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.…

02: Cleansing & pre-processing data in BigData & machine learning with Spark interview Q&As

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 the outliers, encoding the categorical values and scaling down the features.…

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…

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…

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…

What do data analysts, engineers & scientists do?

In addition to the Data Analysts, the Data Engineers & Data Scientists must have the below know-hows.

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