Big Data, Cloud & DevOps

By harnessing data science to its full potential, top-ranking decision makers in all industries, not only make better-informed decisions but make them with clearer predictions of the future. With that advantage on their side, they are able to stabilize businesses that have not always had a clear vision and save businesses that are on the brink of collapse. Once goals have been established, data scientists can work their magic and theorize how to fix it. Data science alone is not an advantage for decision-making, data science combined with good leadership is.

What do I mean by true real-time data? It is data that has just been generated and never been stored. Because once data has been stored, no matter for how long, it is no longer real-time. Can you imagine making vital business decisions based on three-month-old insights? How about a week old? Or a day old? Minutes-old data can be irrelevant for the real-time decisions that matter most to your business, yet many people don’t understand the difference between real-time analytics with real-time data and real-time analytics with stale data.

Data is slowly replacing experience and traditions in the way companies do business. It has already proven its value in different verticals, including finance, healthcare, and of course, retail. The first obstacle is to define the scope of the Big Data project. What are the most critical questions the company needs to answer? What data sources should they analyze? Are these already available? Is the data clean and reliable? While in the initial implementation phase Big Data-related modifications can affect usual workflows and slow down business, the opposite happens after they are set in place.

What do we expect from a Senior Data Scientist? Senior Data Scientists understand that Software/ Machine Learning has a lifecycle and so spend a lot of time thinking about that. They understand that ‘data’ always have flaws. These flaws can be data generating processes, biases in data. They understand the ‘soft’ side of technical decision making, focus on impact and value, and care about ethics. At the very least Senior Data Scientists should read some of the code of ethics in Data Science and have views on these. Ideally, you should have your own code of ethics, and maybe enforce those on yourself.

We have covered the basic terms and definitions for data types and structure on my previous post, let’s dive into the creative and most time consuming side of data science — cleaning and feature engineering. What are some of the basic strategies that data scientists use to clean their data AND improve the amount of information they get from it?The type of cleaning and engineering strategies used usually depend on the business problem and type of target variable, since this will influence the algorithm and data preparation requirements.

Ready to learn Data Science? Browse courses like Data Science Training and Certification developed by industry thought leaders and Experfy in Harvard Innovation Lab. If you are getting started in your data science journey and don’t come from a technical background (like me), then you definitely understand the struggle of keeping up with the terminology of data

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Ready for Big Data Training & Certification? Browse courses like Big Data – What Every Manager Needs to Know developed by industry thought leaders and Experfy in Harvard Innovation Lab. Explore the area of big data Application Performance Management (APM) and why enterprises need it. APM is not a new discipline, but it is a new best

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Ready for Big Data Training & Certification? Browse courses like Big Data – What Every Manager Needs to Know developed by industry thought leaders and Experfy in Harvard Innovation Lab. The title explains it all. No intrigue. We are going to describe big data use cases, and we’ve summed up twenty for our industry-neutral overview. So, let’s