What is Data Science?

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. Getting skilled in Data Science is essential in bagging better opportunities. Check out this Data Scientist Training Course to help you learn from the industry's best experts in a practical approach that will help you surge ahead of your peers.  

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.

What’s the difference between data science, artificial intelligence, and machine learning?

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.

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 analyzing data collected from call centers, so marketing can take action to retain them

Improve efficiency by analyzing traffic patterns, weather conditions, and other factors so logistics companies can improve delivery speeds and reduce costs

Improve patient diagnoses by analyzing 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 behaviors and anomalous actions

Improve sales by creating recommendations for customers based upon previous purchases

Many companies have made data science a priority and are investing in it heavily. In Gartner’s recent survey of more than 3,000 CIOs, respondents ranked analytics and business intelligence as the top differentiating technology for their organizations. The CIOs surveyed see these technologies as the most strategic for their companies, and are investing accordingly.

How data science is conducted

The process of analyzing and acting upon data is iterative rather than linear, but this is how the data science lifecycle typically flows for a data modeling 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 behavior in production. Model evaluation goes beyond raw performance to take into account expected baseline behavior.

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. The data the model was trained on may no longer be relevant for future predictions after some time. For example, in fraud detection, criminals are always coming up with new ways to hack accounts.

Tools for data science

Building, evaluating, deploying, and monitoring machine learning models can be a complex process. That’s why there’s been an increase in the number of data science tools. Data scientists use many types of tools, but one of the most common is open source notebooks, which are web applications for writing and running code, visualizing data, and seeing the results—all in the same environment.

Some of the most popular notebooks are Jupyter, RStudio, and Zeppelin. Notebooks are very useful for conducting analysis but have their limitations when data scientists need to work as a team. Data science platforms were built to solve this problem.

To determine which data science tool is right for you, it’s important to ask the following questions: What kind of languages do your data scientists use? What kind of working methods do they prefer? What kind of data sources are they using?

For example, some users prefer to have a data source-agnostic service that uses open source libraries. Others prefer the speed of in-database, machine learning algorithms.