We all know that data is everywhere. Every click on our favourite mobile app or website is recorded and every sensor out in the world creates a constant stream of data. Almost any action we perform in the physical world is translated one way or another to a digital form as data. Data is the primary energy source that drives multiple organisations and business companies worldwide. These companies use data to manage and optimise their ongoing day-to-day operations and improve and invent new types of products and services. Data is created everywhere and the challenge is how to utilise it effectively across multiple use cases and dimensions. Data science is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. It is an interdisciplinary field that uses scientific methods, algorithms, and visualisation methods to extract knowledge and insights from data. As a result, a growing number of companies are looking for data scientists, data engineers and machine learning experts to develop products, features and projects that will help them unleash the power of data.

Python is powerful, fast, easy to learn,and one of the most popular programming languages for handling data science projects. Python will be our starting point while we enter the practical side of data science. It has a massive list of data science libraries ready to kick off any project. We demonstrate the basic Python syntax, including using variables, strings, lists, dictionaries, functions, classes, if/for-loop statements and much more. In addition, we explain how to install and use the popular JupyterLab tool for creating Jupyter notebooks. The Jupyter notebooks are used to record end-to-end data processing steps performed on raw data and are additionally used to present and share the obtained results with other people.

After gaining the basic knowledge and experience with Python fundamentals, we will continue our momentum by exploring panda, an essential data science Python library. The pandas software library is written for the Python programming language for data manipulation and analysis. It performs many of the most basic operations while working with raw data, including data loading, saving, inspection and analysis, cleaning, normalisation and much more. Explore how to use the panda library to load large datasets from CSV files, organise the data structure and content in a tabular view and perform initial data analysis and exploration. Then perform data cleaning and transformation as a pre-processing step before moving into data visualisation or applying machine learning algorithms. So what are you waiting for? Enrol today and start your journey to become a data scientist! This course has not been updated with the use of Generative AI models, like ChatGPT.