What is Data Science?

  Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter epertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.

Data science is an interdisciplinary field that uses algorithms, procedures, and processes to eamine large amounts of data in order to uncover hidden patterns, generate insights, and direct decision making. To create prediction models, data scientists use advanced machine learning algorithms to sort through, organize and learn from structured and unstructured data. 

While Data Science focuses on finding meaningful correlations between large datasets, Data Analytics is designed to uncover the specifics of etracted insights. In other words, Data Analytics is a branch of Data Science that focuses on more specific answers to the questions that Data Science brings forth. 

Data analytics focuses more on viewing the historical data in contet while data science focuses more on machine learning and predictive modeling.

Data Analytics — Knowledge of Intermediate Statistics and ecellent problem-solving skills along with

Deterity in Ecel and SQL database to slice and dice data.

Eperience working with BI tools like Power BI for reporting

Knowledge of Stats tools like Python, R or SAS

Data Science — Math, Advanced Statistics, Predictive Modelling, Machine Learning, Programming along with –

Proficiency in using big data tools like Hadoop and Spark

Epertise in SQL and NoSQL databases like Cassandra and MongoDB

Eperience with data visualization tools like QlikView, D3.js, and Tableau.

Deterity in programming languages like Python, R, and Scala.

Data is being regularly collected by businesses and companies for transactions and through website interactions. Many companies face a common challenge – to analyze and categorize the data that is collected and stored. A data scientist becomes the savior in a situation of mayhem like this. Companies can progress a lot with proper and efficient handling of data, which results in productivity.

A regulation for data protection was passed by California in 2020. This created co-dependency between companies and data scientists for the need of storing data adequately and responsibly. In today’s times, people are generally more cautious and alert about sharing data to businesses and giving up a certain amount of control to them, as there is rising awareness about data breaches and their malefic consequences. Companies can no longer afford to be careless and irresponsible about their data. The GDPR will ensure some amount of data privacy in the coming future.

Career areas that do not carry any growth potential in them run the risk of stagnating. This indicates that the respective fields need to constantly evolve and undergo a change for opportunities to arise and flourish in the industry. Data science is a broad career path that is undergoing developments and thus promises abundant opportunities in the future. Data science job roles are likely to get more specific, which in turn will lead to specializations in the field. People inclined towards this stream can eploit their opportunities and pursue what suits them best through these specifications and specializations. 

Data is generated by everyone on a daily basis with and without our notice. The interaction we have with data daily will only keep increasing as time passes. In addition, the amount of data eisting in the world will increase at lightning speed. As data production will be on the rise, the demand for data scientists will be crucial to help enterprises use and manage it well.

The earliest applications of data science were in Finance. Companies were fed up of bad debts and losses every year. However, they had a lot of data which use to get collected during the initial paperwork while sanctioning loans. They decided to bring in data scientists in order to rescue them from losses.

Over the years, banking companies learned to divide and conquer data via customer profiling, past ependitures, and other essential variables to analyze the probabilities of risk and default. Moreover, it also helped them to push their banking products based on customer’s purchasing power.

According to a study, the data generated by every human body is 2 terabytes per day. This data includes activities of the brain, stress level, heart rate, sugar level, and many more. To handle such a large amount of data, now, we have more advanced technologies and one of them is Data Science. It helps monitor patients’ health using recorded data.

With the help of the application of Data Science in healthcare, it has now become possible to detect the symptoms of a disease at a very early stage. Also, with the advent of various innovative tools and technologies, doctors are able to monitor patients’ conditions from remote locations.

The drug discovery process is highly complicated and involves many disciplines. The greatest ideas are often bounded by billions of testing, huge financial and time ependiture. On average, it takes twelve years to make an official submission.

Data science applications and machine learning algorithms simplify and shorten this process, adding a perspective to each step from the initial screening of drug compounds to the prediction of the success rate based on the biological factors. Such algorithms can forecast how the compound will act in the body using advanced mathematical modeling and simulations instead of the “lab eperiments”. The idea behind the computational drug discovery is to create computer model simulations as a biologically relevant network simplifying the prediction of future outcomes with high accuracy.

Now, this is probably the first thing that strikes your mind when you think Data Science Applications.

When we speak of search, we think ‘Google’. Right? But there are many other search engines like Yahoo, Bing, Ask, AOL, and so on. All these search engines (including Google) make use of data science algorithms to deliver the best result for our searched query in a fraction of seconds. Considering the fact that, Google processes more than 20 petabytes of data every day.

Had there been no data science, Google wouldn’t have been the ‘Google’ we know today.

If you thought Search would have been the biggest of all data science applications, here is a challenger – the entire digital marketing spectrum. Starting from the display banners on various websites to the digital billboards at the airports – almost all of them are decided by using data science algorithms.

This is the reason why digital ads have been able to get a lot higher CTR (Call-Through Rate) than traditional advertisements. They can be targeted based on a user’s past behavior.

This is the reason why you might see ads of Data Science Training Programs while I see an ad of apparels in the same place at the same time.

Aren’t we all used to the suggestions about similar products on Amazon? They not only help you find relevant products from billions of products available with them but also add a lot to the user eperience.

In the aviation and airlines industry, companies use data for putting up their prices, optimizing routes, and carrying out preemptive maintenance. 

Data Scientists are needed to collect and analyze the airline’s data such as route distance and altitudes, aircraft type and weight, weather, etc. By having a better grasp of how passengers function using Data Science, it will be easy to enhance the services provided to them. This industry created more than 3,000 new jobs for Data Science in 2021.

Due to an increase in online transactions and Internet usage, fraudulent activities have also increased. Organizations are adopting Data Science techniques to detect such fraudulent activities and to prevent losses. It gives a scientific method for detecting hostile assaults on digital infrastructure. It also incorporates machine learning technologies to understand the patterns of data and to create effective algorithms to protect the data. Data Scientists help to manage large amounts of data and derive the best solutions. This will boost the demand for Data Scientists by opening up more than 5,000 jobs in 2021.

With vast amounts of data now available, companies in almost every industry are focused on eploiting data for competitive advantage. 

In the past, firms could employ teams of statisticians, modelers, and analysts to eplore datasets manually, but the volume and variety of data have far outstripped the capacity of manual analysis. 

At the same time, computers have become far more powerful, networking has become ubiquitous, and algorithms have been developed that can connect datasets to enable broader and deeper analyses than previously possible. The convergence of these phenomena has given rise to the increasingly widespread business application of data science principles and data-mining techniques.

Probably the widest applications of data-mining techniques are in marketing for tasks such as targeted marketing, online advertising, and recommendations for cross-selling. 

Data mining is used for general customer relationship management to analyze customer behavior in order to manage attrition and maimize epected customer value. The finance industry uses data mining for credit scoring and trading, and in operations via fraud detection and workforce management. 

Major retailers from Walmart to Amazon apply data mining throughout their businesses, from marketing to supply-chain management. Many firms have differentiated themselves strategically with data science, sometimes to the point of evolving into data mining companies.

When the amount of new data generated surpasses the limit of institutions to manage it, and makes it difficult for analysts to analyze it and researchers to deduce any useful conclusions from it, is known as data deluge.

Vast amounts of data will drive new insights and better business decisions, but only if you have a comprehensive data management plan in place.

We are living in the golden age of data. Thanks to phones, cloud apps, and billions of IoT devices, the volume of enterprise data is growing by more than 60 percent per year, according to IDC.

Today, apart from traditional data sources, there are numerous digital data sources like Google trends, social media, data coming from satellites etc. Such huge amounts of data have made it difficult to aggregate, visualize and analyze information efficiently. That’s why it is very important to counter data deluge by retrieving the eact amount of data that not only saves money but also gives required, valued insights.

We are living in the golden age of data. Thanks to phones, cloud apps, and billions of IoT devices, the volume of enterprise data is growing by more than 60 percent per year, according to IDC.

Today, apart from traditional data sources, there are numerous digital data sources like Google trends, social media, data coming from satellites etc. Such huge amounts of data have made it difficult to aggregate, visualize and analyze information efficiently. That’s why it is very important to counter data deluge by retrieving the eact amount of data that not only saves money but also gives required, valued insights.

Don't be a data hoarder

Dismantle your data silos

Foster a data-centric culture

Prep your data for analytics

Identify the right use cases

Most companies already have more data than they know what to do with. They've been collecting it for years without a coherent plan.

Many organizations just collect everything under the assumption they're going to do something smart with this data in the future. But when you start creating large pools of data with no indication of where it came from or why it was collected, then it's open to interpretations that could be wildly wrong.

Storage and compute may seem infinite in the cloud, but it's not free to process and analyze data. Long term, many organizations won't be able to afford to do what they want to do. Unless they figure out a way to commoditize the consumption of data, combined with ultra-efficient resource utilization, they're going to hit a breaking point.

Once you've identified the data that can drive business outcomes, the net step is to figure out where it resides, how it enters and leaves the organization, and who's responsible for managing it.

You need a good understanding of what your data ecosystem looks like and what the challenges are. If you're creating data silos, you need to figure out why. Is it because the data is stuck inside an SQL database that you can't easily share? Have you created a data lake but nobody else in the organization knows it's there?

It's usually a little more subtle than somebody standing there with their arms crossed saying, 'I'm not going to share my data with you. It's more like, 'If there's no benefit to me or my group, then somebody else needs to pay for it.

Managing data at this scale requires a top-down data governance strategy.

Data is growing at an alarming rate, and a majority of it is not analyzed. It's difficult for organizations to ensure trust in data if it's not properly integrated, cataloged, qualified, and made available to the right tools or—more importantly—the right people. Over the net year, data governance is going to be massively important, especially as organizations look to leverage more high-quality data.

It's why many organizations are hiring chief data officers who can reach across different business units to coordinate a unified strategy. usiness units to coordinate a unified strategy, Leone adds. 

The primary reason most enterprises collect massive amounts of data is so they can apply AI to it and make smarter business decisions. But the value of the insights that analytics can provide are only as good as the quality of the data fed to the machine learning models. You know the saying: Garbage in, garbage out.

The biggest driver of successful AI scaling within any organization is having access to well-organized and relevant data. 

Companies that are better at collecting, storing, and analyzing data stand to gain a lot more from the principled introduction of AI into their processes than those that are still figuring out what data gives them an advantage and how to collect it.

Organizations also need to identify what data sources are the most useful for analytics purposes and the proper use cases to apply them to.

On its own, data is about as useful as oil when you don't have an engine to put it in. Data is only useful in the contet of a particular problem you're trying to solve or a particular inference you're trying to get to—something that's actually going to drive business value.

Analytics architecture refers to the systems, protocols, and technology used to collect, store, and analyze data. The concept is an umbrella term for a variety of technical layers that allow organizations to more effectively collect, organize, and parse the multiple data streams they utilize.

When building analytics architecture, organizations need to consider both the hardware — how data will be physically stored — as well as the software that will be used to manage and process it.

Analytics architecture also focuses on multiple layers, starting with data warehouse architecture, which defines how users in an organization can access and interact with data. Storage is a key aspect of creating a reliable analytics process, as it will establish both how your data is organized, who can access it, and how quickly it can be referenced.

Structures like data marts, data lakes, and more standard warehouses are all popular foundations for modern analytics architecture. On the user side, creating easier processes for access means including tools like natural language processing and ad-hoc analytics capabilities to reduce the need for specialized workers and wasted resources. When seen as a whole, analytics architecture is a key aspect of business intelligence.

No matter what kind of organization you have, data analytics is becoming a central part of business operations. The fast-rising amount of data your multiple touch points collect means that using a simple spreadsheet is quickly becoming unfeasible.

Analytics architecture helps you not just store your data but plan the optimal flow for data from capture to analysis. Understanding these steps can give you a better idea of your hardware and logistics needs and clue you in on the best tools to use.

One important use for analytics architecture in your organization is the design and construction of your preferred data storage and access mechanism. Many companies prefer a more structured approach, using traditional data warehouses or data mart models to keep data more organized and easily sorted for access later.

Focusing first on profiles more oriented to data analysis, Data Analyst is a profile that came before Data Scientist. In some cases they are referred to as "Junior Data Scientists ".

They have a fairly generalist role, covering a wide range of functions that include mining, obtaining and/or retrieving data as well as its processing, advanced study and visualization.

It is the "evolution of Data Analyst". In many cases they are considered the same profile with a different approach. For us, it is a more specific role and less aligned with the business vision.

Like the DA, it requires knowledge of mathematics, statistics and Machine Learning, programming languages ​​such as R or Python, the use of notebooks and Big Data ecosystems, but what we believe differentiates the Data Scientist is that they are responsible for etracting value from data.

Already focusing on the storage and processing of data, we find ourselves with the role of Data Engineer. This is our role in the Aura project at Telefónica and here is one of the reasons why we are going to give it a lot of importance.

Perhaps the most relevant is that it provides the Big Data project with a value very different from the one provided by a Data Scientist or Data Analyst.

We know that the latter are the ones that work with the data, but where do they get it from? How does the environment in which they do their analysis work? It is the task of the Data Engineer to prepare the entire ecosystem so that others can obtain their data clean and prepared for analysis.

The Data Engineers are those who design, develop, build, test and maintain the data processing systems in the Big Data project.