1. Introduction to Data Science with Python
- What is Data Science?
- Data Science Workflow
- Setting up the Environment
- Jupyter Notebook
- Google Colab
- Python Virtual Environments
- Installing Data Science Libraries
- Working with CSV, Excel and JSON Files
Unlock the power of Python for data analysis, visualisation, and machine learning with this comprehensive Python for Data Science course. Designed for learners who already have a solid understanding of Python programming, this hands-on course focuses on the essential libraries, tools, and techniques used by data scientists to collect, clean, analyse, visualise, and interpret data.
Throughout the course, you will learn how to work with NumPy, Pandas, Matplotlib, and Seaborn to manipulate datasets, perform statistical analysis, create insightful visualisations, and prepare data for machine learning. You will also explore the fundamentals of machine learning using Scikit-learn, including data preprocessing, model training, evaluation, and prediction.
By the end of the course, you will be able to analyse real-world datasets, build data-driven solutions, create interactive visualisations, and develop machine learning models that solve practical business problems. This course is ideal for software developers, data analysts, aspiring data scientists, engineers, and anyone looking to transition into the field of data science using Python.
Course Prerequisites:
Basics of Python programming