Data Science Professional Certificate

Get into the field of Data Science with Python

16 weeks | 5 courses | Online

Get into the field of Data Science with Python

Get access to an extensive resource library built in partnership with Coursera & IBM
Complete hands-on projects in data science
Be part of a community of peers & professionals and exchange ideas
Take your learning to the next level with the help of student success mentors

PROGRAMME OVERVIEW

You go through two stages in this certificate programme:

Starter Certificate

Complete the first course in the programme and earn your starter certificate of completion
Week 1 - 3

Professional Certificate

Complete the rest of all five courses, successfully pass your data science project, and earn your career credential
Week 4 - 16

Why a Professional Certificate in Data Science?

Our programme is designed to give you the knowledge and skills you need to build the essentials in the field of data science. By completing our programme, you’ll be able to demonstrate to potential employers that you have a high level of skill and knowledge in the field, making you more competitive in the job market.

Not only will you be more marketable to potential employers, but you’ll also be able to command higher salaries as a result of your professional certification. Plus, you will be able to apply the skills you learn in our programme to a real-world data science project, giving you the practical experience you need to succeed.

The programme offers a curriculum that covers the key areas of data science, including:

Understanding the fundamentals of data science
Mastering the latest data science tools and techniques
Learning the industry-standard data science methodology
Gaining proficiency in Python for data science
Completing a real-world data science project

Don’t miss out on this opportunity to take your data science career to the next level. Enrol in our free professional certification programme today and start your journey towards becoming a top-performing data scientist!”

CURRICULUM

Introduction to the Field of Data Science

In this course you will:

Define data science and its importance in today’s data-driven world.
Describe the various paths that can lead to a career in data science.
Summarise advice given by seasoned data science professionals to data scientists who are just starting out.
Explain why data science is considered the most in-demand job in the 21st century.

Course 2: Data Science Tools

In this course you will;

Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools
Utilise languages commonly used by data scientists like Python, R, and SQL
Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilise their various features
Create and manage source code for data science using Git repositories and GitHub.

Course 3: Data Science Methodology

In this course you will;

Describe what a methodology is and why data scientists need a methodology.
Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyse a case study.
Determine an appropriate analytic model including predictive, descriptive, and classification models to analyse a case study.
Decide on appropriate sources of data for your data science project.

Course 4: Introduction to Python for Data Science and AI

Describe Python Basics including Data Types, Expressions, Variables, and Data Structures.
Apply Python programming logic using Branching, Loops, Functions, Objects & Classes.
Demonstrate proficiency in using Python libraries such as Pandas, Numpy, and Beautiful Soup.
Access web data using APIs and web scraping from Python in Jupyter Notebooks.

Course 5: Data Science Project

In this final course, you will demonstrate your skills in Python – the language of choice for Data Science and Data Analysis. You will apply Python fundamentals, Python data structures, and work with data in Python. By working on a real project, you will model a Data Scientist or Data Analyst’s role, and build a dashboard using Python and popular Python libraries using Jupyter notebook.

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