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Simon Allardice
We all work with data — but often don’t consider the big picture around it. In this fast-paced, practical introduction, quickly get up to speed on multiple ideas, tools and technologies around “data” in the business world today.
Read more
We all work with data — but often don’t consider the big picture around it. In this fast-paced, practical introduction, quickly get up to speed on multiple ideas, tools and technologies around “data” in the business world today.
Read more
We all work with data — but often don’t consider the big picture around it. In this fast-paced, practical introduction, quickly get up to speed on multiple ideas, tools and technologies around “data” in the business world today.
These days, every business role needs to understand the core ideas, tools and technologies their organizations rely on. This course is a fast-paced, practical, and pragmatic introduction to multiple ways to work with (and think about) data. First, we’ll quickly see how “data” differs from other terms like “information”, “knowledge” — or even “wisdom”. Next, we’ll get clear on the most important jargon and terminology, including “Big Data”, data silos and data warehouses, working with structured and unstructured data, and more. When you’re finished with this course, you’ll recognize the various — and sometimes unpredictable — ways that data can add value to your product, team, or organization.
Register for this course and see more details by visiting:
OpenCourser.com/course/bxrawe/data
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What's inside
Syllabus
Data Explained
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops an understanding of the increasing role of data in today's business
Builds a foundation to think critically about managing data
Emphasizes the importance of data management techniques in business
Suitable for beginners who want to learn the basics of data management
Introduces key concepts of data management in a clear and concise manner
Save this course
Save Data Explained to your list so you can find it easily later:
Save
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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 Explained with these
activities:
Read “The Data Science Handbook” by Field Cady
Show steps
Gain a comprehensive overview of the data science field and its applications.
View
The Data Science Handbook
on Amazon
Show steps
-
Purchase or borrow the book.
-
Allocate dedicated time for reading and note-taking.
Start with reviewing basic data vocabulary
Show steps
Ensure a strong understanding of the foundational principles of data science.
Show steps
-
Review terms like “data”, “information”, “knowledge”, and “wisdom”.
-
Understand the characteristics of big data and structured vs unstructured data.
Create a comprehensive notes repository
Show steps
Stay organized and improve your ability to recall key concepts.
Show steps
-
Gather all course materials, including notes, assignments, and quizzes.
-
Organize the materials into a logical structure.
-
Review the materials regularly to reinforce your understanding.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Identify data types using online quizzes
Show steps
Sharpen your ability to recognize and classify different data types.
Browse courses on
Data Types
Show steps
-
Take quizzes on data type identification.
-
Practice converting data between different types.
Attend industry meetups to connect with data professionals
Show steps
Expand your network and learn from experts in the field.
Show steps
-
Find industry meetups or conferences related to data science.
-
Attend the events and actively participate in discussions.
-
Connect with other professionals on LinkedIn.
Follow tutorials on data visualization techniques
Show steps
Gain practical experience in presenting data effectively.
Browse courses on
Data Visualization
Show steps
-
Find tutorials on creating charts, graphs, and other visualizations.
-
Practice using data visualization tools to create your own visualizations.
Contribute to an open-source data science project
Show steps
Enhance your skills through practical experience and collaboration.
Show steps
-
Identify open-source data science projects on platforms like GitHub.
-
Review the codebase and identify areas where you can contribute.
-
Submit a pull request with your contributions.
Write a blog post on a data-related topic
Show steps
Enhance your understanding and communication skills by sharing your knowledge.
Browse courses on
Data Analysis
Show steps
-
Choose a specific data-related topic to write about.
-
Research the topic thoroughly to gather accurate information.
-
Write a well-structured blog post that presents your insights and findings.
Volunteer with a data-driven nonprofit organization
Show steps
Apply your skills to make a positive impact while gaining valuable experience.
Show steps
-
Identify nonprofit organizations that leverage data for their operations.
-
Contact the organizations to inquire about volunteer opportunities.
-
Offer your assistance with data analysis, visualization, or other relevant tasks.
Read “The Data Science Handbook” by Field Cady
Show steps
Gain a comprehensive overview of the data science field and its applications.
View
The Data Science Handbook
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow the book.
- Allocate dedicated time for reading and note-taking.
Start with reviewing basic data vocabulary
Show steps
Ensure a strong understanding of the foundational principles of data science.
Show steps
Show steps
Show steps
- Review terms like “data”, “information”, “knowledge”, and “wisdom”.
- Understand the characteristics of big data and structured vs unstructured data.
Create a comprehensive notes repository
Show steps
Stay organized and improve your ability to recall key concepts.
Show steps
Show steps
Show steps
- Gather all course materials, including notes, assignments, and quizzes.
- Organize the materials into a logical structure.
- Review the materials regularly to reinforce your understanding.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Identify data types using online quizzes
Show steps
Sharpen your ability to recognize and classify different data types.
Browse courses on
Data Types
Show steps
Show steps
Show steps
- Take quizzes on data type identification.
- Practice converting data between different types.
Attend industry meetups to connect with data professionals
Show steps
Expand your network and learn from experts in the field.
Show steps
Show steps
Show steps
- Find industry meetups or conferences related to data science.
- Attend the events and actively participate in discussions.
- Connect with other professionals on LinkedIn.
Follow tutorials on data visualization techniques
Show steps
Gain practical experience in presenting data effectively.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find tutorials on creating charts, graphs, and other visualizations.
- Practice using data visualization tools to create your own visualizations.
Contribute to an open-source data science project
Show steps
Enhance your skills through practical experience and collaboration.
Show steps
Show steps
Show steps
- Identify open-source data science projects on platforms like GitHub.
- Review the codebase and identify areas where you can contribute.
- Submit a pull request with your contributions.
Write a blog post on a data-related topic
Show steps
Enhance your understanding and communication skills by sharing your knowledge.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a specific data-related topic to write about.
- Research the topic thoroughly to gather accurate information.
- Write a well-structured blog post that presents your insights and findings.
Volunteer with a data-driven nonprofit organization
Show steps
Apply your skills to make a positive impact while gaining valuable experience.
Show steps
Show steps
Show steps
- Identify nonprofit organizations that leverage data for their operations.
- Contact the organizations to inquire about volunteer opportunities.
- Offer your assistance with data analysis, visualization, or other relevant tasks.
Career center
Learners who complete Data Explained will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst designs and executes data-gathering strategies, analyzes and interprets data, and formulates strategic recommendations based on their findings. People in this role work closely with cross-functional teams, leveraging their analytical abilities to find opportunities for improvement within an organization's processes and operations. The course *Data Explained* can help Data Analysts understand the various types of data, how to collect and organize it, and how to use it to make informed decisions.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst analyzes data to understand business performance and identify opportunities for growth. They use their skills in data mining, statistical analysis, and data visualization to create reports and dashboards that help executives make informed decisions. The course *Data Explained* can help Business Intelligence Analysts develop the skills they need to collect, analyze, and interpret data, and to communicate their findings to stakeholders in a clear and concise way.
Data Scientist
Data Scientist
A Data Scientist develops and applies statistical models and machine learning algorithms to data in order to extract insights and make predictions. They use their skills in programming, statistics, and data mining to solve complex business problems. The course *Data Explained* can help Data Scientists understand the fundamentals of data analysis, including data collection, cleaning, and transformation, as well as the statistical techniques used to model and analyze data.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and databases. They use their skills in data modeling, data integration, and data management to ensure that data is accurate, consistent, and accessible to all users. The course *Data Explained* can help Data Engineers understand the different types of data, how to collect and organize it, and how to use it to build data pipelines and databases.
Product Manager
Product Manager
A Product Manager leads the development and launch of new products and features. They work closely with cross-functional teams to define product requirements, prioritize features, and track progress. The course *Data Explained* can help Product Managers understand how to use data to make informed decisions about product development and marketing.
Marketing Manager
Marketing Manager
A Marketing Manager develops and executes marketing campaigns to promote products and services. They use their skills in market research, data analysis, and creative storytelling to create marketing materials that reach and engage target audiences. The course *Data Explained* can help Marketing Managers understand how to use data to identify target audiences, track campaign performance, and measure ROI.
Sales Manager
Sales Manager
A Sales Manager leads a team of sales representatives and is responsible for achieving sales targets. They use their skills in sales strategy, negotiation, and customer relationship management to close deals and build long-term relationships with customers. The course *Data Explained* can help Sales Managers understand how to use data to identify sales opportunities, track sales performance, and forecast future sales.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make recommendations on investment decisions. They use their skills in accounting, finance, and data analysis to evaluate companies, industries, and markets. The course *Data Explained* can help Financial Analysts understand the different types of financial data, how to collect and analyze it, and how to use it to make informed investment decisions.
Operations Manager
Operations Manager
An Operations Manager plans and executes the day-to-day operations of a business. They use their skills in process improvement, supply chain management, and data analysis to ensure that the business runs smoothly and efficiently. The course *Data Explained* can help Operations Managers understand how to use data to identify and solve operational problems, improve efficiency, and reduce costs.
Human Resources Manager
Human Resources Manager
A Human Resources Manager leads the human resources department of a company and is responsible for all aspects of employee management. They use their skills in recruiting, training, performance management, and data analysis to attract, retain, and develop employees. The course *Data Explained* can help Human Resources Managers understand how to use data to track employee performance, identify training needs, and make informed decisions about employee management.
Information Technology Manager
Information Technology Manager
An Information Technology Manager plans and executes the technology strategy of a company. They use their skills in IT infrastructure, software development, and data management to ensure that the company's technology systems are aligned with business goals. The course *Data Explained* can help Information Technology Managers understand how to use data to make informed decisions about technology investments, improve system performance, and reduce risk.
Market Researcher
Market Researcher
A Market Researcher collects and analyzes data on consumer behavior and market trends. They use their skills in survey design, data analysis, and forecasting to help businesses understand their customers and make informed decisions about product development and marketing.
Statistician
Statistician
A Statistician designs and conducts statistical studies to collect and analyze data. They use their skills in probability, sampling, and data analysis to draw conclusions about populations based on sample data. The course *Data Explained* can help Statisticians understand the different types of data, how to collect and organize it, and how to use it to draw valid conclusions.
Database Administrator
Database Administrator
A Database Administrator designs, builds, and maintains databases. They use their skills in data modeling, data security, and data performance to ensure that databases are reliable, efficient, and secure. The course *Data Explained* can help Database Administrators understand the different types of data, how to collect and organize it, and how to use it to build and maintain databases.
Data Entry Clerk
Data Entry Clerk
A Data Entry Clerk enters data into computer systems. They use their skills in data entry, data validation, and data processing to ensure that data is accurate, complete, and consistent. The course *Data Explained* may be useful for Data Entry Clerks who want to learn more about the different types of data, how to collect and organize it, and how to use it to make informed decisions.
For more career information including salaries, visit:
OpenCourser.com/course/bxrawe/data
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
Data Explained.
Provides a comprehensive introduction to data science for business professionals. It covers the core concepts of data science, including data collection, data analysis, and data visualization. The book also includes case studies of how data science is being used to solve real-world business problems.
Save
Provides a comprehensive overview of the big data phenomenon. The authors discuss the challenges and opportunities of big data, and they offer advice on how businesses can use big data to their advantage.
Provides a practical introduction to data visualization. The author covers the basics of data visualization, including how to choose the right charts and graphs for your data. The book also includes case studies of how data visualization is being used to communicate insights from data.
Provides a comprehensive guide to data warehousing. The authors cover the basics of data warehousing, including how to design and build a data warehouse. The book also includes case studies of how data warehouses are being used to support business intelligence and analytics.
Save
Provides a comprehensive introduction to databases. The authors cover the basics of databases, including how to design and build a database. The book also includes case studies of how databases are being used to support business applications.
Save
Provides a practical introduction to machine learning for business professionals. The author covers the basics of machine learning, including how to choose the right machine learning algorithms for your data. The book also includes case studies of how machine learning is being used to solve real-world business problems.
Provides a comprehensive introduction to reinforcement learning. The authors cover the basics of reinforcement learning, including how to design and train reinforcement learning algorithms. The book also includes case studies of how reinforcement learning is being used to solve real-world problems.
Save
Provides a comprehensive introduction to deep learning. The authors cover the basics of deep learning, including how to design and train deep learning models. The book also includes case studies of how deep learning is being used to solve real-world problems.
Provides a comprehensive introduction to natural language processing. The authors cover the basics of natural language processing, including how to design and train natural language processing models. The book also includes case studies of how natural language processing is being used to solve real-world problems.
Save
Provides a comprehensive introduction to computer vision. The author covers the basics of computer vision, including how to design and train computer vision models. The book also includes case studies of how computer vision is being used to solve real-world problems.
Provides a comprehensive introduction to information retrieval. The authors cover the basics of information retrieval, including how to design and train information retrieval models. The book also includes case studies of how information retrieval is being used to solve real-world problems.
Save
Provides a comprehensive introduction to data mining. The authors cover the basics of data mining, including how to design and train data mining models. The book also includes case studies of how data mining is being used to solve real-world problems.
Save
Provides a comprehensive introduction to machine learning. The author covers the basics of machine learning, including how to design and train machine learning models. The book also includes case studies of how machine learning is being used to solve real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bxrawe/data
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Time
39 hours
Level
Introductory
Via
Pluralsight
Instructor
Simon Allardice
Language
English
Good to know
Develops an understanding of the increasing role of data in today's business
Builds a foundation to think critically about managing data
Emphasizes the importance of data management techniques in business
Suitable for beginners who want to learn the basics of data management
Introduces key concepts of data management in a clear and concise manner
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