A library of interview experience questions for big data analytics interns

Big data analytics is an attractive area. I t's profitable, you have the opportunity to work on interesting projects, and you're always learning new things. A s a result, access to big data analytics is highly competitive. One of the best ways to start a career in big data analytics is through an internship in big data analytics.

In the Big Data Analytics Intern Interview Question Library, we'll look at the general level of knowledge required, the components of a typical interview process, and some examples of interview questions. Note that the emphasis on the term "universal" is due to the fact that the circumstances vary from company to company.

What are the expectations of big data analytics internship interviews?

The biggest difference between a big data analytics internship interview and a full-time big data analyst is that you are often not expected to learn extremely specific details about machine learning or deep learning concepts.

However, you'll expect to have basic building blocks on which you can build - including Python R or SQL statistical and probability bases, and basic machine learning concepts.

Python and R

You should have programming experience in a scripting language (preferably Python or R). If you're a Python programmer, you should also have a basic understanding of popular libraries such as Scikit-learn and Pandas.

(Recommended tutorial: python tutorial)

What you should know: You should know how to write basic functions and have a basic understanding of the various data structures and their uses. Y ou should also understand the basic (but still essential) features of Scikit-learn such as test_train_split and StandardScaler For Pandas you should manipulate DataFrame as comfortably as writing queries with SQL

For example, you might want to build a simple machine learning model to predict how many products will be sold. In this case, if you're a Python user, it's useful to know the Scikit-Learn library because it already provides many pre-built functions, such as those mentioned above.

How to prepare: Try a big data analytics project on Kaggle or a field trip on Interview Query to see what you might need to accomplish.

To better understand Scikit-Learn it's a good idea to use it to build a simple machine learning model, or to step through some of the big data analytics projects that others have already completed.

(Recommended micro-course: python3 basic micro-course)

Finally, try practicing Python questions on Interview Query to see what they might ask you.

SQL

You won't be expected to have much experience with relational databases, but at least you should understand how SQL works. I f you're looking for an internship as a big data analyst, you're likely to work for a company with a lot of data. You will need to explore this data yourself to resolve the issue.

(Recommended course: SQL tutorial.) )

What you should know: You should be able to write basic queries, and you should know how to manipulate data using SQL queries. It is common for a company to include SQL in its real-world case studies, so you must have a good understanding of SQL

Example question:

Write a SQL query to get the second highest salary from the Employee table. F or example, given the Employee table below, the query should return 200 as the second highest salary. If there is no second highest salary, the query should return null .

+ ---- + ---------- +

| I D | Salary |

+ ---- + ---------- +

| 1 | 100 |

| 2 | 200 |

| 3 | 300 |

+ ---- + ---------- +

How to prepare: Patterns provide a great resource for learning basic SQL which you can find here. In addition, you'll find a large number of SQL practice questions and practice case studies online.

(Recommended micro-class: SQL micro-class.) )

Statistics and probabilities

You should have an understanding of basic statistics and probabilities. T hese concepts are the basis of most machine learning and big data analytics concepts. Similarly, many of the interview questions that require big data analytics positions are statistically relevant.

What you should know: You should have a solid understanding of the basic concepts, including, but not limited to, probability bases, probability distributions, estimates, and hypothesis tests. A very common application of statistics is conditional probability, for example, assuming that a customer has purchased product C, what is the probability of purchasing product B?

How to prepare: If you're new to these concepts, you can take advantage of many free resources, such as Khan Academy or Georgia Georgia Institute of Technology

Machine learning concepts

Although you don't want to be an expert, you should have a good understanding of basic machine learning models and concepts. This is especially true if the job description indicates that you are about to build a model.

What you should know: This includes, but is not limited to, linear regression, supporting concepts such as vector machines and clustering. Ideally, you should have a basic understanding of these concepts and know when machine learning methods are appropriate.

For example, you might want to implement a linear regression of the price point of a product to determine the quantity sold. Having said that, you won't need to produce or deploy machine learning models as interns.

domain knowledge

You should have domain knowledge of the area you are applying for (and if not, you should learn).

For example, if you are applying for a big data analytics position in the marketing department, learn about different marketing channels (e.g. social media, membership, television) and core metrics (e.g. LTV, CAC).

Big Data Analytics Internship Interview Process

Similarly, the interview process ultimately depends on the company you are applying for. But in general, most, if not all, companies have some general steps in the interview process, which I'll explain below.

As an intern, the worst thing is not to study the work of the company, which is a cultural mission and values.

Preliminary screening

Typically, initial screening (usually telephone screening) is performed by the company's recruiter or hiring manager. The goal is to give respondents a better understanding of their role and to give visitors a better understanding of the interviewees.

You should expect them to ask you about your interest in the position and the company, why you think it's appropriate, and questions about your past experience. In rare cases, you may also be asked one or two simple technical questions.

The interviewer is just making sure you're really interested in the company, that you're a good communicator, and that you don't have any red flags.

take home the situation

For many of today's big data analytics internships, the company will ask you to complete a real-world challenge. This means that they will give you time to complete the case studies they have given you, which often reflects the problems you encounter in your actual role.

This is done to understand how you solve the problem (that is, the thought process) and whether you have the basics you need to complete the problem. Examples of cases include cleaning up datasets and building machine learning models to make a given prediction or query dataset and analyze data or a combination of both.

Live interview

Finally, live interviews can include up to six rounds of interviews. T hese interviews consist of behavioral and technical interview questions. You may also need to complete a round of cases on site.

As they try to ensure that you have a deep understanding of the basics you need to succeed in the role, they will also evaluate your motivations and ultimately whether you are fit for the team or not. Make sure you're at your best, but don't forget to be yourself!

Interview questions

Here are a few examples of some of the interview questions you'd like to know about:

1) What is a p-value?

2) What is regularization and what problems does it try to solve?

3) How do you convert the relationship between age and income into a linear model?

4) If you have two dice of equal weight, what is the probability of the sum being 4?

5) What steps do I need to take to organize and clean up my dataset?

6) What is cross-validation and why is it necessary?

7) Give examples of how accuracy is not the best indicator when determining the effectiveness of machine learning models.

8) What is the difference between INNER and OUTER JOIN

The above is about big data big data analysis interns interview experience library related to the introduction, I hope to help you.