Author Archives: Ajitesh Kumar

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking

Linear Discriminant Analysis (LDA) Concepts & Examples

Linear Discriminant Analysis LDA and Fisher Criterian

You may have heard of Linear Discriminant Analysis (LDA), but you’re not sure what it is or how it works. In the world of machine learning, Linear Discriminant Analysis (LDA) is a powerful algorithm that can be used to determine the best separation between two or more classes. With LDA, you can quickly and easily identify which class a particular data point belongs to. This makes LDA a key tool for solving classification problems. In this blog post, we will discuss the key concepts behind LDA and provide some examples of how it can be used in the real world! What is Linear Discriminant Analysis (LDA) and what are its …

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Posted in Data Science, Machine Learning. Tagged with , .

Digital Twins & its Types: Concepts & Examples

digital twins of aeroplane

You’ve probably heard of the term “digital twin” but aren’t sure what it means or how it can help your business. A digital twin is a virtual replica of a physical object or system. There are various different types of digital twins such as component, asset, system and process twins. Digital twins have become an important part of Industry 4.0 and the Internet of Things (IoT). They allow companies to improve product quality, optimize operations and reduce costs. For example, Airbus uses digital twins to monitor the health of its aircraft engines in real time. This helps them to identify potential problems early and take preventative action. In this blog …

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Posted in digital transformation, Digital Twin. Tagged with , .

100 Interview Questions for Deep Learning

Interview questions deep learning

If you’re looking for a job in deep learning, you’ll need to be prepared to answer some tough questions. In order to help you get started, we’ve put together a list of 100 interview questions for deep learning. While many of these questions are related to deep learning concepts, we have also listed several frameworks (Tensorflow, Pytorch, etc) related questions. By being prepared for these questions, you’ll be able to demonstrate your knowledge and expertise in this area, and increase your chances of landing the job! What is deep learning? How does machine learning differ from deep learning? What are the differences between shallow and deep learning? How does deep …

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Posted in Career Planning, Data, Data Science, Deep Learning, Interview questions, Machine Learning. Tagged with , , .

Code Quality Interview Questions for Software Developers

software code quality interview questions

Code quality concepts must be understood well by software developers to write a good quality code. Also, technical lead and technical architects must understand the code quality (or coding) concepts very well in order to review the code (termed as code review) and ensure that good quality software is delivered to the customer. Pictorially, the following represents some of the experiences that software developers, tech leads or technical architects go through while developing software or reviewing software code quality. Enjoy the pictures :).   Often code quality review of poorly written code results in lot of frustrations among project stakeholders. Thus, it is of utmost important to understand different nuances of writing …

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Posted in Interview questions, Software Engg, Software Quality. Tagged with , .

Building Data Analytics Organization: Operating Models

Data analytics organization

Most businesses these days are collecting and analyzing data to help them make better decisions. However, in order to do this effectively, they need to build a data analytics organization. This involves hiring the right people with the right skills, setting up the right infrastructure and creating the right processes. In this article, we’ll take a closer look at what it takes to set up a successful data analytics organization. We’ll start by discussing the importance of having the right team in place. Then we’ll look at some of the key infrastructure components that need to be put in place. Finally, we’ll discuss some of the key process considerations that …

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Posted in Big Data, Data, Data analytics, data engineering, Data lake, Data Science. Tagged with , , .

Differences: Data Analyst & Business Analyst

business analyst vs data analyst

Data analysts and business analysts are two very different positions in the world of business. Data analysts are responsible for examining data and manipulating it into a format that is easy to understand, while business analysts are responsible for taking this data and using it to make informed business decisions. This is not to say that one job is more important than the other – both positions are necessary for a well-functioning company. However, it is important to understand the distinctions between these two jobs so that you can better identify which role you might be interested in pursuing. Data Analysts vs Business Analysts Data analysts and business analysts are …

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Posted in Data, Data analytics, Product Management. Tagged with , .

Who is a Data Scientist? Test your Knowledge

Interview questions

Do you know what a data scientist is? You may think you do, but take this quiz to find out for sure! Data scientists are essential to modern business and it’s important to know who they are and what they do. This quiz is just for fun, but it’s also a great opportunity to learn more about one of the most in-demand professions today. So put your data scientist knowledge to the test and see how well you really know this profession! And, feel free to share your thoughts if you disagree with the answer of any of the questions. Here are a few related posts on this topic: What …

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Posted in Career Planning, Data, Data analytics, Data Science, Interview questions, Machine Learning. Tagged with , .

What are Actionable Insights: Examples & Concepts

actionable insights concepts examples

The idea of actionable insights is something that has gone mainstream across different departments in any and every business due to the onset of digital transformation initiatives at large. Today, actionable insights are at the heart of many successful business decisions, and are used to help your company grow further than ever before. Actionable insights are key to any data analytics initiatives including decision-centric data analytics which are at the heart of digital transformation. Analytics centered around actionable insights is also termed actionable analytics. In this blog post, actionable insights are explained with examples along with few actionable analytics tools which are used when dealing with actionable insights. What are …

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Posted in Data analytics. Tagged with .

PCA Explained Variance Concepts with Python Example

In this post, you will learn about the concepts of explained variance which is one of the key concepts related to principal component analysis (PCA). The explained variance concepts will be illustrated with Python code examples. Check out the concepts of Eigenvalues and Eigenvectors in this post – Why & when to use Eigenvalue and Eigenvectors. What is Explained Variance? Explained variance is a statistical measure of how much variation in a dataset can be attributed to each of the principal components (eigenvectors) generated by the principal component analysis (PCA) method. In very basic terms, it refers to the amount of variability in a data set that can be attributed to …

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Posted in Data Science, Machine Learning, Python. Tagged with , , .

Feature Scaling in Machine Learning: Python Examples

In this post you will learn about a simple technique namely feature scaling with Python code examples using which you could improve machine learning models. The models will be trained using Perceptron (single-layer neural network) classifier. First and foremost, lets quickly understand what is feature scaling and why one needs it? What is Feature Scaling and Why does one need it? Feature scaling is a method used to standardize the range of independent variables or features of data. In data processing, it is also known as data normalization or standardization. Feature scaling is generally performed during the data pre-processing stage, before training models using machine learning algorithms.  The goal is to …

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Posted in AI, Data Science, Machine Learning. Tagged with , , .

To Become a Data Analyst, Data Scientist or Data Engineer?

data analysts vs data scientists vs data engineers

There is a lot of confusion surrounding the job designations or titles such as “data analyst,” “data scientist,” and “data engineer“. What do these job titles mean, and what are the differences between them? Before selecting one of these career path, it will be good to get a good understanding about these job titles or designations, related roles & responsibilities and career potential. In this blog post, we will describe each title / designation and discuss the key distinctions between them. By the end of this post, you will have a better understanding of which career path and related designations are right for you! Shall I become a data analyst? …

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Posted in Career Planning, Data, Data analytics, data engineering, Data Science.

One-hot Encoding Concepts & Python Examples

One-hot encoding concepts and python examples

In this post, you will learn about One-hot Encoding concepts and code examples using Python programming language. One-hot encoding is also called as dummy encoding. In this post, OneHotEncoder class of sklearn.preprocessing will be used in the code examples. As a data scientist or machine learning engineer, you must learn the one-hot encoding techniques as it comes very handy while training machine learning models. What is One-Hot Encoding? One-hot encoding is a process whereby categorical variables are converted into a form that can be provided as an input to machine learning models. It is an essential preprocessing step for many machine learning tasks. The goal of one-hot encoding is to …

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Posted in Data Science, Machine Learning, Python. Tagged with , , .

Interns – Machine Learning Interview Questions & Answers: Set 1

interns machine learning interview questions and answers

This page lists down first set of machine learning / data science interview questions and answers for interns / freshers / beginners. If you are an intern or a fresher or a beginner in machine learning field, and, you are looking for some practice tests before appearing for your upcoming machine learning interview, these practice tests would prove to be very useful and handy. Machine Learning topics covered in Test In this set, some of the following topics have been covered: Machine learning fundamentals (Supervised and unsupervised learning algorithms) Different types of machine learning problems and related algorithms with examples Concepts related with regression, classification and clustering Practice Test (Questions …

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Posted in Career Planning, Data Science, Freshers, Interview questions, Machine Learning. Tagged with , , , .

Data-centric vs Model-centric AI: Concepts, Examples

Data centric vs model-centric AI

There is a lot of discussion around AI and which approach is better: model-centric or data-centric. In this blog post, we will explore both approaches and give examples of each. We will also discuss the benefits and drawbacks of each approach. By the end of this post, you will have a better understanding of both AI approaches and be able to decide which one is right for your business! As product managers and data science architects, you should be knowledgeable about both of these AI approaches so that you can make informed decisions about the products and services you build. Model-centric approach to AI Model-centric approach to AI is about …

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Posted in AI, Data, Data analytics, Data Science, Machine Learning. Tagged with , , .

Data Science Architect Interview Questions

interview questions

In this post, you will learn about interview questions that can be asked if you are going for a data scientist architect job. Data science architect needs to have knowledge in both data science/machine learning and cloud architecture. In addition, it also helps if the person is hands-on with programming languages such as Python & R. Without further ado, let’s get into some of the common questions right away. I will add further questions in the time to come. Q1. How do you go about architecting a data science or machine learning solution for any business problem? Solving a business problem using data science or machine learning based solution can …

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Posted in Career Planning, Data Science, Enterprise Architecture, Interview questions, Machine Learning. Tagged with , , , .

Gartner Data Analytics Trends for 2022

Gartner data analytics trends 2022

Every year, Gartner releases a report on the latest data analytics trends that will be influential for businesses in the coming year. These reports are always insightful, and provide valuable information for companies who want to stay ahead of the curve. This year is no exception, and Gartner released their predictions for data analytics trends in earlier in 2022. In this blog post, we will take a look at some of the most important trends that Gartner has identified. Although it is a bit late to publish this post. However, this post discusses the concepts in detail and will be updated from time-to-time. Stay tuned for more insights into the …

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Posted in Data, Data analytics, Data lake, Data Science. Tagged with .