Explore Data Science Foundational Concepts
Full skill from Data Science Fundamentals (ITCA). Preview the training IT teams trust.
Updated
Skill 1 of 16 in Data Science Fundamentals (ITCA)
What you'll learn
- Explain data science as the process of extracting valuable insights from raw or unprocessed data
- Describe how domain knowledge guides data-driven business decisions such as targeted marketing and stock planning
- Define data as information that can be collected, measured, manipulated, and analyzed
- Explain why dimensionality, connectivity, and transformability make data meaningful for analysis
- Apply dimensionality reduction to simplify a data set by keeping only the features the context requires
- Import a CSV file into Google Colab and identify its data types and dimensions
Free 44-minute video lesson with a full transcript and knowledge checks. No account needed to watch.
Welcome to the first skill of the Data Science Fundamentals course, where you'll Explore Data Science Foundational Concepts! In this first video, we'll review everything you're going to learn in this skill :
- Explore Data Science Foundational Concepts Overview
- What is Data Science?
- What is Data?
- Explore Dimensionality with Google's Colab
- What is Data Connectivity?
- CHALLENGE 🎉
Explore Data Science Foundational Concepts Overview
In this first video, we'll explore all of the concepts in this course and what we'll cover in this first skill. See you there!
Knowledge Check
True/False: Data Science is a larger domain that includes many domains such as statistics, software engineering, business analysis, and other fields.
What is Data Science?
In this video, we'll get more granular and answer questions like "what is Data science?" using hands-on examples using Google Colab. See you in the video!
Knowledge Check
What is Data Science?
What is Data?
In this video, we'll get more granular and answer questions like "what is data?" using hands-on examples using Google Colab. See you in the video!
Knowledge Check
What is data?
Explore Dimensionality with Google's Colab
In this video, we'll actually use Colab for a hands-on demonstration of dimension reduction. No coding needed, just run each cell and make a note of the outputs in the context of dimensionality. See you in the video!
Launch: Google Colab Notebook
Knowledge Check
What is dimensionality?
What is Connectivity?
Without connectivity, our data doesn't offer much value. For example, a row of numbers representing sales data isn't very useful without being connected to other data, such as date, customer_Id, and store_number.
Knowledge Check
What is Connectivity?
CHALLENGE
It's time to check your knowledge of everything you have learned so far by loading a CSV file into Google Sheets using everything you've learned up to this point. See you in the video!
CSV File Download:
Knowledge Check
What is the data type of the following labels? "hp", "mpg", "weight", and "cylinders"
Knowledge Check
What is the data type of the actual values?
Knowledge Check
Which is the most important feature when trying to explore fuel efficiency?
Knowledge Check
How can we reduce the dimensionality of our dataset?
Solution Video
Knowledge Check
How would you rate the difficulty of this skill for you on a scale from 1 to 5, with 1 being very easy and 5 being very challenging?
This interactive assessment is available in the full learning experience.
Frequently asked questions
What does the Explore Data Science Foundational Concepts lesson cover?
The lesson defines data science as the process of extracting valuable insights from raw or unprocessed data, and defines data as information that can be collected, measured, manipulated, and analyzed. It explains why dimensionality, connectivity, and transformability make data meaningful, and shows how domain knowledge guides business decisions such as targeted marketing and stock planning.
How long is this lesson and where does it fit in the ISACA Data Science Fundamentals (ITCA) course?
The lesson runs 44 minutes and is the first skill in the ISACA Data Science Fundamentals (ITCA) course, which takes roughly 12.5 hours per learner and maps to the ITCA exam. It sets the vocabulary the team member needs before the course moves into data structures, statistical analysis, governance, and machine learning concepts.
What is dimensionality reduction and how is it explained in this lesson?
Dimensionality is the number of features or variables in a data set, and dimensionality reduction simplifies that data set by keeping only the features the context requires. The trainer's example drops seconds from a time data set when only the date and hour matter, reducing complexity without losing what the analysis needs.
Does the lesson include hands-on practice with Google Colab?
Yes. The team member runs Python in a Google Colab notebook, then completes a challenge that imports a CSV file and identifies its data types and dimensions. The challenge is not graded, and the trainer walks through how he imported the file and answered each question in the follow-up video.
What background does a team member need before taking this lesson?
The course is rated Beginner and fits new IT Practitioners with about a year of IT experience, as well as experienced team members who need to validate core data science knowledge. The trainer uses simple terms without complicated jargon and pairs concepts with stories, use cases, and hands-on application.
How can a Training Manager roll this course out to an IT team?
Assign it before the team takes on data-heavy reporting, analytics, or governance work so everyone starts from the same data science vocabulary. Playlists can sequence the rollout, and Team Reporting shows per-learner progress and completion with completion exports for audit evidence.
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