Atharv Gyan Beta

Data Science, Data Analysis, and Data Engineering: What's the Difference? Data science, data analysis, and data engineering are all closely related fields that involve working with data. However, there are some key differences between these three disciplines. Data Science Data science is a broad field that encompasses the collection, analysis, interpretation, and presentation of data. Data scientists use a variety of tools and techniques to extract insights from data, including machine learning, statistical analysis, and visualization. Data scientists typically have a strong background in mathematics, statistics, and computer science. Data Analysis Data analysis is a more focused field than data science. Data analysts use data to answer specific questions or solve particular problems. They typically use a variety of tools and techniques, such as SQL, Excel, and Tableau. Data analysts typically have a strong background in mathematics, statistics, and business. Data Engineering Data ...

Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision- making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data analysis often employs statistical and mathematical methods, but it also includes techniques such as machine learning and artificial intelligence. The goal of data analysis is to extract knowledge from data in order to make better decisions. Data analysis is a rapidly growing field, driven by the increasing availability of data and the development of new data analysis techniques. The future of data analysis is bright, with the potential to revolutionize many industries. What is Data Analysis? Data analysis is the process of extracting knowledge from data. It involves collecting, cleaning, organizing, and analyzing data to ide...

Introduction A call stack is a data structure that keeps track of the functions that are currently being executed in a JavaScript program. It is a stack data structure, which means that functions are added to the top of the stack when they are called, and they are removed from the top of the stack when they return. The call stack is used by the JavaScript engine to keep track of the execution context of each function. The execution context of a function includes information such as the function's name, its arguments, its local variables, and its scope chain. The call stack is also used to debug JavaScript programs. When a JavaScript error occurs, the call stack can be used to track down the source of the error. How the Call Stack Works When a JavaScript program starts executing, the JavaScript engine creates a global execution context. This is the topmost function on the call stack. When a function is called, the JavaScript engine creates a new function execution context for the fu...

In JavaScript, the substring() method is used to extract a substring from a string. A substring is a part of a string, starting at a specific index and ending at another specific index. The substring( ) method takes two arguments: the starting index and the number of characters to extract. For example, the following code will extract the first five characters from the string "Hello, world!": Code snippet const str = "Hello, world!"; const substring = str.substring(0, 5); console.log(substring); // "Hello" The substring() method returns a new string, which contains the extracted substring. The original string is not modified. The substring() method is a very versatile tool, and it can be used for a variety of tasks. For example, you can use it to extract the username from a user's email address, or to extract the product name from a product description. In this Javascript  substring , we will take a comprehensive look at the substring() method. W...

  Introduction MongoDB Atlas, which launched in June of 2016, is a battle-tested database-as-a-service platform (DBaaS) artfully designed and built by the same team that created and continues to nurture and grow  MongoDB . MongoDB Atlas is a true blessing to the development community; it provides all of the features of its database counterpart, without the operation and heavy lifting normally required when building new applications, letting you focus on what you do best. One of the most convenient parts about MongoDB Atlas is that it is available on-demand through a pay-as-you-go model and billed on an hourly basis, meaning you don’t have to worry about monthly subscriptions or paying for things you don’t use. Sound intriguing? Let’s jump in! Getting Started MongoDB Atlas features a minimalistic interface that makes it dead simple to get up and running. Simply select your instance size, instance region, and any features you need; Atlas will provision the instance accordingly a...