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- Introduction to Linear Search Algorithm: Introduction & Features [With Examples]
Introduction to Linear Search Algorithm: Introduction & Features [With Examples]
Updated on 25 November, 2022
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Table of Contents
What is Searching?
Searching is the process of finding a given element in a list of elements. It helps in the searching of a particular record. Therefore, it is a technique of identifying the place of a given item. The success of a searching process depends on whether the item to be searched has been identified or not.
The Data structure allows the searching of data through two methods.
- Linear search or sequential search
- Binary search
Linear Search
Linear search algorithms are a type of algorithm for sequential searching of the data. This algorithm finds a given element with O(n) complexity. It is applied to a collection of items. Each and every item of the data is searched sequentially, and returned if it matches the searched element. If no matches are found, then the search keeps on continuing till the end of the collected data. It is basically a technique of exploring every element while traversing the list. The searching algorithm can be applied to both the sorted and the unsorted data. Practically linear search is rarely used because of the faster searching options provided by other search algorithms like binary search algorithms and hashtables.
Steps in the linear search algorithm
- Reading of the search element by the user.
- The element to be searched is compressed with the first element of the list.
- If the elements match, then a return is generated.
- If the elements don’t match then the element to be searched is compared to the second element of the list.
- The process is repeated until the element is matched.
Features of linear search algorithms
- It is usually applied to a small list of unsorted or unordered data.
- Time is linearly dependent on the number of elements, therefore having a time complexity if O(n).
- The implementation is very simple.
Linear search algorithms
A continuous looping method goes on unless and until the item is found
- algorithm Seqnl_Search(list, item)
- Pre: list != ;
- Post: return the index of the item if found, otherwise: 1
- index <- fi
- while index < list.Cnt and list[index] != item //cnt: counter variable
- index <- index + 1
- end while
- if index < list.Cnt and list[index] = item
- return index
- end if
- return: 1
- end Seqnl_Search
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Example of linear search
Problem: Given an array arr[] of n elements, write a function to search a given element x in arr[].
Figure 1: An example of code showing the implementation of linear search algorithm
Linear search algorithms can be used in several programming languages.
1. Linear search in Python
Figure 2: An example of code showing a linear search algorithm in Python language
Output: Element is present at index 3
2. Linear search in C
Figure 3: An example of code showing a linear search algorithm in C language
Output: Element is present at index 3
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3. Linear search in Data structure
Pseudocode for a linear search problem in Data structure is
Figure 4: The pseudocode for linear search algorithm
Binary Search
Binary search is an algorithm to search elements in an array of elements. Compared to the linear search algorithm, the binary search algorithm is applied to a sorted list of data.
Binary search algorithm includes the following steps
- Comparison of the element to be searched with the element from the middle of the list or array.
- If the element matches with the element from the list, it returns the index of the middle element.
- If no match is returned, it is checked whether the element is greater than or lesser than the element in the middle.
- For an element of greater value than the middle element, the search is carried on the right side of the array.
- Similarly, if the element is lesser in value than the middle element, then the search is carried on to the left side of the list.
Therefore, binary search is best applied when the data contains large number rod elements in a sorted manner. This makes it a requisite for the search algorithm that the list/array should be sorted.
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Features of Binary Search
- The binary search algorithm is useful for searching a large number of elements in an array.
- The binary search algorithm has a time complexity of O(logn).
- Implementation of a binary search algorithm is simple.
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Binary Search Algorithm
- Algorithm Binary_Search(list, item)
- Set L to 0 and R to n: 1
- if L > R, then Binary_Search terminates as unsuccessful
- else
- Set m (the position in the mid element) to the floor of (L + R) / 2
- if Am < T, set L to m + 1 and go to step 3
- if Am > T, set R to m: 1 and go to step 3
- Now, Am = T,
- the search is done; return (m)
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Conclusion
In this article, we have looked into what is a Linear Search Algorithm and also studied in detail how to search for a certain element from a list using the Linear Search Algorithm. Lastly, we also saw how we could practically implement the Linear Search Algorithm using Python 3 as a language and get our desired output.
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Frequently Asked Questions (FAQs)
1. How is linear search different from binary search?
The following illustrates the major differences between linear search and binary search:
Linear Search -
1. Elements need not be in any specific order for linear search.
2. In Linear search, elements are sequentially accessed.
3. O(n), where n is the number of array elements.
4. Linear Search is preferred when the data set is relatively smaller.
Binary Search -
1. Elements must be sorted for binary search.
2. Elements are randomly accessed in binary search.
3. O(log n), where n is the number of array elements.
4. Binary Search is generally preferable for a larger data set.
2. What are the applications of linear searching?
The following are some of the significant applications of linear search:
Linear search is efficient for searching in data sets having a smaller number of elements. If only a single search is needed to be performed in unordered data, then the linear search is preferable over other searching algorithms.
Searching a node in a linked list becomes efficient when a linear search is performed. Furthermore, binary search and linear search have the same time complexities in linked lists. Binary Search can even get complex for performing search operations in linked lists.
If the elements in the data set are modified repeatedly, then in such cases linear search is the preferred choice.
3. Give examples where linear search can be seen in real life?
The linear search algorithm is analogous to real-life searching. There are several examples that prove this:
Searching for a book in a pile of 100 books. You will linearly scan the name of each book until you find the right one
Finding your cab in the parking lot. When you book a cab ride, you have the license plate number of the cab. To find your cab, the obvious method would be to match every car’s license plate with your number.
Finding your favourite cookies on the store shelves. From a huge collection of cookies in a store, you will search every row one by one.
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Announcing PG Diploma in Data Analytics with IIIT Bangalore
Data is in abundance and for corporations, big or small, investment in data analytics is no more a discretionary spend, but a mandatory investment for competitive advantage. In fact, by 2019, 90% of large organizations will have a Chief Data Officer. Indian data analytics industry alone is expected to grow to $2.3 billion by 2017-18. UpGrad’s survey also shows that leaders across industries are looking at data as a key growth driver in the future and believe that the data analytics wave is here to stay.
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This growth wave has created a critical supply-demand imbalance of professionals with the adequate know-how of making data-driven decisions. The scarcity exists across Data Engineers, Data Analysts and becomes more acute when it comes to Data Scientists. As a result of this imbalance, India will face an acute shortage of at least 2 lac data skilled professionals over the next couple of years.
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How Organisations can Benefit from Bridging the Data Scientist Gap
Note: The article was originally written for LinkedIn Pulse by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions.
Data Scientist is one of the fastest-growing and highest paid jobs in technology industry. Dr. Tara Sinclair, Indeed.com’s chief economist, said the number of job postings for “data scientist” grew 57% year-over-year in Q1:2015. Yet, in spite of the incredibly high demand, it’s not entirely clear what education someone needs to land one of these coveted roles. Do you get a degree in data science? Attend a bootcamp? Take a few Udemy courses and jump in?
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It depends on what practice you end up it. Data Sciences has become a widely implemented phenomenon and multiple companies are grappling to build a decent DS practice in-house. Usually online courses, MOOCs and free courseware usually provides the necessary direction for starters to get a clear understanding, quickly for execution.
But Data Science practice, which involves advanced analytics implementation, with a more deep-level exploratory approach to implementing Data Analytics, Machine Learning, NLP, Artificial Intelligence, Deep Learning, Prescriptive Analytics areas would require a more establishment-centric, dedicated and extensive curriculum approach. A data scientist differs from a business analyst ;data scientist requires dwelling deep into data and gathering insights, intelligence and recommendations that could very well provide the necessary impetus and direction that a company would have to take, on a foundational level. And the best place to train such deep-seeded skill would be a university-led degree course on Data Sciences.
It’s a well-known fact that there is a huge gap between the demand and supply of data scientist talent across the world. Though it has taken some time, but educationalists all across have recognized this fact and have created unique blends of analytics courses. Every month, we hear a new course starting at a globally recognized university.
Data growth is headed in one direction, so it’s clear that the skills gap is a long-term problem. But many businesses just can’t wait the three to five years it might take today’s undergrads to become business-savvy professionals. Hence this aptly briefs an alarming need of analytics education and why universities around the world are scrambling to get started on the route towards being analytics education leaders. Obviously, the first mover advantage would define the best courses in years to come i.e. institutes that take up the data science journey sooner would have a much mature footing in next few years and they would find it easier to attract and place students.
Strategic Benefits to implementing Data Science Degrees
Data science involves multiple disciplines
The reason why data scientists are so highly sought after, is because the job is really a mashup of different skill sets and competencies rarely found together. Data scientists have tended to come from two different disciplines, computer science and statistics, but the best data science involves both disciplines. One of the dangers is statisticians not picking up on some of the new ideas that are coming out of machine learning, or computer scientists just not knowing enough classical statistics to know the pitfalls. Even though not everything can be taught in a Degree course, universities should clearly understand the fact that training a data science graduate would involve including multiple, heterogeneous skills as curriculum and not one consistent courseware. They might involve computer science, mathematics, statistics, business understanding, insight interpretation, even soft skills on data story telling articulation.
Beware of programs that are only repackaging material from other courses
Because data science involves a mixture of skills — skills that many universities already teach individually — there’s a tendency toward just repackaging existing courses into a coveted “data science” degree. There are mixed feelings about such university programs. It seems to me that they’re more designed to capitalize on the fact that the demand is out there than they are in producing good data scientists. Often, they’re doing it by creating programs that emulate what they think people need to learn. And if you think about the early people who were doing this, they had a weird combination of math and programming and business problems. They all came from different areas. They grew themselves. The universities didn’t grow them. Much of a program’s value comes from who is creating and choosing its courses. There have been some decent course guides in the past from some universities, it’s all about who designs the program and whether they put deep and dense content and coverage into it, or whether they just think of data science as exactly the same as the old sort of data mining.
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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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.
Read More07 Dec'16