model workflows.

Module 12: Practical Data Science Project, Model Evaluation and Final Assessment
Bring together your programming and analytical knowledge in a practical project. Explore model evaluation, review your findings and complete the final assessment.

• Beginners interested in Python programming and data science.

• Students studying computing, business, mathematics or related subjects.

• Graduates exploring data-focused career opportunities.

• Professionals who want to develop practical data analysis skills.

• Business owners interested in understanding information and trends.

• Aspiring junior data analysts and machine learning learners.

• Anyone curious about turning datasets into meaningful insights.

• No previous data science or Python experience is required.

• Basic computer literacy and familiarity with using files and folders.

• A computer or laptop capable of running Python and Jupyter Notebook.

• An internet connection for accessing software documentation and learning resources.

• Basic mathematics is helpful, particularly arithmetic, percentages and averages.

• A willingness to practise coding and work through data exercises.

This foundation may support progression towards junior data analyst, Python developer, data science trainee, reporting analyst or machine learning learner roles. Further study, practical projects and relevant experience are generally needed for professional positions.