Big Data, Cloud & DevOps

First think about what kind of data scientist they want to be. The reason this is crucial is that data science isn’t a single, well-defined field, and companies don’t hire generic, jack-of-all-trades “data scientists”, but rather individuals with very specialized skill sets. Think instead about the kind of value you want to help companies build, and get good at delivering that value. That, more than anything else, is the best way to get in the door.

The days of a clear distinction between Dev and Ops are over. This set-up; “build the product” (Dev) and “maintain the product” (Ops) create a “them and us” attitude where ideas aren’t shared and collaboration is discouraged. When selecting a Digital Transformation strategy, these newly formed DevOps teams must look at the methods they use to adopt an Agile strategy. Continuous Integration (CI), Continuous Delivery (CD) and Continuous Testing (CT) are the top three methods – and while each serves a slightly different objective, when combined they can significantly help achieve velocity and quality.

Data certainly has the potential to grease the wheels of the digital economy, but with that are both opportunities and threats. It all boils down to privacy. Data has the potential to support the discovery of new medical treatments. It could transform healthcare for the better — and it is hard to find anyone who would not be in favour of that. But at what price? Regulators seem to have decided that in some cases the price is too high.

Machine learning can yield compelling insights within the scope of the information it has, but it lacks the wider context to know which results are truly useful. In addition, machines need people to tell them which datasets will be useful to analyze; if AI isn’t programmed to take a variable into account, it won’t see it. Business users must sometimes provide the context — as well as plain common sense — to know which data to look at, and which recommendations are useful.

What’s less well covered by the media, but still crucial to business growth, is what’s happening behind the scenes in application development and testing. The DevOps function – in theory the seamless integration of app development, testing and quality assurance – is increasingly being recognised as a strategic business function, as it powers the delivery of products and services with maximum efficiency, speed and quality. Innovations in this field may be make or break for a business.So let’s have a look at the game changing innovations in 2019.

Every Data Science project starts with a problem you aim to solve. It’s important to keep this in mind. Too often, Data Scientists run around looking to solve problems with Machine Learning. It should be the other way around. As a real-world Data Scientist, you should be aware of the following challenges. You need to convince management and stakeholders to sponsor your new project. Check for the right licensing when incorporating existing models or datasets. Most of the work you’re doing is research and data preparation.

Data scientists who are developing their first tensorflow models often struggle with the non-obvious behavior of some parts of the framework, which are hardly understandable and quite complicated to debug. The main point is that making a lot of mistakes when working on this library is perfectly fine, and for any other thing it is perfectly fine too, and asking questions, diving deep into the docs and debugging every goddamn line is very much okay too. Everything comes with practice, and hope this article will be able to make this practice a bit more pleasant and interesting.

Full stack development is a buzz word nowadays. More and more companies are hiring full stack developers to save their time and cost. But most of the people still confused about the homonyms like Full stack developers, MEAN stack developers, MERN stack developers, etc. What is full stack developer? How to become a full stack developer? What does a full stack developer do? How to hire full stack developers? Let’s find answers in this article!!

It is now universally agreed that DevOps has revolutionised testing and development – and it is here to stay. So, there’s a lot to consider, and it’s a complex road to getting it right. But, ultimately, we believe that leaders who don’t adequately support DevOps within their organisations — whether it’s in getting the right tools, hiring the right teams, employing the right processes or backing new ways of working — will pay dearly for their decisions in the long term.

There is a stark difference between large data and big data. Using Pandas with large data could help you explore another useful feature in Pandas to deal with large data by reducing memory usage and ultimately improving computational efficiency. Typically, Pandas has most of the features that we need for data wrangling and analysis. Pandas is seriously a game changer when it comes to cleaning, transforming, manipulating and analyzing data. In simple terms, Pandas helps to clean the mess.

Product-related risks could refer to the definition and the actual development/ implementation of Minimum Viable Product. A poorly-defined product, regardless of how well-built, will probably fail to solve the problem and deliver value to its users. Poor implementation of a well-defined product will also fail to create value to the user. Engineering-heavy startups tend to put more effort than needed on the technical aspects of the product. You need to apply agile and experimentation principles, an effective way to improve your product and create value to your users.