3 Weeks Beginners Guide to Ace Data Science Interview: #Day 5
By: Vinay Vikram
Tags: Interview, Data Science, Interview Questions, Python, Machine Learning

3 Weeks Beginners Guide to Ace Data Science Interview: #Day 5

About the Series Data Science field is an exciting career choice and seeing a lot of hiring across fresh, lateral and experienced job positions. It’s one thing to know the concepts and totally another to crack the rigorous interviews for data science positions. If a candidate is aware of the different questions and the interview process, he is on the right path to an excellent career in the evolving Data Science field. This 3-week beginners guide to Ace Data Science Interview will be a useful asset for individuals who are preparing for the Data Science interviews. Every day for the next 21 days, we will talk about the different areas of the Data Science field and cover them elaborately. So sit back and start reading the article to get a finer understanding of the Data Science field and go prepared for the interviews.

You and your friends decide to play a football match but when you go out you see there is a possibility of rain so you decide instead to stay back at home. Why did you do this? What does taking a decision really mean?

The art of decision making is just this — choosing a plan of action when faced with uncertainty.

There are two ways to make a decision

Intuitive way, wherein one takes a decision out of a “gut feeling”.

Another is the method that employs data or information.

The Intuitive way is purely a personal and artistic way of making a decision. But the latter is a logical and scientific way of arriving at the right approach with available historical data.

This quantitative approach which enables decision making is called “Statistics”.

Statistics and Data Scientist

A Data Scientist is only as good as the questions they ask. Data Scientist asks probing questions like:

Does the small Rise and dip in temperature affect the ice cream sales in the market?

How a person's salary impacts his online shopping behavior?

How is Google able to “guess” my search question?

Statistics is the art of connecting numbers to these questions so that the “answers” evolve! To establish quantitative connections to largely qualitative questions is the heart of statistics.

This reminds me of a famous saying for DataScientist:

“A Data Scientist is one who knows more statistics than a programmer and more programming than a statistician”

Observe the picture below :

So Basically Data Science is that sweet spot that sits perfectly in the mid of computer programming, statistics and the domain on which the analysis is performed. To perform all of the above, the Data Scientist needs to have a piece of fair domain knowledge.

A Deep Dive into the world of Statistics:

Giphy

A brief background:

The word “Statistics” is derived from the Latin word “status”, which refers to information related to a state or a province. It’s actually said to be an ancient technique used by the kings to know the details about their state or province. So considering traditional statistics.

So in a very Naive way, Statistics is concerned with developing and studying different methods for collecting, analyzing and presenting the empirical data.

The field of statistics is composed of two broad categories-

Descriptive Statistics

Inferential statistics

Both fulfill different statistical aspects and give us different insights about the data.

Before we deep dive with descriptive and inferential statistics let us get the basic idea of population and sample, without these two terms, the explanation of statistics is baseless.

Population:

In statistics, population refers to the total set of observations that can be made. For example, if we are studying the weight of adult women, the population is the set of weights of all the women in the world.

The population is not necessarily people rather it could be a batch of batteries, measurements of rainfall in an area or a group of people.

Sample:

It is a part of the population, which is selected randomly for the study. The sample should be selected such that it represents all the characteristics of the population. The process of selecting the subset from the population is called sampling and the subset selected is called the sample.

Descriptive Statistics:

Fundamentally, all three(mean, mode, median)refer to one single aspect called the “Central Tendency”. The idea of central tendency is that there may be one single value that can possibly describe the data to the best extent.

It describes the important characteristics/ properties of the data using the measures of central tendency like mean/ median/mode and the measures of dispersion like range, standard deviation, variance, etc.

Three musketeers of descriptive statistics

Data can be summarized and represented in an accurate way using charts, tables, and graphs.

“A statistical chart speaks a thousand words more than the data itself which has only 1000 words!”

Inferential Statistics

This is actually statistical inference, wherein, we can make an inference about a large data set based on “testing” a small sample population of the data.

It is about using data from the sample and then making statistical inferences about the larger original population from which the sample is drawn. The goal of inferential statistics is to draw conclusions from a sample and generalize them to the population.

The most common methodologies used are

Hypothesis tests

Analysis of variance

Analysis of Means

Some differences to remember!

So again, I am reiterating the same point on Domain + Coding + Statistics = Data Scientist.

Descriptive Statistics Based Questions: