Data Fundamentals
Data Fundamentals
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Grade 11-12 | 05 Courses
Course Description
Course Description
This credential earner demonstrates knowledge of data analytics concepts, methodologies and applications of data science, and the tools and programming languages used in the data ecosystem. The individual has a conceptual understanding of how to clean, refine, and visualize data using IBM Watson Studio. The earner is aware of the job outlook in data and is familiar with the skills required for success in various roles in the domain.
Course 1: 1 hour and 15 minutes | Course 2: 2 hours | Course 3: 1 hour and 30 minutes | Course 4: 1 hour and 35 minutes | Course 5: 50 minutes.
Learning Objectives
Learning Objectives
Chapter 1: Introduction to Data Concepts
Chapter 2: Data Science in Our World
Chapter 3: Overview of Data Tools and Languages
Chapter 4: Clean, Refine, and Visualize Data with IBM Watson Studio
Chapter 5: Your Future in Data: The Job Landscape
Skill Focus
Skill Focus
Enhances Technical Skills
After completing Data Fundamentals, you should be able to:
1. Describe fundamental data concepts, including types of data, big data, analytics techniques, typical steps in the data analytics process, and data visualization.
2. Identify widely adopted data science methodologies and explain the activities in a typical data science project.
3. Identify applications of data science across industries worldwide.
4. Describe the role of a data analyst, data scientist, and data engineer.
5. Identify the purpose and use of some common data analysis and visualization tools.
6. Clean, refine, and visualize data using IBM Watson Studio with the data refinery tool.
7. Recognize the job market, responsibilities, and skill sets of a data analyst and data scientist, as well as resources and learning opportunities to explore.
Chapter 1: Introduction to Data Concepts
In this course, you will become familiar with fundamental data concepts, such as types of data, big data, the typical steps in the data analytics process, and data visualization. After completing this course, you should be able to:
Explain the importance of data in a digital world.
Differentiate between unstructured and structured data.
Identify the purpose of a database.
Differentiate between quantitative and qualitative data.
Describe the five V’s of big data.
Describe the four types of data analytics.
Explain the work involved in each step of the data analysis process.
Identify the purpose of data visualization.
Recognize different charts to display data visualizations in the best way.
Chapter 2: Data Science in Our World
In this course, you will learn about the field of data science, including widely adopted methodologies, an example project as it moves through the steps of a data science methodology, the application of data science in our world, and the role of data scientists and their colleagues. After completing this course, you should be able to:
Define data science.
Recognize the importance of being curious to solve problems with data.
Differentiate between the fields of data analytics and data science.
Identify three widely adopted data science methodologies.
Explore a data project scenario and identify key tasks as it moves through a methodology.
Recognize industries and applications of data science that help solve problems and discover innovations.
Compare the roles and characteristics of a data analyst, data scientist, and data engineer.
Chapter 3: Overview of Data Tools and Languages
In this course, you will get an introduction to common data analysis and visualization tools so you’re familiar with them and can recognize how data science projects use them. After completing this course, you should be able to:
Recognize the value of collaborating and using open source in the field of data science.
Identify the purpose of GitHub.
Identify the purpose of a selection of common tools to analyze and visualize data, including:
Microsoft Excel
Google Sheets
Structured Query Language (SQL)
Python
IBM Watson Studio
Tableau
Matplotlib
Identify factors businesses can consider when selecting a data transformation tool.
Identify factors individuals can consider when selecting a data transformation tool.
Chapter 4: Clean, Refine, and Visualize Data with IBM Watson Studio
In this course, you will practice cleaning, refining, and visualizing data in a series of simulations using IBM Watson Studio with the data refinery tool. After completing this course, you should be able to:
Explain the purpose and key features of IBM Watson Studio.
Set up a new project in IBM Watson Studio.
Import a data set.
Clean data using the data refinery tool.
Refine data using the data refinery tool.
Create a data visualization.
Draw insights from a data visualization.
Chapter 5: Your Future in Data: The Job Landscape
In this course, you will learn about the data job market and projections, the responsibilities and skill sets of a data analyst and data scientist, and resources and learning opportunities so you can explore more. After completing this course, you should be able to:
Recognize the global demand for data analysts and data scientists in the job market.
Recognize the future of the field of data analytics.
Identify industries in which data professionals work.
Identify the primary responsibilities of a data analyst and data scientist.
Identify the skills that data professionals need.
Identify the tools to know when starting out in the field.
Identify resources to learn more and stay up to date in the field of data science.
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Resource Links
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