As Josh Wills once said,
“Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”
Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. In fact, Mathematics is behind everything around us, from shapes, patterns, and colors, to the count of petals in a flower. Mathematics is embedded in each and every aspect of our lives.
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Big data is the new competitive advantage and it is necessary for businesses. With the growing proliferation of data sources such as smart devices, vehicles, and applications, the need to process this data in real-time and to deliver relevant insights is more urgent than ever. The 2019 Guide to Big Data will explore tools and ecosystems for analyzing big data and relevant use cases ranging from sustainability science to autonomous vehicles.
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In this ever-growing network of physical devices, Internet of Things (IoT) has become much more realistic, with smart devices that allow less human input and more machine output. Today, it is very much a reality that smart home hubs, wearable devices, connected cars, industrial Internet, smart retail, and many more consider IoT for functionality.
The desktop apps, servers, and mobile apps use some programming languages that are more or less the same. So it makes us believe that there is no difference in smart objects as they are like minicomputers. IoT comprises of a three-tier architectural environment that consists of generating the data, organizing the data by the local gateways, or hubs and centralized servers that are geographically distant where all the data ends up.
In the freelancing industry, excellent skills are always required in order to deliver projects exactly as desired. These skills are constantly changing, and it is imperative that freelancers all over the world evolve accordingly.
Clients demand excellence and anything less is not acceptable. These expectations act as the propellant for the acquisition of the new skills in the market.
Data Science, Machine Learning, Deep Learning, and Artificial Intelligence are really hot at this moment and offering a lucrative career to programmers with high pay and exciting work. It’s a great opportunity for programmers who are willing to learn these new skills and upgrade themselves. It’s also important from the job perspective because Robots and Bots are getting smarter day by day, thanks to these technologies and most likely will take over some of the jobs which many programmers do today. Hence, it’s important for software engineers and developers to upgrade themselves with these skills. Programmers with these skills are also commanding significantly higher salaries as data science is revolutionizing the world around us. Machine Learning specialist is one of the top paid technical jobs in the world. However, most developers and IT professionals are yet to learn these valuable set of skills.
For those, who don’t know what is a Data Science, Machine Learning, or Deep Learning, they are very related terms with all pointing towards machine doing jobs which is only possible for humans till date and analyzing the huge set of data collected by modern day application.
In preparation for my talk at the Philadelphia Open Source Conference, Apache Deep Learning 201, I wanted to have some good images for running various Apache MXNet Deep Learning Algorithms for Computer Vision.
Using Apache open source tools – Apache NiFi 1.8 and Apache MXNet 1.3 with GluonCV I can easily ingest live traffic camera images and run Object Detection, Semantic Segmentation and Instance Segmentation.
Logic programming is a programming paradigm that sees computation as automatic reasoning over a database of knowledge made of facts and rules. It is a way of programming and is based on formal logic. A program in such a language is a set of sentences, in logical form, one that expresses facts and rules about a problem domain. Among others, Datalog is one such major logic programming language family.
Let’s talk about facts and rules. Facts are true statements — say, Bucharest is the capital of Romania. Rules are constraints that lead us to conclusions about the problem domain. These are logical clauses that express facts. We use the following syntax to write a rule (as a clause):
Before anything else, you will need to Install Python on your machine. So, here are the steps to install Python.
You can download from the official website:
If you’re at all interested in Artificial Intelligence (AI) — and it seems likely that you are, since you’re reading this in the AI Zone here on DZone — it’s unlikely to be news to you that there is an AI skills shortage. Businesses are increasingly looking to invest in AI and are on the hunt for suitably skilled workers since traditional software teams without the experience of AI often encounter a number of challenges, as I described in a recent article in this zone.
Anyone thinking about joining the AI workforce will want to learn the subject, initially by doing some reading and research, but without committing to paying too much. But where to start? As the need to recruit skilled AI staff has grown, so a number of businesses and individuals have set out to provide training courses, books, and e-learning, and the price and quality of these vary, as you would expect. As with all education, if you commit a chunk of your time, you don’t want to find it wasted on out-of-date or incorrect information or to find that you are missing out on key skills after spending time and money on a course that promises to equip you appropriately.
