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Data Science Landscape

Data Science Landscape_112021A
[Data Science Landscape]
 
 

- Modern Data Science Technology

Data Science is the buzzword in the present market. As the market constantly changes in various ways, data science is getting popular among businesses to learn about their customers and increase profitability. Data science even assists technologies like AI (artificial intelligence) and ML (machine learning). As technical tools are evolving, data science techniques are also maturing with them. Everyone cannot be a data genius or a tech whiz, but other professionals will soon need a data science process.

Data science has become a very helpful tool for solving problems in almost any field. In economics you can assess risks or forecast trends. Health care processes the information generated to construct case studies for studying certain diseases while medical device manufacturers implement artificial intelligence to help hospital administrators improve efficiencies and clinician’s productivity. 

Uncover patterns and build predictions using data, algorithms, machine learning and AI techniques, data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data. 

The computer processing power available today, combined with the explosion in the amount of data available to us in a digital world, means smart, self-teaching machines are now commonplace. Although, they are often hidden away behind services or web interfaces where we may not even notice them, unless we know what we’re looking for! But behind the scenes at Google, Facebook, Netflix or any of the hundreds of organisations which have deployed this revolutionary technology, vast data warehouses and lightning-fast processing units crunch through huge volumes of information to make this a reality.

 

- Data Science Tools and AI-driven Innovation

Data Science includes obtaining the value from data. It is all about understanding the data and processing it to extract the value out of it. Data Scientists are the data professionals who can organize and analyze the huge amount of data. The functions that data scientists perform include identifying relevant questions, collecting data from different data sources, data organization, transforming data to the solution, and communicating these findings for better business decisions. 

Data science tools can be of two types. One for those who have programming knowledge and another for the business users. Tools which are for business users, automate the analysis. Python and R are the most popular languages among data scientists.

Using data science tools and solutions, you can accelerate AI-driven innovation with:

  • An intelligent data fabric 
  • The ability to run any AI model with a flexible deployment
  • Trusted and explainable AI
  • Etc.

In other words, you get the ability to operationalize data science models on any cloud while instilling trust in AI outcomes. Moreover, you'll be able to manage and govern the AI lifecycle, optimize business decisions with prescriptive analytics and accelerate time to value with visual modeling tools.

 

Data Science_Main Formulas for ML_111921A
[Data Science: Main Formulas for Machine Learning]

- Future of Data Science

The future of industry is intelligent and powered by data. Big data refers to extremely large datasets that are difficult to analyze with traditional tools. It is often boiled down to a few varieties of data generated by machines, people, and organizations. When needs for data collection, processing, management, use, and analysis go beyond the capacity and capability of available methods and software systems. These constraints are often defined by volume, variety, velocity, veracity, etc.. Big Data can create efficient challenging solutions in health, security, government and more; and usher in a new era of analytics and decisions. 

Different industries need to wake up to the importance of data at its disposal. Companies in the retail industry must analyze customer buying data to predict what their customers will buy next and understand which products they are interested in. Similarly, companies in the engineering and manufacturing sectors must analyze the data of their machinery available to them, to predict which machine may breakdown in the future

Big data is being generated by everything around us at all times. Every digital process and social media exchange produces it. Systems, sensors and mobile devices transmit it. Big data can be either structured, semi-structured, or unstructured. IDC estimates that 90 percent of big data is unstructured data. Many of the tools designed to analyze big data can handle unstructured data. The unstructured data usually refers to information that doesn't reside in a traditional row-column database. It is the opposite of structured data - the data stored in fields in a database. 

 

- Future of Data Analytics

Big data is arriving from multiple sources at an alarming velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics capabilities and skills. In most business use cases, any single source of data on its own is not useful. Real value often comes from combining these streams of big data sources with each other and analyzing them to generate new insights. The organization that can quickly extract insight from their data AND leverage the data achieves an advantage.  

Analyzing large data sets, so-called big data, will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.

As more companies adopt big data analytics, more technologies will be developed to provide more accurate predictions. This is like a chain where one factor affects the other, and if all the factors are only increasing and joining hands and helping the market, big data analytics is only going to grow and come up with more variations. 

Though the big data is expected to grow, it is still a raw unstructured field to a certain extent. Of course, it is helping a lot of companies and is helping the market too, but one still needs to understand how to leverage big data analytics more effectively

 

 

 

[More to come ...]

 

 

 
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