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Data Science 1: Data Wrangling and Python

This introductory-level course provides a solid foundation with which to build and apply data science skills via the python programming language.


Starts 29th May 2024

About This Course

This course provides a gentle introduction to the core ideas behind Data Science. You will be exposed to some of the most popular Python libraries while we explore the fundamentals of the Python programming language for Data Science, learn how to wrangle with unclean data found in the real world, and understand how features can be modified to help us achieve our goals. First, we set up our python environment by following a step-by-step guide on the most popular platforms. The programming in this course will be done inside Jupyter notebooks. Then, we explore Pandas dataframes and efficient ways to use NumPy to solve common problems. Finally, after exploring feature engineering techniques, we learn how to build basic models and explore the various metrics that can be used to evaluate the usefulness of such models.

Who Is This Course For

This course is for anyone interested in data science. The only pre-requisite is a basic understanding of programming. The knowledge gained in this course can serve as the first step of a deep dive into any of the concepts. This class certification will be advantageous to anyone seeking an entry-level data science job or looking to advance their education in the fields of data science, machine learning, and/or artificial intelligence.

What you'll learn

  • Fundamental data science concepts
  • Exposure to the most popular python libraries used to tackle data science problems
  • How to work with unclean data
  • How to engineer new features to achieve your goals
  • How to manipulate data and build basic models

Course Content

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Lesson Content
0% Complete 0/2 Steps
Part 1 Fundamental of Python for Data Science
Part 2 Data Wrangling

Meet Your Instructor

Dr. Sterling Ramroach

Sterling has a Ph.D. in Machine Learning from The University of the West Indies. He moved to London after his Ph.D. to pursue a Computer Vision Researcher position in the industry. His Ph.D. research focused on accelerating machine learning techniques with parallel processing and applying it to bioinformatics. His work is published in peer-reviewed scientific journals including Molecular Omics, Expert Systems with Application, and BMJ Innovations.

Our Partners

We are proud to partner with the leading In2scienceUK as a step towards achieving our mission of imparting quality education and knowledge across the globe.

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Usually, there are no general prerequisites when enrolling for any individual courses on cbehx.co.uk. However, some courses are certainly more advanced than others that are of introductory and intermediate level. For advanced-level courses, prior knowledge on the topic will be helpful for understanding complex concepts but not mandatory.

The relevant information about the suggested knowledge and background that might be helpful to follow each course is provided on the respective course registration page.

Yes, the courses are flexible and self-paced to suit your schedule. The term “self-paced” implies that the courses do not follow a pre-assigned schedule for learning within the course duration. You will be able to access all the course materials for each course that you have enrolled in as soon as the course begins.

The course completion certificate is CPD accredited. For more informations please visit https://cpduk.co.uk