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UNIT-7
DATA ANALYSIS
(COMPUTATIONAL THINKING)
Learning Outcomes
• Understanding Data • Representation of Data
• Exploring Data—Pattern Recognition • Case, Variables, and Levels of Measurement
• Data Matrix and Frequency Tables • Mean, Median and Mode
• Range, Interquartile Range and Box Plot • Variance and Standard Deviation
• Z-scores
It is common knowledge that Artificial Intelligence (AI) is essentially based on data. AI involves turning large amounts of
raw data into information that has practical value and is actionable. Therefore, it is essential to understand the statistical
concepts and principles of artificial intelligence and machine learning. Statistical methods are required to find answers
to our questions about the data. Statistics and AI have a lot in common. Both disciplines have a lot to do with planning,
reasoning and decision-making. Statistical methods are required to understand the data used to train a machine learning
model and to interpret the results of testing different machine learning models.
In this unit, you will learn about different types of data and how to represent data using different visualisation tools.
You will also learn about case, variables, and levels of measurement. After that you will learn about various statistical
concepts like mean, median, mode, etc.
Understanding Data
Individual facts, statistics, or units of information, mostly numeric, that are gathered by observation are referred to as
data. Data are also referred to as a set of values for qualitative or quantitative variables about one or more people or
objects. The data is generally collected in raw format and is therefore difficult to understand. No matter how accurate
and valid the captured data is, it would be useless if it was not presented effectively.
Types of Data
We already know that data is of two types structured and unstructured.
Structured
Data
Unstructured
Data Analysis (Computational Thinking) 243

