Class 8th Chapter -6 Computer (Introduction to Data Science)

 Chapter 6: Introduction to Data Science

Checkpoint (Pg.110)

Fill in the blanks.

1. outcomes

2. data collection

3. experiences

4. trends

5. Quantitative

Exercises (Pg. 113-115)

A. Tick (✓) the correct answers.

1. b

2. b

3. b

4. a

5. b

B. Fill in the blanks.

1. data cleaning

2. Qualitative data

3. data visualisation

4. Modelling

5. Optimisation and deployment

C. Write T for True and F for False.

1. T

2. T

3. T

4. T

5. F

A. Answer the following questions in short.

Q.1    How many types of  data are in data science? 

Ans:-     Data can be classified into two types :- qualitative data and quantitative data. 

Q.2    Who coined the term "Data Science"? 

Ans:-     Peter Naur in 1974.

Q.3    Who laid the foundation of data analysis in 1962?

Ans:-      John Tukey.

Q.4  What is a common tool used in data science today? 

Ans:-    Python

B. Answer the following questions in long.

Q.1    What is Data Science and how does it contribute to decision making ?

Ans:- Data science is the field that focuses on collecting  mathematics, statistics, and computer science to collect and analyse data, uncover insights, and predict trends. It empowers industries to solve problems, optimise operations, and make smarter decisions through machine learning, AI, and big data technologies.

Q.2    Describe the key stages in the data science process. 

The data science process includes six key stages: problem identification, data collection, cleaning, exploration, modeling, and deployment. It begins by defining the problem and gathering relevant data. After cleaning and analyzing patterns, predictive models are built and optimized. Finally, the model is deployed to make informed decisions, such as identifying customer churn and enabling proactive retention strategies.

Q.3    List Five uses of Data Science ? 

Ans:-    Data science is used across a wide range of fields, from business and healthcare to sports and entertainment. Here are some of the most important applications of data science:

• Healthcare: In healthcare, data science is used to analyse patient records, predict disease outbreaks, and even develop personalised treatment plans based on a patient’s medical history. 

• Finance: Financial institutions use data science to analyse market trends, assess risks, and detect fraud. 

• E-commerce: Online stores like Amazon use data science to recommend products to customers, track purchasing behaviour, and optimise pricing.

• Sports: Teams use data science to analyse player performance, predict the outcome of games, and develop strategies. 

Q.4 Write three advantages and disadvantages of data science . 

Ans:-   Advantages of Data Science

• Data science provides insights that help individuals and businesses make more informed decisions.

• Data science allows for the automation of repetitive tasks, which increases efficiency and reduces human error.

• Data science can forecast future trends, giving businesses a competitive advantage by helping them prepare for future events.

Disadvantages of Data Science

• The collection and analysis of large amounts of personal data raises concerns about privacy and security.

• If the data used for analysis is biased or unrepresentative, the conclusions drawn from it can be incorrect or misleading.

• Implementing data science projects can be expensive due to the need for specialised tools, software, and experts.