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Introduction to Data Science
Learn how to extract information from data using data science methods such as classification and clustering algorithms.
Description
Data science is the key to modern business, and an incredibly valuable set of knowledge with almost infinite uses. Consequently, data scientists are in demand around the world and have very profitable, secure, and interesting careers. This course is designed to give you all the skills you need to enter the world of data science and get those rewards.
The course will first introduce you to the field of data science and the methodologies used in the data science process. This general overview will make the rest of your data science training much clearer and easier to manage. You will then learn some of the most important algorithms used in machine learning. Next, the course will guide you through regression and classification, and through two of the most commonly used clustering algorithms in data science. You will also be introduced to Azure Machine Learning(ML) Studio and be shown why you would use Azure ML Studio for your data science projects.
By the end of the course, you will be much closer to starting your career in this field. That’s not a bad reward for a course that only takes 3 hours. As a prerequisite for this course, you will need to have some programming in Python knowledge, but don’t worry! You can gain this knowledge with our free Introduction to Programming with Python course. If you don’t already have Python, check out that course. Otherwise, get started on your exploration of data science today!
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Modules
Module 1: Overview of Data Science
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Learning Outcomes
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What is Data Science?
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The Data Science Process
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Introduction to Machine Learning
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Lesson Summary
Module 2: Overview of Machine Learning
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Learning Outcomes
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Regression
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Classification
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Clustering
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Recommendation
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Introduction to Data Science Technologies
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Lesson Summary
Module 3: Introduction to Data Science Assessment
Learning Outcomes
Having completed this course you will be able to: - Describe what data science is used for. - List the stages in the data science process. - Explain what machine learning is and the parts that make it up. - Discuss the use of regression and the different types of regression. - Describe the different types of classification algorithms available for you to use. - Describe how the two most popular clustering algorithms work. - Discuss why you would use Azure ML for your data science projects.
Certification
All Alison courses are free to enrol, study and complete. To successfully complete this Certificate course and become an Alison Graduate, you need to achieve 80% or higher in each course assessment. Once you have completed this Certificate course, you have the option to acquire an official Certificate, which is a great way to share your achievement with the world. Your Alison Certificate is:
Ideal for sharing with potential employers - include it in your CV, professional social media profiles and job applications
An indication of your commitment to continuously learn, upskill and achieve high results
An incentive for you to continue empowering yourself through lifelong learning
Alison offers 3 types of Certificates for completed Certificate courses:
Digital Certificate - a downloadable Certificate in PDF format, immediately available to you when you complete your purchase
Certificate - a physical version of your officially branded and security-marked Certificate, posted to you with FREE shipping
Framed Certificate - a physical version of your officially branded and security-marked Certificate in a stylish frame, posted to you with FREE shipping
All Certificates are available to purchase through the Alison Shop. For more information on purchasing Alison Certificates, please visit our FAQs. If you decide not to purchase your Alison Certificate, you can still demonstrate your achievement by sharing your Learner Record or Learner Achievement Verification, both of which are accessible from your Dashboard. For more details on our Certificate pricing, please visit our Pricing Page.
