Data Sciences is a very vast field and in recent times, there is a high demand of professionals in this field. Dealing with data is not easy. Data sets available with companies are very large and to extract meaningful data is a tough job. Thus, the job of data scientist is becoming very important for decision-making and is based on automation and machine learning. The main role of data scientist is to organize and analyse data. Other than this, data can help in predictions, pattern detection analysis etc. All this can be done the help of some software which is specially designed for the task. The responsibilities of data scientist begin with data collection and ends with decision making on the basis of data.

To know more about the key roles of data scientist, requirements and skills visit: https://www.cio.com/article/3217026/data-science/what-is-a-data-scientist-a-key-data-analytics-role-and-a-lucrative-career.html#tk.cio_rs

With advancement of technology, one field that will be highly demanded in upcoming years is turning up to be Artificial Intelligence. It is bringing changes that is transforming the world. AI comes with its sub streams such as data mining, machine learning, neural networks etc. This field has already become the area of interest for many programmers and developers. However, still there are not many developers in this stream. 

Schools, Colleges and Organizations have started providing courses on AI. It is one of the best career option. But Artificial Intelligence is just a main stream. One should have a clear mind about his career opportunities. Following are few options you can opt if interested in Artificial Intelligence and want to have a career towards it:

1. A.I. Research Scholar
2. A.I. based Software Developer
3. Data Scientist
4. Machine Learning Engineer
5. Automation Engineer

To know more about them visit: https://www.technotification.com/2018/04/top-5-career-opportunities-in-artificial-intelligence.html

Data Science is a new field of interest and used in every sector. Whether it is a business, production line or a tech company, each of them wants someone to analyse their data. This would further help them to make decisions. Even though there is so much need of data scientist, still the number of data scientist is low. There are many characteristics that could define a good data scientist. 

Few of them starting with C are: Curious, Careful, Clever, Confident, Creative, Capable, Communicative, Considerate, Candid and Collaborative. 

To know further about these words visit: https://medium.com/@tableaucoach/characteristics-of-a-data-scientist-ten-cs-4e3b185cc7cd

Often the outputs of data models developed by data, scientists end up in a report which summarizes the state of business and used by stakeholders to make decisions. But it is necessary to achieve a system that can predict the future outcomes in real time. This can be done by integrating the model in a production environment, however, it requires advance engineering skills and data scientists cannot do it alone. The process of deployment follows broadly 7 steps :  1.Refactor the model code

2. Walk through the code and determine how it slots into the engineering cycle

3.Re-write into a production stack language or PMML

4.Implement it into the tech stack

5. Test performance

6. Tweak the model based on test results

7.Slowly roll out the model.

Today many companies are adopting tools to make this process faster to reap the benefit of data driven decision making.

Read more at : https://www.datascience.com/blog/navigating-the-pitfalls-of-model-deployment

Data Science has become an inevitable charter in our everyday lives where every action of ours is measured, plotted, classified and logged. Businesses have also realized that they should adopt and embrace these changes now or risk being left behind in this fast moving digital world. Data Monetization is the new paradigm for organizations and slowly but steadily data is becoming their currency of trade.
Data Science is more like an art of turning data into actionable insights. Though we consume data regularly, we never cared to look behind the scenes on the rigorous processes, data preparation and machine learning algorithms that give us accurate data to devour. And this looks like some deep mystery but in reality it’s not a mystery, it’s just an intelligent use of data and various resources available to so called wizards: Data Scientists. To know more read the complete article by Prakash Pasupathy at: http://www.datasciencecentral.com/profiles/blogs/solving-the-data-science-mystery

In today’s digital age the world has become smaller.Gone are the days, when organizations used to load data in their data warehouse overnight and take decision based on BI, next day. Today organizations need actionable insights faster than ever before to stay competitive.With real-time analytics, the main goal is to solve problems quickly as they happen, or even better, before they happen. The lead role in revolutionizing real-time analytics is played by Internet of Things(IoT) . Now, with sensor devices and the data streams they generate, companies have more insight into their assets than ever before.
But it is so great as it looks , indeed it is as it helps getting the right products in front of the people looking for them, or offering the right promotions to the people most likely to buy using the real time recommender system.
are the days of waiting long hours to know the analytics of your data , now is the time to move beyond just collecting, storing & managing the data to take rapid actions on the continuous streaming data – Real-Time!! You can read the full article at http://www.datasciencecentral.com/profiles/blogs/do-you-know-what-is-powerful-real-time-analytics

A traditional data science department is comprised of Data Scientists, Data Engineers and Infrastructure Engineers. This model has a drawback that one role is always dependent on other and likely to criticize them for task failures because they didn't do their job well. These conflicts may reflect in the quality of final data product. So, what went wrong? You probably don't have big data. Jeff Magnusson (Director of Algorithms Platform at Stitch Fix) suggested a clever approach of forming a "High Functioning Data Science Department" which involves building an environment which allows autonomy, ownership, and focus for everyone involved yet at the same time clearly distinguishing the roles of Data Scientists and Data Engineers. Data scientist can't suddenly become talented engineers nor is that engineers will be ignorant of all business logic, the partnership is inherent to the success of this model. You can read more at: http://multithreaded.stitchfix.com/blog/2016/03/16/engineers-shouldnt-write-etl/

Aim-Lever-Data-Implement (ALDI) is an approach to integrate marketing analytics with Data Science, i.e making data the primary object of various decisions. So is it something very difficult or some kind of rocket science , no it’s a simple paradigm which follow the following approach :

• Aim :

The aim of the analysis needs to be fixed by the strategy teams, before any data scientists gets involved, as they are are ones who know what exactly is needed.

• Lever :

It is very important for an organization to know what actions it is going to take as a result of the analysis, not what the organization’s strengths are.

• Data :

Once the objectives have been defined the next step is then to gather the appropriate data and then perform the analysis. This is where data scientists would really come in.

• Implement :

This is the final step of the problem where results from analysis are used to make further decision on how the problem is going to be tackled and what all needs to be done by the various departments.

You can read the full article by Srividya Kannan Ramachandran at http://www.datasciencecentral.com/profiles/blogs/aldi-a-new-paradigm-for-integrating-marketing-analytics-with-data

Data Science is one of the hottest job of the 21st century. But is it easy to be a data scientist , and the answer is it’s not hard. But here are some of the things that one need to avoid to be a bad data scientist :

• Focus on tools rather than business problems :

Yaa tools do matter, but what is even more important is the problem you are working on, it should be the basis of all your decisions.

• Planning communication last :

Communication helps in getting various insights about our ideas and hence help in improving our approach to handle a particular problem.

• Data analysis without a question / plan :

Data without a plan or motive is useless and we often end with more of the things we don’t need than the things we really need.

• Don’t read enough :

This is a mistake that everyone makes, to be updated with the recent development tends helps in constantly improving our skill set and hence benefits the organization.

• Fail to simplify :

Data Scientist generally fail to maintain the core idea behind their product and hence end upwith something which is much complicated for the end users.

• Don’t sell well :

The job of data scientist doesn’t end with creating the product , he must know how to sell it, how to reach to the end users.

You can read more at http://ucanalytics.com/blogs/6-worst-mistakes-for-data-scientists-and-how-to-avoid-them-explained-with-quotable-quotes/

Data Scientist has been coined as the hottest job for the year 2016 by many leading platforms. But should it be the only reason to choose data science as a profession. Not at all. So here are some more reasons that can help you. The starting salaries are on the rise, you will earn more, PhDs are no more essential. There are enormous opportunities in various industries including finance, healthcare, and transportation. You get a chance to make a difference. Read the complete article here: :http://www.itworld.com/article/3063499/5-more-reasons-to-be-a-data-scientist.html

Companies are looking for talented personnel especially in the analytics and big data sector. According to a new report, start-ups are offering phenomenal packages to data scientists as they give them a competitive advantage. Indian start-ups are willing to pay around 10.8 lacks to a talented analyst. Although the global market has been very fluctuating for the past five years but the need for a specialist is growing with the emerging work in this sector. Demand has grown but the supply has failed to meet .An Experienced Analyst (minimum 5 years) demands 12.3 lac to companies. Kolkata in India holds maximum number of analysts or data scientists. The average package salary of an analyst professional is 9.36 lacks.

To know more: http://economictimes.indiatimes.com/jobs/analytics-big-data-to-see-robust-hiring-high-pay-packets-report/articleshow/51105814.cms

Big data analytics require skilled data scientists who are paid unreasonably high amount of money, because of their ability to ask right question and create the most effective algorithm in order to extract meaningful information from tons of data. But, not anymore. Researchers at MIT teamed had developed a machine of automated analytics that explores patters and designs in data structures. Read more at:- http://blogs.csc.com/2015/10/16/can-automated-analytics-reduce-need-for-data-scientists/

Data analytics is no longer limited to the field of business and market research but it has spread across the financial sector as well. Stock market investors are using data driven insights and predictions to make crucial investment decisions. By observing the market conditions and various metrics related to it, the data scientists are being able to come up with accurate inferences about the market, and the data analysis is becoming increasingly precise and data intensive.  This is due to the availability of financial data and decreasing cost of information technology. The growth of analytics in this sector is likely to continue and is going to revolutionize the way financial trading is done. To know more read: http://www.huffingtonpost.com/irene-aldridge/why-big-data-matters-in-f_b_7553438.html?ir=India&adsSiteOverride=in

When one starts a new project that involves analyzing his company's data especially when the data is stored across functional areas, that person is in trouble. The data lake model helps in this case. To get access to data doesn't require an integration effort, because data is already there in the lake and one can apply MapReduce and other algorithms to use it. In the lake some data are unstructured or not structured by us for a given project. To construct a data lake one needs to learn some of the Hadoop stack such as Sqoop, Oozie and Flume. Next a data scientist should be found who understands Hadoop as well as business and the company’s business data in particular. Then one should start with basic cases and use simple and familiar tools like Tableau to make nice charts, graphics, and reports demonstrating that he can do something useful with the data. Next security up front should be considered, as well as who can access what data. Use of core Hadoop platform is beneficial. Apart from this one should keep in mind that lake security may have business unit implications and one should not have a lot of mini lakes i.e. data ponds that are separate and not equal. Read more at:http://www.infoworld.com/d/application-development/how-create-data-lake-fun-and-profit-246874?page=0,0

Sampling error can cause problems if they are not taken care of. Errors in judgment about sample size can be fixed easily and sample sizes must be considered seriously if big data is being used for predictive analysis. A leader trying to use big data in predictive analysis should always consult the data scientist. The way to understand whether enough data has been collected or not for the purpose of prediction involves understanding the tolerance of the risk associated to accept the assumptions drawn from the sample size characteristics. There are two types of risk: the risk that you're going to take some action when you shouldn't and the risk that you are not going to take some action when you should. Also enough information should be available about the sample variation and precision of measurement to know whether enough data has been collected to make prediction. To know more about importance of sample size in predictive analytics, go to John Weathington (President and CEO of Excellent Management Systems, Inc.)'s link: http://www.techrepublic.com/blog/big-data-analytics/why-samples-sizes-are-key-to-predictive-data-analytics/ 

Human element plays an important role in the application of big data. Though data science is based upon business analytics, it is different from business analytics. Data science collects data and other information from different systems and then asks many different questions. To know more about whether it is necessary to include data scientist when a company is deploying big data solutions or not, go through the article by Daniel Kusnetzky, software engineer and product manager in Kusnetzky group.

http://www.zdnet.com/the-human-element-is-critical-in-applying-big-data-7000028983/