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- What is Nominal Data? Definition, Variables and Examples
What is Nominal Data? Definition, Variables and Examples
Updated on 03 September, 2024
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Table of Contents
Introduction
Nominal data is categorical data that effectively categorize items into discrete categories without inherent order or ranking. A comprehensive grasp of nominal data is paramount for precise data interpretation and analysis across diverse domains, encompassing research, marketing, and social sciences. Researchers are enabled to discern patterns, relationships, and frequencies within various categories.
Understanding the inherent characteristics of nominal data aids in identifying suitable statistical techniques and visual depictions, thereby facilitating the extraction of significant insights from the data. A comprehensive grasp of nominal data facilitates making well-informed decisions and deriving precise conclusions based on the available information.
In this article, the concepts of nominal data will be explained. So, just take out your notepad and start taking notes.
What is Nominal Data?
Nominal data is a specific form of categorical data wherein variables are categorized into discrete groups or classes without any inherent order or numerical significance. The nominal level of measurement represents the lowest tier within the four levels of measurement scale, alongside ordinal, interval, and ratio data. Nominal data is predominantly employed to classify and categorize qualitative attributes that are not amenable to quantification.
Variables related to Nominal Data exhibit the characteristics of mutual exclusivity, whereby each observation is assigned to a single category, and exhaustiveness, which guarantees that all potential categories are encompassed.
When gathering data about the gender of individuals, the potential categories encompass male, female, and non-binary. Each individual is limited to membership in a single category, and there is no inherent ranking or hierarchy among these categories.
Nominal data is widely utilized across disciplines, such as sociology, psychology, biology, and market research, to effectively organize qualitative attributes for analysis and interpretation. Academic researchers frequently utilize nominal data to categorize data to facilitate comparisons and analyze patterns or relationships between distinct categories.
What is the Nominal Data Scale?
The nominal data scale is considered the most basic measurement level. It is used to show classified data that has different groups or categories. The numerical scale is different because the data points are put into groups that can’t be used together. The inference is that each fact is only put into one group, but all categories are included. But it’s important to note that these groups don’t have any natural order or number value. Nominal data represents general characteristics that can’t be measured in numbers.
For example, individuals can be classified into various groups based on the color of their eyes. The colors range from blue, brown, green, grey, and hazel. Individuals are assigned to discrete categories according to their eye color without any apparent hierarchy or discernible sequential arrangement among these categories.
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Characteristics of Nominal Data
Nominal data exhibits unique attributes that differentiate it from other data types. These characteristics determine the organization, classification, and analysis of nominal data within a dataset.
- Categorical Classification: Using nominal data to categorize qualitative characteristics into discrete groups or categories effectively. Every instance within the dataset is assigned to a singular category, guaranteeing that the groups are completely separate and all-encompassing.
- No order: The absence of inherent order characterizes nominal data, as opposed to ordinal data, which possesses a rank order but lacks a quantifiable distance between categories. The categorizations are simply designations employed to organize characteristics without any quantifiable import.
- Nonexistence of Numeric Quantification: Nominal data does not encompass numerical values or measurements. The categories encompass qualitative attributes that elude quantification through numerical values.
- No use of arithmetic operations: In the case of nominal data, it is not suitable to engage in arithmetic operations such as addition, subtraction, multiplication, or division due to the absence of numerical values associated with the data points. The available data can solely be structured as frequency counts or proportions.
- Utilization of Labels: Within the realm of nominal data, categories are frequently denoted by labels or names, thereby facilitating comprehension and interpretation of the data. The labels may encompass linguistic units, alphabetical characters, or symbolic representations denoting distinct categories.
- Frequencies and Percentage: When it comes to analyzing nominal data, frequency distribution tables and bar charts are the primary methods employed. The provided graphical representations depict the distribution of individual categories and the corresponding relative frequencies or percentages of observations within each category.
- Mode as a measure of Central Tendency: The mode, which signifies the category that occurs most frequently, is considered the suitable measure of central tendency for nominal data. The dataset is analyzed to identify the category that exhibits the utmost frequency.
- Comparisons Involving Non-Numeric Data: Nominal data facilitates non-numeric comparisons, wherein the objective is to ascertain the similarity or dissimilarity between two categories. Researchers are empowered to discern patterns, associations, or disparities among diverse groups.
- More use of Categorical Studies: Nominal data finds extensive application in diverse domains such as social sciences, market research, and biology. Its primary purpose is to categorize and arrange qualitative attributes into significant groupings, facilitating subsequent analysis and interpretation.
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Nominal vs. Ordinal Data
Nominal data, in contrast to ordinal data, lacks any inherent order or ranking. Its sole purpose is to embody categories or groups. The system can exclusively categorize information into discrete and independent classifications. Examples of nominal data include colors, genders, types of fruits, and marital status (e.g., single, married, divorced). Labeling data points can resemble the absence of a discernible sequence or hierarchical structure, much like the nature of categories.
In contrast, ordinal data showcases a perceptible arrangement or ranking among the diverse categories. The utilization of relative differences in magnitude allows for the comparison of categories, albeit with the acknowledgment that the intervals between them may not demonstrate uniform spacing.
When contemplating ordinal data, it facilitates the capacity to assess and order items, but it falls short in conveying precise details about the magnitude of differences between the categories. Ordinal data, such as different levels of education (e.g., elementary, high school, college) or client satisfaction ratings (e.g., very dissatisfied, dissatisfied, neutral, satisfied, very satisfied), serve as apt illustrations.
Researchers frequently use visual representations and statistical tests to analyze the data types. Commonly employed methods for analyzing nominal data encompass bar charts, pie charts, and chi-square tests. In contrast, ordinal data can be effectively examined using bar charts featuring ordered categories alongside non-parametric tests such as the Mann-Whitney U test or the Wilcoxon signed-rank test.
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Nominal vs. Interval Data
Categorization | Nominal and interval data share similarities in assigning items to discrete categories or groups.
|
Interval data is a variant of numerical data that exhibits a significant order and uniform intervals between values |
Arithmetic Operations | Arithmetic operations, including addition, subtraction, and multiplication, exhibit meaningfulness when applied to numerical data, whereas nominal data lacks the same level of suitability for such operations. | Arithmetic operations, like addition and subtraction, hold importance when performed on interval data, whereas multiplication and division lack importance without a true zero. |
Data Analysis | The analysis of nominal data primarily focuses on the enumeration of frequencies and the computation of percentages, distinguishing it from other forms of data analysis.
|
Interval data is analyzed using data visualization, hypothesis testing, correlation analysis, and calculating measures of central tendency. |
Examples | Examples of nominal data include gender (male, female), colors (red, blue, green), and types of animals (dog, cat, bird). | Interval data, like temperature measured in Celsius or Fahrenheit, dates on a calendar, and IQ scores, serve as instances of interval data. |
Nominal vs. Ratio Data
The distinction between nominal and ratio data lies in their respective levels of measurement and the specific analyses that can be performed on them. Nominal data, being of a categorical nature, is predominantly employed for classification. On the other hand, it is worth noting that ratio data exhibit numerical characteristics that entail a significant sequence, equivalent intervals, and an authentic zero point.
These attributes facilitate a more comprehensive examination and comprehension. Comprehending the differentiation among these data types is paramount when selecting suitable statistical methodologies and deriving precise inferences from the data.
Conclusion
In conclusion, we studied that nominal data is a prevalent form of categorical data, wherein items are categorized into distinct groups without inherent hierarchy. A comprehensive comprehension of numerical data is important for precise data analysis and evaluation in various domains such as research, marketing, and the social sciences. Through the analysis of nominal data features and the application of appropriate statistical methods, valuable insights can be obtained, leading to enhanced decision-making capabilities and the ability to draw definitive conclusions from categorical data.
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Frequently Asked Questions (FAQs)
1. What is the methodology used for the analysis of nominal data?
Nominal data is commonly subjected to analysis through the utilization of frequency tables and chi-square tests.
2. Is it possible to depict nominal data using numerical values?
Yes, nominal data can be assigned numerical codes for the sake of convenience. However, it is important to note that these numerical codes lack any inherent numerical value or significance.
3. What are the instances of nominal data observed in daily experiences?
Nominal data encompasses various examples, such as the hues of one's eyes, the diverse brands of automobiles, and the designations assigned to one's occupation.
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Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.
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The Theories on Theory
A recurring theme throughout my conversations was the role of theory and its extension to practical approaches, case studies and live projects. A good recommendation to aspiring data scientists would be to find a university that offers a bachelor’s degree in data science. Learn it at the bachelor’s level and avoid getting mired in only deep theory at the PostGrad level. You’d think the master’s degree dealing with mostly theory would be better, but I don’t think so. By the time you get to the MS you’re working with the professors and they want to teach you a lot of theory. You’re going to learn things from a very academic point of view, which will help you, but only if you want to publish theoretical papers.
Hence, universities, especially those framing a PostGrad degree in Data Science should make sure not to fall into orchestrating a curriculum with a long drawn theory-centric approach. Also, like many of the MOOCs out there, a minimum of a capstone project would be a must to give the students a more pragmatic view of data and working on it. It’s important to learn theory of course. I know too many ‘data scientists’ even at places like Google who wouldn’t be able to tell you what Bayes’ Theorem or conditional independence is, and I think data science unfortunately suffers from a lack of rigor at many companies. But the target implementation of the students, which would mostly be in corporate houses, dealing with real consumer or organizational data, should be finessed using either simulated practical approach or with collaboration with Data Science companies to give an opportunity to students to deal with real life projects dealing with data analysis and drawing out actual business insights.
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Don’t Forget About the Soft Skills
In an article titled The Hard and Soft Skills of a Data Scientist, Todd Nevins provides a list of soft skills becoming more common in data scientist job requirements, including:
Manage teams and projects across multiple departments on and offshore.
Consult with clients and assist in business development.
Take abstract business issues and derive an analytical solution.
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The article also emphasizes the importance of these skills, and criticizes university programs for often leaving these skills out altogether: “There’s no real training about how to talk to clients, how to organize teams, or how to lead an analytics group.”
Data science is still a rapidly evolving field and until the norms are more established, it’s unlikely every data scientist will be following the same path. A degree in data science will definitely act as the clay to make your career. But the part that really separates people who are successful from that are not is just a core curiosity and desire to answer questions that people have — to solve problems. Don’t do it because you think you can make a lot of money, chances are by the time you’re trained, you either don’t know the right stuff or there’s a hundred other people competing for the same position, so the only thing that’s going to stand out is whether you really like what you’re doing.
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Computer Center turns Data Center; Computer Science turns Data Science
(This article, written by Prof. S. Sadagopan, was originally published in Analytics India Magazine)
There is an old “theory” that talks of “power shift” from “carrier” to “content” and to “control” as industry matures. Here are some examples
In the early days of Railways, “action” was in “building railroads”; the “tycoons” who made billions were those “railroad builders”. Once enough railroads were built, there was more action in building “engines and coaches” – General Electric and Bombardier emerged; “power” shifted from “carrier” to “content”; still later, action shifted to “passenger trains” and “freight trains” – AmTrak and Delhi Metro, for example, that used the rail infrastructure and available engines and coaches / wagons to offer a viable passenger / goods transportation service; power shifted from “content” to “control”.
The story is no different in the case of automobiles; “carrier” road-building industry had the limelight for some years, then the car and truck manufacturers – “content” – GM, Daimler Chrysler, Tata, Ashok Leyland and Maruti emerged – and finally, the “control”, transport operators – KSRTC in Bangalore in the Bus segment to Uber and Ola in the Car segment.
In fact, even in the airline industry, airports become the “carrier”, airplanes are the “content” and airlines represent the “control”
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It is a continuum; all three continue to be active – carrier, content and control – it is just the emphasis in terms of market and brand value of leading companies in that segment, profitability, employment generation and societal importance that shifts.
We are witnessing a similar “power shift” in the computer industry. For nearly six decades the “action” has been on the “carrier”, namely, computers; processors, once proprietary from the likes of IBM and Control Data, then to microprocessors, then to full blown systems built around such processors – mainframes, mini computers, micro computers, personal computers and in recent times smartphones and Tablet computers. Intel and AMD in processors and IBM, DEC, HP and Sun dominated the scene in these decades. A quiet shift happened with the arrival of “independent” software companies – Microsoft and Adobe, for example and software services companies like TCS and Infosys. Along with such software products and software services companies came the Internet / e-Commerce companies – Yahoo, Google, Amazon and Flipkart; shifting the power from “carrier” to “content”.
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This shift was once again captured by the use of “data center” starting with the arrival of Internet companies and the dot-com bubble in late nineties. In recent times, the term “cloud data center” is gaining currency after the arrival of “cloud computing”.
Though interest in computers started in early fifties, Computer Science took shape only in seventies; IITs in India created the first undergraduate program in Computer Science and a formal academic entity in seventies. In the next four decades Computer Science has become a dominant academic discipline attracting the best of the talent, more so in countries like India. With its success in software services (with $ 160 Billion annual revenue, about 5 million direct jobs created in the past 20 years and nearly 7% of India’s GDP), Computer Science has become an aspiration for hundreds of millions of Indians.
With the shift in “power” from “computers” to “data” – “carrier” to “content” – it is but natural, that emphasis shifts from “computer science” to “data science” – a term that is in wide circulation only in the past couple of years, more in corporate circles than in academic institutions. In many places including IIIT Bangalore, the erstwhile Database and Information Systems groups are getting re-christened as “Data Science” groups; of course, for many acdemics, “Data Science” is just a buzzword, that will go “out of fashion” soon. Only time will tell!
As far as we are concerned, the arrival of data science represents the natural progression of “analytics”, that will use the “data” to create value, the same way Metro is creating value out of railroad and train coaches or Uber is creating value out of investments in road and cars or Singapore Airlines creating value out of airport infrastructure and Boeing / Airbus planes.
More important, the shift from “carrier” to “content” to “control” also presents economic opportunities that are much larger in size. We do expect the same from Analytics as the emphasis shifts from Computer Science to Data Science to Analytics. Computers originally created to “compute” mathematical tables could be applied to a wide range of problems across every industry – mining and machinery, transportation, hospitality, manufacturing, retail, banking & financial services, education, healthcare and Government; in the same vein, Analytics that is currently used to summarize, visualize and predict would be used in many ways that we cannot even dream of today, the same way the designers of computer systems in 60’s and 70’s could not have predicted the varied applications of computers in the subsequent decades.
We are indeed in exciting times and you the budding Analytics professional could not have been more lucky.
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About Prof. S. Sadagopan
Professor Sadagopan, currently the Director (President) of IIIT-Bangalore (a PhD granting University), has over 25 years of experience in Operations Research, Decision Theory, Multi-criteria optimization, Simulation, Enterprise computing etc.
His research work has appeared in several international journals including IEEE Transactions, European J of Operational Research, J of Optimization Theory & Applications, Naval Research Logistics, Simulation and Decision Support Systems. He is a referee for several journals and serves on the editorial boards of many journals.
Read More11 May'16
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Enlarge the analytics & data science talent pool
Note: The articlewas originally written by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions.
A Better Talent acquisition Framework
Although many articles have been written lamenting the current talent shortage in analytics and data science, I still find that the majority of companies could improve their success by simply revamping their current talent acquisition processes.
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We’re all well aware that strong quantitative professionals are few and far between, so it’s in a company’s best interest to be doing everything in their power to land qualified candidates as soon as they find them. It’s a candidate’s market, with strong candidates going on and off the market lightning fast, yet many organizational processes are still slow and outdated. These sluggish procedures are not equipped to handle many candidates who are fielding multiple offers from other companies who are just as hungry (if not more so) for quantitative talent.
Here are the key areas I would change to make hiring processes more competitive:
Fix your salary bands – It (almost) goes without saying that if your salary offerings are outdated or aren’t competitive to the field, it will be difficult for you to get the attention of qualified candidates; stay topical with relevant compensation grids.
Consider one-time bonuses – Want to make your offer compelling but can’t change the salary? Sign-on bonuses and relocation packages are also frequently used, especially near the end of the year, when a candidate is potentially walking away from an earned bonus; a sign-on bonus can help seal the deal.
Be open to other forms of compensation – There are plenty of non-monetary ways to entice Quants to your company, like having the latest tools, solving challenging problems, organization-wide buy-in for analytics and more. Other things to consider could be flexible work arrangements, remote options or other unique perks.
Pick up the pace – Talented analytics professionals are rare, and the chances that qualified candidates will be interviewing with multiple companies are very high. Don’t hesitate to make an offer if you find what you’re looking for at a swift pace – your competitors won’t.
Court the candidate – Just as you want a candidate who stands out from the pack, a candidate wants a company that makes an effort to stand apart also. I read somewhere, a client from Chicago sent an interviewing candidate and his family pizzas from a particularly tasty restaurant in the city. I can’t say for sure that the pizza was what persuaded him to take the company’s offer, but a little old-fashioned wooing never hurts.
Button up the process – Just as it helps to have an expedited process, it also works to your benefit is the process is as smooth and trouble-free as you can make it. This means hassle-free travel arrangements, on-time interviews, and quick feedback.
Network – make sure that you know the best of the talent available in the market at all levels and keep in touch with them thru porfessional social sites on subtle basis as this will come handy in picking the right candidate on selective basis
Redesigned Interview Process
In the old days one would screen resumes and then schedule lots of 1:1’s. Typically people would ask questions aimed at assessing a candidate’s proficiency with stats, technicality, and ability to solve problems. But there were three problems with this – the interviews weren’t coordinated well enough to get a holistic view of the candidate, we were never really sure if their answers would translate to effective performance on the job, and from the perspective of the candidate it was a pretty lengthy interrogation.
So, a new interview process need to be designed that is much more effective and transparent – we want to give the candidate a sense for what a day in the life of a member on the team is like, and get a read on what it would be like to work with a company. In total it takes about two days to make a decision, and there be no false positives (possibly some false negatives though), and the feedback from both the candidates and the team members has been positive. There are four steps to the process:
Resume/phone screens – look for people who have experience using data to drive decisions, and some knowledge of what your company is all about. On both counts you’ll get a much deeper read later in the process; you just want to make sure that moving forward is a good use of either of both of your time.
Basic data challenge – The goal here is to validate the candidate’s ability to work with data, as described in their resume. So send a few data sets to them and ask a basic question; the exercise should be easy for anyone who has experience.
In-house data challenge – This is should be the meat of the interview process. Try to be as transparent about it as possible – they’ll get to see what it’s like working with you and vice versa. So have the candidate sit with the team, give them access to your data, and a broad question. They then have the day to attack the problem however they’re inclined, with the support of the people around them. Do encourage questions, have lunch with them to ease the tension, and check-in periodically to make sure they aren’t stuck on something trivial.
At the end of the day, we gather a small team together and have them present their methodology and findings to you. Here, look for things like an eye for detail (did they investigate the data they’re relying upon for analysis), rigor (did they build a model and if so, are the results sound), action-oriented (what would we do with what you found), and communication skills.
Read between the resume lines
Intellectual curiosity is what you should discover from the project plans. It’s what gives the candidate the ability to find loopholes or outliers in data that helps crack the code to find the answers to issues like how a fraudster taps into your system or what consumer shopping behaviors should be considered when creating a new product marketing strategy.
Data scientists find the opportunities that you didn’t even know were in the realm of existence for your company. They also find the needle in the haystack that is causing a kink in your business – but on an entirely monumental scale. In many instances, these are very complex algorithms and very technical findings. However, a data scientist is only as good as the person he must relay his findings to. Others within the business need to be able to understand this information and apply these insights appropriately.
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Good data scientists can make analogies and metaphors to explain the data but not every concept can be boiled down in layman’s terms. A space rocket is not an automobile and, in the brave new world, everyone must make this paradigm shift.
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And lastly, the data scientist you’re looking for needs to have strong business acumen. Do they know your business? Do they know what problems you’re trying to solve? And do they find opportunities that you never would have guessed or spotted?
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UpGrad partners with Analytics Vidhya
We are happy to announce our partnership with Analytics Vidhya, a pioneer in the Data Science community. Analytics Vidhya is well known for its impressive knowledge base, be it the hackathons they organize or tools and frameworks that they help demystify. In their own words, “Analytics Vidhya is a passionate community for Analytics/Data Science professionals, and aims at bringing together influencers and learners to augment knowledge”.
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We are joining hands to provide candidates of our PG Diploma in Data Analytics, an added exposure to UpGrad Industry Projects. While the program already covers multiple case studies and projects in the core curriculum, these projects with Analytics Vidhya will be optional for students to help them further hone their skills on data-driven problem-solving techniques. To further facilitate the learning, Analytics Vidhya will also be providing mentoring sessions to help our students with the approach to these projects.
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This collaboration brings great value to the program by allowing our students to add another dimension to their resume which goes beyond the capstone projects and case studies that are already a part of the program.
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Through this, we hope our students would be equipped to showcase their ability to dissect any problem statement and interpret what the model results mean for business decision making. This also helps us to differentiate UpGrad-IIITB students in the eyes of the recruiters.
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Transformation & Opportunities in Analytics & Insights
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Data Analytics Student Speak: Story of Thulasiram
When Thulasiram enrolled in the UpGrad Data Analytics program, in its first cohort, he was not very different for us, from the rest of our students in this. While we still do not and should not treat learners differently, being in the business of education – we definitely see this particular student in a different light. His sheer resilience and passion for learning shaped his success story at UpGrad.
Humble beginnings
Born in the small town of Chittoor, Andhra Pradesh, Thulasiram does not remember much of his childhood given that he enlisted in the Navy at a very young age of about 15 years. Right out of 10th standard, he trained for four years, acquiring a diploma in mechanical engineering.
Thulasiram came from humble means. His father was the manager of a small general store and his mother a housewife. It’s difficult to dream big when leading a sheltered life with not many avenues for exposure to unconventional and exciting opportunities. But you can’t take learning out of the learner.
“One thing I remember about school is our Math teacher,” reminisces Thulasiram, “He used to give us lot of puzzles to solve. I still remember one puzzle. If you take a chessboard and assume that all pawns are queens; you have to arrange them in such a way that none of the eight pawns should die. Every queen, should not affect another queen. It was a challenging task, but ultimately we did it, we solved it.”
Navy & MBA
At 35 years of age, Thulasiram has been in the navy for 19 years. Presently, he is an instructor at the Naval Institute of Aeronautical Technology. “I am from the navy and a lot of people don’t know that there is an aviation wing too. So, it’s like a dream; when you are a small child, you never dream of touching an aircraft, let alone maintaining it. I am very proud of doing this,” says Thulasiram on taking the initiative to upskill himself and becoming a naval-aeronautics instructor.
When the system doesn’t push you, you have to take the initiative yourself. Thulasiram imbibed this attitude. He went on to enroll in an MBA program and believes that the program drastically helped improve his communication skills and plan his work better.
How Can You Transition to Data Analytics?
Data Analytics
Like most of us, Thulasiram began hearing about the hugely popular and rapidly growing domain of data analytics all around him. Already equipped with the DNA of an avid learner and keen to pick up yet another skill, Thulasiram began researching the subject.
He soon realised that this was going to be a task more rigorous and challenging than any he had faced so far. It seemed you had to be a computer God, equipped with analytical, mathematical, statistical and programming skills as prerequisites – a list that could deter even the most motivated individuals.
This is where Thulsiram’s determination set him apart from most others. Despite his friends, colleagues and others that he ran the idea by, expressing apprehension and deterring him from undertaking such a program purely with his interests in mind – time was taken, difficulty level, etc. – Thulasiram, true to the spirit, decided to pursue it anyway. Referring to the crucial moment when he made the decision, he says,
If it is easy, everybody will do it. So, there is no fun in doing something which everybody can do. I thought, let’s go for it. Let me push myself — challenge myself. Maybe, it will be a good challenge. Let’s go ahead and see whether I will be able to do it or not.
UpGrad
Having made up his mind, Thulasiram got straight down to work. After some online research, he decided that UpGrad’s Data Analytics program, offered in collaboration with IIIT-Bangalore that awarded a PG Diploma on successful completion, was the way to go.
The experience, he says, has been nothing short of phenomenal. It is thrilling to pick up complex concepts like machine learning, programming, or statistics within a matter of three to four months – a feat he deems nearly impossible had the source or provider been one other than UpGrad.
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Favorite Elements
Ask him what are the top two attractions for him in this program and, surprising us, he says deadlines! Deadlines and assignments. He feels that deadlines add the right amount of pressure he needs to push himself forward and manage time well.
As far as assignments are concerned, Thulasiram’s views resonate with our own – that real-life case studies and application-based learning goes a long way. Working on such cases and seeing results is far superior to only theoretical learning.
He adds, “flexibility is required because mostly only working professionals will be opting for this course. You can’t say that today you are free, because tomorrow some project may be landing in your hands. So, if there is no flexibility, it will be very difficult. With flexibility, we can plan things and maybe accordingly adjust work and family and studies,” giving the UpGrad mode of learning, yet another thumbs-up.
Amongst many other great things he had to say, Thulasiram was surprised at the number of live sessions conducted with industry professionals/mentors every week. Along with the rest of his class, he particularly liked the one conducted by Mr. Anand from Gramener.
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“Have learned most here, only want to learn..”
Interested only in learning, Thulasiram made this observation about the program – compared to his MBA or any other stage of life. He signs off calling it a game-changer and giving a strong recommendation to UpGrad’s Data Analytics program.
We are truly grateful to Thulasiram and our entire student community who give us the zeal to move forward every day, with testimonials like these, and make the learning experience more authentic, engaging, and truly rewarding for each one of them.
If you are curious to learn about data analytics, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
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Decoding Easy vs. Not-So-Easy Data Analytics
Authored by Professor S. Sadagopan, Director – IIIT Bangalore. Prof. Sadagopan is one of the most experienced academicians on the expert panel of UpGrad & IIIT-B PG Diploma Program in Data Analytics.
As a budding analytics professional confounded by jargon, hype and overwhelming marketing messages that talk of millions of upcoming jobs that are paid in millions of Rupees, you ought to get clarity about the “real” value of a data analytics education. Here are some tidbits – that should hopefully help in reducing your confusion.
Some smart people can use “analytical thinking” to come up with “amazing numbers”; they are very useful but being “intuitive”, they cannot be “taught.”
For example:
Easy Analytics
Pre-configuring ATMs with Data Insights
“We have the fastest ATM on this planet”
Claimed a respected Bank. Did they get a new ATM made especially for them? No way. Some smart employee with an analytical mindset found that 90% of the time that users go to an ATM to withdraw cash, they use a fixed amount, say Rs 5,000. So, the Bank re-configured the standard screen options – Balance Inquiry, Withdrawal, Print Statement etc. – to include another option. Withdraw XYZ amount, based on individual customer’s past actions.
This ended up saving one step of ATM operation. Instead of selecting the withdrawal option and then entering the amount to be withdrawn, you could now save some time – making the process more convenient and intuitive. A smart move indeed, however, this is something known as “Easy Analytics” that others can also copy. In fact, others DID copy, within three months!
A Start-Up’s Guide to Data Analytics
Hidden Data in the Weather
In the sample data-sets that used to accompany a spreadsheet product in the 90’s, there used to be data on the area and population of every State in the United States. There was also an exercise to teach the formula part of the spreadsheet to compute the population density (population per sq. km). New Jersey, with a population of 467 per sq. km, is the State with the highest density.
While teaching a class of MBA students in New Jersey, I met an Indian student who figured out that in terms of population density, New Jersey is more crowded than India with 446 people per sq. km! An interesting observation, although comparing a State with a Country is a bit misleading. Once again, an Easy Analytics exercise leading to a “nice” observation!
Some simple data analytics exercises can be routinely done, and are made relatively easier, thanks to amazing tools:
B-School Buying Behavior Decoded
In a B-School in India that has a store on campus, (campus is located far from the city center) some smart students put several years of sales data of their campus store. They were excited by the phenomenal computer power and near, idiot-proof analytics software.
The real surprise, however, was that eight items accounted for 85% of their annual sales. More importantly, these eight items were consumed in just six days of the year! Everyone knew that a handful of items were the only fast-moving items, but they did not know the extent (85%) or the intensity (consumption in just six days) of this.
It turns out that in the first 3 days of the semester the students would stock the items for the full semester! The B-School found it sensible to request a nearby store to prop up a temporary stall for just two weeks at the beginning of the semesters and close down the Campus Store. This saved useful space and costs without causing major inconvenience to the students. A good example of Easy Analytics done with the help of a powerful tool.
Top 4 Data Analytics Skills You Need to Become an Expert!
The “Not So Easy” Analytics needs deep analytical understanding, tools, an ‘analytical mindset’ and some hard work. Here are two examples, one taken from way back in the 70’s and the other occurring very recently:
Not-So-Easy Analytics
To Fly or Not to Fly, That is the Question
Long ago, the American Airlines perfected planned overbooking of airline seats, thanks to SABRE Airline Reservation system that managed every airline seat.
Armed with detailed past data of ‘empty seats’ and ‘no show’ in every segment of every flight for every day through the year, and modeling airline seats as perishable commodities, the American Airlines was able to improve yield, i.e., utilization of airplane capacity. They did this through planned overbooking – selling more tickets than the number of seats, based on projected cancellations.
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If indeed more passengers showed up than the actual number of seats, American Airlines would request anyone volunteering to forego travel in the specific flight, with the offer to fly them by the next flight (often free) and taking care of hotel accommodation if needed. Sometimes, they would even offer cash incentives to the volunteer to opt-out.
Using sophisticated Statistical and Operational Research modeling, American Airlines would ensure that the flights went full and the actual incidents of more passengers than the full capacity, was near zero. In fact, many students would look forward to such incidents so that they could get incentives, (in fact, I would have to include myself in this list) but rarely were they rewarded!)
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What American Airlines started as an experiment has become the standard industry practice over the years. Until recently, a team of well-trained (often Ph.D. degree holders) analysts armed with access to enormous computing power, was needed for such an analytics exercise to be sustained.
Now, new generation software such as the R Programming language and powerful desktop computers with significant visualization/graphics power is changing the world of data analytics really fast. Anyone who is well-trained (not necessarily requiring a Ph.D. anymore) can become a first-rate analytics professional.
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Unleashing the Power of Data Analytics
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Cab Out of the Bag
Uber is yet another example displaying how the power of data analytics can disrupt a well-established industry. Taxi-for-sure in Bangalore and Ola Cabs are similar to Uber. Together, these Taxi-App companies (using a Mobile App to hail a taxi, the status monitor the taxi, use and pay for the taxi) are trying to convince the world to move from car ownership to on-demand car usage.
A simple but deep analytics exercise in the year 2008 gave such confidence to Uber that it began talking of reducing car sales by 25% by the year 2025!
After building the Uber App for iPhone, the Uber founder enrolled few hundreds of taxi customers in San Francisco and few hundreds of taxi drivers in that area as well. All that the enrolled drivers had to do was to touch the Uber App whenever they were ready for a customer. Similarly, the enrolled taxi customers were requested to touch the Uber App whenever they were looking for a taxi.
Thanks to the internet-connected phone (connectivity), Mobile App (user interface), GPS (taxi and end-user location) and GIS (location details), Uber could try connecting the taxi drivers and the taxi users.
The real insight was that nearly 90% of the time, taxi drivers found a customer, less than 100 meters away! In the same way, nearly 90% of the time, taxi users were connected with their potential drivers in no time, not too far away.
Unfortunately, till the Uber App came into existence, riders and taxi drivers had no way of knowing this information. More importantly, they both had no way of reaching each other! Once they had this information and access, a new way of taxi-hailing could be established.
With back-end software to schedule taxis, payment gateway and a mobile payment mechanism, a far more superior taxi service could be established. Of course, near home, we had even better options like Taxi-for-sure trying to extend this experience even to auto rickshaws. The rest, as they say, is “history in the making!”
Deep dive courses in data analytics will help prepare you for such high impact applications. It is not easy, but do remember former US President Kennedy’s words
“we chose to go to the Moon not because it is easy, but because it is hard!”
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Launching UpGrad’s Data Analytics Roadshow – Are You Game?
We, at UpGrad, are excited to announce a brand new partnership with various thought leaders in the Data Analytics industry – IIIT Bangalore, Genpact, Analytics Vidhya and Gramener – to bring to you a one-of-a-kind Analytics Roadshow!
As part of this roadshow, we will be conducting several back-to-back events that focus on different aspects of analytics, creating interaction points across India, to do our bit for a future ready and analytical, young workforce.
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Here is the line-up for the roadshow, to give you a better sense of what to expect:
9 webinars
– These webinars (remote) will be conducted by industry experts and are aimed at increasing analytics awareness, providing a way for aspirants to interact with industry practitioners and getting their tough questions answered.
11 workshops
– The workshops will be in-person events to take these interactions to the next level. These would be spread across 6 cities – Delhi, Bengaluru, Hyderabad, Chennai, Mumbai and Pune. So, if you are in any of these cities, we are looking forward to interact with you.
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2 Conclaves
– These conclaves are larger events with a pre-defined agendas and time for networking. The first conclave is happening on the 17th of December in Bengaluru.
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Hackathon
– Time to pull up your sleeves and showcase your nifty skills. We will be announcing the format of the event shortly.
“We find that the IT industry is absorbing almost half of all of the analytics jobs. Banking is the second largest, but trails at almost one fourth of IT’s recruiting volume.
It is interesting that data rich industries like Retail, Energy and Insurance are trailing near the bottom, lower than even construction or media, who handle less data. Perhaps these are ripe for disruption through analytics?”
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Mr. S. Anand, CEO of Gramener, wonders aloud.
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What’s Cooking in Data Analytics? Team Data at UpGrad Speaks Up!
Team Data Analytics is creating the most immersive learning experience for working professionals at UpGrad.
Data Insider recently checked in to me to get my insights on the data analytics industry; including trends to watch out for and must-have skill sets for today’s developers. Here’s how it went:
How competitive is the data analytics industry today? What is the demand for these types of professionals?
Let’s talk some numbers, a widely-quoted McKinsey report states that the United States will face an acute shortage of around 1.5 million data professionals by 2018. In India, which is emerging as the global analytics hub, the shortage of such professionals could go up to as high as 200,000. In India alone, the number of analytics jobs saw a 120 percent rise from June 2015 to June 2016. So, we clearly have a challenge set out for us. Naturally, because of acute talent shortage, talented professionals are high in demand.
Decoding Easy vs. Not-So-Easy Analytics
What trends are you following in the data analytics industry today? Why are you interested in them?
There are three key trends that we should watch out for:
Personalization
I think the usage of data to create personalized systems is a key trend being adopted extremely fast, across the board. Most of the internet services are removing the anonymity of online users and moving towards differentiated treatment. For example, words recommendations when you are typing your messages or destinations recommendations when you are using Uber.
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End of Moore’s Law
Another interesting trend to watch out for is how companies are getting more and more creative as we reach the end of Moore’s Law. Moore’s Law essentially states that every two years we will be able to fit double the number of transistors that could be fit on a chip, two years ago. Because of this law, we have unleashed the power of storing and processing huge amounts of data, responsible for the entire data revolution. But what will happen next?
IoT
Another trend to watch out for, for the sheer possibilities it brings. It’s the emergence of smart systems which is made possible by the coming together of cloud, big data, and IoT (internet of things).
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What skill sets are critical for data engineers today? What do they need to know to stay competitive?
A good data scientist sits at a rare overlap of three areas:
Domain Knowledge
This helps understand and appreciate the nuances of a business problem. For e.g, an e-commerce company would want to recommend complementary products to its buyers.
Statistical Knowledge
Statistical and mathematical knowledge help to inform data-driven decision making. For instance, one can use market basket analysis to come up with complementary products for a particular buy.
Technical Knowledge
This helps perform complex analysis at scale; such as creating a recommendation system that shows that a buyer might prefer to also buy a pen while buying a notebook.
How Can You Transition to Data Analytics?
Outside of their technical expertise, what other skills should those in data analytics and business intelligence be sure to develop?
Ultimately, data scientists are problem solvers. And every problem has a specific context, content and story behind it. This is where it becomes extremely important to tie all these factors together – into a common narrative. Essentially all data professionals need to be great storytellers.
In this respect, one of the key skills for analysts to sharpen would be, breaking down the complexities of analytics for others working with them. They can appreciate the actual insights derived – and work toward a common business goal.
In addition, what is as crucial is getting into a habit of constantly learning. Even if it means waking up every morning and reading what’s relevant and current in your domain.
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What should these professionals be doing to stay ahead of trends and innovations in the field?
Professionals these days need to continuously upskill themselves and be willing to unlearn and relearn. The world of work and the industrial landscape of technology-heavy fields such as data analytics is changing every year. The only way to stay ahead, or even at par with these trends, is to invest in learning, taking up exciting industry-relevant projects, participating in competitions like Kaggle, etc.
How important is mentorship in the data industry? Who can professionals look toward to help further their careers and their skills?
Extremely important. Considering how fast this domain has emerged, academia and universities, in general, have not had the chance to keep up equally fast. Hence, the only way to stay industry-relevant with respect to this domain is to have industry-specific learning.
This can only be done in two ways – through real-life case studies and mentors who are working/senior professionals and hail from the data analytics industry.
In fact, at UpGrad, there is a lot of stress on industry mentorship for aspiring data specialists. This is in addition to a whole host of case studies and industry-relevant projects.
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While it’s important for budding or aspiring data professionals to tap into their networks to find the right mentors, it is admittedly tough to do so. There are two main reasons that can be blamed for this.
First, due to the nascent stage, the industry is at, it is extremely difficult to find someone with the requisite skill sets to be a mentor.
Even if you find someone with considerable experience in the field, not everybody has the time and inclination to be an effective mentor. Hence most people don’t know where to go to be mentored.
That’s where platforms like UpGrad come in, which provide you with a rich, industry-relevant learning experience. Nowhere else are you likely to chance upon such a wide range of industry tie-ups or associations for mentorship from very senior and reputed professionals.
How Can You Transition to Data Analytics?
What resources should those in the data analytics industry be using to ensure they’re educated and up-to-date on developments, trends, and skills?
There are many. For starters, here are some good and pretty interesting blogs and resources that would serve aspiring/current data analysts well to keep up with Podcasts like Data Skeptic, Freakonomics, Talking Machines, and much more.
This interview was originally published on Data Insider.
Read Moreby Rohit Sharma
23 Dec'16