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Foundations of Data Science

K-Means Clustering in Python

Dr Matthew Yee-King, Dr Betty Fyn-Sydney, Dr Jamie A Ward, and Dr Larisa Soldatova

Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government.

This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary for typical data analysis tasks.

You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques. During the course, you will be asked to do a series of mathematical and programming exercises and a small data clustering project for a given dataset.

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What's inside

Syllabus

Week 1: Foundations of Data Science: K-Means Clustering in Python

This week we will introduce you to the course and to the team who will be guiding you through the course over the next 5 weeks. The aim of this week's material is to gently introduce you to Data Science through some real-world examples of where Data Science is used, and also by highlighting some of the main concepts involved.

Week 2: Means and Deviations in Mathematics and Python

Week 3: Moving from One to Two Dimensional Data

Week 4: Introducing Pandas and Using K-Means to Analyse Data

Week 5: A Data Clustering Project

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Begins by asking important questions that encourage background research in the field

Establishes a groundwork of basic math, statistics, and programming with a focus on industry-level applications

Provides exercises to solidify the fundamentals of data science

Suitable for foundational learning and building a strong foundation in data science

Aimed at novice and beginner-level students with little to no prior knowledge of Data Science

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Reviews summary

K-means clustering in data science

Learners say that this well-structured course gently introduces the data science field and K-means clustering. Engaging videos and clear explanations make the content easy to understand, even for beginners. Although some mathematical concepts are explained in detail, learners may need a base level of mathematics and statistics to grasp them. The hands-on assignments and practical final project prepare learners for real-life data science work. Peer reviews help learners improve their work, though some may be subjective or incomplete. Overall, learners recommend this course for those new to data science or as a refresher for those with some experience.

Practical assignments and final project

"The hands-on approach, particularly the final project, provided practical experience that boosted my confidence."

"I really enjoyed the data science projects and I wish we had few more of the projects to use the knowlege gained."

Clear explanations and engaging presentations

"Great Course which was not an easy course but fabulous tutors competently delivered the course in a structured way and made my learning curve very enjoyable."

"Learned much from this course thanks to all great instructors."

Appropriate for beginners

"Perfect for beginners and those who have no experience in data science and phyton."

"The course content is great. I especially enjoyed Week 4—the lecturer was concise and engaging."

Informative and engaging

"Very interesting course! The lecturers explain concepts thoroughly which makes the concepts easy to understand even for people without much knowledge in Data Science"

"Method of instruction was easy to understand yet the content was very rich."

Can be subjective or incomplete

"Occasionally, frustration may arise when fellow learners subjectively assess assignments due to limited understanding of the subject matter, language barriers, personal biases related to names or preconceived notions, and so on."

"After submitting assignments you are at the mercy of others reviewing them correctly (or even at all)."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Foundations of Data Science: K-Means Clustering in Python with these activities:

Practice implementing core Python data structures

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Sharpen your programming skills by practicing with core Python data structures, providing a solid foundation for data analysis.

Browse courses on Python Basics

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Review and practice using Python data structures such as lists, tuples, dictionaries

Review essential data preprocessing techniques

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Refresh your knowledge of fundamental data preprocessing techniques to strengthen your foundation.

Browse courses on Data Preprocessing

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Review concepts of data cleaning, normalization, and transformation

Explore online tutorials on Python libraries

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Enhance your Python skills by exploring and practicing with different libraries used in data science.

Browse courses on Python Libraries

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Identify and locate relevant Python libraries for data analysis
Follow step-by-step tutorials to utilize these libraries

Six other activities

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Practice basic mathematics exercises

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Solidify your understanding of basic mathematics operations and reinforce your Python skills.

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Solve problems involving means and deviations
Use Python to calculate means and deviations

Join a study group or form a learning community

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Enhance your learning through collaboration and peer support, fostering a deeper understanding.

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Connect with fellow learners
Discuss course concepts and share insights

Analyze data using K-Means in Python

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Develop practical skills in using K-Means algorithm for data clustering.

Browse courses on Data Clustering

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Apply the K-Means algorithm to cluster data
Interpret the results of K-Means clustering

Gather and organize resources on data science tools and frameworks

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Expand your knowledge and stay up-to-date by gathering resources on industry tools and frameworks, complementing your coursework.

Browse courses on Data Science Tools

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Identify and research relevant tools and frameworks
Compile a comprehensive list of resources

Complete a small data clustering project

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Apply your knowledge and skills to a real-world data clustering problem, solidifying your understanding.

Browse courses on K-Means

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Collect and prepare a dataset
Apply K-Means algorithm to cluster the data
Visualize and interpret the results
Write a brief report summarizing your findings

Contribute to open-source data science projects

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Enhance your practical skills, contribute to the community, and gain valuable real-world experience by participating in open-source projects.

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Identify and join open-source projects
Contribute code or documentation

Career center

Learners who complete Foundations of Data Science: K-Means Clustering in Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge of mathematics, statistics, and programming to develop and implement data-driven solutions to business problems. This course will help you build a solid foundation in these essential skills, and the focus on K-Means clustering will give you a valuable advantage in your career. K-Means clustering is a widely used technique for data analysis, and it is essential for Data Scientists to be able to understand and apply this technique in order to extract meaningful insights from data.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course will help you build a solid foundation in the fundamental concepts of Data Science, including mathematics, statistics, and programming. These skills are essential for Machine Learning Engineers, who need to be able to understand and interpret data in order to develop and deploy effective machine learning models. Additionally, the course's focus on K-Means clustering, a common machine learning technique, will give you a valuable advantage in your career.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use their knowledge of business and data analysis to help businesses make informed decisions. This course will help you build a solid foundation in the fundamental concepts of Data Science, including mathematics, statistics, and programming. These skills are essential for Business Analysts, who need to be able to understand and interpret data in order to make sound business decisions. Additionally, the course's focus on K-Means clustering, a common data analysis technique, will give you a valuable advantage in your career.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use their knowledge of mathematics, statistics, and programming to develop and implement solutions to operational problems. This course will help you build a solid foundation in these essential skills, and the focus on K-Means clustering will give you a valuable advantage in your career. K-Means clustering is a widely used technique for data analysis, and it is essential for Operations Research Analysts to be able to understand and apply this technique in order to develop and implement solutions to operational problems effectively.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians use their knowledge of mathematics and statistics to collect, analyze, and interpret data. This course will help you build a solid foundation in these essential skills, and the focus on K-Means clustering will give you a valuable advantage in your career. K-Means clustering is a widely used technique for data analysis, and it is essential for Statisticians to be able to understand and apply this technique in order to extract meaningful insights from data.

See salaries and explore the career path for Statistician

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course provides a solid foundation in the fundamental concepts of Data Science, including mathematics, statistics, and programming. These skills are essential for Data Analysts, who need to be able to understand and interpret data in order to draw meaningful conclusions. Additionally, the course's focus on K-Means clustering, a common data analysis technique, will give you a valuable advantage in your career.

See salaries and explore the career path for Data Analyst

Database Administrator

Database Administrators are responsible for designing, building, and maintaining databases. This course will help you build a solid foundation in the fundamental concepts of Data Science, including mathematics, statistics, and programming. These skills are essential for Database Administrators, who need to be able to understand and interpret data in order to design and build effective databases. Additionally, the course's focus on K-Means clustering, a common data analysis technique, will give you a valuable advantage in your career.

See salaries and explore the career path for Database Administrator

Financial Analyst

Financial Analysts use their knowledge of mathematics, statistics, and finance to analyze and make recommendations on investments. This course will help you build a solid foundation in these essential skills, and the focus on K-Means clustering will give you a valuable advantage in your career. K-Means clustering is a widely used technique for data analysis, and it is essential for Financial Analysts to be able to understand and apply this technique in order to analyze and make recommendations on investments effectively.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts use their knowledge of mathematics, statistics, and finance to assess and manage risk. This course will help you build a solid foundation in these essential skills, and the focus on K-Means clustering will give you a valuable advantage in your career. K-Means clustering is a widely used technique for data analysis, and it is essential for Risk Analysts to be able to understand and apply this technique in order to assess and manage risk effectively.

See salaries and explore the career path for Risk Analyst

Data Engineer

Data Engineers are responsible for designing, building, and maintaining data infrastructure. This course will help you build a solid foundation in the fundamental concepts of Data Science, including mathematics, statistics, and programming. These skills are essential for Data Engineers, who need to be able to understand and interpret data in order to design and build effective data infrastructure. Additionally, the course's focus on K-Means clustering, a common data analysis technique, will give you a valuable advantage in your career.

See salaries and explore the career path for Data Engineer

Data Visualization Specialist

Data Visualization Specialists use their knowledge of data visualization techniques to create visual representations of data. This course will help you build a solid foundation in the fundamental concepts of Data Science, including mathematics, statistics, and programming. These skills are essential for Data Visualization Specialists, who need to be able to understand and interpret data in order to create effective data visualizations. Additionally, the course's focus on K-Means clustering, a common data analysis technique, will give you a valuable advantage in your career.

See salaries and explore the career path for Data Visualization Specialist

Quantitative Analyst

Quantitative Analysts use their knowledge of mathematics, statistics, and programming to develop and implement quantitative models for financial institutions. This course will help you build a solid foundation in these essential skills, and the focus on K-Means clustering will give you a valuable advantage in your career. K-Means clustering is a widely used technique for data analysis, and it is essential for Quantitative Analysts to be able to understand and apply this technique in order to develop and implement effective quantitative models.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use their knowledge of mathematics, statistics, and finance to assess and manage risk. This course will help you build a solid foundation in these essential skills, and the focus on K-Means clustering will give you a valuable advantage in your career. K-Means clustering is a widely used technique for data analysis, and it is essential for Actuaries to be able to understand and apply this technique in order to assess and manage risk effectively.

See salaries and explore the career path for Actuary

Market Researcher

Market Researchers use their knowledge of mathematics, statistics, and marketing to conduct research and analyze data to help businesses make informed decisions. This course will help you build a solid foundation in these essential skills, and the focus on K-Means clustering will give you a valuable advantage in your career. K-Means clustering is a widely used technique for data analysis, and it is essential for Market Researchers to be able to understand and apply this technique in order to conduct research and analyze data effectively.

See salaries and explore the career path for Market Researcher

Data Governance Analyst

Data Governance Analysts use their knowledge of data management and governance to ensure that data is used in a consistent and ethical manner. This course will help you build a solid foundation in the fundamental concepts of Data Science, including mathematics, statistics, and programming. These skills are essential for Data Governance Analysts, who need to be able to understand and interpret data in order to develop and implement effective data governance policies and procedures. Additionally, the course's focus on K-Means clustering, a common data analysis technique, will give you a valuable advantage in your career.

See salaries and explore the career path for Data Governance Analyst

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Foundations of Data Science: K-Means Clustering in Python.

An Introduction to Statistical Learning

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Provides a comprehensive overview of statistical learning methods, including supervised and unsupervised learning algorithms. It can serve as an excellent reference for the more advanced statistical concepts and techniques that may be briefly introduced in this course.

An Introduction to Statistical Learning: with...

Hardcover

$$$

Python for Data Analysis

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Focuses on the use of Python for data analysis and provides a comprehensive introduction to the pandas library. It can serve as a valuable reference and supplement for the programming exercises and data analysis tasks covered in this course.

Foundations of Data Science

K-Means Clustering in Python

What's inside

Syllabus

Good to know

Save this course

Reviews summary

K-means clustering in data science

Activities

Career center

Reading list

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