English | Size: 8.6 GB
Genre: eLearning
Learn data science without coding using R, SPSS, jamovi & Sheets. Build a strong foundation in concepts, tools, & skills
What you’ll learn
Gain a clear, non-technical introduction to data science and understand its real-world relevance across industries
Learn how data science integrates statistics, programming, and domain expertise to generate actionable insights
Discover how to source data from traditional and specialized methods specific to data science workflows
Explore essential mathematical concepts that support data analysis, with intuitive explanations and real-world examples
Understand core statistical practices such as data exploration, estimation, and feature selection—without needing prior experience
Get familiar with tools used by data science professionals, including Google Sheets, jamovi, R, and SPSS
Build a conceptual foundation in coding for data science, understanding how languages like R and Python are used alongside visual tools
Develop the ability to approach data problems creatively, applying foundational knowledge to practical challenges
Prepare for more advanced courses in applied data science by building a strong, well-rounded foundation
Are you curious about data science but unsure where to start? This beginner-friendly course offers a clear introduction to the core concepts, tools, and real-world applications of data science—no coding experience required.
Whether you’re looking to transition into a data role, boost your analytical skills, or just understand how data powers the world, this course gives you a comprehensive foundation to get started and understand how to use tools like Google Sheets, jamovi, SPSS, and R to work with data
1. DATA SCIENCE: An Introduction
Data science sits at the intersection of statistics, computer programming, and domain expertise. This non-technical overview introduces the basic elements of data science and how it is relevant to work in the real world.
2. DATA SOURCING
Data science can’t happen without data. That means the first task in any project is source – that is, to get – the raw materials that you will need. This course discusses some of the more familiar methods of gathering data and some of the less familiar that are specific to data science.
3. CODING
Data science professionals rely on a range of tools, from basic spreadsheets to advanced languages like R and Python. In these videos, we’ll cover the basic elements of the most important tools for data science
4. MATHEMATICS
Data science relies on several important aspects of mathematics. In this course, you’ll learn what forms of mathematics are most useful for data science, and see some worked examples of how math can solve important data science problems.
5. STATISTICS
Statistics is distinct from – but critical to – data science. In this non-technical, conceptual overview, you can learn how statistical practices such as data exploration, estimation, and feature selection give data science its power and insight.
6. The Tools of Data
In working with data, you’ll confront novel problems that require creative solutions. A wide range of tools can help you find these solutions efficiently and accurately. This course offers tutorials on the most important tools for working constructively with data. This course includes modules on Google Sheets, the open-source data analysis program jamovi, the statistical programming language R, and the analytical application SPSS.
6.1. Google Sheets: Google Sheets is a free, web-based spreadsheet application that is part of the Google Drive office suite, along with Google Docs and Google Slides. Google Sheets has a clean, human-friendly design that encourages collaboration and facilitates insight into your data. In this course, you’ll learn how you can use Google Sheets to enter, organize, refine, analyze, and visualize your data to present your story.
6.2. R: R is a free, open-source, statistical programming language and it is possibly the single most important tool in data science. In this non-technical overview, learn what makes R special and how it can be used to make data science easy, efficient, and insightful.
6.3. Jamovi: jamovi is a free, open-source application that makes data analysis easy and intuitive. jamovi menus and commands are designed to simplify the transition from programs like SPSS but, under the hood, jamovi is based on the powerful statistical programming language R. jamovi has a clean, human-friendly design that facilitates insight into your data and makes it easy to share your work with others. In this introductory course, you’ll learn how you can use jamovi to refine, analyze, and visualize your data to get critical insights.
6.4. SPSS: Learn about the popular statistical application SPSS from IBM. This course provides a concise overview of how you can use SPSS to explore and analyze your data for actionable insights.
All datasets, exercise files, and assignment files are available for download under each lecture.
This course includes materials licensed under the Creative Commons Attribution license. Original work by datalab has been used across the course.
Who this course is for:
Beginners curious about data science who want a non-technical and accessible introduction to the field
Students or professionals looking to explore data science tools without a programming-heavy approach
Researchers and social scientists seeking user-friendly tools like jamovi, SPSS, or Google Sheets for data analysis
Educators and teachers who want to integrate practical data tools and concepts into their curriculum
Business professionals and decision-makers interested in understanding data-driven insights and methods
Aspiring data analysts who prefer to start with visual tools before transitioning into coding environments like R
Anyone needing a foundational understanding of statistics, data sourcing, and analysis techniques
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