Big Data, Cloud & DevOps

Confidence Intervals are always a headache to explain even to someone who knows about them, let alone someone who doesn’t understand statistics. In statistics, a confidence interval (CI) is a type of estimate computed from the statistics of the observed data. This proposes a range of plausible values for an unknown parameter. The interval has an associated confidence level that the true parameter is in the proposed range. This post is about explaining confidence intervals in an easy to understand way without all that pretentiousness.

Leadership, creativity, and collaboration are some of the skills that students need to have to survive in today’s competitive industries. Thanks to technology, it’s now easier for teachers to provide them with hands-on experience, enhancing skill learning. This model doesn’t place teachers at the centre of attention. Instead, they’re there to observe how students approach a specific problem and whether they communicate with their peers to solve the issue at hand. These and other educational methods will help educators instil leadership skills in students and ensure that they can thrive in today’s fast-paced society.

Data analysts turn data into information. They play a vital role by making data actionable for decision makers.  Data analysts often take data provided by data engineers, analyze it, and make recommendations. They create visualizations to display their findings in dashboards and presentation.  Unlike data scientists, data analysts don’t usually create predictive models based on machine learning algorithms.If you want to become a data analyst or make yourself more marketable, this article suggests you learn the certain technologies, in order of priority.

Culture and governance are key to drive change around cyber security behaviours, but too many awareness programmes focus simply on superficial technical gimmicks. Stay clear of empirical and ready-made solutions: Start with focus groups, questionnaires, and interviews and measure upfront levels of staff security maturity and engagement with corporate values. There are 3 clichés that have been dominating the security awareness arena for the past decade. And here are 5 key points to build a successful cyber security culture change programme.

In earlier versions of SQL Server, storage of unstructured data posed many challenges in terms of maintaining consistency between structured and unstructured data, managing backup/restore procedure, performance issues, scalability, etc. This post explains to you how to use SQL Server FILESTREAM to store unstructured data. Here you can also read the positive and negative sides of FILESTREAM. Here are some other SQL queries to help you use it.

Take a look at the latest stats and facts that show how popular and commonly used Ruby on Rails is. Ruby on Rails is known for its multiple already inbuilt solutions that are really beneficial for rapid software development.  It is up to you how to get these gems in order to make your development faster, safer and easier. You can try to use these gems but at the same time, you are suggested to explore more and more new tools that are related to this field and are recommended to you. 

There are many types of SaaS business models. Some serve consumers directly while others use a channel partner to go to market. To achieve today’s most valued status (the platform unicorn), SaaS companies need to begin to think about their business not just as B2B but also B2C. And the restructuring of relationships is the single largest challenge of the change. It will require specialized sales and marketing skills that tune the company’s offerings to meet the needs of each member of the network simultaneously.

The python programming language has a large number of both built-in functions and libraries for data analysis. Combining some of these libraries can produce very powerful methods of summarising, describing and filtering large amounts of data. This article shares some tips on how to combine pandas, matplotlib  and some built-in python functionality to very quickly analyse a dataset. All the methods described can be extended to create much richer and more complex analyses.

Real-life Data Science never finds you working alone on a project and your workmates or clients usually won’t know much about the data you’ll be using. Being able to explain your thinking process is a key part of any data-related job. That’s why copying and pasting are not enough and charts personalization becomes key. This blog goes through 5 techniques to make better charts that are useful. Some of them are day-to-day tools, while others you’ll use them every now and then.

Data downtime refers to periods of time when your data is partial, erroneous, missing or otherwise inaccurate, and almost every data organization struggles with it. Data downtime refers to any time data when data teams find themselves answering “no” to common questions such as is the data in this report up-to-date, or is the data complete, and more. This blog post will cover an approach to managing data downtime that has been adopted by some of the best teams in the industry.

Data engineers play a vital role for organizations by creating and maintaining pipelines and databases for injesting, transforming, and storing data. They are responsible for storing and making data usable by others. Data engineers set up pipelines to injest streaming and batch data from many sources. Eventually the data finds its way into dashboards, reports, and machine learning models. Which tech skills are most in demand for data engineers? How do they compare to the most in demand tech skills for data scientists? Read on to find out!