How To Build Your Data Science Competency For A Post-Covid Future

The world collectively has been bracing for a change in the job landscape. Driven largely by the emergence of new technologies like data science and artificial intelligence (AI), these changes have already made some jobs redundant. To add to this uncertainty, the catastrophic economic impact of the Covid-19 pandemic has brought in an urgency to upskill oneself to adapt to changing scenarios.

While the prognosis does not look good, this could also create the demand for jobs in the field of business analytics. This indicates that heavily investing in data science and AI skills today could mean the difference between you being employed or not tomorrow.

By adding more skills to your arsenal today, you can build your core competencies in areas that will be relevant once these turbulent times pass over. This includes sharpening your understanding of business numbers and analysing consumer demands – two domains which businesses will heavily invest in very soon.



But motivation alone will not help. You need to first filter through the clutter of online courses that the internet is saturated with. Secondly, you need to create a study plan that ensures that you successfully complete these courses.

We have a solution.


W3Schools


Developed with the objective of providing you a comprehensive understanding of key concepts that are tailored to align with the jobs of the future, we are launching a series of special short-term courses. These courses will not only help you upskill yourself, it will also ensure that you complete these courses in a matter of a few days.

These short-term courses will have similar content as regular ones, but packed in a more efficient way. Whether you are looking for courses in business analytics, applied AI, or data analytics, these should hold you in good stead for jobs of the future.

Analytics Edge (Data Visualization & Analytics)

About The Course: This all-encompassing data analytics certification course is tailor-made for analytics beginners. It would cover key concepts around data mining, and statistical and predictive modelling skills, and is curated for candidates who have no prior knowledge about data analytics tools. What is more, the inclusion of popular data visualization tool Tableau makes it one of the best courses available on the subject today. Additionally, it also puts an emphasis on widely used analytics tools like R, SQL and Excel, making this course truly unique.

Duration: While the original data analytics course this short-term course is developed from includes 180 hours of content and demands an average of 10-15 hours of weekly online classes and self-study. This course will enable you to acquire the same skills, but within a shorter period of time.

Target Group: While anyone with an interest in analytics can pursue this course, it is especially targeted at candidates with a background in engineering, finance, math, and business management. It will also be a useful skill-building course for candidates who want to target job profiles based around R programming, statistical analysis, Excel-VBA or tableau-based BI analyst profiles.

Data Science Using Python


About the course: Adapted to greatly help candidates when searching for data science roles, this certification covers all that they need to know on the subject using Python as the programming language. While other languages like R are also commonly used today, Python has emerged as one of the more popular options within the data science universe.

This ‘Python for Data Science’ course will make you proficient in defly handling and visualizing data, and also covers statistical modelling and operations with NumPy. It also integrates these with practical examples and case studies, making it a unique online training data science course in Python.

Duration of the course: While the original data science course this short-term course is developed from includes 220 hours of content and demands an average of 15-20 hours of weekly online classes and self-study, this course will enable you to acquire the same skills, but within a shorter period of time.

Target Group: While anyone with an interest in analytics can pursue this course, it is especially targeted at candidates with a background of working with data analysis and visualization techniques. It will also help people who want to undergo Python training with advanced analytics skills to help them jumpstart a career in data science.

Machine Learning & Artificial Intelligence

About this course: This course delves into the applications of AI using ML and is tailor-made for candidates looking to start their journey in the field of data science. It will cover tools and libraries like Python, Numpy, Pandas, Scikit-Learn, NLTK, TextBlob, PyTorch, TensorFlow, and Keras, among others.

Thus, after successful completion of this Applied AI course, you will not only be proficient in the theoretical aspects of AI and ML, but will also develop a nuanced understanding of its industry applications.

Duration of the course: While the original ML and AI course this short-term course is developed from includes 280 hours of content and demands an average of 8-10 hours of weekly self-study, this Applied AI course will enable you to acquire the same skills, but within a shorter period of time.

Target Group: While anyone with an interest in analytics can pursue this course, it is especially targeted at candidates with a background in engineering, finance, math, statistics, and business management. It will also help people who want to acquire AI and machine learning skills to head start their career in the field of data science.

Summary

While the Covid-19 pandemic has witnessed a partial – or even complete – lockdown at several places across the globe, people have been reorienting their lives indoors. But with no end in sight, it necessitates that professionals turn these circumstances into opportunities to upskill.

Given an oncoming recession and economic downturn, it behoves them to adapt to these changes to remain employable in such competitive times. In this setting, Covid-19 could emerge as a tipping point for learning, with virtual learning offering the perfect opportunity to self-learn.

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COVID-19 Impact: Temporary Surge in Sales of Big Data Analytics in Automotive Product Observed …

Big Data Analytics in Automotive Market 2018: Global Industry Insights by Global Players, Regional Segmentation, Growth, Applications, Major Drivers, Value and Foreseen till 2024

The report provides both quantitative and qualitative information of global Big Data Analytics in Automotive market for period of 2018 to 2025. As per the analysis provided in the report, the global market of Big Data Analytics in Automotive is estimated to growth at a CAGR of _% during the forecast period 2018 to 2025 and is expected to rise to USD _ million/billion by the end of year 2025. In the year 2016, the global Big Data Analytics in Automotive market was valued at USD _ million/billion.

This research report based on ‘ Big Data Analytics in Automotive market’ and available with Market Study Report includes latest and upcoming industry trends in addition to the global spectrum of the ‘ Big Data Analytics in Automotive market’ that includes numerous regions. Likewise, the report also expands on intricate details pertaining to contributions by key players, demand and supply analysis as well as market share growth of the Big Data Analytics in Automotive industry.

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Big Data Analytics in Automotive Market Overview:

The Research projects that the Big Data Analytics in Automotive market size will grow from in 2018 to by 2024, at an estimated CAGR of XX%. The base year considered for the study is 2018, and the market size is projected from 2018 to 2024.

The report on the Big Data Analytics in Automotive market provides a bird’s eye view of the current proceeding within the Big Data Analytics in Automotive market. Further, the report also takes into account the impact of the novel COVID-19 pandemic on the Big Data Analytics in Automotive market and offers a clear assessment of the projected market fluctuations during the forecast period. The different factors that are likely to impact the overall dynamics of the Big Data Analytics in Automotive market over the forecast period (2019-2029) including the current trends, growth opportunities, restraining factors, and more are discussed in detail in the market study.

Leading manufacturers of Big Data Analytics in Automotive Market:

The key players covered in this study

Advanced Micro Devices

Big Cloud Analytics

BMC Software

Cisco Systems

Deloitte

Fractal Analytics

IBM Corporation

Rackspace

Red Hat

SmartDrive Systems

Market segment by Type, the product can be split into

Hardware

Software

Services

Managed

Professional

Market segment by Application, split into

Product Development

Manufacturing & Supply Chain

After-Sales, Warranty & Dealer Management

Connected Vehicles & Intelligent Transportation

Marketing, Sales & Other Applications

Market segment by Regions/Countries, this report covers

North America

Europe

China

Japan

Southeast Asia

India

Central & South America

The study objectives of this report are:

To analyze global Big Data Analytics in Automotive status, future forecast, growth opportunity, key market and key players.

To present the Big Data Analytics in Automotive development in North America, Europe, China, Japan, Southeast Asia, India and Central & South America.

To strategically profile the key players and comprehensively analyze their development plan and strategies.

To define, describe and forecast the market by type, market and key regions.

In this study, the years considered to estimate the market size of Big Data Analytics in Automotive are as follows:

History Year: 2015-2019

Base Year: 2019

Estimated Year: 2020

Forecast Year 2020 to 2026

For the data information by region, company, type and application, 2019 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

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Some important highlights from the report include:

  • The report offers a precise analysis of the product range of the Big Data Analytics in Automotive market, meticulously segmented into applications
  • Key details concerning production volume and price trends have been provided.
  • The report also covers the market share accumulated by each product in the Big Data Analytics in Automotive market, along with production growth.
  • The report provides a brief summary of the Big Data Analytics in Automotive application spectrum that is mainly segmented into Industrial Applications
  • Extensive details pertaining to the market share garnered by each application, as well as the details of the estimated growth rate and product consumption to be accounted for by each application have been provided.
  • The report also covers the industry concentration rate with reference to raw materials.
  • The relevant price and sales in the Big Data Analytics in Automotive market together with the foreseeable growth trends for the Big Data Analytics in Automotive market is included in the report.
  • The study offers a thorough evaluation of the marketing strategy portfolio, comprising several marketing channels which manufacturers deploy to endorse their products.
  • The report also suggests considerable data with reference to the marketing channel development trends and market position. Concerning market position, the report reflects on aspects such as branding, target clientele and pricing strategies.
  • The numerous distributors who belong to the major suppliers, supply chain and the ever-changing price patterns of raw material have been highlighted in the report.
  • An idea of the manufacturing cost along with a detailed mention of the labor costs is included in the report.

You can Buy This Report from Here @ https://www.researchmoz.com/checkout?rep_id=2636782&licType=S&source=atm

The Questions Answered by Big Data Analytics in Automotive Market Report:

  • What are the Key Manufacturers, raw material suppliers, equipment suppliers, end users, traders And distributors in Big Data Analytics in Automotive Market ?
  • What are Growth factors influencing Big Data Analytics in Automotive Market Growth?
  • What are production processes, major issues, and solutions to mitigate the development risk?
  • What is the Contribution from Regional Manufacturers?
  • What are the Key Market segment, market potential, influential trends, and the challenges that the market is facing?

And Many More….

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Oracle #1 in Forrester's Translytical Data Platforms Wave

Oracle came out top in Forrester’s Translytical Data Platforms Wave published today (10/23/2019). Analyst firm, Forrester, describe “translytical” as, “…a hot, emerging market that delivers a unified data platform to support all kinds of workloads. The sweet spot is the ability to perform all of these workloads within a single database, leveraging innovation in memory, multimodel, distributed, and cloud architectures. Translytical can support various use cases, including real-time insights, machine learning (ML), streaming analytics, extreme transactional processing, and operational reporting.” Click here to read the full report.

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‘Personal Data Network’ Veriglif Launches, Seeks Funds

‘Personal Data Network’ Veriglif Launches, Seeks Funds

May 22 2019

Veriglif, a New York-based company which promises to ‘unlock value for buyers, sellers & creators of personal data’, is emerging from ‘stealth’ mode and beginning a rapid push towards full commercialization.

James WilsonBacked among others by insights publisher and IIeX organizer GreenBook, Veriglif says it does not directly compete with anyone in the existing research, data and insights ecosystem but offers a ‘network of networks’ allowing consumer data to be verified and exchanged with full permission for the mutual benefit of all. The platform can validate, link, store and transact any permissioned data at the individual level including behavioral, passive, transactional, geolocation, social media and opinion data, but ensures network participants have full control over every transaction using it. As such, it promises significant improvements to targeted surveys, data augmentation, audience analytics and personalized marketing; and aims to address the ever-more challenging regulatory environment post-GDPR.

The network is built on IBM’s Hyperledger blockchain protocols, and the firm says it has worked with the IT giant and more than 100 leaders in the marketing insights & analytics value chain over the past nine months to design and build its soution. In addition to Hyperledger, it makes use of a series of automated API integrations, an AI-driven data inventory solution, and a transactional processing portal.

The firm cites ‘significant demands worth billions of dollars for verified, accurate, and permissioned consumer data that meets the requirements of increasingly stringent privacy legislation’ – and says there is currently ‘no existing platform that offers validated, privacy-compliant data linked to the same consumer across multiple suppliers that also pays incentives to the consumer’. CEO James Wilson (pictured) says the founding team of eleven industry professionals have ‘a detailed behind-the-scenes understanding of this problem, and what the solution needs to be. Veriglif will fundamentally change how the world deals with individual-level data’.

Having raised $464k to date from early stage investors, the company has just launched an equity crowdfunding campaign via WeFunder to get the solution to market – accessed via https://wefunder.com/veriglif .

GreenBook’s Lenny Murphy says his organisation’s support is in line with its mission to ‘connect supply and demand via innovative new models that can support the future growth of the industry’. He adds: ‘In this case we are applying that to the personal data supply chain by creating a new platform that we believe will solve many of the challenges with the current paradigm, while working ‘within the system’ vs. trying to replace it’.

Web site: www.veriglif.com .

All articles 2006-19 written and edited by Mel Crowther and/or Nick Thomas.

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Mining for Gold in Worldwide Centers of Excellence

With the ever-growing flood of data hitting today’s enterprises, we’re in the midst of a new gold rush. To twist around a line from a Mark Twain character, you might say “there’s gold in them thar hills of data.” But this is true only for those organizations that can put high-performance computing systems, data analytics and artificial intelligence to work to capture nuggets of business value from streams of data. So how do you get started down this path? Mining value from business data is, arguably, a lot more complicated than panning for gold in mountain … READ MORE

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Accelerating Insight Using 2nd Generation Intel® Xeon® Scalable Processors with Deep Learning Boost

Artificial Intelligence (AI) techniques are quickly becoming central to businesses’ digital transformation by augmenting, and in many cases supplanting, traditional data analytics techniques. These techniques bring proactive and prescriptive capabilities to a company’s data-driven decision-making process, giving companies that adopt them early a distinct competitive advantage. Those that adopt them late will be left behind. Intel recognizes that AI methods, most notably machine learning and deep learning, are now critical components of company workloads. To address the need to both train and, arguably more importantly, have AI models make decisions faster, Intel has put these workloads … READ MORE

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Data & AI: The Crystal Ball into Your Future Success

Years ago, the future was much opaquer. Now, it’s tangible, visible and rising up all around us. It seems to be taking shape in real time, much of which can be attributed to innovation in data and infrastructure, across their respective and collective aspects.

As innovation in these areas accelerates, it rapidly gains in capabilities, particularly for enterprises who have reached a point of digital maturity, ensuring access to quality data and accelerated infrastructure at scale. Yet, for others, their data and analytics initiatives are still lacking. As their data continues to expand, they do not have the right building blocks in place to grow and change with it. In fact, a recent McKinsey survey of more than 500 executives found that more than 85% acknowledged they are only somewhat effective at meeting the goals they set for their data and analytics initiatives.

With both growing and mature data sets, the effects of enterprise deep learning and machine learning can be significant – automating processes, identifying trends in historical data and uncovering valuable intelligence that strengthens fast and accurate decision-making abilities – all of which can be used as a virtual crystal ball to refine predictions about the future and potentially its successes.

To do this correctly, companies should look at using their data AI and analytics capabilities to not only improve their core operations, but also to launch entirely new business models and applications. First, they must solve for problems in the way data is generated, collected, organized and acted upon. Because, while the mechanics are important, the ultimate value of data doesn’t come from merely collecting it, but acting on the insights derived from it.

The key lies in a fundamental mind shift of evolving your organization into a technology company with a data-first mentality.

In my experience, there are three certainties for every company:

  1. Your data is going to grow faster than you expected.
  2. The use cases for this data are going to change.
  3. The business is always going to expect outcomes to be delivered faster.

The first step in the journey to becoming a technology company is simplifying the infrastructure by moving from legacy data systems to a more nimble, flexible modernized data architecture that can bridge both structured and unstructured data to deliver deeper insights and performance at scale. Once consolidated onto a single, scalable, analytics platform, the pace of discovery and learning can be accelerated to drive a more accurate strategic vision for both today and tomorrow.

At Dell EMC, we are dedicated to bringing new and differentiated value and opportunities to our customers globally. We are always looking toward current and future trends and technologies that will help customers better manage and take advantage of their growing data sets with deep learning and machine learning at scale.

Dell EMC Isilon does just that.

As an industry leading scale-out network-attached storage, designed for demanding enterprise data sets, Isilon simplifies management and gives you access to all your data, scaling from tens of terabytes to tens of petabytes per cluster. We also deliver all-flash performance and file concurrency up to the millions, allowing us to support the bandwidth needs of 1000’s of GPUs running the most complex neural networks available. As a bonus, we accomplish this this very economically, with over 80% storage utilization, data compression and automated-tiering across flash and disk in a single cluster. Finally, Isilon based AI increases operational flexibility with multiprotocol support, allowing you to bring analytics to the data to accelerate AI innovation with faster cycles of learning, higher model accuracy and improved GPU utilization.

In an era of change and ongoing data expansion, creating a crystal ball for your business is not a matter of luck or fortune telling. It takes place through a focused strategy for doing more with the data you have at hand. By offering innovative new ways to store, manage, protect and use data at scale, Isilon moves customers that much closer to both becoming technology companies and future proofing their businesses.

To learn more, attend our April 1st webinar event, “Your Future Self is Calling, Will You Pick Up? with Dell EMC, NVIDIA & Mastercard. We look forward to seeing you there.

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