Neural Network Explained with Perceptron Example

Single layer neural network

Neural networks are an important part of machine learning, so it is essential to understand how they work. A neural network is a computer system that has been modeled based on a biological neural network comprising neurons connected with each other. It can be built to solve machine learning tasks, like classification and regression problems. The perceptron algorithm is a representation of how neural networks work. The artificial neurons were first proposed by Frank Rosenblatt in 1957 as models for the human brain’s perception mechanism. This post will explain the basics of neural networks with a perceptron example. You will understand how a neural network is built using perceptrons. This …

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

Chi-square test – Types, Concepts, Examples

Chi-square goodness of fit - Tossing coin

The Chi-square (χ2) test is a statistical test used to determine whether the distribution of observed data is consistent with the distribution of data expected under a particular hypothesis. The Chi-square test can be used to compare two distributions, or to assess the goodness of fit of a given distribution to observed data. In this blog post, we will discuss the types of Chi-square tests, the concepts behind them, and how to perform them using Python / R. As data scientists, it is important to have a strong understanding of the Chi-square test so that we can use it to make informed decisions about our data. We will also provide …

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

Hypothesis Testing Steps & Real Life Examples

Hypothesis Testing Workflow

Hypothesis testing is a technique that helps scientists, researchers, or for that matter, anyone test the validity of their claims or hypotheses about real-world or real-life events. Hypothesis testing techniques are often used in statistics and data science to analyze whether the claims about the occurrence of the events are true, whether the results returned by performance metrics of machine learning models are representative of the models or they happened by chance. This blog post will cover some of the key statistical concepts including steps and examples in relation to what is hypothesis testing, and, how to formulate them. The knowledge of hypothesis formulation and hypothesis testing holds the key …

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Insurance Machine Learning Use Cases

insurance machine learning use cases

As insurance companies face increasing competition and ever-changing customer demands, they are turning to machine learning for help. Machine learning / AI can be used in a variety of ways to improve insurance operations, from developing new products and services to improving customer experience. It would be helpful for product manager and data science architects to get a good understanding around some of the use cases which can be addressed / automated using machine learning / AI based solutions. In this blog post, we will explore some of the most common insurance machine learning / AI use cases. Stay tuned for future posts that will dive into each of these …

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Posted in AI, Data Science, Insurance, Machine Learning, Product Management. Tagged with , , , .

Tail Spend Management & Spend Analytics

Tail spend analysis and analytics and machine learning

Do you know where your business is spending its money? And more importantly, do you know where your business SHOULD be spending its money? Many businesses don’t have a good handle on their tail spend – the money that’s spent on things that are not essential to the core operations of the company. Tail spend can be difficult to track and manage, but with the help of spend analytics tools and machine learning, it’s becoming easier than ever before. In this blog post, we’ll discuss what tail spend is, how to track it, and how to use analytics and machine learning to make better decisions about where to allocate your …

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

Procurement 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 , , .

When to Use Z-test vs T-test: Differences, Examples

When it comes to statistical tests, z-test and t-test are two of the most commonly used. But what is the difference between z-test and t-test? And when should you use Z-test vs T-test? In this blog post, we will answer all these questions and more! We will start by explaining the difference between z-test and t-test in terms of their formulas. Then we will go over some examples so that you can see how each test is used in practice. As data scientists, it is important to understand the difference between z-test and t-test so that you can choose the right test for your data. Let’s get started! Difference between …

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Posted in Data Science, statistics. Tagged with , .

Two sample Z-test for Proportions: Formula & Examples

two proportion z-test formula and examples

In statistics, a two-sample z-test for proportions is a method used to determine whether two samples are drawn from the same population. This test is used when the population proportion is unknown and there is not enough information to use the chi-squared distribution. The test uses the standard normal distribution to calculate the test statistic. As data scientists, it is important to know how to conduct this test in order to determine whether two proportions are equal. In this blog post, we will discuss the formula and examples of the two-proportion Z-test. What is two proportion Z-test? A two-proportion Z-test is a statistical hypothesis test used to determine whether two …

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OKRs vs KPIs vs VGIs: Differences and Examples


The difference between OKRs , KPIs and VGIs is not always clear, but the concept is a great way to measure the progress towards achieving your business objectives. As business analysts, product managers, project or team leaders, it is important to understand the concepts of OKRs, VGIs & KPIs, and what’s the difference between OKR, VGIs and KPIs. In this blog post, we will discuss OKR vs VGI vs KPI and how they can be used for setting goals / objectives and measure different aspects of your team’s and organization’s performance in relation to achieving those goals. We’ll also go over real-world examples for OKRs, VGIs and KPIs so you can …

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

Decision Making Models: Concepts, Examples, Importance

Rational decision making model

Making decisions is a critical part of business operations. However, making the right decision is not always easy. There are a number of different decision models that organizations can use to make better decisions. In this blog post, we will discuss some of the most popular decision models, what is their importance, and explain how they can be used to create desired business outcomes with the help of examples. In addition, we will also learn how could data and insights be used to drive decisions while implementing different kind of decision models. A decision scientist should be aware of these concepts fairly well. Decision models & different types Decision making …

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Loan Eligibility Prediction using Machine Learning

loan eligibility prediction using machine learning

It is no secret that the loan industry is a multi-billion dollar industry. Lenders make money by charging interest on loans, and borrowers want to get the best loan terms possible. In order to qualify for a loan, borrowers are typically required to provide information about their income, assets, and credit score. This process can be time consuming and frustrating for both lenders and borrowers. In this blog post, we will discuss how AI / machine learning can be used to predict loan eligibility. As data scientists, it is of great importance to understand some of challenges in relation to loan eligibility and how machine learning models can be built …

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What is Data-Driven Decision Making? Why & How?

data driven decision making what why how

Data-driven decision-making is a data-driven approach to making decisions to achieve desired outcome. More precisely, data-driven decision making is an insights-driven approach to drive decisions and related actions. The data can come from internal and external data sources. Data-driven decision-makers use data in their decision process to validate existing actions or take new actions (predictive or prescriptive analytics). They make decisions based on the actionable insights generated from the data. The goal is to make informed decisions while ensuring transparency across the stakeholders. In this blog post, we will discuss what data-driven decision-making is, how it differs from other types of decision-making, and why you should consider going for this …

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Healthcare & Machine Learning Use Cases / Projects

Healthcare and AI and Machine Learning Use cases and projects

AI & Machine learning is being used more and more in the healthcare industry. This is because it has the potential to improve patient outcomes, make healthcare more cost-effective, and help with other important tasks. In this blog post, we will discuss some of the healthcare & AI / machine learning use cases that are currently being implemented. We will also talk about the benefits of using machine learning in healthcare settings. Stay tuned for an exciting look at the future of healthcare! What are top healthcare challenges & related AI / machine learning use cases? Before getting into understand how machine learning / AI can be of help in …

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

Marketing Analytics Machine Learning Use Cases

marketing analytics machine learning use cases

If you’re like most business owners, you’re always looking for ways to improve your marketing efforts. You may have heard about marketing analytics and machine learning, but you’re not sure how they can help you. Marketing analytics is an essential tool for modern marketers. In this blog post, we will discuss some of the ways marketing analytics and AI / machine learning / Data science can be used to improve your marketing efforts. We’ll also give some real-world examples of how these technologies are being used by businesses today. So, if you’re ready to learn more about marketing analytics and machine learning, keep reading! What is marketing and what are …

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

Machine Learning Use Cases for Climate Change

Climate change is a serious issue facing the world. The climate changes which are already affecting our planet can be seen in rising sea levels, melting ice caps and glaciers, more severe storms and hurricanes, more droughts, and wildfires increased precipitation in some areas of the world while other regions experience less rainfall. It’s important that we do what we can to reduce climate change risks by reducing greenhouse gas emissions into the atmosphere as well as adapting to climate impacts. Artificial intelligence (AI), machine learning (ML)/ deep learning (DL), data science, advanced analytics have been widely used for decades across different industries such as finance, healthcare, etc., but their …

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