Big Data, Cloud & DevOps

Interviews can be nerve wrecking, especially when it comes to Big Data. But as someone who has spent decades in this industry, I think it doesn’t have to be. If one is prepared, then it can just turn out to be a good dialogue and explain your value to prospective employers. But as they say, one who has prepared well has half won the battle! So to make things easier, I’m going to share a few questions that I’ve been asked over the years and some that I used while interviewing candidates when building a data science team. And by no means are they exhaustive. 

In this digital day and age, marketing is more and more about data, analytics, and intelligence. The idea is no longer to just promote a business’s offerings, but also get a better understanding of the customer’s needs, interactions, and choices. From the beginning of commerce, the most advanced marketers understood the value of storing and organizing information about their customers. But this information happened to be widely heterogenous and inherently managed by each source, and channel of collection across various silos.

I recently discovered data science practice problems on Analytics Vidhya. These allow you access to simple datasets on which to practice your machine learning skills and benchmark yourself against others. I think they offer a great introduction to approaching these problems before perhaps moving onto something a bit more challenging such as Kaggle competitions. 

Women encompass half of the world’s population. However, their numbers are not reflected in technological fields and corporate boardrooms. Data science is one of the highest-ranking careers for employee satisfaction. Big data leadership opportunities can offer women a successful career path. Hopefully, positive career prospects and salaries will encourage more women to pursue prosperous technology careers. By following the example of great women leaders in technology, aspiring female executives might one day take on the role of empowering their coworkers and organizations.

What problems do fintechs need to solve to scale up and grow profits? They need building an easily scalable software product, partnering with other companies and engaging new customer segments, and complying with regulation and security standards while scaling up. In our series of articles we will dwell on how technologies can help you solve these 3 key challenges. We’ve collected and analysed findings from PWC, CBInsights, Forbes, etc., and fintech software development cases to elaborate a strategy on how to build a successful fintech business that generates profits, attracts investments and can achieve economies of scale.

Today’s marketers and salespeople are harnessing technology advancements to unearth insights in real-time, which was unimaginable few years ago. However, the key question is – are we looking at the right data? If our intent is to collect and understand customer feedback, we are looking at only partial information. A lot of information is usually in hidden in what is usually referred to as “dark data” or in simple terms unstructured data. You need to go beyond structured data and demystify the dark data that can give you relevant information. 

Scrapy is a framework built to build web scrapers more easily and relieve the pain of maintaining them. Basically, it allows you to focus on the data extraction using CSS selectors and choosing XPath expressions and less on the intricate internals of how spiders are supposed to work. If you need to scrape something a bit harder, you can do it on your own. With that, let’s get started. 

What is prescriptive analytics? Prescriptive analytics is the area of business analytics dedicated to finding the best course of action for a given situation. Based on prior experiences, prescriptive analytics enables quality improvement, service enhancements, cost reduction, and productivity increase.  Whatever you call it. Predictive analytics leaves a huge gap between “knowing” and “doing”. Prescriptive analytics goes beyond knowing. Prescriptive analytics provides recommended actions based on prior outcomes. A recommended course of action to achieve a specific outcome. Prescriptive analytics provide specific recommendations based on prior experiences and outcomes.

The combination of DevOps, Continuous Delivery, and Continuous Integration is transforming the practice of product management. DevOps is now the next phase in modern software development and product management. Product managers, more than ever, must do a better job of prioritizing requirements. DevOps requires product management and other parts of the organization to also change. It’s an exciting time to be a product manager. Agile and DevOp create new opportunities.   Add security into your product DNA. Work closely with your teammates to maximize process delivery and customer value. Support the changes required to ensure successful product launches and customer engagement

In this turbulent legal environment, corporate governance and ethical standards are critically important for care provider organizations. Staff members must have a clear understanding of proper ethical and moral behavior and the innate, or learned, fortitude to make the right decisions. This kind of decisiveness may involve overlooking opportunities for short-term profit, avoiding programmed work behaviors and making choices that require more effort among available alternatives. This kind of ethical decision-making improves the standing that a facility has in the community and makes a health care institution appear more appealing to potential employees and patrons.

It’s never been easier to get started with machine learning. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. You are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems. This requires you to put on a programming hat. It’s widely believed that Python helps developers to be more productive from development to deployment and maintenance.  This article will focus on some essential hacks and tricks in Python focused on machine learning.