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Introduction to Data Science and Its Growing Importance

Data Science and Its Growing Importance – An interdisciplinary field, data science deals with processes and systems that extract knowledge or insights from large amounts of data.

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Data extracted can be either structured or unstructured. It is a continuation of data analysis fields like data mining, statistics, and predictive analysis.

A vast field, it uses many theories and techniques that are a part of other fields like information science, mathematics, statics, chemometrics, and computer science.

Some data science methods include probability models, machine learning, signal processing, data mining, statistical learning, database, data engineering, visualization, pattern recognition and learning, uncertainty modeling, and computer programming.

It is not restricted to big data, a big field, because big data solutions focus on organizing and pre-processing rather than analyzing the data.

Also, machine learning has enhanced data science’s growth and importance in the last few years.

What is the origin of Data Science?

Over the years, it has become integral to many industries, like agriculture, marketing optimization, risk management, fraud detection, marketing analytics, and public policy.

It tries to resolve many issues within individual sectors and the economy by using data preparation, statistics, predictive modeling, and machine learning.

It emphasizes the use of general methods without changing their application, irrespective of the domain. This approach differs from traditional statistics and focuses on providing solutions specific to particular sectors or domains.

The traditional methods depend on providing solutions tailored to each problem rather than applying the standard solution.

Today, it has far-reaching implications in many fields, both academic and applied research domains like machine translation, speech recognition, and digital economy on the one hand, and fields like healthcare, social science, and medical informatics on the other hand.

It affects the growth and development of a brand by providing a lot of intelligence about consumers and campaigns through data mining and data analysis techniques.

This history can be traced over fifty years back and was used as a substitute for computer science in 1960 by Peter Naur.

In 1974, Peter published the Concise Survey of Computer Methods, where he used the term data science in his survey of contemporary data processing methods.

These methods were then used in several applications. Almost twenty-two years later, in 1996, the International Federation of Classification Societies met Kobe for their biennial conference. Data science was used for the first time in the conference title, Data Science, classification, and related methods. C.F. Jeff Wu 1997 gave an inaugural lecture on the topic in which he spoke about statistics being a form of data science.

The report mentions six areas that he thought formed the base of data science: multidisciplinary investigations, models, methods for data, pedagogy, computing with data, theory, and tool evaluation.

In the next year, in 2002, the International Council for Science: Committee on Data for Science and Technology started the publication of Data Science Journal, which focuses on issues related to data science like a description of data systems, their publication on the internet, application, and legal issues.

In January 2003, Columbia University also began the publication of the Journal of Data Science, a platform for data workers to share their opinions and exchange ideas about the use and benefits of data science.

A journal that was devoted to applying statistical methods and qualitative research, this journal was a platform that provided data workers with a voice of their own in the field of data science.

In 2005, the National Science Board published Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century.

Their primary activity is to conduct creative inquiry and analysis so that data can be utilized properly and effectively by organizations across all sectors.

The growing importance of data science has, in turn, led to the growth and importance of data scientists. These data scientists professionals are now integral to brands, businesses, public agencies, and non-profit organizations.

These data scientists work tirelessly to make sense of a large amount of data and discover relevant patterns and designs to be effectively utilized to realize future goals and objectives.

This means that data scientists are gaining prime importance, and understanding data properly is reflected in their rising salaries.

According to a recent study by McKinsey Global Institute, there is a shortage of analytical and managerial talent, especially as they need to make sense of the large amount of data available in the world.

This is one of the most pressing challenges in current times. Further, this report estimates that by 2023, there will be a requirement of four to five million data analysts.

There is also a need for close to one million managers and analysts who can help consume big data results to help organizations reach their goals using resources strategically and helpfully.

Why is data science so important?

Over the past few years, it has come a long way. That is why they are integral to understanding many industries’ working, however complex and intricate.

Here are ten reasons why it will always remain an integral part of the culture and economy of the global world:

It helps brands to understand their customers in a much enhanced and empowered manner. Customers are the soul and base of any brand and have a great role in their success and failure. With data science, brands can connect with their customers in a personalized manner, thereby ensuring better brand power and engagement.

One of the reasons why it is gaining so much attention is because it allows brands to communicate their story in such an engaging and powerful manner. When brands and companies comprehensively utilize this data, they can share their story with their target audience, creating better brand connections. After all, nothing connects with consumers like an effective and powerful story that can inculcate all human emotions.

Big Data is a new field that is constantly growing and evolving. With so many tools being developed, big data is almost regularly helping brands and organizations solve complex problems in IT, human resources, and resource management effectively and strategically. This means effective use of resources, both material and non-material.

One of the most important aspects of data science is that its findings and results can be applied to almost any sector, like travel, healthcare, and education. Understanding the implications of data science can go a long way in helping sectors analyze their challenges and address them effectively.

It is accessible to almost all sectors. A large amount of data is available today, and utilizing them properly can spell success and failure for brands and organizations. Properly utilizing data will hold the key to achieving goals for brands, especially in the coming times.

That being said, it is taking on a big and prime role in brands’ functioning and growth process. Being a data scientist is a prime position for any person as they have the big task of managing data and providing solutions for their problems, both within and outside the organization.

Link new and different data to offer products that meet the aspirations and goals of their target customers

Use señor data to detect weather conditions and reroute supply chains.

Uncover frauds and anomalies in the market

Advance the speed at which data sets can be accessed and integrated

Identify the best and most innovative way to use the internet so that brands can comprehensively use opportunities.

Data science can have huge implications in retail and beyond, such as recreating the personal touch of local shopkeepers.

This shopkeeper was able to meet the customer’s needs in a personalized manner. With time, however, this personalized attention got lost in the emergence and growth of supermarkets.

However, data analytics can help brands to create this personal connection with their customers. Using this, brands must develop a better and deeper understanding of how customers use their products.

This means competitive retailers will have to understand better how customers use their products. Efficiency means that retailers will have to match the right product to the right customer, even though both these objects are constantly evolving.

What is the future of data science and data scientist?

Data science can have far-reaching implications beyond retail. These include healthcare, energy, and education. Because these fields are constantly evolving, their importance is also rapidly increasing. Healthcare needs to balance discovering new drugs with improving patient care. With data-driven solutions, healthcare can take patient care and satisfaction to the next level.

The healthcare industry is constantly evolving, and it can help them create better care for patients at all stages. Another field that can truly benefit from data science is education. Smartphones and laptops in education can create better opportunities for constructive learning and knowledge enhancement.

Another example of how it can help society is through its application and use of energy. The energy sector is today on the cusp of radical change and transformation. From oil to gas to renewable energy, we need to find new and innovative ways to use energy.

It can help us meet the increasing demand and sustainable future challenges while ensuring the best solutions. Data scientists will have to develop a wide range of solutions to meet challenges across all sectors.

This is not an easy task, so they need the resources and systems that will help them achieve this goal. Data scientists must use high-end tools to create cross-sector solutions and become creative thinkers.

All in all, data scientists are the future of the world today. Data scientists will be integral to addressing global challenges with far-reaching impacts.

Developing the skill and creativity of data scientists worldwide can transform people’s experience of life, products, and services.

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Importance Of Internships In Data Science


Internships and apprenticeships are two of the most popular methods of learning on the job and gaining crucial skills. The concept of an apprentice started dying out once masters of various sciences and crafts were replaced by employers. Eventually, internships took over from the 20th century as a standardised approach for gaining domain experience.

Internships are officially commissioned by the company and also feature documentation or certificates that justify an intern’s time at the company. Internships are essential for gaining hands-on experience and are one of the best methods of adapting to job roles that you like. For instance, if you complete an internship as a Data Scientist or a data analyst, then you will become prepared to function in any of the respective professions with ease. Internships also make other companies more likely to hire you, assuming that you are already trained in the job role.

Another factor we must take into consideration is the tools and technologies that are associated with sectors such as Data Science. There are many software and methodologies which are domain-specific and are not necessarily covered in degree programmes. Thus, individuals can use the time during their internship to acquire these necessary skills. Yes, there are online courses that teach these skills but an internship allows companies to believe that you do not need any further technical training (unless a company is using different technologies). There are programmes such as Data Science Immersive Bootcamp that can provide you with real-world job training or internships as well as teach you all the necessary skills. 

There are also various soft skills that internships help you pick up. Internships are the best approach for gaining experience for freshers. Even if you are an exceptional student, being an intern first is recommended as it is sometimes a requirement for top MNCs that are dealing with domains such as Data Science.

Benefits of Data Science Internships

Joining and then completing an internship in Data Science can help you in a lot of ways along your path to becoming a Data Scientist, a data analyst or a data engineer. Internships serve as proof of your accomplishments and your foundational abilities. Through your internship and the projects you have worked on, employers can find out your capabilities and how well you fit inside a Data Science process or a pipeline. Also, without an internship, it is almost impossible to get jobs as a Data Scientist.

Let us check out some of the main reasons why internships are essential for Data Science jobs.

Crucial Skills

The best thing about internships is that interns are not expected to know much when they join and are able to learn while being on the job. Unlike degree programmes and courses, internship roles require interns to carry out many tasks that help them gain practical experience. This helps one acquire enough knowledge about all the necessary tools, technologies, techniques and methodologies.

Formal education mostly covers foundational topics and not specialised tools and skills. Thus, one will always learn something new while being an intern. Generic curriculum from formal education generally features outdated technology while companies operating on the ground adapt to modern practices for meeting business requirements.

For example, you might have been taught Python for programming and Excel for foundational analytics during your education. However, the company you are working for requires you to use Microsoft Power BI, Azure and various libraries for Python such as Matplotlib. Nowadays, Power BI caters to various operational and strategic requirements of a company. By learning other technologies from your internship, you will seem more alluring to employers who are also using the same systems or tools.

Similarly, you can learn skills related to data pre-processing, data mining, data warehousing and tools associated with cloud computing, artificial intelligence and machine learning. 

Experience and Domain Knowledge

One can gain crucial experience with the help of internships. From the daily tasks during internships, interns can learn important domain information that will help them become better employees in the future. With enough exposure during your internship, you can even become a domain expert. For example, you might become excellent in noise removal or visualisation just by carrying out these job responsibilities during your internship.

With more experience and knowledge, you will also feel more confident, essentially reflecting your skills through high-quality work. Also, you will be adding value to the company you are working for, providing you with enough job satisfaction. Many companies have specialised training facilities and internal resources that interns can use for growing.


Freshers find it hard to get good jobs due to not having any experience. A lot of companies assume that it will take additional time and money to train freshers and thus, many of them prefer freshers who have completed an internship. Companies expect interns to already have domain knowledge and an understanding of how Data Science processes function. 

This enables people who have completed their internships to become more employable. Let us take an example where there are two candidates where one has completed an internship and the other has not. In this kind of situation, it is almost guaranteed that a company will choose a candidate who has completed an internship. Many internships also convert into full-time jobs upon the completion of the intern period.

Networking and Career Prospects

By joining an internship, you can grow your network and become acquainted with more people working in the field who wish to be involved in the future. This can help you learn the opinions of other professionals in the domain. You can also identify the future of the sector better with the help of other senior resources. Other professionals working in Data Science can help better guide you in terms of upskilling yourself and your career. For instance, domain experts might recommend a tool such as SAS to upskill yourself in.

For Data Science, growing your network is crucial to stay updated about the latest market trends and innovations in technology. There are also many great career prospects you might find out about after joining an internship. Many companies also refer their interns to their partners or to other organisations.


An internship is like a journey that allows you to gain skills without any compulsory cost. As a matter of fact, many internships even compensate interns, allowing them to learn while they earn. Finally, without an internship, it might be extremely hard to get a good data scientist job, if not impossible. Bootcamps such as Data Science Immersive Bootcamp by Analytics Vidhya can solve this problem by offering a 100% job guarantee. Read more about a comprehensive comparison among Data Science bootcamps vs degree vs online courses here. 


Business Leadership And Its Importance

Strong business leadership is an essential aspect of every successful corporation. A team with capable, strong leadership is more likely to produce results than one without. Business leadership is the capacity of a company’s management to meet objectives, make quick decisions, and outperform its rivals while fostering a culture of performance.

Because it affects internal and external stakeholders inside the sector and beyond, business leadership is significant. In this article, we will learn about Business leadership and its importance.

What is Business Leadership?

Business leadership is how people make choices, establish goals, and give direction in a workplace setting. Although there are many distinct types of business leadership, most often, the remainder of the team is led and motivated by the CEO or other senior personnel. Business leadership aims to find the best leadership style for a certain organization and its workforce.

Because it affects internal and external stakeholders inside the sector and beyond, business leadership is significant. You may run a firm regardless of your position if you possess the necessary talents.

Importance of Business Leadership Improved Business Performance

Effective business leaders can guide their organizations to achieve positive results, including increased profits and growth. This may involve setting clear goals and expectations, analyzing data and making informed decisions, and motivating and inspiring the team to do their best work.

Greater Job Satisfaction

Leading a successful team can be extremely rewarding and lead to greater job satisfaction for the leader. This may involve feeling a sense of accomplishment, pride in the team’s achievements, and the opportunity to take on new challenges and responsibilities.

Increased Influence

Successful business leaders are often respected and influential within their industries, which can open up new opportunities and help them shape their field’s direction. This may involve being asked to speak at conferences, being invited to join industry groups or committees, or being recognized as an expert in their field.

Improved Team Morale

Successful leaders can inspire and motivate their team, increasing productivity and creating a positive work environment. This may involve setting clear goals and expectations, providing support and guidance, and recognizing and rewarding hard work.

Enhanced Reputation Greater Work-Life Balance

Successful leaders can often delegate tasks and manage their time effectively, leading to a better work-life balance. This may involve setting clear priorities, strategically allocating their time, and creating a culture of work-life balance within their organization.

Stronger Decision-Making

Good business leaders can analyze data and make informed decisions that drive the success of their organization. This may involve gathering and analyzing data from various sources, considering multiple options and their potential outcomes, and making decisions based on that analysis.

Improved Customer Satisfaction

Effective leaders can create a positive customer experience, increasing customer loyalty, and satisfaction. A good leader knows that customer satisfaction is one of the key aspects of building a brand. By implementing a healthy workspace and strategic approach to implementing ideas, business leaders help to achieve this goal.

Enhanced Innovation

Successful business leaders can foster a culture of innovation and creativity, leading to new products, services, and processes that drive growth. This may involve encouraging team members to bring new ideas to the table, creating a safe space for experimentation and risk-taking, and investing in research and development.

Stronger Competitive Advantage

Overall, business leadership is critical to the success of any organization. By focusing on strong communication skills, strategic thinking, and the ability to inspire and motivate others, business leaders can drive the success of their teams and organizations and achieve a wide range of personal and professional benefits.

How to Improve Business Leadership Skills?

There are many ways to improve your business leadership skills. Here are a few strategies you may consider −

Seek Feedback − One of the best ways to improve your leadership skills is to seek feedback from your team and colleagues. This can help you to identify areas where you can improve and give you specific action steps to take to address any weaknesses.

Take Courses or Attend Workshops − Many courses and workshops are available to help you develop your leadership skills. Look for programs focusing on specific skills, such as communication, strategic thinking, or problem-solving.

Seek out Leadership Opportunities − Look for opportunities to take on leadership roles within your organization or in your community. This can help you to gain practical experience and build your leadership skills.

Practice Active Listening − One of the key skills of a successful leader is listening actively and understanding others’ perspectives. Practice active listening by focusing on what others say, asking clarifying questions, and showing empathy and understanding.

Network and Seek Mentors − Building a network of mentors and peers can be a valuable way to improve your leadership skills. Look for opportunities to connect with other leaders and seek out mentors who can offer guidance and support.

Read and Learn from Others − Many books and articles written by successful leaders can offer valuable insights and strategies for improving your leadership skills. Take the time to read and learn from these resources, and look for ways to apply what you learn to your leadership style.


Business leadership is unique because it calls for technical knowledge and soft abilities. It’s an empowering yet demanding work that constantly presents difficulties and unknowns. To control all the moving elements and ensure that the company not only turns a profit but also becomes self-sustaining, it needs a skilled leader.

Why Should Data Science Embrace Blockchain As Its Next Big Thing?

This article was published as a part of the Data Science Blogathon.


Data science and blockchain technology are two of the most cutting-edge and disruptive technologies in the world today. Data science analyzes and interprets the raw data to understand how a system works. Blockchain technology is an innovative way of keeping track of transactions and storing financial information. The combination of these two concepts has led to incredible innovations in software development, finance, and more.

This article will explain data science and blockchain and how they work together to make a difference.

Definition of Data science and Blockchain Technology

Data Science is one of the rapidly-growing domains in technology today. Predictive analytics, Diagnostic analytics, and Descriptive analytics are just a few of the many subfields within science that are always evolving. The goal is to derive insights from existing data, whether structured or unstructured.

For example, Netflix Recommendations – Netflix can provide recommendations based on a user’s video viewing history and ratings. As a result, users can receive suggestions for new films and series relevant to their interests based on their preferences. This can boost the company’s revenue by keeping users engaged on such sites.

Blockchain is a decentralized digital ledger capable of storing any type of data. Blockchain technology is an encrypted database that multiple users share without an intermediary overseeing it. This allows for a tamper-proof system for storing information about transactions between parties.

For example, Cryptocurrencies – A cryptocurrency is a digital currency that uses blockchain technology to record and secure every transaction. Bitcoin, for example, can be used as digital cash to buy everything from groceries to cars.

It has several applications, including financial transactions, digital identity verification, and supply chain management. As such, data scientists have been tasked with improving the efficiency of these processes by identifying patterns in transaction data or predicting how particular actions will impact the system as a whole.

Implications of Data Science and Blockchain Technology

Data is the foundation of blockchain technology. Data also plays a critical role in addressing several critical pain points in the industry. For example, to improve transparency and mitigate fraud, we need to analyze patterns and trends of past user behaviors and correlate them with current activities.

Both have made significant contributions to the modern world. Data scientists have been investigating using the blockchain to store data for years. The most well-known example of this is Factom, which recently partnered with Microsoft on its Cocoa Framework project. This will allow companies to use the Blockchain to store their sensitive data on an enterprise level.

Data science has Impacted Blockchain Technology

In blockchain technology, data science ensures that transactions are secure and tamper-proof. It helps to maintain the integrity and security of blockchain transactions. In addition, it can be used to make sure that transactions are executed promptly.

Any suspicious activity on the blockchain network can be detected using data science. Additionally, it can categorize various transactions depending on their features, allowing for easier collection and analysis. This would make it easier for companies to track criminals using blockchain networks for nefarious purposes such as money laundering or terrorist financing activities.

Blockchain technology offers many benefits for businesses leveraging its decentralized features for authentication or record-keeping purposes. However, it also presents some challenges when it comes to analyzing the data stored on a blockchain network. The distributed nature of blockchains means that there are no centralized servers where one can run queries or perform statistical analysis on the data stored within them. To overcome these limitations, researchers have developed new techniques for performing analytics on blockchains by leveraging concepts from areas such as AI, machine learning (ML) and deep learning (DL).

Blockchain Uses Cases in Data science

Data Integrity:

The quality of the data recorded on it ensures its reliability because it has undergone a rigorous verification process. Furthermore, since the activities and transactions that occur on the blockchain network can be traced, it provides transparency.

In most cases, data integrity is secured by storing and automatically verifying the origin and transactions of a data block on the blockchain.

Ensures high-quality data and accuracy:

Allows Data Traceability:

It’s easier for people to form partnerships with each other using the blockchain. For example, if a published account fails to describe any technique adequately, any peer can analyze the entire process and conclude how the results were produced.

Real-time analysis:

Real-time data analysis is extremely challenging. The best approach to identifying scammers is observing the changes in real-time. With blockchain’s distributed nature, businesses can discover any inconsistencies in their databases from the start.

A blockchain-enabled solution can help enterprises that require large-scale real-time data analysis. With blockchain, banks and other organizations can detect changes in data in real-time, enabling them to make prompt choices, such as blocking a suspicious transaction or monitoring aberrant behaviors.

Making prediction (Predictive analysis) :

One of the simplest ways is through predictive analytics. Just like other types of data, blockchain data can be analyzed to get valuable insights into behaviors and patterns and to predict future events. In addition, blockchain delivers organized data collected from individuals or devices.

Data scientists use predictive analysis to accurately forecast social events, including consumer preferences, customer lifetime value, dynamic prices, and organizational churn rates. As a result, almost any occurrence can be predicted with the correct data analysis, whether it’s social attitudes or investment signals.


Both industries are relatively new, but they’re growing rapidly in tandem. Several companies can benefit from using these technologies together to examine blockchain networks for security purposes, determine more about their users, and begin making better decisions about the technology they produce. Overall, Data science has plenty of potential applications in this brave new world of blockchain technology, and we look forward to seeing what the future holds!

Key Takeaway:

Big data focuses on the quantity of data, whereas blockchain is concerned with quality.

Data Science is a field of study that uses diverse scientific methods, algorithms, and procedures to extract information from large volumes of data.

This technology is decentralized, distributed ledger that tracks the origin of a digital asset. The inherent security mechanisms and public ledger of blockchain make it an ideal tool for virtually every industry.

As the adoption of blockchain technology continues to rise, data scientists have begun building blockchain-based solutions.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


Data Science Jobs Are Hot—And With A State

Data Science Jobs Are Hot—and with a State-of-the-Art Building, New Faculty, and a New Major, BU Is Ready Data and mathematical science occupations are projected to grow more than 30 percent by 2030

BU’s 19-story Center for Computing & Data Sciences is scheduled to open in 2023. Founding faculty come from areas as diverse as law, medicine, sociology, theology, and education, as well as computer science and engineering. Photo by Janice Checchio

Data Science

Data Science Jobs Are Hot—and with a State-of-the-Art Building, New Faculty, and a New Major, BU Is Ready Data and mathematical science occupations are projected to grow more than 30 percent by 2030

In game one of the 2023 NBA Eastern Conference semifinal series, the Atlanta Hawks drilled one long-range three-point shot after another against the heavily favored Philadelphia 76ers. So many that the Hawks set a franchise playoff record—20 three-pointers for one game—and upset Philadelphia 128-124.

“We gave up a lot of corner threes,” says Grant Fiddyment, the 76ers manager of research. In the next two games, Philadelphia was ready. The 76ers cut their turnovers and put in an aggressive defensive display. After making 42.6 percent of their attempted three-pointers in game one, the Hawks managed only 36.7 percent in game two; by game three, they were sinking just 26.1 percent. The 76ers edged ahead 2-1 in the series. “We really shut that down, and you could see the defensive difference,” says Fiddyment.

Like many other professional sports teams, the 76ers have a cadre of analysts and data scientists picking over reams—or more accurately, gigabytes— of stats and information, from training schedules to player performance. Fiddyment (MED’16), who has been with the 76ers since 2023, is one of them, using that data to show the team ways it might improve on the court.

Around 2013, he says, the NBA started getting data from cameras mounted in arena ceilings, tracking every player in every moment of the game. Each step, bounce, screen, shot, and block became a piece of data for teams to study. And that’s exactly what he did after that first game three-point frenzy: scrutinized the granular game data and looked for ways to snuff out the Hawks’ threat.

The 76ers shut down the Hawks’ three-point shooting, with the help of aggressive defense—and data science. Photo by Tim Nwachuku/Getty Images

“We can now dissect the game at a much deeper level,” says Fiddyment, who’s also an adjunct professorial lecturer at American University. It’s a long way from coaches reviewing basic shot-attempt numbers—or just going with their gut. “There’s all the events leading up to a shot that we can go back and analyze or everything that happens after. We can probe all these things in between that used to be dead space from a data perspective.”

Data Science: the Liberal Arts of the 21st Century

The data helped force change in areas Graham cared about, swaying decision-makers or giving impetus to activists. After completing an immersive data science course, Graham signed up for a master’s in computer science at BU—with a concentration in data analytics—to strengthen their technical skills. In August, they also joined BU’s staff and now use data science to support research and efforts to make the tech industry antiracist.

Dawn Graham (MET’22) worked in social services and community organizing roles and sees data science as “something that you can use to effect change.” Photo by Jackie Ricciardi

“I didn’t go into data science as an end goal,” says Graham. “It’s a tool, something that you can use to effect change. A lot of my work has been in things related to racial equity, gender equity, just our general well-being as communities and people. For me, the shift to data science was a question of, how can we more effectively take care of these things and address them?”

As indirect as Graham’s route into the field might seem, Bestavros says it’s common for data science to attract people from disparate backgrounds. Before Fiddyment crunched basketball numbers, for example, he worked in neuroscience, helping epilepsy researchers and surgeons build statistical models to break down the phases of a seizure. Bestavros says that given the wide and varied applications of data science, it’s time to view it as more of a foundational program than a purely vocational one.

“I actually don’t know if data science is a science,” says Bestavros. “Data science is more like the liberal arts of the 21st century. It’s a way of thinking, a way of doing—it has all the elements, the critical thinking, that we associate with the liberal arts.”

A Ramp, Not the Destination

That philosophy is informing BU’s approach to the field. In 2023, the University established the Faculty of Computing & Data Sciences (CDS), a degree-granting academic unit not tied to any college or department. The group’s goal is to cut across disciplines, pulling together researchers and students interested in leveraging the power of computing and data-led inquiry. Founding faculty members come from areas as diverse as law, medicine, sociology, theology, and education, as well as computer science and engineering. This year, CDS launched its first undergraduate major in data science—with a minor coming soon.

Bestavros says the goal of the new bachelor’s degree is to provide students with “the substrate, the base on which you build lots of other professions.” It will check off mathematics, algorithmics, and software engineering, but also topics like social impacts, ethics, and bias.

“The job of a data scientist is different from that of a software engineer,” says Chatterjee (MET’19). “It’s also about communicating our work. It’s what differentiates good data scientists: being able to explain, justify processes, get good feedback, and iterate.”

At the 76ers, Fiddyment calls himself the glue between the coaches and the stats people.

“If you can’t communicate the results of the data you’re working with, then your impact could be just stopped in its tracks,” he says. “You can develop the most fantastic, amazing model, but if you can’t convince people of the importance, then maybe nothing happens.”

Efforts to Diversify the Field

At a time when more companies need that expertise, there’s a shortage of people ready to fill data science jobs, according to Bestavros. In August, venerable life insurance company MassMutual donated $1 million to CDS, in part to boost its own access to new data scientists. It uses customer data—age, health, lifestyle—to help refine and underwrite policies, as well as process claims.

“Talent is hard to find,” Adam Fox, MassMutual’s head of data, told BU Today when the donation was announced. “So one of the biggest drivers for us is the talent at both the undergraduate and graduate levels at BU, and gaining access to that talent pipeline for recruiting.”

The gift also supports a professor of the practice position, experiential learning opportunities, research, and efforts to diversify the field. The latter is an especially pressing issue. According to a 2023 study by executive recruitment firm Burtch Works, only 15 percent of data scientists are women—and other underrepresented groups don’t fare even that well.

It’s not enough to be a software programming whiz—data scientists also need to be good communicators to ensure the conclusions they draw have an impact, according to Oindrilla Chatterjee (MET’19), a data scientist at enterprise software company Red Hat. Photo by Jackie Ricciardi

“In my graduate program [at BU], there were very few women, very few people from diverse backgrounds in general,” says Chatterjee. She says her own team at Red Hat has made inclusion a priority, in part by attending conferences and recruitment events that target underrepresented groups. She says those who don’t pay attention to the industry’s lack of diversity are in danger of letting bias creep into their analyses: “Bias and ethics in machine learning models—and the whole data science domain—is a huge concern. You must be more mindful about where you are gathering the data from; if the data you’re gathering is itself biased and flawed, your models cannot be neutral.”

One goal of the Antiracist Tech Initiative that Graham is working on at the Center for Antiracist Research is to increase industry diversity. They and their colleagues are setting up partnerships with tech firms to gain access to the firm’s data and help them tailor their push for racial equity.

“I’ve been able to witness and experience some of the challenges around what it means to be from an underrepresented group in a certain industry,” says Graham. “To be able to bring that experience directly into some of the work we’re doing now, I think it helps guide that work in a way that is really meaningful.”

Bestavros says increasing industry diversity is also high on the list of priorities for CDS. Along with embedding ethics and lessons about bias throughout its programs, he says, a push to reach students who might not have considered—or had a route into—the field before will help “democratize access to data science.”

In addition to addressing its lack of diversity, the field faces another critical issue: closing a trust gap. Many have very legitimate fears about the power of big data, especially biased data, to shape our lives. For all its benefits—whether positive (supporting vaccine research) or relatively benign (shaping the comedies we watch on Netflix)—plenty of people are deeply skeptical. They don’t want firms or governments using their data to manipulate them.

Bestavros argues that data science is and will be a force for good, and he says opening up the field to more diverse groups of people will only enhance its potential for positive change. He draws lessons from the early days of nuclear energy and the internet: many of those behind the world-shaping breakthroughs only thought about their potential for good, not for harm. It’s a mistake he wants to learn from—and teach to those entering the hot data science job market.

“There are a lot of things that happen that make our life much better because of data science,” he says. “But there are better ways to do data science than others. It’s almost like you are training future doctors—it goes beyond just what works for mice and rats. This is about the human in the loop. We are now introducing technology that is changing how we interact with each other.”

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Frequently Asked Data Science Interview Questions

This article was published as a part of the Data Science Blogathon.


This article will discuss some data science interview questions and their answers to help you fare well in job interviews. These are data science interview questions and are based on data science topics. Though some of the questions may sound basic, these are frequently asked in interviews. Most candidates overlook them and won’t focus on the basics, and they face rejection in job interviews. It is important to start learning the basics to nail the data science job interviews. The following data science interview questions are your guide to performing well in data science job interviews.

Source: SEEK

Frequently Asked Data Science Interview Questions Q1: Elaborate on the differences between Data Science and Data Analytics.

Firstly data analytics is a part of data science. Multiple things come under data science, like data mining, data analytics, data visualization, and many more.

The job of a data analyst is to find the solution to the current problem. At the same time, the job of a data scientist is to find the solution to the present problem and predict the future by taking inputs from the past.

Q2: Explain the Confusion matrix.

A confusion matrix is used to know how well the model is performed.

Source: medium

If the predicted value is positive and the actual result is also positive, then the model will perform well (True positive).

If the predicted value is positive and the actual result is negative, then the model is not performing well (False positive)- Type I error.

If the predicted value is negative and the actual result is also negative, then the model will perform well (True negative).

If the predicted value is negative and the actual result is positive, then the model is not performing well (False negative)- Type II error.

The formula knows the accuracy of the model

(True Positive + True Negative) / Total Observations let us say true positive observations are 5 and true negative observations are 4, out of total observations 10, then the accuracy of the model is (5+4)/10=9/10=0.9

It is 90% accurate.

Q3:Differentiate between the terms error and residual.

The difference between the observed value and the theoretical value gives the error. The difference between the observed and predicted values gives the residual value.

Error = Observed value - Theoretical value Residual = Observed value - Predicted value Q4:What are the precautions taken to avoid overfitting our model?

Suppose the model is performing well on the data sets we are using for our training and testing and not on some other data set, then such a model is said to be overfitting.

Source: kdnuggets


Can be avoided by having our model as simple as possible

Using cross-validation techniques

Using regularization techniques

Using feature engineering

Q5: Differentiate between data science and traditional application programming.

In traditional application programming, we need to analyze the input first. To get the expected output, certain code needs to be written on our own, which could be challenging as it is a manual process.

However, in Data Science, the process is entirely different. We need to have data first and divide it into two sets. One is called the testing data, and the other set is called the training data. With the help of training data and data science algorithms, rules are created to map an input to an output. These rules are tested using the testing data set. If the rule succeeds, it is said to be the model.

Q6:Explain bias in Data Science. Q7: What do you know about dimensionality reduction?

Some Datasets would have more fields than required. Even after removing some fields, the functionality would remain the same. The process of reducing such fields or dimensions while taking care of the functionality is known as dimensionality reduction.

Q8: Mention the popular libraries used in Data Science.

Some of the popular libraries used in data science are:






Q9: Explain the working of a recommendation system.

A recommendation system is either a program or an algorithm based on watch and search history inputs. It analyses the genre, cast, director, and more to recommend movies to the viewers. That is how the recommendation system works for a product-selling platform like Amazon, Myntra, or Flipkart, or an OTT platform like Netflix, Amazon prime Video, Aha, and so on.

Generally, there are three types of recommendation systems.

1. Demographic filtering

2. Content-based filtering

3. Collaboration-based filtering

Demographic filtering: In this, the recommendations are the same for every user regardless of their interests. For example, let’s take the top trending movies column in OTT platforms. These are the same for every user because of the demographic filtering system.

Content-based filtering: Filterings are based on movie metadata. Metadata contains details like movies, songs, genres, cast stories, etc. Based on this data, the system recommends movies related to that data for user consumption.

Collaboration-based filtering: Here, the system will group users with similar interests and recommend movies to them.

Question 10: Explain the benefit of dimensionality reduction.

Some Datasets would have more fields than required, meaning that even after removing some fields, the functionality would remain the same. The process of reducing such fields or dimensions by taking care of functionality is known as dimensionality reduction.

After reducing fields, it requires less time to process the data and to train the model, and speed is increased compared to data with more dimensions. Also, the accuracy of the model is increased.


Data science is a lucrative career option with ample opportunities across diverse sectors. There is a surge in the number of companies based on Data science like machine learning and artificial intelligence and the career options they offer. This write-up helps us cover some frequently asked and basic data science interview questions and their answers.

Overall in this article, we have seen,

Some basics of data science and data analytics

Bias, Error, and Overfitting related topics.

Questions related to data science.

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