Big Data, Cloud & DevOps

This post is not really about how to lie with Data Science. Instead, it’s about how we may be fooled by not giving enough attention to details in different parts of the pipeline. There are different pitfalls that might occur when we try to publish some algorithm results or interpret others. The main idea to take from this is “When it looks too good to be true, it probably is”. When our model (or others) looks surprisingly good, we have to make sure that all of the steps in our pipeline are correct.

Having a portfolio of public evidence of your data science skills can do wonders for your job prospects. Even if you have a referral, the ability to show potential employers what you can do instead of just telling them you can do something is important. A portfolio of public evidence is a way to get opportunities that you normally wouldn’t get. It is important to emphasize that a portfolio is an iterative process. As your knowledge grows, your portfolio should be updated over time.

How do you prove your capability as a Apache Spark data scientist when you don’t have much to show? Perhaps this is because you don’t have much experience. Perhaps it is because the work you do does not lend itself to being shown to others. You may have done impressive work, but have nothing to show for it because most developers work for companies that don’t publish their code as open source.

Big data helps firms unearth information that consumers and clients may not reveal via common market research tools such as surveys and questionnaires. In some cases, big data provides answers to questions that the research tools of the past could never hope to reveal. Big data analysis can help business leaders figure out whether their online presence is helping or hurting their brand. While big data reveals hidden patterns, they do not always result in actionable insights. This makes it important for marketers to understand the meaning behind the results. 

CNNs (convolutional neural networks) are one of the reasons deep learning is so popular today, they can do amazing things that people used to think computers would not be capable of doing for a long, long time. But they have their limits and some fundamental drawbacks and that is why Capsules neural networks are picking up pace, which introduce a new building block that can be used to overcome these limits & drawbacks of CNNs. Capsule networks (CapsNets) are a hot new neural net architecture that may well have a profound impact on deep learning, in particular for computer vision.

With access to data analytics and big data, companies know more about their customers than ever before. For corporations, this has been a huge boon, allowing them to target local markets online. On the other hand, small businesses have had trouble integrating effective customer analysis into their marketing strategies without alienating their local customer base. However, showing love to the local community while harnessing data analytics to expand outreach can help a small business compete in the strange new landscape of the digital age.

Like any other career path, there are benefits and drawbacks to working as a data scientist. Data scientists must acquire a large amount of training to grow proficient in their field. Upon entering the workforce, some specialists are tasked with rebuilding a company’s information structure from scratch. At other firms, institutionalized executives may want the benefits of the latest information technology, but aren’t willing to provide the requisite funding to launch a full data initiative. To prepare the next generation of data science professionals, forward-thinking academics are working to promote a learning environment where students train in near real-world environments and conduct interviews with field experts. 

Professional big data developers are mostly valued when they have a strong technical background and great problem solving skills. Furthermore, the knowledge of data analysis and business requirements analysis are essential for developing a clear understanding of the business needs. Specialists with such skill sets may handle diverse sources and huge amounts of raw data seamlessly and provide valuable insights from it. This enables big data engineers to use technical solutions that leverage innovative technologies to drive real benefits for your business. So which criteria to use when choosing big data developers?

Customer data comes from varied sources such as website forms, social media, email lists, and more. We all come across fake lead information, every now and then. Therefore, as a marketer, this data is not enough. You neither know if the prospect falls under your target industry, organization size, job title, revenue, etc. nor do you know where they are in their purchase cycle. This is where data enrichment as a practice comes into play. Data enrichment apps fill up the gaps of inadequate data or inaccurate information. 

Data is providing feedback to every corporation, which can then use the data to better themselves and get more business. Architects are no different and are coming to use technology in the same ways. With VR improving daily, they can even show clients exactly what they’re paying for before any construction begins. In short, now is a special time to be an architect. Technology working for you and becoming a tool for business is exactly what the world has been waiting for.

In part 1, we introduced the field of Natural Language Processing (NLP) and the deep learning movement that’s powered it was introduced. We also walked you through 3 critical concepts in NLP: text embeddings (vector representations of strings), machine translation (using neural networks to translate languages), and dialogue & conversation (tech that can hold conversations with humans in real time). In part 2, we’ll cover 4 other important NLP techniques that you should pay attention to in order to keep up with the fast growing pace of this research field.