Wednesday 27 November 2019

How to Launch Your Data Science Career?

What Is a Data Scientist?
Data scientists do many of the same elements as data analysts, but they also typically build machine learning models to make accurate predictions based on past data. A data scientist often has more independence to try their ideas and experiment to get exciting designs and aims in the data that management may not have considered.
As a data scientist, you might be asked to evaluate how a change in marketing strategy could affect your company’s bottom line. This would require a lot of data analysis work collecting, cleaning, and visualizing data. Still, it would also probably need building and training a machine learning model that can make sure future predictions based on past data.
Skills Required: 
All of the skills expected of a data analyst, plus:
  • Solid knowledge of both supervised and unsupervised machine learning methods
  • Strong understanding of statistics and the ability to estimate statistical models
  • More advanced data science-related programming skills in Python or R, and possibly experience with other tools like Apache Spark  

Career Prospects
If you are working as a data scientist, your next job title may well be a senior data scientist, a position that will earn you about $20,000 more per year on average. You might also want to specialize further in machine learning as a machine learning engineer, which would also bring a pay increase. Or, you can view more toward management with roles like a lead data scientist. If you need to maximize earnings, your final aim might be a C-suite role in data such as chief data officer, although these parts need management skills and may not require a lot of actual day-to-day work with data.
Why Is Data Science a Good Career to Explore?
Advancements in technology improved data science evolve from cleaning datasets and applying statistical methods to a range that encompasses data analysis, predictive analytics, data mining, business intelligence, machine learning, deep learning, and so much more. Now, there still might be some who believe that data science is just a trend, and the hype around it will finally go away. Of course, nothing could be farther from reality. The truth is, data science is just getting speed as all businesses and government organizations use enormous volumes of data to change what they do and how they do it.
Why Is Data Science Important?
Data science is not just restricted to the F1 racetrack or the big casino business players. There is practically no industry that can’t benefit from it. Retail and e-commerce, logistics and transportation, healthcare, finance, insurance, real estate all these require a robust data science team that can leverage the data within their organization to gain a competitive advantage. That is why, if you are looking for a rewarding career with a substantial impact on any business decision-making process, you should explore the data science career path.
The demand for data science is vast, and employers are investing valuable time and money in Data Scientists. So using the right steps will lead to exponential growth. 

For Enquiry Call: +91 81558 37000

 

Monday 11 November 2019

Boost your career in Artificial Intelligence, Machine Learning & get the salary hike you deserve with Epoch Research Institute

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

Artificial Intelligence History

The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage.
Early AI research in the 1950s explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning. For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Siri, Alexa or Cortana were household names.
This early work paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and smart search systems that can be designed to complement and augment human abilities.
While Hollywood movies and science fiction novels depict AI as human-like robots that take over the world, the current evolution of AI technologies isn’t that scary – or quite that smart. Instead, AI has evolved to provide many specific benefits in every industry. Keep reading for modern examples of artificial intelligence in health care, retail and more.

Why is artificial intelligence important?

AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.

AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become. 

AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.

AI achieves incredible accuracy through deep neural networks – which was previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.

AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.

AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.

How Artificial Intelligence Is Being Used

Every industry has a high demand for AI capabilities – especially question answering systems that can be used for legal assistance, patent searches, risk notification and medical research. Other uses of AI include:

Health Care: AI applications can provide personalized medicine and X-ray readings.  Personal health care assistants can act as life coaches, reminding you to take your pills, exercise or eat healthier.

Retail:AI provides virtual shopping capabilities that offer personalized recommendations and discuss purchase options with the consumer. Stock management and site layout technologies will also be improved with AI.

Manufacturing:AI can analyze factory IoT data as it streams from connected equipment to forecast expected load and demand using recurrent networks, a specific type of deep learning network used with sequence data.   

Banking:Artificial Intelligence enhances the speed, precision and effectiveness of human efforts. In financial institutions, AI techniques can be used to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, as well as automate manually intense data management tasks.

Working together with AI

Artificial intelligence is not here to replace us. It augments our abilities and makes us better at what we do. Because AI algorithms learn differently than humans, they look at things differently. They can see relationships and patterns that escape us. This human, AI partnership offers many opportunities. It can:
  • Bring analytics to industries and domains where it’s currently underutilized.
  • Improve the performance of existing analytic technologies, like computer vision and time series analysis.
  • Break down economic barriers, including language and translation barriers.
  • Augment existing abilities and make us better at what we do.
  • Give us better vision, better understanding, better memory and much more.  

How Artificial Intelligence Works

AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies, as well as the following major subfields:
  • Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.
  • A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
  • Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
  • Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.  
  • Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
  • Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.

Additionally, several technologies enable and support AI:
  • Graphical processing units are key to AI because they provide the heavy compute power that’s required for iterative processing. Training neural networks requires big data plus compute power.
  • The Internet of Things generates massive amounts of data from connected devices, most of it unanalyzed. Automating models with AI will allow us to use more of it.
  • Advanced algorithms are being developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.
    • APIs, or application programming interfaces, are portable packages of code that make it possible to add AI functionality to existing products and software packages. They can add image recognition capabilities to home security systems and Q&A capabilities that describe data, create captions and headlines, or call out interesting patterns and insights in data.
In summary, the goal of AI is to provide software that can reason on input and explain on output. AI will provide human-like interactions with software and offer decision support for specific tasks, but it’s not a replacement for humans – and won’t be anytime soon.
 SAS  Artificial Intelligence Training
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  Whats-app: +91 99789 95178 | +91 63608 17936


EPOCH RESEARCH INSTITUTE OFFERS:
Authorized SAS TRAINING | SAS CERTIFICATION | SOFTWARE PURCHASE | BUSINESS CONSULTING | TECHNICAL SUPPORT ON SAS || SAS STAFFING SOLUTION  

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Saturday 19 October 2019

Live Web Predictive Modeling Training


Enroll in a data science MicroMasters program and get in-depth training in data mining, data modeling and predictive analytics. Adding these key skills to your CV will get you on a path to an exciting career in big data, machine learning, data analytics, BI or a related field.
 
What Is Epoch?
Established in 2009, Epoch Research Institute India Pvt Ltd., is one of India oldest SAS Accredited training partner imparting SKILLS and TALENT DEVELOPMENT, offers multi-disciplinary LEARNING MANAGEMENT, TRAINING, on SAS to individuals, corporations, institutions, across pan India. It has become known, in particular, as a one of primer institute providing quality SAS Training over the years. Along with SAS we offer more than 150 programs  on ILT and e-learning platform.

In Addition to SAS Training Epoch also offers BUSINESS CONSULTING, STAFFING SOLUTIONS, & SAS SOFTWARE RESELL through its division “Epoch Technology & Consulting Services”. We state of art highly developed offshore and near-shore facilities, experienced team of domain specialists. Our innovative approaches to customer experience  management & delivery has been a catalyst in sustaining long-term customer engagements. 


Predictive Modeling Training
Ahmedabad |     Bengaluru         |  Chennai
+91 79 4032 7000| +91 80 9575 7700|+91 99404 52792
  Whats-app: +91 99789 95178 | +91 63608 17936


EPOCH RESEARCH INSTITUTE OFFERS:
Authorized SAS TRAINING | SAS CERTIFICATION | SOFTWARE PURCHASE | BUSINESS CONSULTING | TECHNICAL SUPPORT ON SAS || SAS STAFFING SOLUTION  

SAS Training & Placement Programs with Internship: Epoch Research Institute India Largest and Oldest #SASTraining Institute (#epochsastraining)

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How to code in Python with SAS 9.4 by Epoch Research Institute

The SAS® platform is now open to be accessed from open-source clients such as Python, Lua, Java, the R language, and REST APIs to leverage...