DATA SCIENCE – A Game-Changing Industry

You purchased a product from an online platform like Amazon, Flipkart and in the next couple of minutes, you got a recommendation for a similar product you have purchased. Have you ever thought about how did this happen?

Let’s explore the answer to this very common question.

It’s the?‘DATA’?!!!

I have tried my best to give you a closer look at what is data science and how it is driving us towards the future which we have seen in sci-fi movies.

So, what is Data Science?

Data Science is the field of science in which we deal with the various forms of data to get and develop some informative insights for business development and research.

In other words, “DATA SCIENCE” word can be explained as preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. Analytic applications and data scientists can then review the results to uncover patterns and enable business leaders to draw informed insights.

Following are the ways or we can say as the effects of “Data Science” which will surely affect the human life in near future:

Data science: An untapped resource for machine learning:

Data science is one of the most exciting fields out there today.?

But why is it so important?

Because companies are sitting on a treasure trove of data. As modern technology has enabled the creation and storage of increasing amounts of information, data volumes have exploded. It’s estimated that 90 percent of the data in the world was created in the last two years. For example, Facebook users upload 10 million photos every hour.

But this data is often just sitting in databases and data lakes, mostly untouched.

The wealth of data being collected and stored by these technologies can bring transformative benefits to organizations and societies around the world — but only if we can interpret it.

That’s where data science comes in !!!

Data science reveals trends and produces insights that businesses can use to make better decisions and create more innovative products and services. Perhaps most importantly, it enables machine learning (ML) models to learn from the vast amounts of data being fed to them, rather than mainly relying upon business analysts to see what they can discover from the data.

Data is the bedrock of innovation, but its value comes from the information data scientists can glean from it, and then act upon.

Now let’s try to differentiate between the?Data Science,?Machine Learning?and?Artificial Intelligence

To better understand data science — and how you can harness it — it’s equally important to know other terms related to the field, such as artificial intelligence (AI) and machine learning. Often, you’ll find that these terms are used interchangeably, but there are nuances.

Here’s a simple breakdown:

• AI?means getting a computer to mimic human behaviour in some way.
• Data science?is a?subset of AI,?and it refers more to the overlapping areas of statistics, scientific methods, and data analysis — all of which are used to extract meaning and insights from data...
• Machine learning?is another?subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
• And for good measure, we’ll throw in another definition.

Deep learning?is a?subset of machine learning?that enables computers to solve more complex problems.

Hence, we can say that Data Science is the Heart of automated systems.

How data science is transforming business

Organizations are using data science to turn data into a competitive advantage by refining products and services. Data science and machine learning use cases include:

• Determine customer churn by analysing data collected from call centres, so marketing can take action to retain them.
• Improve efficiency by analysing traffic patterns, weather conditions, and other factors so logistics companies can improve delivery speeds and reduce costs.
• Improve patient diagnoses by analysing medical test data and reported symptoms so doctors can diagnose diseases earlier and treat them more effectively.
• Optimize the supply chain by predicting when equipment will break down.
• Detect fraud in financial services by recognizing suspicious behaviours and anomalous actions.
• Improve sales by creating recommendations for customers based upon previous purchases.

How data science is conducted

The process of analysing and acting upon data is iterative rather than linear, but this is how the data science lifecycle typically flows for a data modelling project:

Planning:?Define a project and its potential outputs.

Building a data model:?Data scientists often use a variety of open-source libraries or in-database tools to build machine learning models. Often, users will want APIs to help with data ingestion, data profiling and visualization, or feature engineering. They will need the right tools as well as access to the right data and other resources, such as computing power.

Evaluating a model:?Data scientists must achieve a high percentage of accuracy for their models before they can feel confident deploying them. Model evaluation will typically generate a comprehensive suite of evaluation metrics and visualizations to measure model performance against new data, and also rank them over time to enable optimal behaviour in production. Model evaluation goes beyond raw performance to take into account expected baseline behaviour.

Explaining models:?Being able to explain the internal mechanics of the results of machine learning models in human terms has not always been possible — but it is becoming increasingly important. Data scientists want automated explanations of the relative weighting and importance of factors that go into generating a prediction, and model-specific explanatory details on model predictions.

Deploying a model:?Taking a trained, machine learning model and getting it into the right systems is often a difficult and laborious process. This can be made easier by operationalizing models as scalable and secure APIs, or by using in-database machine learning models.

Monitoring models:?Unfortunately, deploying a model isn’t the end of it. Models must always be monitored after deployment to ensure that they are working properly. For example, in fraud detection, criminals are always coming up with new ways to hack accounts.

Final Thoughts:

So, being an aspiring data scientist, regardless of the field we are working in, Data Science is an evergreen domain, giving us a chance to convert our imagination into reality via automation and helping us in developing businesses.

The unlimited extent of data in various industries like healthcare, finance, corporate services, media, communication, E-commerce, software, manufacturing, Robotics and many more will drive us in creating a better future.

We all are living in the best era of all time!! I hope we all make this era an unforgettable timeline among all human history.