Presence of generic kamagra oral jelly alternatives in South Africa seriously helps with favour of brand kamagra oral jelly. Its simple to buy kamagra oral jelly without prescription in South Africa. With help of generic pharmacy kamagra oral jelly cost never been so low online from South Africa. Many insurers and some retail pharmacies now offer drugs by mail order. These companies ship prescribed drugs to your home so you don’t have to pick them up in person. Often you can get a three-month supply at a reduced cost. The convenience and savings can pay off in surprising ways. There forever was a cheap way to get medications in online pharmacy australia by visitng this website. From day to day with a need to order viagra online in australia it will be ideal choice to go. Some large pharmaceutical companies support health development through public-private partnerships. In a number of cases, international corporations and foundations have contributed drugs or products free of charge to help in disease eradication. Generic version of viagra cost is always less when purchased from online pharmacy. Practically in australia. Industry relationships with healthcare professionals must support, and be consistent with, the professional responsibilities healthcare professionals have towards their patients. Whilst looking information about naltrexone low dose simply go to this.

subscribe: Daily Newsletter

 

Data analytics and the changing face of industries

0 comments

The days of being transported in self-driving cars and having our phones suggest what we ought to eat for lunch are upon us: this is the age of big data, artificial intelligence and machine learning. Never before have industries had the power to use information to enhance their products and services as they do today.
Jacobus Eksteen, senior data analyst at Compuscan, shares his insights into the significant opportunities that data analysis has brought about in various fields, including the credit industry.
Since the rise of big data, analytics has become a powerful tool which is used by companies across industries to understand what to do, when to do it and how to do it in order to enhance efficiency, minimise cost and maximise profitability. Recent years have seen greater availability of highly sophisticated software packages and algorithms, an increase in processing power as well as a decrease in processing cost. As a result, analysts, businesses and industries on the whole have become ever more empowered given that more data can be processed and stronger models can be built in shorter periods of time.
Statistical modelling – a facet of data analytics which is rooted in mathematics and deals with finding relationships between variables to predict an outcome
– is commonly used in financial services companies and consumer facing businesses as they are required to make a high number of decisions within limited time periods, and these are relatively easy to standardise.
Within the credit industry, analytics has enabled credit providers to make informed decisions on the creditworthiness of prospective borrowers within split seconds. Calculations and decisions are increasingly being made in the ‘cloud’ and analytics isn’t only being used to predict repayment and response behaviour but also to understand customer behaviour, fraud and brand affiliation; to do benchmarking, profit and provision modelling and to optimise call centres.
Given the evolution of data generation, storage and analysis coupled with the introduction of cloud computing, the realm of data analysis has expanded and ‘machine learning’ is now is equipping a variety of industries where a great amount of data is generated to make quicker and better decisions. Machine learning is an evolutionary by-product of artificial intelligence and computer science which has enabled computers to “learn” without being explicitly programmed to perform certain tasks.
A number of articles describing how the implementation of machine learning is shaping industries have been published in the past few months. One in particular indicates how disease assessment, diagnostics and treatment plans are being enhanced via machine learning. This technology also allows the monitoring and prediction of epidemic outbreaks based on satellite data, web information and social media updates, among other sources.
Another focusses on machine learning in the aerospace industry and how this may become be applied in the form of “data-driven adaptive training” to optimise the time taken for each trainee to become a proficient, thus meeting the need to increase the global training capacity of pilots without jeopardising flight safety.
In essence, machine learning automates repetitive tasks thus allowing employees the leeway to challenge themselves to a larger extent. Of course, it’s not likely that machines will “take over the world” as some may fear, but a focus on equipping oneself and growing one’s skillsets may be necessary in the near future to avoid the risk of unemployment or redundancy in the workplace. Indeed, some job roles won’t exist in 10 years’ time, but there are also jobs now that didn’t exist 10 years ago. In fact, technologies such as machine learning have opened up new doors for employment opportunities and have drastically changed the way business is done.
It must further be said that machine learning is merely a tool: one of many that needs to be understood and used properly. Nonetheless, the powerful combination of machine learning and human learning, the rapidly changing world of analytics and the resultant opportunities and efficiencies within various industries is tremendously exciting.