Believe In Your DATA SCIENCE Skills but Never Stop Improving Them

Data science
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The world of data science is taking up an important part in the technology industry. Big companies like Google, Facebook, Amazon are looking for data scientists to keep developing their services; while others like LinkedIn and Twitter are making big bets on technologies like Apache Spark (a framework either R or Python) to make sure they receive the required information processed at high speed.

Predictive analysis has become one of the most attractive tools for businesses nowadays. Many organizations have already started using Data Science techniques involving machine learning algorithms to get accurate predictions about future trends within their business environment. This ability brings a competitive advantage over other market players because it helps the company to take decisions that can positively influence into its growth strategy. Thus, being able to create an accurate predictive model, organizations are able to gain more knowledge about their customers and goods in order to make better business decisions. To know more check RemoteDBA.com.

Many are the ways of learning Data Science.

  • There are training courses, degrees at universities or MOOCs (massive open online courses) available over the Internet. It is also very common for IT professionals to attend these courses when they want to acquire new skills in data analysis, modeling techniques or predictive analytics.
  • However, there is an issue that needs to be addressed in this context: all those previous mentioned ways of acquiring Data Science knowledge may not bring the expected final results because people tend to stop their learning process after finishing a course or degree program. This happens even with people who already have experience in this area because they think they know enough and that there will be no need to keep learning new things.
  • In fact, it is required from a Data Scientist to have a wide range of skills in the field because he/she has to deal with data sources from different platforms or databases spread across their organization’s technology cloud. In order for this knowledge to be useful, data scientists should be able to find insights within those datasets which lead them into making better decisions for their business strategy. According to McKinsey Quarterly, it is necessary for organizations “to build a strong talent base, provide appropriate fellowship opportunities and training programs, and ensure that compensation packages remain competitive” if they want to continue generating value from Big Data. There is also a study made by IBM showing that 60 percent of data scientists believe that “quantitative talent shortage (i.e., mathematical, statistical, machine-learning expertise) poses the biggest obstacle to effective analytics”.

There are several ways for organizations to develop their Big Data strategy and take advantage over competitors in the market.

  • The first one involves hiring more staff for this area; however, it is an expensive solution which may not always be possible depending on the company’s budget.
  • Another option would be using Tools or applications which allow companies to make use of third party services instead of creating their own tools internally. This way can also bring savings because many providers already have the entire necessary infrastructure. Ready to work with different kinds of businesses so they need only pay for what they actually use without needing huge investments in software and hardware. The third option is to choose one of those tools available in the market that covers all aspects for this area such as predictive modeling, data processing or machine learning algorithms.

One such tool is Predictive Analytics World (PAW).

PAW is a platform which includes different types of analytics and allows you to use them from an easy-to-use GUI. From Predictive Modeling and Machine Learning to Data Visualization and Clustering, there’s everything you need to build and deploy your data science project. You can also create a very simple prototype using its drag & drop interface without writing any line of code according to Markus Noga, IBM Director Technology Sales at IBM Watson Group. In order for companies to take full advantage of Big Data, it is necessary for them to have skilled data scientists. The number of businesses using predictive tools is still not very high. However the growth in the market is picking up pace quickly because it makes everything faster and easier for companies working with Big Data.

These are some of the reasons why IBM has recently built a new artificial intelligence-based platform called Watson Analytics. With this tool you can make better decisions by gaining insights from your data without having to be an expert in data science. This way, companies may stop depending on data scientists that are becoming really expensive nowadays or spend too much time trying different solutions until they find one that works fine enough for them.

Conclusion:

Big Data has been called the new oil by some people. This is because its value can be extremely high and therefore a limited resource. However it is still possible for different companies to develop specific strategies in order to maximize their performance and take advantage over others in the market. Predictive tools are essential for this purpose because they allow businesses to make better decisions based on insights from Big Data which can help them gain competitive advantages or outpace their competitors. In the future, more companies should rely on predictive tools that provide complete solutions instead of trying to create their own software internally which would involve huge investments in resources and talent.

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