**What are various steps involved in an analytics project?**

The following are the various steps involved in an analytics project:

Understand the business problem

Explore the data and become familiar with it.

Prepare the data for modelling by detecting outliers, treating missing values, transforming variables, etc.

After data preparation, start running the model, analyse the result and tweak the approach. This is an iterative step till the best possible outcome is achieved.

Validate the model using a new data set.

Start implementing the model and track the result to analyse the performance of the model over the period of time.

**What are the differences between overfitting and underfitting?**

In statistics and machine learning, one of the most common tasks is to fit a model to a set of training data, so as to be able to make reliable predictions on general untrained data.

In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data.

Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. Underfitting would occur, for example, when fitting a linear model to non-linear data. Such a model too would have poor predictive performance.

CGI Group Data Science Recently Asked Interview Questions Answers |

**Python or R – Which one would you prefer for text analytics?**

We will prefer Python because of the following reasons:

Python would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools.

R is more suitable for machine learning than just text analysis.

Python performs faster for all types of text analytics.

**How does data cleaning plays a vital role in analysis?**

Data cleaning can help in analysis because:

Cleaning data from multiple sources helps to transform it into a format that data analysts or data scientists can work with.

Data Cleaning helps to increase the accuracy of the model in machine learning.

It is a cumbersome process because as the number of data sources increases, the time taken to clean the data increases exponentially due to the number of sources and the volume of data generated by these sources.

It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.

**Suppose a life insurance company sells a $240,000 one year term life insurance policy to a 25-year old female for $210. The probability that the female survives the year is .999592. Find the expected value of this policy for the insurance company.**

A) $131

B) $140

C) $112

D) $125

Ans: (C)

P(company loses the money ) = 0.99592

P(company does not lose the money ) = 0.000408

The amount of money company loses if it loses = 240,000 – 210 = 239790

While the money it gains is $210

Expected money the company will have to give = 239790*0.000408 = 97.8

Expect money company gets = 210.

Therefore the value = 210 – 98 = $112

**When an event A independent of itself?**

A) Always

B) If and only if P(A)=0

C) If and only if P(A)=1

D) If and only if P(A)=0 or 1

Ans: (D)

The event can only be independent of itself when either there is no chance of it happening or when it is certain to happen. Event A and B is independent when P(Aê“µB) = P(A)*P(B). Now if B=A, P(Aê“µA) = P(A) when P(A) = 0 or 1.

**Suppose you’re in the final round of “Let’s make a deal” game show and you are supposed to choose from three doors – 1, 2 & 3. One of the three doors has a car behind it and other two doors have goats. Let’s say you choose Door 1 and the host opens Door 3 which has a goat behind it. To assure the probability of your win, which of the following options would you choose.**

A) Switch your choice

B) Retain your choice

C) It doesn’t matter probability of winning or losing is the same with or without revealing one door

Ans: (A)

I would recommend reading this article for a detailed discussion of the Monty Hall’s Problem.

**Cross-fertilizing a red and a white flower produces red flowers 25% of the time. Now we cross-fertilize five pairs of red and white flowers and produce five offspring. What is the probability that there are no red flower plants in the five offspring?**

A) 23.7%

B) 37.2%

C) 22.5%

D) 27.3%

Ans: (A)

The probability of offspring being Red is 0.25, thus the probability of the offspring not being red is 0.75. Since all the pairs are independent of each other, the probability that all the offsprings are not red would be (0.75)5 = 0.237. You can think of this as a binomial with all failures.

**What is power analysis?**

An experimental design technique for determining the effect of a given sample size.

**What is K-means? How can you select K for K-means?**

**What is Collaborative filtering?**

The process of filtering used by most of the recommender systems to find patterns or information by collaborating viewpoints, various data sources and multiple agents.

**What is the difference between Cluster and Systematic Sampling?**

Cluster sampling is a technique used when it becomes difficult to study the target population spread across a wide area and simple random sampling cannot be applied. Cluster Sample is a probability sample where each sampling unit is a collection, or cluster of elements. Systematic sampling is a statistical technique where elements are selected from an ordered sampling frame. In systematic sampling, the list is progressed in a circular manner so once you reach the end of the list,it is progressed from the top again. The best example for systematic sampling is equal probability method.

**Are expected value and mean value different?**

They are not different but the terms are used in different contexts. Mean is generally referred when talking about a probability distribution or sample population whereas expected value is generally referred in a random variable context.

For Sampling Data

Mean value is the only value that comes from the sampling data.

Expected Value is the mean of all the means i.e. the value that is built from multiple samples. Expected value is the population mean.

For Distributions

Mean value and Expected value are same irrespective of the distribution, under the condition that the distribution is in the same population.

**What does P-value signify about the statistical data?**

P-value is used to determine the significance of results after a hypothesis test in statistics. P-value helps the readers to draw conclusions and is always between 0 and 1.

• P- Value > 0.05 denotes weak evidence against the null hypothesis which means the null hypothesis cannot be rejected.

• P-value <= 0.05 denotes strong evidence against the null hypothesis which means the null hypothesis can be rejected.

• P-value=0.05is the marginal value indicating it is possible to go either way.

**If you dealt 13 cards, what is the probability that the 13th card is a King?**

A) 1/52

B) 1/13

C) 1/26

D) 1/12

Ans: (B)

Since we are not told anything about the first 12 cards that are dealt, the probability that the 13th card dealt is a King, is the same as the probability that the first card dealt, or in fact any particular card dealt is a King, and this equals: 4/52

**A fair six-sided die is rolled 6 times. What is the probability of getting all outcomes as unique?**

A) 0.01543

B) 0.01993

C) 0.23148

D) 0.03333

Ans: (A)

For all the outcomes to be unique, we have 6 choices for the first turn, 5 for the second turn, 4 for the third turn and so on

Therefore the probability if getting all unique outcomes will be equal to 0.01543

**You are given a data set consisting of variables having more than 30% missing values? Let’s say, out of 50 variables, 8 variables have missing values higher than 30%. How will you deal with them?**

We can deal with them in the following ways:

Assign a unique category to missing values, who knows the missing values might decipher some trend

We can remove them blatantly.

Or, we can sensibly check their distribution with the target variable, and if found any pattern we’ll keep those missing values and assign them a new category while removing others.