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Discover the Best Industries to Have a Career in Data Science

Discover-the-Best-Industries-to-Have-a-Career-in-Data-Science

Data fires up everything, nowadays. And data science is gaining exceptional traction in the job world, as data analytics, machine learning, big data, and data mining are fetching relevance in the mainstream tech world. By 2025, it is being expected that data science industry will reach $16 billion in value – this is why landing a job in data science domain is the next big thing!

The skills you will imbibe as a data scientist would be incredible, powerful and extremely valuable. You can easily a bag a dream job in corporate moguls, like Coca-Cola, Uber, Ford Motors and IBM, as well as play a significant role in any pro-social or philanthropic endeavors to make this world a better place to live in.

Check out these extremely interesting fields you could start your career in data science:

Biotechnology

No wonder, science and medicine are intricately related to each other. As the technology pushes boundaries, more and more companies are recommitting themselves towards a better public health by nabbing biotechnology. Being a data scientist, you would help in unraveling newer ways of studying large amounts of data – including machine learning, semantic and interactive technologies. Eventually, they would influence treatments, drugs-usage, testing procedures and much more.

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Energy

Power industry functions on data – and tons of it. Whether it’s about extracting mineral wealth from the earth’s crust or transporting crude oil or planning better storage facilities, the demand for data scientists is on the rise. Just as expanding oil fields ask for humongous amounts of data study, installing and refining cleaner energy production facilities relies on data about the natural environment and ways of modern construction. Data scientists are often given a ring to enhance safety standards and help companies recommit themselves towards better safety and environmental regulations.

Transportation

Recently, transportation is undergoing a robust change. For example, Tesla paved a new road of development and turned countless heads by unveiling a long-haul truck that could drive on its own. Though it’s not the first time, they are prone to lead the change.

Beyond self-driving vehicle technology, the transportation industry is looking for more efficient ways to preserve and transport energy. These advancements in technology works wonders when combined with better battery technology development – in simple terms, every individual field in transportation industry is believed to benefit from a motley team of data scientists.

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Telecommunications

The internet is not only about tubes, but all about data. The future of the internet is here, with ever-increasing networks of satellites and user devices establishing communication through blockchain. Though they are yet to be used on large-scale, they have started making news. In situations like this, it would be difficult not to highlight the importance of data science and data architecture as they are becoming major influencers in the internet world. Whenever there is a dire need to make the public aware of a new product, we rely on user data – hence the role of data scientists is the key to a better future.

Today, data science is an interesting field to explore, and it is going to play an integral role as the stride in technology and globalization keeps expanding its base. If you have a keen eye for numbers, charts, patterns and analytics, this niche is perfectly suitable for you.

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Bringing Back Science into “Data Science”

Bringing Back Science into “Data Science”

Far from the conventional science disciplines, like physics or mathematics, Data Science is a budding discipline: which means there are no proper definition to explain what data science is and what role it does play.

Nevertheless, the internet is full of working definitions of data science. As per Wikipedia, Data Science is

(an) interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.

To that note, a very important aspect is left behind in this explanation: Data Science is a science first, which means a proper scientific method should be devised to tackle different data science practices. By scientific method, we mean a healthy process of asking questions, collecting information, framing hypothesis and analyzing the results to draw conclusions thereafter.

Go below, the process breakup is as follows..

Ask questions

Start by asking what is the business problem? How to leverage maximum gains? What ways to implement to increase return on investment? The finance industry takes help from data science for myriad reasons. One of the most striking reasons is to enhance the return on investment out of marketing campaigns.

What Sets Apart Data Science from Big Data and Data Analytics – @Dexlabanalytics.

Collect data

A predictive modeling analyst has access to vast data resources, which eventually makes the entire research and gathering data process much less complex. However, it is only in theory, because rarely data is stored in the desired format an analyst wants, making his job easier.

Data Science – then and now! – @Dexlabanalytics.

Devise a hypothesis

After getting to the heart and soul of the problem, we start to develop hypotheses. For example, you believe your firm’s profit is leveraged by an optimistic customer reaction towards your product quality and positive advertising capabilities of your firm. Through this example, we explained a nomological network, where you are in a position to infer casualties and correlations. While dealing in Data Science, assessing customer perception is very crucial, and so is the analysis of financial datasets.

Data Science: Is It the Right Answer? – @Dexlabanalytics.

Testing and experiments

Formulating a hypothesis is not enough; a predictive modeler relies on statistical modeling techniques to forecast the future in a probabilistic manner. Keep a note, this doesn’t result in indicating “X will occur”, instead it refers “Given Y, the probability of X occurring is 75%.”

Any proper experiment includes control groups and test, meaning a modeler when preparing a predictive model should divide the dataset so as to ensure availability of few data for testing predictive equation.

Now, if we talk about marketing – consider logistic regression. It offers a probability whether a binary event of interest will take place or not.

Enroll in an R Predictive Modelling Certification program to go through the mechanics of this problem. Reach us at DexLab Analytics.

Tracing Success in the New Age of Data Science – @Dexlabanalytics.

Evaluate results and infer conclusions

Now is the time to make a decision: do you prefer the quantitative approach? As social media is totally unstructured, the qualitative approach needs to be implemented using Natural Language Processing, which can be a tad difficult. Now, how about making a longitudinal analysis, while transforming data into time series? Do all these questions rake your mind? Yes? Then you are on the right track.

Keep Pace with Automation: Emerging Data Science Jobs in India – @Dexlabanalytics.

Reporting of results

This is the final battle scene for all predictive modelers. It calls for all the documents, based on which a modeler made his decision during the development process. All the assumptions taken have to be identified and highlighted beside the results.

And with it comes the end of our Science in Data Science process!

For more interesting updates and blogs, follow us at DexLab Analytics. Opt for our impressive Data Science Courses in gurgaon and lead the road of success!

 

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3 Stages of a Reliable Data Science Solution to Attack Business Problems

Today, businesses are in a rat race to derive relevant intuition and make best use of their data. Several notable organizations are skimming with cutting edge data science terms and resolving intricate problems (some being more successful than others).

 

3 Stages of a Reliable Data Science Solution to Attack Business Problems

 

However, the crux lies in determining the present stage of data science your organization has embraced, followed by ascertainment of the desired level of data science.

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Quantum Internet Is Now Turning Into a Reality

Quantum Internet Is Now Turning Into a Reality
 

Scientists across the globe are looking forward towards formulating new methods to realize ‘quantum internet’, an unhackable internet, which connects particles linked together by the principle of quantum entanglement. In simple terms, quantum internet will entail multiple particles striking information at each other in the form of quantum signals – but specialists are yet to figure out what it actually does beyond that. The term ‘quantum internet’ is quite sketchy at this moment. There’s no real definition of it as of now.

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Televisory Launches Data Analytics & Operational Benchmarking Platform

Televisory Launches Data Analytics & Operational Benchmarking Platform
 

Televisory, a start-up based out of India and Singapore, has launched its data analytics and operational benchmarking platform. The platform can measure real-time operational and financial performance of companies. While the firm has chosen to launch its platform from the US, its services are available globally.

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Data Journalism: What is it and how it works

The internet has killed some newspapers’ lunch, but it also presented them something truly remarkable – Data Journalism.

 
Data Journalism: What is it and how it works

Introducing Data Journalism

Data journalism is an amalgamation of a nosy reporter’s news sniffing capabilities and a statistician’s fondness for data analysis. By scrounging through vast amounts of data sets that are available through extensive connectivity, data journalists are using this data to etch out interesting stories.

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Business Intelligence: Now Every Person Can Use Data to Make Better Decisions

The fascinating world of Business Intelligence is expanding. The role of data scientists is evolving. The mysticism associated with data analytics is breaking off, making a way for non-technical background people to understand and dig deeper into the nuances and metrics of data science.
 
Business Intelligence: Now Every Person Can Use Data to Make Better Decisions
 

“Data democratization is about creating an environment where every person who can use data to make better decisions, has access to the data they need when they need it,” says Amir Orad, CEO of BI software company Sisense. Data is not to be limited only in the hands of data scientists, employees throughout the organization should have easy access to data, as and when required.

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Data Science: Is It the Right Answer?

‘Big Data’, and then there is ‘Data Science’. These terms are found everywhere, but there is a constant issue lingering with their effectiveness. How effective is data science? Is Big Data an overhyped concept stealing the thunder?

Summing this up, Tim Harford stated in a leading financial magazine –“Big Data has arrived, but big insights have not.” Well, to be precise, Data Science nor Big Data are to be blamed for this, whereas the truth is there exists a lot of data around, but in different places. The aggregation of data is difficult and time-consuming.

Look for Data analyst course in Gurgaon at DexLab Analytics.

Statistically, Data science may be the next-big-thing, but it is yet to become mainstream. Though prognosticators predict 50% of organizations are going to use Data Science in 2017, more practical visionaries put the numbers closer to 15%. Big Data is hard, but it is Data Science that is even harder. Gartner reports, “Only 15% organizations are able to channelize Data Science to production.” – The reason being the gap existing between Data Science expectations and reality.

Big Data is relied upon so extensively that companies have started to expect more than it can actually deliver. Additionally, analytics-generated insights are easier to be replicated – of late, we studied a financial services company where we found a model based on Big Data technology only to learn later that the developers had already developed similar models for several other banks. It means, duplication is to be expected largely.

However, Big Data is the key to Data Science success. For years, the market remained exhilarated about Big Data. Yet, years after big data infused into Hadoop, Spark, etc., Data Science is nowhere near a 50% adoption rate. To get the best out of this revered technology, organizations need vast pools of data and not the latest algorithms. But the biggest reason for Big Data failure is that most of the companies cannot muster in the information they have, properly. They don’t know how to manage it, evaluate it in the exact ways that amplify their understanding, and bring in changes according to newer insights developed. Companies never automatically develop these competencies; they first need to know how to use the data in the correct manner in their mainframe systems, much the way he statisticians’ master arithmetic before they start on with algebra. So, unless and until a company learns to derive out the best from its data and analysis, Data Science has no role to play.

Even if companies manage to get past the above mentioned hurdles, they fail miserably in finding skillful data scientists, who are the right guys for the job in question. Veritable data scientists are rare to find these days. Several universities are found offering Data Science programs for the learners, but instead of focusing on the theoretical approach, Data Science is a more practical discipline. Classroom training is not what you should be looking for. Seek for a premier Data analyst training institute and grab the fundamentals of Data Science. DexLab Analytics is here with its amazing analyst courses in Delhi. Get enrolled today to outshine your peers and leave an imprint in the bigger Big Data community for long.

 

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Move Your Career towards Big Data Analytics: The Future Looks Bright

Move Your Career towards Big Data Analytics: The Future Looks Bright

With state-of-the-art technology looming on the horizon, the $150-billion Indian IT industry has a high appetite for workers accomplished in the fields, like AI, Data Science, Big Data, and more.

Soon, it wouldn’t be enough to flash an engineering degree or some minor knowledge in Java or Python – the need for data science and artificial intelligence is on the rise. Automation is going to be the key to change. Globally, 12% of employers have started thinking of downsizing their workforce owing to technological advancement. Amidst all this, don’t think India would be spared. Indian bosses fear automation will reduce their headcount too. But fret not, it’s not all a bad news – there is always a silver lining after rains and that is Big Data jobs.

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Shine bright with Big Data

In India, the number of job openings in the Analytics field almost doubled from the last year. Digital natives, like Amazon, Citi, HCL, IBM, and Accenture are waiting to fill close to 50000 positions, according to a study conducted by Analytics India Magazine and Edvancer. All these definitely signify parting off the dark clouds, and I can’t agree more!

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Artificial Intelligence and Machine Learning are building a base of its own. Moreover, AI is deemed to be the hottest technical sector in the next 5 years and would beam in success. Along with top-of-the-line tech firms, more than 170 startups have transfixed their gaze on this field. To surf on the next wave of IT jobs, candidates need to step aside from low-in-demand stale skills to excel on budding Analytics skills. Every single HR Manager out there is seeking professionals who can manipulate algorithms and work wonders in various machine-learning models and you can be one of them!

Get better, get evolved

Expertise in languages, like Java/C/C++ gives you a certain edge, but to enter the dominating field of Big Data, techies will be asked to master intricate languages, such as Scala and Hive that are less conventional. Millennial recruiters are also looking out for those who have a keen insight for good design and flawless code architecture. “Programmers who focus on good design principals are always preferred over programmers who can just code,” Rajat Vashishta, founder of Falcon Minds, a resume consulting firm, says. “User experience matters a lot more than it used to, say, five years ago.”100793293-102628471r.1910x1000

Where skills in technology, like business intelligence, artificial intelligence, machine learning and DevOps are flourishing, minute attention need to be given on proper implementation of these skills, according to Aditya Narayan Mishra, chief executive officer of CIEL HR Services, a recruitment firm, otherwise all of it would be a total waste.

It’s all in the layout

Presentation matters, you agree or not! Make your resume ready to strike the job criteria you are applying for. For example, if a user interface developer wants to become a full stack developer, he must mention back-end programming skills in the profile. This will give an instant boost to the resume. The design of a resume has also changed over the years. Now, the shorter your resume the better response you get. “Most techies write pages and pages of projects in their resumes. While it is important, in most cases, the same information gets repeated. Anything above two pages is a big no,” says Vashishta.

Feel free to get in touch with our in-house experts for a data analyst course at DexLab Analytics, the premier platform for Data Science Online training in Noida.


 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
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