Last year was the year, which saw artificial intelligence, went mainstream.
By that, we do not mean just getting filtered raunchy photos on Twitter or getting the fake news suggestions on Facebook.
Here is what to look for in Artificial Intelligence for this New Year:
Driven by unprecedented financial support (along with a growing open source ecosystem), founders have been delivering artificial intelligence start-ups at a record high rate.
GE, Google, Intel, Microsoft, Facebook, Apple, Salesforce and Samsung, and several other name brands made rigorous AI investments last year.
There are now five million homes, which, are talking about their music and shopping choices with the help of Alexa from Amazon.
There is a whole new department of U.S. Department of Transportation Committee for self-driving cars. Even a few years ago, there were people talking about 2025 or so for the accessibility of self-driving cars (of level 5 autonomy), but this is a reality now, much before we could reach 2020. It is also amazing to think that self-driving cars may whittle down the 1.2 million annual deaths from automobiles.
Also in other interesting news, two AI unicorns just grew their horns, the Cylance in Silicon Valley and iCarbonX in China.
Also more than one-fifth of the MIT 50 smartest companies list, include AI as a core approach these days.
The Canadian Olympic company has recently teamed up with a major Big Data Company to ramp up the analytics for the benefit of the athletes.
Recently the COC made an announcement about an eight-year, cash and services sponsorship deal with SAS, which is an analytics software with a brag-worthy client list from varied industries, like universities, hotels, banks casinos and much more.
The PUT statement in SAS for programmers who have completed a SAS certification in the DATA step and the %PUT macro statements are highly useful statements, which will help to enable you to display the values of variables and macro variables, respectively.
And almost by default the output will appear in the SAS logs. In this article we will share a few tips which will allow you to make use of these statements more efficiently.
It is a well-known fact that Python, R and SAS are the most important three languages to be learnt for data analysis.
If you are a fresh blood in the data science community and are not experienced in any of the above-mentioned languages, then it makes a lot of sense to be acquainted with R, SAS or Python.
To those of you unaware of the developments in the SAS world, JSON is the new XML and the number of users who need to access JSON has really grown in the recent times. This is mainly due to proliferation of REST-based APIs and other web services. A good reason for JSON being so popular is the fact that it is structured data in the format of text. We have been capable of offering simple parsing techniques, which make use of data step and most recently PROC DS2. Now with the SAS 9.4 and Maintenance 4 we now have a built-in libname engine for the JSON XML.
Many still believe that Big Data is a grossly misunderstood, mega trending buzzword in the tech field even today. However, there is still no denying of the fact that the recent development of AI and machine learning push is related on the synthesis and labelling of huge amounts of training data. A latest trend report by the advisory firm Ovum predicted that the Big Data market which currently is valued to be USD 1.7 billion, will further rise to be USD 9.4 billion by 2020.
Then what do the insiders in the data analytics market see it happening in the upcoming year ahead? We at DexLab Analytics, the premiere Big Data Hadoop institute in Delhi spoke to several leaders in this field to discover.
Here is what we found to be the five most important trends that will shape the future of machine learning, AI and data analytics in 2017 from the industry experts:
The predictions strongly emphasize the need for more talent and skilled personnel in this vast field of data analytics, thus, a growing demand for Big Data training and Big Data courses will be witnessed worldwide.
Let us not waste any time on reading long-worded introductions, and get right down to our 5 top tips for creating different map styles in Tableau.
You will be able to find out about these tips and a lot more on Tableau with our Tableau training courses.
Removing map layer:
A simple way to create a clean map for best visualization is by removing the map layers. In order to do this, all you need to do is Select Map on the toolbar menu and “map layer”. Then simply click on the uncheck box and everything in the map layer window, this will leave behind only the outline of the map data.
When you use map, you can change the border colours of the map under the option ‘Color’. Simply select the colour and choose the border colour that you desire.
Image Source: dataplusscience.com
Making the USA map outline:
Combining the above mentioned step 1 and 2 and by clicking to remove the fill colour, we can create an outline of a map. To do so, first remove the map layer shown in step 1 and then set the map fill colour to match with the background colour. And voila! After selecting the desirable colour for the borders (as shown in step 2) we now have created just a simple outline of the map of USA!
Image Source: dataplusscience.com
Making a minimalistic map:
To do this in Tableau, you must remove the base layer which is similar as shown in the first step, we can then alter the Pane colour to change the colour of the water on the map to any colour we want. Once we get the base layer to be unchecked, all we have to do is simply right-click on the map and select ‘Format’ then select “Shading” and then alter the “Pane” colour to any shade of our choice.
Creating the countries of the world:
Just like the previously discussed step (step 3) we can start by making the borders of the countries. In this case we have also combined the step 4 where the pane colour has been changed to match. This has given rise to a red coloured outline of the countries of the world on a pitch-black background.
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It is no new news that the Big Data and software analytics have had a huge impact on the modern industries. There are several industry disruptors that have surfaced in the past few years and totally changed the lives of all connected humans. Like Google, Tesla and Uber! These are the companies that have used the long list of benefits presented to us by Big Data to expand and infiltrate newer markets, they have helped themselves improve customer relations and enhance their supply chains in multiple segments of market.
As per the latest IDC Research projects the sale of Big Data and analytics services will rise to USD 187 billion in 2019. This will be a huge leap from the USD 122 billion which was recorded in 2015. A major industry that stands to benefit greatly from this expansion which is the manufacturing industry, with the industry revenue projections ranging to USD 39 billion by 2019.
The industry of manufacturing has come a long way from the age of craft industries. But back then, the manufacturing process involved a slow and tedious production processes which only yielded limited amounts of products.
The effects of Big Data Analytics on the Manufacturing sector:
Automated processes along with mechanization have resulted in a generation of large piles of data, which is, much more than what most manufacturing enterprises know what to do with them.
But such data can yield beneficial insights for the manufacturing units to improve their operations and increase their productivity. Here are a few notable ones:
Image Source: mckinsey.com
Savings in cost:
Big data analytics can really help transform the manufacturing process and revolutionize the way they are carried out. The obtained information can be used to reduce the cost of production and packaging during manufacturing. Moreover, companies which implement data analytics can also reduce the cost of transport, packaging along with warehousing. This is in turn can help inventory costs and return i huge savings.
Improvement in safety and quality:
A lot of manufacturing companies are now making use of computerised sensors during the production to sift through low quality products while on the assembly line. With the right software analytics enterprises can use the data generated from such sensors to improve the quality and safety of the products instead of simply throwing away the low quality products after the production.
Image Source: blogs-images.forbes.com
Tightening up the workforce efficiency:
They can also use this data to improve management and employee efficiency. Big data analytics can be used to study the error rates on the production floor and use that information to analyse specific regions where employees are good when they perform under pressure.
Moreover, data analytics may help to speed up the production process n the production floor. S will be especially useful for large firms, which work with large volumes of data.
Better collaboration:
A great advantage of having an IT based data collection and analysis infrastructure is improved information movement within the manufacturing organization. The synergy of flow of information within the management and engineering departments as well as in the quality control sector and between the machine operators and other departments of the company helps them work more efficiently.
The manufacturing industry is much more complex than any other industry, which have implemented the big data analytics. Companies must effectively time the implementation of this software so that there are no losses. And should also pay attention as to from where they can mine the data and the right analytics tools to use for producing feasible and actionable results.
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We know that you have probably heard many times that predictive analysis will further optimize and accentuate your marketing campaigns. But it is hard to envision that in more concrete terms what it will achieve. This makes it harder to choose and direct analytics technology.
Wondering how you can get a functional value for marketing, sales and product directions without being an expert? The solution to all your problems lies in how predictive analytics may offer with benefits for the current marketing operations. But to use it you must learn a few specifics about how it works.
