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.
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.
Online and digital tools and technologies have a lot to offer the modern professional. But entering the digital age and using cloud technologies to the best effect means cybersecurity is now your concern just like it's everybody else's — and possibly to an even greater extent if recent European legislation becomes widely popular and continues to change what clients expect of the companies and professionals they do business with. Let's look at some real-world advantages of building and scaling a legal-focused business using a strong technological backbone that doesn't sacrifice security robustness for convenience.
Today, rebounding from rationalist dominance and fuelled by big data, AI and medicine tend strongly to empiricism. This alignment threatens to entrench the incumbents in cycles of codependency and reinforcing errors. Examples of these cycles include data as a cure-all, theory-free science, and the misuse of statistics. A range of differences across medicine and AI may bring out the best that each community has to offer. Hype and disillusionment are to be expected a natural part of the process. But this isn’t another AI winter. These pendulum swings and ideological corrections are essential mechanisms of progress.
Want to make the most out of social media? It is time to start leveraging data. Here's your beginner's guide to getting started with data driven social media marketing, for maximum results.
This an article about Neural Networks dedicated to programmers who want to understand basic math behind the code and non-programmers who want to know how to turn math into code. In the last years applications of Deep Learning made huge advancements in many domains arousing astonishment in people that didn’t expect the technology and world to change so fast. Deep Learning is currently excelling in tasks like image recognition, Autonomous Vehicles, games like Chess, and also computer games, Language Translation, and speech recognition.
Location and spatial data are becoming much more crucial to smooth and successful business operations. Spatial data and analytics are vital to problem-solving and decision-making processes, especially in a local environment. This type of information can be tied back to every other form of data, and it helps to bring a context-heavy tone to everything. Here are three reasons why spatial analytics could prove to be beneficial to the future of your business and, as such, should be a part of your data strategy.
“Data for good” is a very new term. “Data for good” refers to a subset of data projects. “Data for good” is an odd descriptor because it implies that some data is not being used for good or is at least ambivalent in the nature of its application. The subjective nature of the word “good” as a qualifier means that there may be multiple valid definitions used at the same time. Four criteria used to qualify a project as falling under the “data for good” umbrella.
Retail coupled with the benefits and challenges leverages with adopting today’s leading technologies like AI and machine learning. Specifically, the key to humanizing experiences and influencing action is to treat each outcome as unique and dynamically respond to each customer individually, a feat which can only be scaled with machine learning. But in order to meaningfully impact the customer experience on this one-to one level, machine learning algorithms need to ingest vast amounts of data. And the more data sources, the more successful an algorithm will be at predicting the desired outcome for each user.
Today’s leaders are recognizing that technology can improve not only commerce, but society as well, and as consumers grow more tech-savvy, commerce is shifting from a business- to a consumer-driven marketplace. These influences may lead to the reduction of economic disparities and inequality. Additionally, contemporary managers want to make sure that employees are making the most of their time on the job. Managers want staff members that produce quality work, and plenty of it. These days, quality is just as important as quantity.
Marketing analytics is changing the way businesses are making marketing and sales related decisions. Here's what you need to know about why it is so important to dig deeper into data.
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.
Companies see big data as a big opportunity and plan to maintain or increase their big data investments. But it’s not enough to be sold on the idea, you have to successfully implement a big data solution before you can enjoy the benefits and that’s easier said than done. If you approach the task with a strategy for tackling the major challenges you’re going to face, then you can boost your chance of success significantly. Here are four major challenges that anyone building a big data solution is going to have to overcome.
With the increasing use of machine learning and AI, retailers can boost efficiency and productivity while actively engaging with consumers via digital and mobile platforms. This is the step forward that retailers should be taking, while keeping in mind the need to reduce waste and significantly improve efficiency. There is a real need for automation and deeper data analysis to identify patterns that support cost-effective and accurate decisions. Retailers have to figure out now how to meaningfully connect with consumers who expect intuitive and convenient shopping experiences.
We interviewed our expert Rebecca D. Wooten. Here's her insights on emerging technologies, and how apps and gaming softwares are changing.
Considerable business value in data science comes with the right application of exploratory analytics or statistical techniques. The bottom line is the need for data science capability in organisations to identify pertinent solutions to business problems, by leveraging the data at hand. How one can go about incubating a data science practice, or for that matter a startup with analytics offerings? Here are the 3 distinctive phases of growth, focus areas and skills needed in each, and share intelligence on how to acquire the right talent.
If your product or service isn’t working, you deliver the necessary assistance to your customers. To this end, you need an army of tireless, helpful employees and representatives to interface and interact with your influx of customers. In other words, your customer support channels need to be open and active nearly 24/7 to accommodate customer and audience demands. Chatbots are transforming how this process works, especially in regard to reliability and efficiency:. You might not believe it, but automated chatbots and messaging tools can supplement live support reps.
Machine Learning has made great advances in pharma and biotech efficiency. AI is also helping us more efficiently diagnose diseases, develop drugs, personalize treatments, and even edit genes. But this is just the beginning. The more we digitize and unify our medical data, the more we can use AI to help us find valuable patterns — patterns we can use to make accurate, cost-effective decisions in complex analytical processes. Machine Learning is particularly helpful in areas where the diagnostic information a doctor examines is already digitized. This post summarizes the top 4 applications of AI in medicine today.
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.
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.
General Data Protection Regulation (GDPR) is an attempt to regulate the source of information and legalize the flow of information based on the prospect or customer’s permission. GDPR is a regulation that defines how organizations need to protect the personal data of EU citizens. If your idea of prospect reach-out was mass emailers, cold calls or anything that intrudes a prospect’s experience – then GDPR is going to affect you badly. However, there is immense opportunity for marketers and sales people to build quality relationships and engagement, and more importantly an opportunity to improve the quality of data you work on.
Foundation of trust is slowly fading as a new generation of AI-doctored videos find their way into the mainstream. Famously known as “deepfakes,” the synthetic videos are created using an application called FakeApp, which uses artificial intelligence to swap the faces in a video with those of another person. Educating people on the capabilities of AI algorithms will be a good measure to prevent the bad uses of applications like FakeApp having widespread impact—at least in the short term. We also need technological measures to back up our ethical and legal safeguards against deepfakes and other forms of AI-based forgery.
Getting the most out of your upcoming trade show will require a lot of work before, during and after the show takes place. There are leads to follow, people to mingle with and a brand to build. Doing a trade show is like networking among the experts. While a trade show can be a fun experience, you're there to get the best deals possible. Unfortunately, the leads you generate from trade shows may not turn into customers if you leave them alone. Leads take work, and you must follow the leads to work.