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The Data Science Companion provides an introduction to data science. You will gain a quick background in data science and core machine learning concepts, such as regression and classification. You’ll be introduced to the practical knowledge of data processing and visualization using low-code solutions, as well as an overview of the ways to integrate multiple tools effectively to solve data science problems. You will then leverage cloud resources from Amazon Web Services to scale data processing and accelerate machine learning model training. By the end of this short course, you will have a high-level understanding of important data science concepts that you can use as a foundation for future learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/zthlpw/data
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What's inside
Syllabus
Background
Explore or refresh your knowledge of the core purpose of data science and the two main categories of machine learning models, regression and classification.
Read more
Syllabus
Background
Explore or refresh your knowledge of the core purpose of data science and the two main categories of machine learning models, regression and classification.
Read more
Low Code Solutions
Perform core tasks in data processing and visualization, experimenting with different options with the help of interactive, graphical tools, before committing to a solution in code.
Integrating with Other Tools
Leverage the benefits of combining multiple tools to solve a data science problem.
Scaling to the Cloud
Scale the processing of large data sets and speed up the training time of machine learning models in MATLAB by using cloud resources available from Amazon Web Services.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a high-level overview of important data science concepts
Introduces core machine learning concepts, including regression and classification
Focuses on practical knowledge of data processing and visualization using low-code solutions
Includes an overview of integrating multiple tools to solve data science problems
Leverages cloud resources from Amazon Web Services to scale data processing and accelerate machine learning model training
Save this course
Save Data Science Companion to your list so you can find it easily later:
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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 Data Science Companion with these
activities:
Brush up on regression and classification concepts
Show steps
Revisiting these core concepts will help you grasp the subsequent course material more easily.
Browse courses on
Regression
Show steps
-
Review lecture notes or textbooks on regression and classification
-
Complete practice problems or online quizzes on these topics
-
Discuss these concepts with a peer or mentor
Engage in discussion forums or meetups with other data science learners
Show steps
Connecting with peers can provide different perspectives, insights, and support throughout your learning journey.
Show steps
-
Join online discussion forums or communities related to data science
-
Participate in meetups or virtual events to connect with other learners
-
Share your knowledge and experiences, and engage in discussions to deepen your understanding
Practice data processing and visualization using low-code tools
Show steps
Hands-on practice will enhance your proficiency in data manipulation and visualization techniques.
Browse courses on
Data Processing
Show steps
-
Explore interactive tutorials or online resources on low-code data processing
-
Experiment with different low-code tools to perform data cleaning, transformation, and visualization tasks
-
Share your findings or insights with a study group
Two other activities
Expand to see all activities and additional details
Show all five activities
Explore cloud resources for scaling data processing and machine learning model training
Show steps
Gaining familiarity with cloud resources will prepare you for handling larger datasets and complex modeling tasks.
Browse courses on
Cloud Computing
Show steps
-
Enroll in online courses or workshops on cloud computing platforms like AWS
-
Follow guided tutorials on how to use cloud services for data processing and machine learning
-
Experiment with cloud-based tools and services to gain practical experience
Develop a small-scale data science project
Show steps
Applying your knowledge to a real-world project will solidify your understanding and prepare you for future data science endeavors.
Show steps
-
Identify a small dataset of interest
-
Apply data processing and visualization techniques to explore and analyze the dataset
-
Develop a machine learning model to solve a specific problem related to the dataset
-
Create a report or presentation to showcase your findings and insights
Brush up on regression and classification concepts
Show steps
Revisiting these core concepts will help you grasp the subsequent course material more easily.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on regression and classification
- Complete practice problems or online quizzes on these topics
- Discuss these concepts with a peer or mentor
Engage in discussion forums or meetups with other data science learners
Show steps
Connecting with peers can provide different perspectives, insights, and support throughout your learning journey.
Show steps
Show steps
Show steps
- Join online discussion forums or communities related to data science
- Participate in meetups or virtual events to connect with other learners
- Share your knowledge and experiences, and engage in discussions to deepen your understanding
Practice data processing and visualization using low-code tools
Show steps
Hands-on practice will enhance your proficiency in data manipulation and visualization techniques.
Browse courses on
Data Processing
Show steps
Show steps
Show steps
- Explore interactive tutorials or online resources on low-code data processing
- Experiment with different low-code tools to perform data cleaning, transformation, and visualization tasks
- Share your findings or insights with a study group
Two other activities
Expand to see all activities and additional details
Show all five activities
Explore cloud resources for scaling data processing and machine learning model training
Show steps
Gaining familiarity with cloud resources will prepare you for handling larger datasets and complex modeling tasks.
Browse courses on
Cloud Computing
Show steps
Show steps
Show steps
- Enroll in online courses or workshops on cloud computing platforms like AWS
- Follow guided tutorials on how to use cloud services for data processing and machine learning
- Experiment with cloud-based tools and services to gain practical experience
Develop a small-scale data science project
Show steps
Applying your knowledge to a real-world project will solidify your understanding and prepare you for future data science endeavors.
Show steps
Show steps
Show steps
- Identify a small dataset of interest
- Apply data processing and visualization techniques to explore and analyze the dataset
- Develop a machine learning model to solve a specific problem related to the dataset
- Create a report or presentation to showcase your findings and insights
Career center
Learners who complete Data Science Companion will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make better decisions. With the advent of big data, Data Analysts are in high demand across a variety of industries. By covering data processing and visualization, this course will help you develop the hands-on skills you need to succeed as a Data Analyst.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are at the forefront of developing and implementing machine learning models. They are responsible for the entire lifecycle of a machine learning project, from data collection and preparation to model training and deployment. By focusing on machine learning, particularly model building, regression and classification, this course will help you prepare for a career as a Machine Learning Engineer.
Data Scientist
Data Scientist
Data Scientists use mathematical and computational models to extract insights from data. With the rapid growth of data in nearly every field, Data Scientists have become increasingly in demand. This course offers a solid background in the core concepts of data science. Whether you're a recent graduate or a working professional, this course will help you gain the foundational skills needed to join the field or advance your career as a Data Scientist.
Business Analyst
Business Analyst
Business Analysts help companies to improve their processes and performance by analyzing data and making recommendations. This course will help you develop the skills you need to gather, analyze, and interpret data to help businesses make better decisions. By providing a solid grounding in the fundamentals of data science, this course can provide you with an edge over other candidates for Business Analyst roles.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that is used to store and process data. With the increasing importance of data, Data Engineers are in high demand across a variety of industries. By covering data processing and scaling to the cloud, this course will help you build a foundation in the skills needed to succeed as a Data Engineer.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts (Quants) use mathematical and statistical models to analyze financial data. They play a key role in the investment banking industry, and their skills are also in demand in other areas, such as risk management and trading. The focus on machine learning in this course is particularly relevant to the role of a Quant, since machine learning is increasingly being used in financial modeling and trading.
Statistician
Statistician
Statisticians use statistical methods to analyze data and draw conclusions. They work in a variety of fields, including finance, healthcare, and marketing. This course will help you develop the foundational skills you need to succeed as a Statistician.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They work in a variety of fields, including insurance, finance, and healthcare. By providing a solid foundation in data science, particularly probability and statistics, this course can help you prepare for a career as an Actuary.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in a variety of industries, such as manufacturing, transportation, and healthcare. By providing a solid foundation in data science, this course can help you prepare for a career as an Operations Research Analyst.
Software Engineer
Software Engineer
Software Engineers design and develop computer software. By covering data processing and visualization, this course will help you develop some of the skills needed to succeed as a Software Engineer with a specialization in data science.
Data Science Manager
Data Science Manager
Data Science Managers are responsible for leading and managing teams of data scientists. They play a key role in setting the strategic direction of data science projects and ensuring that they are successful. This course may be useful for aspiring Data Science Managers by providing a high-level understanding of the field and the skills and knowledge needed to be successful in a leadership role.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. By covering data processing and scaling to the cloud, this course may be useful for aspiring Database Administrators by providing a foundation in the skills needed to manage large and complex databases.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. By providing a solid foundation in data science, particularly data analysis and visualization, this course may be useful for aspiring Product Managers by providing the skills needed to understand and analyze customer data.
Financial Analyst
Financial Analyst
Financial Analysts provide financial advice to individuals and organizations. By providing a solid foundation in data science, particularly data analysis and visualization, this course may be useful for aspiring Financial Analysts by providing the skills needed to analyze financial data.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about consumers and markets. By providing a solid foundation in data science, particularly data collection and analysis, this course may be useful for aspiring Market Researchers by providing the skills needed to conduct market research studies.
For more career information including salaries, visit:
OpenCourser.com/course/zthlpw/data
Reading list
We've selected ten 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
Data Science Companion.
Provides a comprehensive overview of data science, including the core concepts, techniques, and tools used in the field. It valuable resource for those who want to learn more about data science and its applications in business.
Provides a practical introduction to data science, covering the core concepts and techniques used in the field. It valuable resource for those who want to learn more about data science and its applications.
Provides a comprehensive overview of Python for data analysis, covering the core concepts and techniques used in the field. It valuable resource for those who want to learn more about Python and its applications in data analysis.
Save
Provides a comprehensive overview of R for data science, covering the core concepts and techniques used in the field. It valuable resource for those who want to learn more about R and its applications in data science.
Provides a comprehensive overview of data science with MATLAB, covering the core concepts and techniques used in the field. It valuable resource for those who want to learn more about data science and its applications in MATLAB.
Provides a comprehensive overview of machine learning with Python, covering the core concepts and techniques used in the field. It valuable resource for those who want to learn more about machine learning and its applications in Python.
Provides a comprehensive overview of data science with Python, covering the core concepts and techniques used in the field. It valuable resource for those who want to learn more about data science and its applications in Python.
Provides a comprehensive overview of data science on AWS, covering the core concepts and techniques used in the field. It valuable resource for those who want to learn more about data science and its applications on AWS.
Provides a comprehensive overview of data science for dummies, covering the core concepts and techniques used in the field. It valuable resource for those who want to learn more about data science and its applications.
Provides a comprehensive overview of machine learning for dummies, covering the core concepts and techniques used in the field. It valuable resource for those who want to learn more about machine learning and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zthlpw/data
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Effort
Less than 2 hours.
Level
Introductory
Via
Coursera
Institution
MathWorks
Instructor
MathWorks Instructors
Language
English
Good to know
Provides a high-level overview of important data science concepts
Introduces core machine learning concepts, including regression and classification
Focuses on practical knowledge of data processing and visualization using low-code solutions
Includes an overview of integrating multiple tools to solve data science problems
Leverages cloud resources from Amazon Web Services to scale data processing and accelerate machine learning model training
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