Mastering Data Science Foundation: SMDH’s Course for Aspiring Data Scientists
In the era of big data, data science has emerged as one of the most powerful tools for driving business insights and innovation. Understanding the core concepts of data science, along with practical skills in Python, machine learning and data visualisation is essential for anyone looking to excel in this field.
Our Data Science Foundations course at SMDH Academy is designed to provide you with a strong foundation in data science, equipping you with the knowledge and practical experience to thrive in this rapidly evolving industry.
What you can expect from the course:
Module 1: An Introduction to Data Science Foundations (Part 1), An Introduction to Python and The Fundamentals of Python – This module offers a comprehensive introduction to the fundamentals of data science, exploring various application areas where data science is leveraged. You will also dive into Python programming and JupyterLab, essential tools for any data scientist. Each module will conclude with a practical session, allowing you to gain hands-on experience with Python development and apply the concepts you’ve learned to real-world problem-solving.
Module 2: An Introduction to Data Science Foundations (Part 2), NumPy and Pandas – In the second part of the Data Science Foundations module, you’ll explore the data science project lifecycle. You will also be introduced to NumPy and Pandas, two critical Python libraries. The practical session focuses on using Pandas for data manipulation and analysis, helping you become proficient in cleaning, transforming and analysing data – a crucial skill for any data scientist or analyst.
Module 3: An Introduction to Data Science Foundations (Part 3), Feature Selection and Extraction and Data Visualisation – this delves into advanced topics such as feature engineering, feature selection, dimension reduction and data visualisation. You’ll learn how to refine and prepare data for machine learning models, ensuring it is in the optimal format for analysis. The practical session focuses on feature extraction using Scikit-learn, giving you the tools to extract and format data features for machine learning algorithms effectively.
Module 4: Machine Learning, Supervised Learning and Unsupervised Learning – The final module introduces the fundamentals of machine learning, covering both supervised and unsupervised learning techniques. You’ll learn what each approach entails and how to apply them in various contexts. The module concludes with a hands-on Scikit-learn tutorial, designed to help you get started with Python based machine learning, providing a solid foundation to build upon in your data science journey.
Who should enrol?
This course is perfect for:
By the end of this course, you will have a comprehensive understanding of data science foundations, practical skills in Python and the ability to implement machine learning techniques. Each module is designed to build your confidence and competence in using data science tools, from data manipulation with Pandas to feature extraction and machine learning with Scikit-learn. Whether you’re just starting your data science journey or looking to sharpen your skills, this course will provide you with the essential knowledge and experience to succeed in the data-driven world.
Enrol today and take the first step towards becoming a proficient data scientist, capable of turning data into actionable insights.