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    Hypothesis testing is a method used in Inferential Statistics to observe a small section of the population called a sample, in order to draw insights that can tell us about the population at large. Hypothesis testing forms the very foundation of statistical analysis, to the point where the main purpose of learning statistics is so that we can perform hypothesis testing. Take for example, your friend suggests a great place to order. Now if there is some basis to why he or she said that, which can be used to identify other great restaurants, then and only then would it be useful. If not, we would probably just be “tasting” by fluke.   So, what is a hypothesis? Put simply, it is a calculated guess about something in the world around us based on an inference or insight drawn from an observation. Ofcourse, this should be proven and thus, we perform hypothesis testing which we’ll soon learn more about. For now, let’s take a look at what hypothesis looks like:   Whether

  Introduction There is a podcast for everything, from expanding your vocabulary in data science to staying current with industry news, learning new data skills, and even obtaining guidance on landing your first data science job.   Whether you're just starting out or a seasoned veteran, listening to any of these below listed podcasts may be a terrific method to develop yourself as a data professional. Also, you may listen while doing other activities like exercise, grocery shopping, or housework while on the move.   Podcasts that provide an introduction to data science These podcasts provide a thorough, high-level overview of a variety of data subjects, regardless of your degree of academic or professional interest in data science. If you're new to data science or want to listen to a variety of podcasts, this is a fantastic place to start.   Analytics Power Hour Episode runtime: around one hour Frequency: Every two weeks The idea of this podcast is

    Introduction Repurposing material is a regular and crucial component of many content marketing strategies. Yet, it is a time-consuming and laborious procedure that many teams avoid. Frequently, a piece of content, such as a blog post, a video, or other assets such as white papers and case studies, is developed, published, and that's the end of the tale. Automata automates the process of converting content into forms that may be delivered across many channels to reach the greatest number of people.   Existing material must be examined and reused by marketers. Small marketing teams that rely heavily on content as their primary acquisition route. We specialise in software and professional services, but any industry that focuses on content creation across various platforms is a natural fit.   AI content production has limits when it comes to fact-based writing and providing value to smart, business marketers in specialised sectors, particularly technical B2B material.

  Python is a popular programming language that is widely used for a variety of tasks, such as data analysis, web development, and artificial intelligence . For beginner Python developers, it can be challenging to know where to start with projects.   In this article, we will explore ten project ideas that are great for beginner Python developers to work on.     ●      Number Guessing Game   The number guessing game is a classic beginner project that can help you practice your programming skills. The objective of the game is to guess a randomly generated number within a certain range. You can use Python's random library to generate a random number and use conditional statements to check if the player's guess is correct.     ●      Mad Libs Generator   Mad Libs is a fun word game that involves filling in the blanks with different words to create a funny story. In this project, you can create a Mad Libs generator using Python's string formatting fu

    What is Central Tendency and why do we use it? Let’s say you made 1400 sales of ice creams in one year, however, you being smart, you sold the ice cream at different prices depending on the season. Now at the end of the year, you want to calculate how much you earned on an average so you can make various other calculations such as how much money you make per month or year and how much would you like to increase this number by in order to hit your goal. This is where you come up with your central values if you have plotted all the sales figures and the money earned.   Technically, central tendency is the conclusion for a data set describing its central value or key focus area which we can use to find out more advanced information. Central tendency tells us where most values fall in the distribution of data points, specifically over the plot of sales figures from the example earlier. To find out this central tendency, statistics uses three measures namely Mean, Median and M

    Introduction: In today's data-driven world, data science has emerged as a promising career path, offering numerous opportunities for growth and innovation. As organizations increasingly rely on data-driven decision-making, the demand for skilled data scientists continues to soar. This essay aims to provide a comprehensive guide on how to build a successful career in data science, highlighting the essential steps and strategies to pursue.   Acquiring a Strong Foundation: To embark on a career in data science, it is crucial to develop a strong foundation in relevant disciplines. This includes a solid understanding of mathematics, statistics, and programming languages such as Python or R. Gaining proficiency in these areas forms the basis for advanced data science techniques and methodologies.   Pursuing Relevant Education and Certifications: Formal education plays a vital role in establishing credibility and deepening knowledge in data science . Pursuing