Most businesses today have made some investment in data science. Data science projects have a habit of sprouting up team by team within an organisation, resulting in a disconnected approach that is neither scalable nor cost-effective.

Consider how data science is generally implemented in today’s businesses: A line-of-business organisation that wants to make more data-driven choices typically engages a data scientist to develop models tailored to their needs. Another business unit decides to hire a data scientist to construct its R or Python apps after seeing that group’s performance improve. Repeat until every department or division within the company has its own isolated data scientist or data science team.

Furthermore, no two data scientists or teams are likely to use the same set of technologies. The great majority of data science tools and packages are currently open-source and available for download from forums and websites. And, because data science innovation moves at breakneck speed, even a new version of the same package might cause a previously high-performing model to generate incorrect predictions suddenly and without warning.

As a result, the IT group has no visibility into a virtual “Wild West” of different, disconnected data science projects across the firm.

To address this issue, businesses must entrust IT with the creation of scalable, reusable data science environments. This process can be facilitated by training employees to learn Data Science and get the best online course for business analytics .

Currently, each data science team extracts the data they require or desire from the company’s data warehouse, then replicates and manipulates it for their own reasons. They construct their own “shadow” IT infrastructure to meet their computational demands, entirely distinct from the leading IT group. Unfortunately, these shadow IT environments store critical artefacts (such as deployed models) locally, on shared servers, or in the public cloud. This exposes your company to significant risks (such as lost work when major employees leave) and the lack of reproducible evidence for audit proof or proof of compliance.

What exactly is Data Science, and how does it work?

For competent professionals, data science remains one of the most promising and in-demand job paths. Today’s influential data professionals recognise that they must go beyond the traditional abilities of large-scale data analysis, data mining, and programming. Data scientists must master the complete spectrum of the data science life cycle and possess a level of flexibility and awareness to maximise returns at each stage of the process to unearth meaningful intelligence for their organisations.

The data science life cycle is divided into five stages: 

  • Capture: Includes data entry, data acquisition, data extraction, and signal reception
  • Maintain: Includes data cleansing, data warehousing, data processing, data staging, and data architecture.
  • Process: Includes data mining, data modelling, classification/clustering, and data summarization.
  • Analyse: Includes confirmatory/exploratory, regression, text mining, predictive analysis, and qualitative analysis.
  • Communicate: Includes data visualisation, data reporting, decision making, and business intelligence.

Influential data scientists can develop relevant questions, acquire data from various sources, organise the data, translate results into solutions, and present their findings in a way that favourably influences business decisions. Because these talents are required practically in every industry, skilled data scientists are becoming increasingly valuable to businesses.

What is the Role of a Data Scientist?

During the previous decade, data scientists have become crucial assets in almost every company. These professionals are data-driven, well-rounded individuals with superior technical talents who can build complex quantitative algorithms to synthesise and organise large amounts of data to answer questions and drive company strategy. This is complemented with the communication and leadership abilities needed to provide tangible outcomes to a diverse group of stakeholders within an organisation or a company.

Data scientists must be curious and results-oriented, with extensive industry knowledge and communication skills that allow them to communicate highly technical results to non-technical colleagues. They have a strong quantitative foundation in linear algebra and statistics and programming skills, focusing on mining, data warehousing, and modelling, which they use to analyse and build algorithms. All these skills and more can be obtained from the best courses on data science at Greatlearning.

IT and the next phase of Data Science

Let’s move on from the data to the tools that data scientists employ to clean and alter it to build these sophisticated predictive models. Data scientists can choose from an extensive range of mostly open-source tools, and they usually do so freely. Every data scientist or group has a preferred language, tool, or technique, and each data science team develops unique models. This absence of uniformity may appear insignificant, but it means there is no repeatable path to production. When a data science team collaborates with IT to put its model(s) into production, the IT team must constantly reinvent the wheel.

The previously outlined model is neither tenable nor sustainable. Most importantly, it isn’t scalable, which will be critical in the coming decade, when companies will employ hundreds of data scientists and thousands of continually learning and improving models.

IT has a unique opportunity to play a crucial leadership role in developing a scalable data science function. The CIO can tame the “Wild West” by leading the charge to make data science a corporate part rather than departmental expertise by establishing strong governance, standards guidance, repeatable processes, and reproducibility — all of which IT has experience with.

When IT takes the lead, data scientists have the freedom to try out new tools or algorithms while remaining completely governed, allowing their work to be elevated to the level necessary across the enterprise. A clever centralization strategy based on Docker, Kubernetes, and modern microservices, for example, not only saves money for IT but also expands the value that data science teams can offer to the table. Containers’ magic allows data scientists to experiment with their preferred tools without concern of disrupting shared systems. IT can give data scientists the freedom they require while also standardising a few golden containers for use by a larger audience. GPUs and other specific configurations that today’s data research team’s demands can be included in this golden container.

Models and their accompanying data may be tracked throughout their lifecycle, fulfilling compliance and audit needs, thanks to a centrally controlled, collaborative architecture that allows data scientists to collaborate in a uniform, containerized manner. Data science assets, such as underlying data, discussion threads, hardware tiers, software package versions, parameters, outcomes, and the like, can be tracked to enable new data science team members onboard faster. Tracking is essential because when a data scientist departs an organisation, they typically take their institutional knowledge. Bringing data science under the IT umbrella provides the necessary controls to prevent “brain drain” and ensure that anybody may replicate any model at any point in the future.


Furthermore, by setting up systems that allow data scientists to self-serve their own needs, IT can aid expedite data science research. While data scientists have easy access to the data and compute power they require, IT maintains control and can track consumption and assign resources to the teams and projects which need them the most. It’s a win-win situation.

However, CIOs must first take action. The influence of our COVID-era economy currently necessitates the development of new models to deal with rapidly changing operating circumstances. So now is the moment for IT to take command and provide some order to this chaotic atmosphere.

Data science and data analytics are growing at a very high speed, and companies are now looking for professionals to filter through the data gold mine and help them make fast and efficient business decisions. IBM forecasts that the number of vacancies for all data professionals in the US will increase from 364,000 to 2,720,000 by 2020. To find out what science is we met Eric Taylor, the Senior Data Scientist working at CircleUp, in a Simplilearn Fireside. 

This article covers the following topics, which will give you a clear understanding of the meanings, differences, and skills needed to become a scientist and data analyst, as well as more topics in detail, including:

  • What is data science?
  • Major skills that are requisite to Become a Data Scientist
  • What does a Data Scientist do?
  • What is data analysis?
  • Skills that are requisite to Become a Data Analyst
  • What does a Data Analyst do?
  • Difference between a data science and a data analysis

What is data science?

People have been trying to explain data science for over a period of 10 years, and the best way to answer the question is with a Venn diagram. This Venn Diagram was created by Hugh Conway in 2010 and consists of three circles: math and statistics, technical competence (knowledge of the domain to be summarized and calculated), and hacking skills. In essence, if you can do all three, you are already well versed in data science.

Data science is a concept against big data and includes data cleaning, preparation, and analysis. A data scientist collects data from various/vast sources and applies predictive analytics, machine learning, and sentiment analysis to bring out critical information from the collected datasets. They understand data from a business perspective and can provide accurate forecasts and information that can be used to make important business decisions.

Skills Required to Become a Data Scientist

Anyone looking to build a solid career in this area must develop essential skills in three departments: Analysis, Programming, and Domain Knowledge. If you take it a step further, the following skills will help you find a niche for yourself as a data scientist:

  • Good knowledge of Python, Scala, SAS, R
  • Hands-on experience in coding SQL databases
  • Ability to work with unstructured data from multiple sources such as video and social media
  • Understand various analytic functions
  • machine-learning knowledge

What does a data scientist do?

A data scientist will generally be more involved in outlining data modeling processes, designing algorithms and forecasting models. As a result, data scientists can spend more time designing tools, automation systems, and data structures.

Comparatively a data scientist may be more concentrated on developing new tools and methods to pull out the information the business needs to resolve compound problems than a data analyst. It’s also good to have business instinct and critical thinking skills to understand the entanglement of the data. Some in this field might recount a data scientist as someone who not only has hacking skills but also math and statistics to approach problems in new ways.

What is data analysis?

A data analyst is generally the person who can create basic descriptive statistics, visualize data, and communicate data points to wind up. You should have a basic knowledge of statistics, a perfect command of databases, the ability to create new views, and insight into data views. Data analysis can be seen as the necessary level of data science.

Skills Requisite to Become a Data Analyst

A data analyst must be able to answer a specific question or topic, discuss what the data looks like, and present it to relevant stakeholders across the organization. If you want to become a data analyst, you need to learn these four key skills:

  • knowledge of mathematical statistics
  • Good understanding of R and Python
  • Data dispute
  • Understand the PIG/HIVE

What does a data analyst do?

A data analyst generally gathers data to recognize trends that will be helpful to business leaders to make deliberate decisions. The adherent focuses on performing statistical analysis to answer questions and resolve problems. A data analyst uses tools like SQL to query relational databases. A data analyst can also clean the data or put it into a usable format, remove irrelevant or unusable information​​or figure out how to deal with missing data. For more details Visit Data Science Training in Hyderabad

A data analyst usually works as part of an interdisciplinary team to find out business goals and then manages the data extraction, cleansing, and review process. The data analyst uses programming languages ​​like R and SAS, visualization tools like Power BI and Tableau, and communication skills to develop and communicate their results.

Data science vs. Data analysis

  • Data science is a wide-ranging term that encloses data analysis, machine learning, data mining, and many other related subjects.
  • While a data scientist is supposed to forecast the future based on past patterns of the earlier, data analysts pull out important information from a diversity of data sources. 
  • While a data analyst finds answers to already existing questions, a data scientist raises questions.

Data Science is evolving rapidly to occupy its position in all the different verticals of the industries. From finance to healthcare, every domain is leveraging the applications of data science to transform these sectors. Blending science, technology, and bio-medicine in this digital era have revealed new data-driven systems that can make precise healthcare systems, health reports for clinical decisions & drug delivery. This article will focus on the impact of data science in healthcare.

Applications of Data Science in Healthcare 

Data science is witnessing rapid progress in the healthcare industry. It is because it can cater to a vast collection of libraries. Also, technology can now render Big Data through advanced tools and frameworks. Healthcare companies can cultivate large data sets and extract valuable insights from them.

The top 5 data science applications in healthcare.

  1. Data Science for Genomics: Genomics is a popular branch of biology that deals with the study and analysis of sequencing genomes. Every organism contains genes, and these genes comprise DNA and other existential traits. After the Human Genome Project, scientists have been working hard for the advancement in genetic engineering. They are blending the ideas of big-data and data science to extract. Healthcare and tech companies are spending billions of dollars to analyze the genetic sequences in humans and other animal species. Before the advent of data science, such projects were expensive. But through it, researchers and scientists can interpret and acquire insights from the human gene in a significantly shorter time & at a much cheaper cost. Researchers use data analysis and visualization tools and libraries to genomic strands to explore the irregularities and deficiencies in various organisms. Analyzing the genetic sequences through data science tools will automate the process bringing down the time and cost. Also, data science algorithms can help in finding a correlation between various parameters. Detection of defects or diseases also becomes easy to locate.
  2.  Data Science in Medical Imaging: One of the most prominent uses of data science in the healthcare industry is in medical imaging. The conventional imaging techniques used in medical science are MRI scan, X-Ray, and CT scan. These scanning and imaging techniques help in visualizing the inner body parts in humans. The traditional approach was to manually examine these images & spot the problem(s). However, it becomes challenging to detect microscopic deformities. That is where doctors could not perform proper diagnostics. Therefore, to reduce such issues, healthcare systems use data science and deep learning algorithms to detect granular-level defects in those scanned images. The deep learning algorithms are fed with deformities data (as images) that the system understands. This system then tries to identify these defects in the scanned images fed by the doctor.
  3.  Data Science in Wearables for monitoring health problems: As we all know, the human body generates around two terabytes of data daily. With traditional equipment, it was hard to capture all that data. But with the advancement in technology, companies can collect most of it. New wearables makers are leveraging technology and data science to accumulate blood glucose, sleep patterns, stress levels, heart rate, and brain activity-data. With such a massive collection of health data, scientists and researchers are working closely with data analysts to push the boundaries in health monitoring. Popular companies working with wearables are Qualcomm, IBM, Google, Fitbit, AltexSoft, Strata Decision Technology, etc. Apple recently joined the race to make the healthcare system better through data science and machine learning through CareKit and ResearchKit. Modern technological devices also help monitor the patients, remotely help in understanding the chronic disease, and productively increase the pharmaceutical logistics and supply chains system. The wearables sent the data back to the server. Researchers and scientists then use data science to extract meaningful patterns and data-driven methods focusing on disease prevention.
  4. Drug Discovery with Data Science: Discovering drugs is a complicated discipline and requires a high level of precision. That is where pharmaceutical industries rely heavily on it for solving their problems & nurturing better drugs for humans. Drug Discovery and researching new drugs is a highly time-consuming process. Traditional drug discovery methods involve heavy financial expenditure in doing adequate research, looking for the exact chemical composition, and complicated testing. But with data science and machine learning approaches, researchers can improve the efficiency and accuracy of the chemical composition that helps prepare an accurate drug. Overall, this increases the success rate for a drug-making company and makes it stand out from its competitors.
  5.  Providing Virtual Assistance: Rendering virtual assistance through disease predictive modeling is another significant role of it in healthcare. Data scientists and healthcare researchers worked together to bring to the table a comprehensive virtual platform that can assist the patients. Many healthcare firms have created platforms where patients can fill the medical symptoms as input and acquire insights. All such applications utilize data analysis and machine learning algorithms in their background. These ML algorithms can instantly check various possible diseases based on the training model and confidence rate.

Virtual assistance applications can also assist patients with their daily habits, routines, and tasks. A famous example of a virtual assistant is Ada – created by a startup in Berlin. Another well-known ML-based chatbot is Woebot – developed at Stanford University. It can produce therapy procedures for patients who are experiencing depression.

Various other roles of it in healthcare industries are tracking and preventing pandemics, turning patient responsibility into precision medicine, optimizing clinic performance, predicting medicine needs in different areas of a country, etc. Data scientists use supervised ML, dimensionality reduction, unsupervised ML, medical classification systems, etc. techniques. These techniques help in enhancing the healthcare industry.

Hope this comprehension helped you get a clear picture of how healthcare industries, together with data scientists, are adopting it. Extracting real value from unstructured patient data can eventually contribute to providing adequate healthcare services. Leveraging it into healthcare systems makes it more efficient, personalized, and accessible.