Friday, September 20, 2019

Data Science – Challenges and Opportunities - Why Choose Data Science for Your Career

Data science is a multidisciplinary scientific field that uses mathematics, statistics, data analysis, information science, machine learning and their related methods, processes, algorithms.
A data scientist collects large sets of structured and unstructured data from varying sources, and analyzes data for actionable insights, cleans and validates data to ensure accuracy, completeness, standardization, and uniformity.
Data Science
Data Science and Data Scientist: Challenges and Opportunities, What are the main challenges that data scientists face and how to deal with them. 

Data Science – Challenges and Opportunities - Why Choose Data Science for Your Career

Introduction
Scientists believe that the bulk of the data and intellectual output on earth and throughout history has been achieved only in the past few years, but with the continuous development of science and technology and the immersion of humans in more electronic worlds, we believe that the volume of data and information produced by man has not yet reached its maximum speed.

It is estimated that 90 percent of the world's data has been generated in the past two years. For example, Facebook users upload 10 million images per hour. 
The number of connected devices in the world - the Internet of Things (IoT) - is expected to increase to more than 75 billion by 2025.

Big Data has become a treasure trove of knowledge that can be used to achieve various gains, but this is not possible without relying on powerful expert systems, and specialists who are able to prospect and analyze this data and extract what benefits and whatnot. 

There is no point in collecting and accumulating data unless there are clear objectives to exploit it.

Without the expertise of professionals who turn state-of-the-art technology into actionable insights, Big Data is better than nothing. 
Today, many organizations are opening their doors to big data and unlocking their power - raising the value of a data scientist who knows how to manipulate actionable insights from big data.

The wealth of data collected and stored can bring transformational benefits to organizations and communities around the world, but only if we can interpret them. This is where data science comes into play




Data Science – Challenges and Opportunities 

Data science is a vast field and has its fair share of challenges and opportunities. 
Many people want to become data scientists, so it is necessary to weigh the pros and cons of data science and give a real picture.
Therefore, here we will discuss the challenges and opportunities of data science.

This article will help you to assess yourself, make the right decision in the field of data science and select data science for your career.

What Are the Major Challenges Faced by Data Scientists?

At the moment, data science is expanding its branches all over the world. But it involves a lot of challenges that create many obstacles for data scientists when dealing with data. 

There are many companies in the world that suffer from inefficient team workflows because different people use different tools and processes that do not work well together. 

Without better integration and disciplined central management knowledge, executives may not see a full return on their investment. This chaotic environment poses many challenges. Let us discuss some of the major obstacles faced by data scientists.

Data scientists cannot work efficiently. Because access to data must be given by an information technology system administrator, data scientists often wait a long time to get the data and resources they have to analyze that data. 

Once they arrive, the data science team can analyze the data using different and possibly incompatible tools. 
For example, a data scientist may develop a model using the python programming language but the application in which it will be used is written in a different language such as R language. 
This is why it may take weeks or even months to publish templates in useful applications.

IT administrators take a lot of time to support. Due to the proliferation of open-source tools, the IT department has a growing list of tools that need support. 
For example, the data scientist in marketing may use different tools than the data scientist in finance. 
Teams may also have different workflows, which means the IT department must constantly re-create and update environments.

Application developers do not have access to use machine learning. Sometimes, there is a need to re-encode machine learning models that developers receive or may not be ready for deployment in applications. 
Because access points may be inflexible, templates cannot be deployed in all scenarios and leave application developer scalability.

Business managers can be removed from data science. The data science workflow is not integrated into decision-making processes and systems at all times, which creates problems for business managers in intentionally collaborating with data scientists.

Without more disciplined central management skills, it is difficult for business managers to understand why it takes longer from prototype to production, and are often less likely to support investment in projects they consider too slow.


What are the Benefits of Learning Data Science?

Data science is considered a technological revolution that everyone wants to talk about.
Data scientists have various skills from different backgrounds such as analytics, mathematics, statistics, modeling and business skills.
These important technical skills help data scientists identify patterns that can help institutions and companies recognize new market opportunities.

Here, you will have a look at the benefits of data science and data science career opportunities.

Why Do We Need Data Science Platform?
When companies faced challenges while using data science in their business, they realized that without an integrated platform, data science would be ineffective, insecure, and difficult to measure. After considering and understanding the potential benefits of data science and possible challenges data scientists face,, they thought that there should be a platform that would deal with emerging challenges properly and then a data science platform emerged in the business world. 

Data science platforms are software hubs around which all data science works. They put the whole process of data modeling in the hands of data science teams so that they can focus on gaining insights from the data and communicating them to key business stakeholders. 
Through central platforms, data scientists can work in a collaborative environment using their favorite open source tools, while synchronizing all their work with a version control system. 

A good core platform reduces many of the challenges of implementing data science and helps companies turn their data into insights faster and more efficiently.



Benefits of Data Science Platform
The data science platform eliminates bottlenecks in workflows by simplifying management and using open source tools, infrastructure, and frameworks. 

Data science also reduces redundancy and stimulates innovation by allowing teams to share code, results, and reports.
For example, the data science platforms may allow data scientists to publish models as an application programming interface (API), making it easier to integrate them into different applications. 

Using the data science platform, data scientists can access tools, data, and infrastructure without having to wait for the IT department.
The demand for data science platforms in the market has increased. 

In fact, digital marketing platforms are expected to grow at a compound annual growth rate (CAGR) of more than 39 percent over the next few years and are projected to reach about the US $ 386 billion by 2025.

What Strategies Do Data Scientists Need in the Platform?
If you're ready to explore the possibilities of data science platforms, here are some key possibilities and important strategies to keep in mind:

Prioritize integration and flexibility: Ensure that the platform includes support for the latest open-source tools, including popular version control providers and ensure tight integration with other resources.

Choose a project-based user interface that helps collaborative work: The platform should enable people to work together on models, from conception to final development. Each team member must be given access to data and resources.

Include enterprise-level capabilities: Make sure the platform is able to expand your business as your team grows. The platform must be highly available, have strong access controls, and support a large number of concurrent users.

Make data science as an improved self-service: Look for a platform that reduces the burden of IT and engineering processes and makes it easier for data scientists to accelerate the turnover of environments instantly, track all their work and easily deploy prototypes in the production phase.



How Data Science Can Improve Your Business Efficiency?
Many organizations and companies use data science teams to transform data into a competitive advantage by improving products and services. 

Most companies have made data science a priority and continue to invest heavily in it. For example, logistics companies analyze weather conditions, traffic patterns, and other factors to improve delivery speeds and reduce costs.

Healthcare companies use data science to analyze medical test data and reported symptoms to help doctors diagnose and treat diseases early and more effectively.

IT companies use data science to analyze data collected from call centers to identify potential customers, so the marketing department can take action to retain them.

 According to a Gartner survey, more than 3,000 Chief Information Officer (CIO) is heavily involved in the business model change, in which participant’s ranked professional analytics and information as the best distinctive techniques for their organizations. 

Many chief executive officers (CEOs) believe that data science tools and technologies are the most strategic for their companies and therefore attract the newest investments.

Why Choose Data Science for Your Career

Here are some important reasons why you should choose data science for your career:
–The demand and requirement for data scientists are booming.
–There is a lack of competition and ease in searching for jobs and career options in data science.
–Data science has the expertise and provides great opportunities for IT professionals from various fields and flows to acquire skills and learn data.
–Data scientist salary across the globe indicates positive and exponential growth.
–Data science is the best option to overcome the shortcomings of career stagnation.
–A variety of training and skill upgrading options are available in the data science field.

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