The DevOps training in Hyderabad is aimed at giving you an in-depth introductory understanding of DevOps. Being one of the best DevOps training in Hyderabad, the training course dives into the tools of DevOps, which govern operations and provide a thoroughly researched documentation of the operations, functions, and parameters of these tools.
Forms authentication is a common feature in many C# MVC .NET web applications. There are a variety of methods for implementing forms authentication in MVC .NET. A key part for each, is the process of storing user specific details that are carried throughout the web application. User details such as Id, Username, Address, and Age may be obtained through various methods, such as by querying the database upon every request or as needed, loading from cache, context, or even loading from Session.
In this tutorial, we’ll walk through the steps of implementing forms authentication in C# MVC .NET, specifically with MVC4. We’ll use a custom MembershipProvider class, along with a custom Principal object. The Principal will hold our custom user details, encrypted within the forms authentication ticket cookie, and allow us to access this data anywhere within the web application.
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Python is fast becoming the preferred language for data scientists – and for good reasons. It provides the larger ecosystem of a programming language and the depth of good scientific computation libraries. If you are starting to learn Python, have a look at learning path on Python.Among its scientific computation libraries, I found Pandas to be the most useful for data science operations. Pandas, along with Scikit-learn provides almost the entire stack needed by a data scientist. This article focuses on providing 12 ways for data manipulation in Python. I’ve also shared some tips & tricks which will allow you to work faster.
Microsoft Corp. snapped up LinkedIn Corp. for $26.2 billion in the largest acquisition in its history, betting the professional social network can rev up the tech titan’s software offerings despite recent struggles by both companies.The deal is Chief Executive Satya Nadella’s latest effort to revitalize Microsoft, which was viewed not long ago as left behind by shifts in technology. Mr. Nadella hopes the deal will open new horizons for Microsoft’s Office suite as well as LinkedIn, both of which have saturated their markets, and generally bolster Microsoft’s revenue and competitive position.
Many of those who call themselves statisticians just won't admit that data science heavily relies on and uses (heretical, rule-breaking) statistical science, or they don't recognize the true statistical nature of these data science techniques (some are 15-year old), or are opposed to the modernization of their statistical arsenal. They already missed the train when machine learning became a popular discipline (also heavily based on statistics) more than 15 years ago. Now machine learning professionals, who are statistical practitioners working on problems such as clustering, far outnumber statisticians.
Web apps are now more interactive than ever. Getting that last drop of performance can do a great deal to improve your end-users' experience. Read the following tips and learn if there is anything more you can do to improve latency, render times and general performance!
Rising alongside the relatively new technology of big data is the new job title data scientist. While not tied exclusively to big data projects, the data scientist role does complement them because of the increased breadth and depth of data being examined, as compared to traditional roles.
So what does a data scientist do?
A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.
Over the years, working as a data modeler and database architect, I have noticed that there are a couple rules that should be followed during data modeling and development. Here I describe some tips in the hope that they might help you. I have listed the tips in the order that they occur during the project lifecycle rather than listing them by importance or by how common they are.