## 01: Q01 – Q07 General Big Data, Data Science & Data Analytics Interview Q&As

Q01. How is Big Data used in industries?
A01. The main goal for most organisations is to enhance customer experience, and consequently increase sales. The other goals include cost reduction, better targeted marketing, fraud detection, identifying data breaches to enhance security, making existing processes more efficient, medical records to drug discovery and genetic disease exploration, and the list goes on.

Q02. What do you understand by the terms personalization, next best offer, next best action, and recommendation engines?
A02. Big data processing and machine learning techniques can be used for customer personalization. By gathering historical data from all users and using this data within a big data framework to generate statistical models which predict the probability that a user will find a product, service, document, web page, destination, service, etc to be useful.

The historical data include current geographical location, home location, age, gender, activities on the web site (pages viewed, items viewed, etc), purchase activities (e.g. items rated, items in the shopping cart, signing up for loyalty programs, use of discount coupons, etc), activities on social media, etc. This will be lots of data.

Once valid & statistical models for personalization are available, they can be used in real time to personalize the results for individual users.

Next-best offer refers to the use of predictive analytics solutions to identify the products or services your customers are most likely to be interested in for their next purchase.… Read more ...

## 02: Cleansing & pre-processing data in BigData & machine learning with Spark interview questions & answers

Q1. Why are data cleansing & pre-processing important in analytics & machine learning? A1. Garbage in gets you garbage out. No matter how good your machine learning algorithm is. Q2. What are the general steps of cleansing data A2. General steps involve Deduplication, dropping/imputing missing values, fixing structural errors, removing…

## 03: Simple Linear Regression interview Q&As

Q01. What is a gradient? A01. In algebra we can represent a straight line with: y = mx + c A parabola is represented as: y = m1x2 + m2x + c, and so on. The diagram depicts the parabola y = x2. A gradient in maths is the slope…

## 04: Residuals, Cost/Loss functions, R-squared & Gradient Descent interview Q&As

Q01. What do you understand by the terms mean, variance, and standard deviation of the sample Vs. the population? A01. Given that the following are the number of job applications sent by 6 individuals:

Where X is the Sample. Mean: To calculate the mean we add up the observed…

## 05: Linear regression outputs, null hypothesis, t-test & p-value interview Q&As

Q1. How do you produce & interpret Linear Regression output? A1. Scatter plots can only detect obvious relationships between variables by looking at the graph, but we can use statistics to comment about the variable relationships as outlined below. The link 11A: Databricks – Spark ML – Pandas Dataframe &…

## What do data analysts, engineers & scientists do?

Today’s world run on data and no organisation would survive without data-driven decision making and strategic planning. There are several roles in the industry today like data analysts, data engineers, data scientists & business analysts that deal with data. Some of the skills required overlap among these roles. For example, SQL, Microsoft Excel, Data visualisation & basic data collection & management skills are must have for all these 3 roles. The data engineers must have solid programming & software engineering skills whereas the data scientists must have skills in maths, statistics & algorithms.

Q01. What do you understand by the terms Descriptive, Predictive & Prescriptive Analytics
A01. Businesses analyse various data points (e.g. historical, social media, IoT, etc) to derive insights that help executives, managers and operational employees make better, more informed business decisions.

#### Descriptive Analytics

Descriptive analytics is used for analysing “what has happened?” by using historical data that is collected, organised and then presented in a way that is easily understood. Descriptive analytics is NOT used to draw any inferences or predictions from its findings. Descriptive analytics is used for reporting KPIs (i.e. Key Performance Indicators) like Sales Volumes, Gross Adds, Revenues, Churn Rates, Attrition Rates, Profit Margins, etc. It uses simple maths and statistical tools to calculate averages, percentages, sum, cumulative totals, etc and visual tools such as graphs & charts. SQL analytic functions interview questions.… Read more ...

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