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Data Science Or Data Analytics. What Is The Buzzword?

Puzzled? let us find out about Data Science and Data Analytics

Data science and data analytics

Harvard Business Review article declared data scientist to be the sexiest job of the 21st century. But is working with data still a novel or as exciting as it was then? Data scientists’ most basic, universal skill is the ability to write code.

Now, what exactly is data science, and is it different from data analytics? This question bugged me because my students came to me to ask some of their doubts before going in for an interview. They said that the placement manager had sent them to me as there were some companies coming for interviews.

One of the major questions was “am I qualified for being a data scientist or a data analyst?”

I tried to explain to them in the simplest manner that although data scientist and data analytics are words that are used synonymously there are differences in their role and scope.

Data scientist

A data scientist's job is based on interdisciplinary work. Such a job connects scientific methods, processes, algorithms, and systems to pick out the data that is required for use in a situation from both, structured, and unstructured data, and then apply it to the required situations.

What exactly is Data Science? Data science is a combination. It is a package consisting of the scientific method, math and statistics, specialized programming, advanced analytics, and AI. to extract business insights buried in data.

A data scientist has to prepare data for analysis, cleansing, and manipulating the data to perform advanced data analysis.

There are certain steps a data scientist has to take.

Step 1 Collect the data: This means to collect raw structured and unstructured data from all relevant sources either manually and web scraping or by capturing the data from systems and devices in real-time.

Step 2 Preparing and maintaining data: The raw data has to be made into an organized format for analytics or machine learning or deep learning models.

Step 3 processing data: A Data scientist must examine biases, patterns, ranges, and distributions of values to find out if the data is suitable for using it with predictive analytics, machine learning, and deep learning algorithms

Step 4 Analysis of data: The data scientist analyzes data through statistical analysis, predictive analytics, regression, machine learning, and deep learning algorithms, to extract insights from the prepared data.

Step 5 Presenting data: The presentation of data in the form of reports, charts, and other data visualizations that make an impact on the business easy for decision-makers to understand

What is the benefit of data science? any business process can be made more efficient through data-driven optimization, and all kinds of customer experiences can be improved with proper targeting and personalization.

Where can data science be applied?

Data science can be used in any industry. Some of its applications can be used in organizations requiring large or critical data such as banks, police departments, health care industry, e-commerce, digital media etc.

Bank can create a mobile app offering on-the-spot decisions to loan applicants using machine learning-powered credit risk models and hybrid cloud computing is powerful and secure.
A digital media company can create an audience analytics platform that enables its clients to see what’s engaging TV audiences as they’re offered a growing range of digital channels. Analytics and machine learning can help to bring together r real-time insights into viewer behavior.
Police departments can make analysis tools to prevent crime. The data-driven solution can create reports to augment situational awareness.
A healthcare company can develop solutions to various health problems through the combination of sensors, machine learning, analytics, and cloud-based processing.

Data Analytics

A data analyst should have the following skillsets to be able to analyze data through data analytics.

A data analyst must know the basics of Structured Query Language(SQL) It is the standard database language. It is able to handle a large database.
Knowledge of Microsoft Excel is essential. Advanced Excel methods like writing Macros and using VBA are used for quick analytics
A data analyst has to uncover and synthesize connections to make them clear through critical thinking. He should be able to find answers to questions
He should be able to do statistical Programming through Python or R. They are powerful statistical programming languages used to perform advanced analyses and predictive analytics on big data sets.
He must understand Data Visualization. An analyst has to present the data which is visually appealing. They have to learn to use high-quality charts and graphs to present their findings clearly in an organization.
An analyst must have presentation skills. He must focus on the requirements of the audience and prepare well to be able to communicate well.
He must have a thorough knowledge of Machine Learning. This will help in being able to use predictive analytics and artificial intelligence.

data analytics knowledge today means more opportunity and more money in the future.

Types of data analytics: There are four types of data analytics. These are Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics

Descriptive Analytics answers questions about past performances. These strategies can help track successes or failures. This process consists of a collection of relevant data, processing, analysis and visualization of data.
Diagnostic Analytics answer questions about why certain things happened. Statistical techniques are used to find relationships and trends that explain these anomalies.
Predictive analytics answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to recur. They use statistical data such as Neural Networks, Decision Trees, and Regression.
Prescriptive Analytics answers questions about what should be done. It uses predictive analysis to make decisions through Machine Learning to analyze a large set of data to make use for the future.

It can aptly be said that data analytics are extremely important for industries:

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” — By Geoffrey Moore

The takeaways

#1. Data science is comprehensive. It consists of understanding massive data sets and data analytics finds insights that can be applied for improvement.

#2. Data science does not answer questions. It looks for data to extract it for more insights. Data analytics focuses on finding out the answers that can work well in structured datasets.

#3. A data scientist looks into the integrity of the data whereas a data analyst interprets the data.

#4. Data scientist explores business insights but analyst experiments with tools and techniques such as descriptive, predictive, diagnostic and prescriptive analytics.

Conclusion

These types of data scientists and analytics provide the insight that businesses need to make an effective and efficient decision.

Data Analytics is a branch of Data Science that focuses on more specific answers to the questions that Data Science extracts. They go hand in hand.

The use of data is in large and small organizations where data sets are large such as a hospital, the police department, a bank, and many such organizations.

So find out if you want to be a data scientist or a data analyst. It is the future of all organizations in the world.

“Data is the new science. Big Data holds the answers.” — By Pat Gelsinger.