Data Science| season one, lesson 1| understanding data science by @nova001 | 10% to steem.skillshare

Definition of Data Science

Data science is the intersection between the scientific fields of mathematics, computer science and industry-specific expertise. The field of data science deals with the analysis of (large) amounts of data, the detection of anomalies in the data and the prediction of future events. The people working in the data science workspace are referred to as data scientists

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Data Science involves preparing data for analysis, including cleaning, aggregating, and processing data to perform advanced data analysis. Analytical applications and data scientists can then review the results to uncover patterns and enable business leaders to draw informed conclusions.

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Data Science. An undisclosed resource for machine learning

Data Science is one of the most interesting areas today. But why is it so important?

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Because companies are sitting on the treasure trove that is data. Modern tech have made it feasible to create and store unrivalled amounts of data. As a result, data volumes have skyrocketed. According to statistics, about 90% of the world's data was created in the last two years (2013).

But this data often just lies in databases and data lakes and is practically not used.

Companies have the ability to use the data collected and stored using these technologies to develop innovative solutions—but only if they can interpret it. Data Science comes to their aid.

Data Science recognize current trends and extracts awareness that businesses can make use of to construct better decisions and improve products and services.Perhaps most importantly, this allows machine learning models to learn from the vast amount of data that is fed into them, rather than leaving everything to business analysts waiting for what they can find in the data.

Data is the foundation of innovation, but its main value lies in the information that professionals can extract from it for later use.

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What the difference is btw Artificial intelligence, Data Science, Machine Learning?

To better understand The concept of what data science is—and how to study the discipline—it is equally important to know other terms related to the field, such as artificial intelligence (AI) and machine learning. These terms are often used interchangeably, but there are nuances.

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Here is a simple explanation:

AI means programming a computer to mimic human behavior in some way.

Data science is one of the areas of AI that refers more to the overlapping areas of statistics, scientific methods, and data analytics—that are designed to extract meaningful, useful information from large datasets.

Machine learning is one of the areas of AI that consists of methods that allow computers to make decisions based on data and implement AI applications.

Let's give one more definition.

Deep learning is a field of machine learning that allows computers to solve more complex problems.

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How Data Science is transforming business

Companies use Data Science to optimize products and services and gain competitive advantage.

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The folowing are the ways data science & machine language van be used.:

• Identify potential customers by analyzing call center data so that the marketing department can make efforts to retain them.

• Increase efficiency by analyzing traffic congestion, weather conditions and other factors so that logistics companies can speed up delivery and reduce costs.

• Analyze medical test data and symptom descriptions to improve and speed up diagnosis and treat illnesses more effectively.

• Supply chain optimization by predicting when equipment might fail.

• Detect financial services fraud by identifying suspicious behavior and anomalous activities.

• Increasing sales by making recommendations to customers based on past purchases

Many companies have made Data Science a priority and are investing heavily in this area. According to a recent Gartner survey of more than 3,000 CEOs, respondents cited data analytics and business intelligence as the top technologies for success. The CEOs interviewed felt that these technologies were of the greatest strategic importance, so they are investing accordingly.