Experfy Insights

Cloud Computing continued to be one of the hottest topics in the technology and business media throughout 2018. This is no surprise as the Cloud Sector has been growing rapidly for the last few years. So, while steady growth was perhaps expected this year, a few trends of note stand out. When the experts look back at 2018, they will likely stress the following for Cloud Computing trends that shaped the year and will continue to be important in 2019.

The Data Science Unicorn is an expert in statistics, programming, and business. While much has been said and done to help Data Scientists become better at math and coding, this post helps Data Scientists sharpen their business mindset. Find below the curated list of books about business and decision making which will ultimately help you better understand and navigate the world. These books are not hard-core theoretical. Rather, the books are fun to read while they are backed by science and convey important lessons. Let’s start reading. 

In domains like Science and Engineering, documenting your experiments is crucial. Scientists too relied on notes, drawings, annotations and later on pictures — virtually any kind of record — to support their hypotheses and advance the collective knowledge of the scientific community. This article shares and celebrates the work of the Pioneers of Data Visualization, the people who paved the way to in Infographs and data visualization techniques that are so popular and widespread today.

For each use case, find out whether the necessary data is being captured. Specifically, check whether the different datasets you need can be merged. The more machine learning projects you’ve already implemented, the better you can pinpoint the right questions to ask. Build on your previous experience: What are the common pitfalls in projects like this one? Which datasets are the most important to have, and which ones are optional? If you haven’t implemented a similar use case before, talk to a team that has.

If you are considering a Business Intelligence solution, you ought to give some consideration to the concept of Smart Data Visualization and review your prospective solution to determine its capabilities in that regard. Smart Data Visualization provides many benefits to the organization and to the business users, who will leverage the selected BI tools to gather, analyze, share and report on data. Smart Data Visualization goes beyond data display to suggest options for visualization and plotting for certain types of data, based on the nature, dimensions and trends inherent in the data.

Transfer Learning is the reuse of a pre-trained model on a new problem. It is currently very popular in the field of Deep Learning because it enables you to train Deep Neural Networks with comparatively little data. This is very useful since most real-world problems typically do not have millions of labeled data points to train such complex models. This blog post is intended to give you an overview of what Transfer Learning is, how it works, why you should use it and when you can use it. 

Automated machine learning doesn’t replace the data scientist, but it might be able to help you find good models faster. TPOT bills itself as your Data Science Assistant. TPOT is meant to be an assistant that gives you ideas on how to solve a particular machine learning problem by exploring pipeline configurations that you might have never considered, and then leaves the fine-tuning to more constrained parameter tuning techniques such as grid search.

Trading is a gruesomely competitive world. And with AI being painted as the new wonder weapon for everything, it’s understandable that there’s a huge amount of interest in discovering how to use AI for trading. AI does play an important role in trading – but maybe not in the way you’d expect. Unfortunately, AI can’t be used to power a superhuman trading machine that steals human traders’ lunches in every market. Then, how AI helps traders make better decisions & improve high-frequency trading?

Computer vision, the discipline of enabling computers to understand the context of images, is one of the most challenging areas of artificial intelligence. And naturally, like any other technology that creates a huge impact, deep learning became the focus of a hype cycle. Subsequently, deep learning pushed itself into the spotlight as the latest revolution in the artificial intelligence industry, and different companies and organizations started applying it to solve different problems. And like every other hyped concept, deep learning faced a backlash. Many experts believe that deep learning is overhyped, and it will eventually subside.

For medium-to-large-scale enterprises, distributed systems are unsustainable and centralized databases expensive to implement. Gradually transitioning to a federated system is, therefore, an efficient solution. Downsizing the satellite databases while building a central database allows large firms to eliminate unnecessary personnel, equipment, and facilities gradually and strategically, increasing the odds of success. Even if it takes years to complete, a federated database created carefully and managed meticulously will pay in dividends, in part by helping enterprises improve their Data Management Maturity.

Natural language processing (NLP) is an area of computer science and artificial intelligence that is concerned with the interaction between computers and humans in natural language. The ultimate goal of NLP is to enable computers to understand language as well as we do. It is the driving force behind things like virtual assistants, speech recognition, sentiment analysis, automatic text summarization, machine translation and much more. In this post, you will learn the basics of natural language processing, dive into some of its techniques and also learn how NLP benefited from the recent advances in Deep Learning.

Forward-looking organizations can’t afford to overlook the emerging trends in artificial intelligence and analytics in 2019. 8 Most Important AI and Analytics Trends for 2019 unpack the driving forces in AI for the New Year.  Artificial intelligence will deliver approximately $2 trillion worth of business value worldwide over the next year through the use of advanced computing algorithms that identify and optimize business insights that humans cannot spot. Companies that fail to adopt AI will lose out. Some industries may even be wiped out. Be on the look-out for these hot trends.

I use the suitcase dilemma as a metaphor for the types of decisions being made in the analytics technology world by customers. Companies invariably confront with the decision on the type/size/complexity of solutions to implement, and they many times initially demanded the 100% answer to their forecast needs into the mid to long range future.  Be a suitcase-skeptic – don’t be too quick to purchase the largest, handle-all-cases bags. Consider in addition the frugality and simplicity of a 95+% solution, simultaneously planning for, but not implementing, the 100% case.

The tech industry might feel like some monolithic juggernaut, but it's easy to get along with once you know it a little better. If you're a young professional looking to make your start in an emerging and disruptive technology field, however, how can you give yourself the best chance at standing out? Like it or not, it all begins with the humble resume. Here's how to make yours shine in what is projected to remain a very in-demand field, according to employers.

One of the most important elements of advanced data discovery and Advanced Analytics tools is plug n’ play predictive analysis and forecasting tools. These tools can support the enterprise initiative to implement Self-Serve Advanced Analytics and transform business users into Citizen Data Scientists. Plug n’ Play Predictive Analytics provides easy-to-use tools that require no programming or Data Scientist skills and enable the average business user to leverage sophisticated predictive algorithms so users can confidently plan for success.

Financial products are mostly mathematical bets: statistical equations that – on average – should return a profit. In the simpler and more repetetive parts of this game, we can substitute Machine Learning for human brainpower and automate a lot of decisions. There is a lot to be gained for finance businesses in applying AI. And - in fact - plenty are already doing so successfully. To give you an idea - here’s a summary of the top applications of AI in Finance.

Maybe it's hard to believe, but cutting-edge technologies are bringing their magic even to places that once lived and died by their human touch. Take restaurants for example. Places like Applebees, Chili's and more employ the help of tabletop tablets to enhance the customer experience. Similarly, technology has the potential to improve functions such as conferences and events — from the planning of the event to its execution and everywhere in between. Let's take a look at a few of the reasons why.

Notebooks are being used as dashboards and also as artifacts in the data engineering and transformation process. Data visualization in R has grown nearly as robust and interactive as data visualization in BI tools or custom applications. People are growing more comfortable with stylized data visualization. These factors all contribute to what I think will define the third wave of data visualization where modes like notebooks, dashboards, and long-form storytelling converge, as will the tools to create them and the literacy of the audiences they are made for. 

As the amount of data enterprises deal with daily continues to explode, so does the number of different repositories creating data silos across an organization. The more disparate silos an organization has, the more vulnerabilities are likely to exist across the organization. The future needs for information management cannot be fulfilled with data and processes spread across various silos. Today information management thrives on using, analyzing and leveraging interrelated information in the right context. This cannot be done on a silo-based foundation. 

Software project management is the practice of planning and executing software projects. Its concepts need to be understood by every team member to ensure a smooth project flow. There are different methodologies that can be mainly divided into structured and flexible approaches. The most common approach, which gained a lot of popularity in recent years, is called “Agile”. This is a flexible approach based on delivering requirements iteratively and incrementally throughout the project life cycle. This post, will give you a gentle introduction to agile and non-agile project management approaches with the focus on the Scrum Methodology. 

Today’s excellent data scientists, especially on the applied side, tend to get things done more quickly, and with improved computer science skills. The need for understanding the inner-workings of the black box seem to balance with the need to know how to run programs, operate tools, and write code. It is excited to see where the next several years will take us, and look forward to seeing further evolution of the data scientist. Here is a look ath the evolution of the data science function over the past few decades:

Hiring managers and human resources departments will probably always need a human touch to perform their duties well. Even so, we're already seeing the many potential benefits of bringing data analysis and artificial intelligence into the hiring process. Broadly speaking, companies large and small are using advanced technologies to help them source candidates more successful. Hiring can be much more strategic now that human beings have the aid of impartial and vastly more observant AI tools to help make our staffing decisions. Here's a look at how big data and AI are shaking up hiring just about everywhere.

There is a lot of talk about how machine learning, AI, and big data can be used to help B2B companies improve their efforts. While there’s no disputing this, there is a large misconception that B2C businesses cannot be helped in the same way. This is, of course, quite false. B2C focuses on the individual, rather than an organization. So, can machine learning technology still fit into this category of B2C? Absolutely. Here are three key reasons why.

Today, artificial intelligence (AI) business technology, also called business intelligence (BI), is shaking the foundation of commerce. Although the technology is complex, there are numerous ways that it can help business leaders improve their operations and boost profits. Understanding AI and machine learning (ML) is initially challenging for business leaders, but highly beneficial. With the cost of this technology diminishing, a growing number of small to midsize firms are leveraging the innovation to compete in an increasingly competitive marketplace.