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Lecture notes of 6 pages for the course Computer Science at Freshman / 9th grade (NBF FBISE)
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Ms. Amna Altaf Unit: 4 Data & Analysis Computer ScQ1. Sketch the key concepts of data science in your own words.
Ans. Data Science:
Data Science refers to an interdisciplinary field of multiple disciplines that uses mathematics, statistics, data analysis, and machine learning to analyze data and
to extract knowledge and insights from it. It is like a pipeline from data to insights. This insight or knowledge is used to find patterns in the data. The result drawn
can be used for making informed decisions to solve real world problems e.g., medical, education, scientific research, and business etc.
Concepts of Data Science
Data science consists of many components, theories, and algorithms. To understand data science and make its productive usage, following are some key
concepts or components that lay the foundation of data science:
Data: Data is a collection of observations, facts or information collected from different sources. This data can be in the form of numbers, measurements, words,
observations, or in audio or video form. It could be structured(processed) data is in the form of tables or unstructured(unprocessed) data in the form of audio,
video, tweets, pdf files etc.
Dataset: Dataset is a structured or processed collection of data usually associated with the unique body of work. This collection of data usually associated with a
of data is related to each other in some way, for example a collection of brain CT scan of brain tumor patients is a dataset which can be wed to evaluate certain
pattern or trend common in the e entire dataset.
Statistics and Probability: Statistics is the analysis of the frequency of past events and probability is to predict the likelihood of future events. Data scientists use
statistics and is to find patterns and trends in the data.
Mathematics: Mathematics is a fundamental part of data science which helps to solve problems, optimize the model performances, and interpret huge complex
data in to simple & clear results, for decision making.
Machine Learning: Machine learning is a branch of Artificial Intelligence and computer science which emphasis on the use of data and algorithms to imitate
human learning by the computers.
Deep Learning: Deep learning is the subset of Machine learning, with emphasis on the simulation or imitation of human brain's behavior by using artificial neural
networks.
Data Mining: Data mining is the subset of data science which primarily focuses on discovering patterns and relationships in existing datasets. The usage of
techniques and tools is limited in data mining as compared to data science.
Data Visualization: Data visualization is the graphical representation of data using common charts, plots, info graphics, and animations. These visual displays of
information communicate complex data relationships and data-driven insights in a way that is easy to understand.
Big Data: Big data refers to handling large volumes of data. Data scientists use big data to find patterns and trends in datasets, to obtain more accurate and
reliable results. The huge size of data provides more opportunities for machine learning and provides better results.
, Ms. Amna Altaf Unit: 4 Data & Analysis Computer Sc
Predictive Analysis: Predictive analysis is the use of data to predict future trends and events based on historical data.
Natural Language Processing (NLP): It is the study of interaction between human language and computers. The common uses of NLP are chatbots, language
translators and sentiment analysis.
Q2. Develop your own thinking on the various data types used in data science.
Ans. In data science we can mainly classify data into two main types
a. Qualitative (categorical)
b. Quantitative (numeric).
Qualitative or Categorical data: describes an object or a group of objects that can be labeled according to some group or category. It cannot be represented in
numerical form. For example, data including colors, places, etc. it is further sub divided into two types:
i. Ordinal data
ii. Nominal data
i. Ordinal Data:
Ordinal data sees a specific order or ranking, it uses certain scale or measure to group data into categories. Such as in test grades, economic status, or military
rank.
ii. Nominal Data:
Nominal data does not have any order, it can be labelled into mutually exclusive categories, which cannot be ordered meaningfully. For example, if we consider
the categories of transportation as car, bus or train. Similarly, gender, city, color, employment status are also examples of nominal data.
Quantitative or Numerical data: deals with numeric values that can be computed mathematically to draw some conclusions. Examples of numeric data are
height, weight, number of students in a school, fruits in a basket etc. Quantitative data can be further divided into two types:
i. Discrete data
ii. Continuous data
i. Discrete Data: It includes data which can only take certain values and cannot be further subdivided into smaller units. This data can be counted and
has a finite number of values. For example, the number of product reviews, ticket sold, computers in certain departments, employees in a company
etc.
ii. Continuous Data: It refers to the unspecified number of possible measurements between two realistic points or numbers. For example, daily wind
speed, weight of newborn babies, freezer's temperature etc.
Q3. Compare how big data is applicable to various fields of life. Illustrate your answer with suitable examples.
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