The 8 Ops

What is DevOps? DevOps is a culture, set of practices, and tools that combine software development (Dev) and IT operations (Ops). It aims to shorten the development lifecycle and provide continuous delivery with high software quality. In simple terms, DevOps bridges the gap between Developers (who write code) and Operations (who deploy and manage that code in production). DevOps: The Basics Imagine you’re at a car assembly line, where designers plan and engineers build. DevOps is like a streamlined process that allows designers and engineers to work together closely, ensuring that every car model rolls off the line faster, with fewer errors, and with higher quality. Why Do We Need DevOps? Before DevOps, developers and operations teams often worked in silos i.e. isolation or separation. Developers would create code, throw it over the wall, and operations would handle deployment and management. This traditional model led to: Slow releases: Development took time, testing took time, and d

Exciting Changes Ahead: Welcome to The 8 Ops! Dear Readers, I have some exciting news to share! After careful consideration and reflection, I’ve decided to transition my blog from " Data Science Learnings " to " The 8 Ops ". This change marks a new chapter in our journey together, focusing on the dynamic fields of Cloud Computing and DevOps—the future of technology. Why the Change? As we continue to witness rapid advancements in technology, it’s clear that cloud computing and DevOps are not just trends but essential components of modern IT practices. With powerful tools like AWS, Azure, Kubernetes, Docker, Jenkins, Git, Ansible and many more , the possibilities are endless. I believe that these technologies will shape the future of how we build, deploy, and manage applications. By shifting the focus of this blog, I aim to provide you with insights and knowledge in these areas—from foundational concepts to practical applications. Whether you’re a beginner or looking

Introduction Descriptive statistics is one of the ways to describe the data. In data science, descriptive statistics are used to provide an overview of a huge data collection. It's used to see if the data is normally distributed or not. It is presented in the form of a chart, graphs, table, frequency distribution, and so on. It provides information on summary statistics that includes Mean, Standard Error, Median, Mode, Standard Deviation, Variance, Kurtosis, Skewness, Range, Minimum, Maximum, Sum, and Count. Two major information is provided by Descriptive Statistics regarding the data: The measure of Central Tendency The measure of Dispersion Descriptive Statistics Descriptive statistics answer the following questions: What is the value that best describes the data set? How much does a data set speads from its average value? What is the smallest and largest number in a data set? What are the outliers and how it affects the dataset? Measure of Central Tendency It describes a whole

What is Hypothesis Testing? Statistics is all about data. That huge amount of data will only be useful if we are going to analyze it or take out conclusions from it. To find out such important interpretations or conclusions we use hypothesis testing. Statistics Statistical Hypothesis testing is to test the assumption (hypothesis) made and draw a conclusion about the population. This is done by testing the sample representing the whole population and based on the results obtained; the hypothesis is either rejected or accepted. Pre-requisites: DSL | E1 Statistics Steps in Hypothesis Testing The three major steps are: Making an initial assumption. We will take the initial assumption as the null hypothesis, H0. Example: We want to know whether the defendant is guilty or innocent. Thus, we take H0 as Defendant not guilty. Collecting the data. This data will not only be the data but the shreds of evidence as well. Example: Fingerprints, DNA, etc. Gathering evidence to reject/accept the hypot

Introduction Statistics is one of the popularly regarded disciplines this is particularly centered on records collection, records organization, records analysis, records interpretation and records visualization. Earlier, facts become practiced through statisticians, economists, enterprise proprietors to calculate and constitute applicable records of their field. Nowadays, facts have taken a pivotal position in diverse fields like records technology, system learning, records analyst position, enterprise intelligence analyst position, pc technology position, and plenty more. Statistics is a type of mathematical analysis that uses quantified models and representations to analyze a set of experimental data or real-world research. The fundamental benefit of statistics is that information is provided in an easy-to-understand style. Statistical & Non-Statistical Analysis Statistical analysis is used to better understand a wider population by analyzing data from a sample. Statistical analy