Hello NPTEL Learners, In this article, you will find **NPTEL Data Science for Engineers Assignment 1 Week 1 Answers 2023**. All the Answers are provided below to help the students as a reference don’t straight away look for the solutions, first try to solve the questions by yourself. If you find any difficulty, then look for the solutions.

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## NPTEL Data Science for Engineers Assignment 1 Answers 2023:

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#### Q.1. The function “ ls() ” in R will

- set a new working directory path
**list all objects in our working environment**- display the path to our working directory
- list all files in our working directory

#### Q.2. Consider the following code:

If a room temperature of 20 degree Celsius is input to the above code, choose the output displayed:

- “The room is chilly. Please turn the heater on!”
**“The room is warm” “Enjoy the stay!”**- “The room is hot” “Please turn the air conditioning on!”
- None of the above

#### Q.3. Consider the code below to create a dataframe city_data:

Choose the correct answer based on the output expected on running the below code.

**In the column weather in dataframe city_data, “Rain” is replaced by “Snowy”**- In the column name in dataframe city_data, “City C” is replaced by “Snowy”
- No change occurs to the entries in the dataframe city_data
- The code raises an error

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#### Q.4. Consider the code given below.

Choose the correct command to access the variable “Cloudy”

**city_weather[[4]][2]**- city_weather[[5]]
- city_weather[[2]][2]
- None of the above

#### Q.5.** **Consider the following code.

A = matrix(c(1:42), nrow = 6, ncol = 7, byrow = T)

B = A[-2,]

Based on the output of the above code, choose the correct options from the following

- B is a matrix consisting of only elements of the second row of matrix A
- B is a matrix consisting of same elements as that of A
- The code raises an error
**B is a matrix consisting of all elements of A excluding the second row**

#### Q.6. The parameter “collapse” in the function paste()

- adjusts the display of the string to left, right or center
- add space in between two strings
**eliminates the space in between two strings**- eliminates the space within two words in a string

#### Q.7. Consider the following code in R.

If we supply 4 as an argument to the function find.function, after executing the above code, the result displayed would be

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#### Q.8. The operator %in% is used for

- multiplication of a matrix with its transpose
- generating a sequence of numbers in a vector
**identifying if an element belongs to a vector**- none of the above

#### Q.9. The output of compiling and executing the following code in R would be

- The term “Green” “apple” printed 5 times.
- The term “Green apple” printed 3 times.
- The term “Green” printed 4 times
**The term “Green” “apple” printed 4 times.**

#### Q.10. The “next” statement in R programming is useful

**for skipping the current iteration of a loop without terminating it.**- for terminating the current iteration of a loop.
- for evaluating the current iteration of a loop without terminating it.
- None of the above

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#### About NPTEL Data Science for Engineers Course:

**Learning Objectives :**

- Introduce R as a programming language
- Introduce the mathematical foundations required for data science
- Introduce the first level data science algorithms
- Introduce a data analytics problem solving framework
- Introduce a practical capstone case study

**Learning Outcomes:**

- Describe a flow process for data science problems (Remembering)
- Classify data science problems into standard typology (Comprehension)
- Develop R codes for data science solutions (Application)
- Correlate results to the solution approach followed (Analysis)
- Assess the solution approach (Evaluation)
- Construct use cases to validate approach and identify modifications required (Creating)

**Course Outcome:**

**Week 1:**Course philosophy and introduction to R**Week 2:**Linear algebra for data science- 1. Algebraic view – vectors, matrices, product of matrix & vector, rank, null space, solution of over-determined set of equations and pseudo-inverse)
- 2. Geometric view – vectors, distance, projections, eigenvalue decomposition
**Week 3:**Statistics (descriptive statistics, notion of probability, distributions, mean, variance, covariance, covariance matrix, understanding univariate and multivariate normal distributions, introduction to hypothesis testing, confidence interval for estimates)**Week 4:**Optimization**Week 5:**1. Optimization- 2. Typology of data science problems and a solution framework
**Week 6:**1. Simple linear regression and verifying assumptions used in linear regression- 2. Multivariate linear regression, model assessment, assessing importance of different variables, subset selection
**Week 7:**Classification using logistic regression**Week 8:**Classification using kNN and k-means clustering

**CRITERIA TO GET A CERTIFICATE**:

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.

Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

**YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.**

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