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

Z-Score Explained with Ronaldo / Robert Example

In this post, you will learn the concepts of Z-Score with the help from examples including Christiano Ronaldo and Robert Lewandowski. You will learn about how to compare and call out whose performance was better in Champions League 2019-2020. As a data scientist, it will be extremely important to learn the concepts of Z-Scores, also called as Standard scores, as it would help you evaluate / compare a particular data set with past data set. Before getting into the example of Z-scores, lets understand some concepts of Z-scores. What’s Z-Score or Z-statistics? Z-score can be defined as number of standard deviations the data point is above or below the mean …

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Posted in statistics. Tagged with , .

Reinforcement Learning Real-world examples


 In this blog post, we’ll learn about some real-world / real-life examples of Reinforcement learning, one of the different approaches to machine learning where other approaches are supervised and unsupervised learning. Reinforcement learning is a type of machine learning that enables a computer system to learn how to make choices by being rewarded for its successes. This can be an extremely powerful tool for optimization and decision-making. It’s one of the most popular machine learning methods used today. Before looking into the real-world examples of Reinforcement learning, let’s quickly understand what is reinforcement learning. Introduction to Reinforcement Learning (RL) Reinforcement learning is an approach to machine learning in which the agents …

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

Leading & Lagging KPIs – Concepts & Examples

kpi concepts and examples leading lagging KPIs

Key performance indicators (KPIs) are important for any organization. They measure the success or failure of initiatives, projects and products with specific metrics and can be used to make informed decisions about future strategies. However, there is no one single definition of what a KPI is; instead, they come in many forms. KPIs are key metrics for product and project managers and are used to track the success of products and projects. This blog post will explore two types of KPIs – leading KPIs and lagging KPIs – as well as provide some examples. What are KPIs? KPIs are defined as a quantitative measure that indicates the performance of a …

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Posted in Product Management. Tagged with , .

Different Success / Evaluation Metrics for AI / ML Products

Success metrics for AI and ML products

In this post, you will learn about some of the common success metrics that can be used for measuring the success of AI / ML (machine learning) / DS (data science) initiatives / projects / products. If you are one of the AI / ML stakeholders including product managers, you would want to get hold of these metrics in order to apply right metrics in right business use cases. Business leaders do want to know and maximise the return on investments (ROI) from AI / ML investments.  Here is the list of success metrics for AI / DS / ML initiatives: Business value metrics / key performance indicators (KPIs): Business …

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

Degree of Freedom in Statistics: Meaning & Examples

degrees of freedom in statistics - meaning and examples

The degree of freedom (DOF) is a term that statisticians use to describe the degree of independence in statistical data. A degree of freedom can be thought of as the number of variables that are free to vary. When you have one degree, there is one variable that can be freely changed without affecting the value for any other variable. As a data scientist, it is important to understand the concept of degree of freedom, as it can help you better understand your data and how to analyze it. In this blog post, we’ll explore the degree of freedom concept in statistics and provide some examples. What is degree of …

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Posted in Data Science, statistics. Tagged with , .

Null and Alternate hypothesis: Definition & Example

null and alternate hypothesis

Hypothesis testing is a technique used to determine whether an assumption about the population is true. Null hypothesis and alternate hypothesis are two types of hypotheses that you may hear when conducting this type of test. Having a good understanding about null and alternate hypothesis will help you better design good hypothesis tests and understand their results in a nice manner. It is very important for data scientists to be able to distinguish between null and alternate hypothesis and design hypothesis tests. In this blog post, we will understand the definition and examples of the null and alternate hypothesis. What are different scenarios for hypothesis testing? The following are two …

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Posted in Data Science. Tagged with .

Warehouse Management & Machine Learning Use Cases

warehouse management machine learning use cases

Warehouses are a vital part of the supply chain. Not only do they store products, but warehouses also play a role in shipping and receiving goods. As warehouse operations become more complex, it’s important to use technology to help manage them. Warehouses need to be able to efficiently manage the flow of goods in and out while still making room for new deliveries. Increasingly warehouses are turning to machine learning algorithms as a way to improve warehouse efficiency, reduce costs, and increase warehouse productivity. In this blog post, we will explore different machine learning use cases which can be deployed by warehouse managers to create a positive business impact. Machine …

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Posted in Data Science, Machine Learning. Tagged with .

15 Tricky DevOps Architect Interview Questions & Answers

DevOps Architect Interview Topics

DevOps and DevSecOps are two sides of the same coin. They both share some goals, but they also have their differences. It is important to understand what each one means so that you can implement them properly in your organization. In this post, you will learn about some of the questions (and answers) which could be asked in the DevOps Architect interview. The following are some of the topics you might want to cover for doing interview preparation for DevOps Architect position: DevOps & DevSecOps concepts Setting up DevOps implementation DevOps reference architecture DevOps reference implementation Continuous delivery concepts & reference architecture Technologies (Tools s& Frameworks) Here is a related …

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Posted in Architecture, Career Planning, DevOps, Interview questions. Tagged with , .

Normal Distributions Questions and Answers for Interviews

normal distribution with different means and standard deviations

In order to be successful in normal distribution interviews, you need a solid understanding of the normal distribution. This blog post will focus on normal distribution questions and answers that are commonly asked in the data science and statistics interviews. Before jumping into questions and answers, lets quickly understand what normal distribution is. What is normal distribution?  A normal distribution is a symmetric, bell-shaped curve that describes the distribution of many types of data. The normal distribution has two parameters, mean and standard deviation. It is important to know these two parameters because they are used to calculate probabilities associated with the normal distribution. The normal curve describes how data …

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Posted in Career Planning, Data Science, Interview questions, statistics. Tagged with , .

Level of Significance & Hypothesis Testing

level of significance and hypothesis testing

In hypothesis testing, the level of significance is a measure of how confident you can be about rejecting the null hypothesis. This blog post will explore what hypothesis testing is and why understanding significance levels are important for your data science projects. In addition, you will also get to test your knowledge of level of significance towards the end of the blog with the help of quiz. These questions can help you test your understanding and prepare for data science / statistics interviews. Before we look into what level of significance is, let’s quickly understand what is hypothesis testing. What is Hypothesis testing and how is it related to significance …

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Posted in Data Science, statistics. Tagged with , .

P-Value & Hypothesis Testing: Examples

P-value explained with examples

Many describe p-value as the probability that the null hypothesis holds good. That is an incorrect definition. The concept of p-value is understood differently by different people and is considered as one of the most used & abused concepts in statistics, mostly in relation to hypothesis testing. In this blog post, you will learn the P-VALUE concepts with multiple different examples. It is extremely important to get a good understanding of P-value if you are starting to learn data science/machine learning as the concepts of P-value are key to hypothesis testing. Before getting into the description of p-value, let’s quickly go through the hypothesis testing concepts to get a good …

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Posted in Data Science, statistics. Tagged with , .

Type I & Type II Errors in Hypothesis Testing: Examples

This article describes Type I and Type II errors made due to incorrect evaluation of the outcome of hypothesis testing, based on a couple of examples such as the person comitting a crime, the house on fire, and Covid-19. You may want to note that it is key to understand type I and type II errors as these concepts will show up when we are evaluating a hypothesis such as those related to machine learning algorithms (linear regression, logistic regression, etc). For example, in the case of linear regression models, the significance value is compared with the p-value and, the null hypothesis that the parameter/coefficient is equal to zero is …

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Posted in Data Science, statistics. Tagged with , , .

E-commerce Machine Learning Use Cases: Examples

ecommerce machine learning use cases

In e-commerce, machine learning can be used to improve a number of decisions thereby resulting in creating a positive business impact. Not only does it help e-commerce organizations increase conversion rates and find the best deals for their customers, but it also helps them understand the customer better. This blog post will look at various different use cases where AI/machine learning and deep learning have been used in eCommerce. What are some key machine learning use cases in eCommerce? Here are some key areas in eCommerce where AI/machine learning can be leveraged: Product recommendation: One of the key use cases where machine learning has been used is to provide product …

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Posted in AI, ecommerce, Machine Learning. Tagged with .

Cybersecurity Machine Learning Use Cases: Examples

cybersecurity machine learning use cases

Cybersecurity professionals are increasingly finding cybersecurity machine learning use cases in their work. The reason for this is that cybersecurity has become more complicated and the scale of cybersecurity threats is growing exponentially. Machine learning can help to combat these cybersecurity threats by providing security teams with real-time alerts, but there are many cybersecurity machine learning use cases beyond just cybersecurity. Artificial intelligence (AI) technologies, in particular, machine learning models such as logistic regression, SVM and random forest, etc., and deep neural networks models such as CNN, LSTM, etc., have been widely used to fight against cyberattacks. In this blog post, we will look into how machine learning is being …

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Posted in AI, Deep Learning, Machine Learning. Tagged with , .

Python – Matplotlib Pyplot Plot Example

matplotlib pyplot plot example artistic layer

Matplotlib is a matlab-like plotting library for python. It can create both 2D and 3D plots, with the help of matplotlib pyplot. Matplotlib can be used in interactive environments such as IPython notebook, Matlab, octave, qt-console and wxpython terminal. Matplotlib has a modular architecture with each layer having its own dependencies which makes matplotlib very versatile and allows users to use only those modules they need for their applications. matplotlib provides many hooks that allow developers to customize matplotlib features as they need. Matplotlib architecture has a clear separation between user interface and drawing code which makes it easy to customize or create new interfaces for matplotlib. In this blog …

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Posted in Data Science, Python. Tagged with , .

Procurement: Key Advanced Analytics Use Cases

procurement analytics use cases

The procurement analytics applications are poised to grow exponentially in the next few years. With so much data available and the need for digital transformation across procurement organization, it’s important to know how procurement analytics can help you make better business decisions. This blog will cover procurement analytics and key use cases of advanced analytics that will be useful for business stakeholders such as category managers, sourcing managers, supplier relationship managers, business analysts / product managers, and data scientists implement different use cases using machine learning. Procurement analytics will allow you to use data very effectively in achieving data-driven decision making.  One can get started with procurement analytics with focus …

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