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Binary Search Algorithm: Function, Benefits, Time & Space Complexity

Updated on 06 November, 2024

246.78K+ views
18 min read

Introduction 

In any computational system, the search is one of the most critical functionalities to develop. Search techniques are used in file retrievals, indexing, and many other applications. There are many search techniques available. One of which is the binary search technique.
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A binary search algorithm works on the idea of neglecting half of the list on every iteration. It keeps on splitting the list until it finds the value it is looking for in a given list. A binary search algorithm is a quick upgrade to a simple linear search algorithm. 

This article will discuss areas like the complexity of binary search algorithm is and binary search worse case along with giving a brief idea of binary search algorithm first, along with best and worse case complexity of binary search algorithm.

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What is Binary Search Algorithm?

Binary search is a highly efficient search algorithm to locate a specific target value within a sorted array or list. It operates by repeatedly dividing the search interval in half, significantly reducing the number of comparisons required to find the target. The algorithm begins by examining the middle element of the array and comparing it to the target. If the middle element matches the target, the search concludes successfully. If the middle element is greater than the target, the search continues in the left half of the array; if it’s smaller, the search continues in the right half. This process iterates until the target is found or the search interval becomes empty.

Due to its halving nature, Binary search complexity exhibits an impressive time complexity of O(log n), where n represents the number of elements in the array. This makes binary search particularly effective for large datasets, offering a substantial improvement over linear search algorithms with a time complexity of O(n). However, binary search demands a precondition of sorted data, which might necessitate sorting the array initially. While incredibly efficient for sorted data, binary search is less suitable for small or frequently changing data due to the initial sorting overhead.

The history of the binary search algorithm dates back to ancient times when humans were developing manual methods to search for specific elements in a sorted list. While the formal algorithmic description we know today emerged in the field of computer science, the fundamental concept has roots in various historical practices.

1. Ancient Methods

The basic idea of binary search can be traced back to ancient methods of searching for elements in a sorted list. In ancient manuscripts or books, if someone was looking for a particular passage or information, they might start by opening the book in the middle. Based on whether the target passage was before or after the midpoint, they would then eliminate half of the remaining pages and repeat the process until they found the desired information.

2. John Mauchly’s Early Use (1946)

The concept of binary search was formalized in the field of electronic computing during the mid-20th century. John Mauchly used a binary search algorithm in 1946. The ENIAC, one of the earliest electronic general-purpose computers, was programmed to perform a binary search on sorted punched cards.

3. Algorithmic Description by Derrick Henry Lehmer (1948)

The algorithmic description of binary search as we recognize it today is credited to Derrick Henry Lehmer, an American mathematician and computer scientist. Lehmer published a paper in 1948 titled “Teaching an Electronic Computer to Play a Game,” where he described the binary search algorithm as part of a guessing game played on the SWAC (Standards Western Automatic Computer) computer.

4. Inclusion in Sorting and Searching Libraries

As computers evolved, binary search became a fundamental part of sorting and searching libraries. Its efficiency in quickly locating elements in a sorted dataset made it a staple in computer science and programming. Sorting and searching algorithms, including binary search, played a crucial role in the development of early programming languages and paved the way for more sophisticated algorithms.

5. Algorithmic Analysis and Refinement

Over the years, researchers and computer scientists have analyzed the time and space complexity of the binary search algorithm, leading to a better understanding of its performance characteristics. Algorithmic refinements and adaptations have been proposed to address specific use cases and improve efficiency.

6. Integration into Standard Libraries and Programming Languages

As computing became more widespread, binary search found its way into standard libraries and programming languages. It became a foundational tool for developers working with sorted data structures, arrays, and other collections.

7. Continued Relevance

Despite its ancient roots, the binary search algorithm remains relevant in modern computer science and software development. Its logarithmic time complexity makes it particularly valuable for efficiently searching large datasets, and it continues to be taught in introductory computer science courses.

Comparison with Other Search Algorithms 

While comparing search algorithms, the time complexity of binary search distinguishes it as a highly efficient method. Binary search operates with a remarkable time complexity of O(log n), significantly outperforming linear search algorithms with O(n) time complexity. The logarithmic nature of binary search time complexity ensures swift access to elements by halving the search space in each iteration. This efficiency is especially notable for large datasets.

The worst case complexity of binary search occurs when the target element is at an extremity or absent, resulting in a time complexity analyzed through the recurrence relation T(n) = T(n/2) + 1. In contrast, linear search exhibits linear time complexity (O(n)), making it less efficient for extensive datasets. Understanding the time complexities of these algorithms is crucial for selecting the optimal approach based on the specific dataset size and characteristics.

Variations of Binary Search 

Several variations of the binary search algorithm exist, each tailored to specific scenarios, addressing nuances in binary search complexity and time complexity for binary search. One such variant is the Interpolation Search, which adapts to datasets with non-uniformly distributed values, potentially reducing the O(log n) complexity.

Another variation, Exponential Search, combines binary and linear search elements, optimizing for scenarios where the target is closer to the dataset’s beginning, impacting the time complexity for binary search.

These adaptations acknowledge the need to address the worst case time complexity of binary search when the target is at an extremity. While these variations maintain the core principles of binary search algorithm complexity, they showcase the algorithm’s flexibility in accommodating diverse dataset characteristics and optimizing time and space complexity of binary search in specific contexts.

Benefits of Binary Search Algorithm

It offers numerous benefits, some of which are: –

  • Efficiency

Binary search dramatically reduces the comparisons required to find a target element within a sorted dataset. This efficiency is especially noticeable when dealing with large datasets, as the algorithm divides the search space in half with each iteration, resulting in a time complexity of O(log n). This is significantly faster than linear search algorithms with an O(n) time complexity.

  • Fast Retrieval

Binary search complexity suits applications requiring quick data retrieval from sorted collections. Its logarithmic time complexity ensures rapid access to elements even in vast datasets, making it a valuable tool for databases, search engines, and other information retrieval systems.

  • Predictable Performance

The performance of time complexity for binary search is consistent and predictable regardless of the size of the dataset. This reliability makes it a preferred choice when response time is crucial.

  • Optimal for Sorted Data

Binary search is designed specifically for sorted data. When the data is sorted, the algorithm’s effectiveness shines, allowing optimal utilization of the sorted order.

  • Simplicity

The core concept of binary search time complexity is straightforward: compare the target value with the middle element and narrow down the search range based on the comparison. This simplicity makes it relatively easy to implement and understand.

  • Reduced Comparison Count

Binary search minimized the number of comparisons required to locate a target, resulting in improved efficiency and reduced computational load compared to linear search algorithms.

  • Applicability to Various Data Structures

While commonly associated with arrays, the time complexity of binary search can be applied to other data structures, such as binary search trees and certain types of graphs, enhancing its versatility.

  • Memory Efficiency

The binary search typically requires minimal additional memory beyond the existing data structure, making it memory-efficient and suitable for resource-constrained environments.

  • Search Failure Indication

If the algorithm concludes without finding the target, it indicates that the target element is not present in the dataset. This can be useful in decision-making processes.

Algorithmic Optimizations

Algorithmic optimizations play a crucial role in enhancing the efficiency and addressing the complexity of the binary search algorithm. To optimize the time complexity of binary search, adaptive strategies can be used, allowing early exits or intelligent decision-making during the search process. Additionally, considering the worst case time complexity of binary search, specialized algorithms may be implemented to handle edge cases more efficiently.

A focus on reducing the space complexity of binary search involves minimizing additional memory usage beyond the existing data structure. These optimizations, while maintaining the core principles of what is binary search, contribute to refined binary search algorithm complexity and elevate its performance in scenarios where traditional implementations may face challenges.

By strategically addressing complexities, these optimizations contribute to the continued relevance and applicability of the binary search algorithm.

Working of a Binary Search Algorithm

The first thing to note is that a binary search algorithm always works on a sorted list. Hence the first logical step is to sort the list provided. After sorting, the median of the list is checked with the desired value.

  • If the desired value is equal to the central index’s worth, then the index is returned as an answer. 
  • If the target value is lower than the central index’s deal of the list, then the list’s right side is ignored. 
  • If the desired value is greater than the central index’s value, then the left half is discarded. 
  • The process is then repeated on shorted lists until the target value is found. 

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Example #1

Let us look at the algorithm with an example. Assume there is a list with the following numbers:

1, 15, 23, 7, 6, 14, 8, 3, 27

Let us take the desired value as 27. The total number of elements in the list is 9. 

The first step is to sort the list. After sorting, the list would look something like this:

1, 3, 6, 7, 8, 14, 15, 23, 27

As the number of elements in the list is nine, the central index would be at five. The value at index five is 8. The desired value, 27, is compared with the value 8. First, check whether the value is equal to 8 or not. If yes, return index and exit. 

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As 27 is greater than 8, we would ignore the left part and only traverse the list’s right side. The new list to traverse is:

14, 15, 23, 27

Note: In practice, the list is not truncated. Only the observation is narrowed. So, the “new list” should not be confused as making a new list or shortening the original one. Although it could be implemented with a new list, there are two problems. First, there will be a memory overhead. Each new list will increase the space complexity. And second, the original indexes need to be tracked on each iteration.

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The new central index can be taken as the second or third element, depending on the implementation. Here, we will consider the third element as central. The value 23 is compared with value 27. As the value is greater than the central value, we will discard the left half. 

The list to traverse is:

27

As the list contains only a single element, it is considered to be the central element. Hence, we compare the desired value with 27. As they match, we return the index value of 27 in the original list. 

Example #2

In the same list, let us assume the desired value to be 2. 

First, the central value eight is compared with 2. As the desired value is smaller than the central value, we narrow our focus down to the list’s left-hand side. 

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The new traversal will consist of:

1, 3, 6, 7

Let us take the central element as the second element. The desired value two is compared with 3. As the value is still smaller, we again narrow the focus down to the list’s left-hand side. 

The new traversal will consist of:

1

As the traversing list has only one element, the value is directly compared to the remaining element. We see that the values do not match. Hence, we break out of the loop with an error message: value not found. 

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Practical Tips for Implementation

Implementing the binary search algorithm effectively involves considering key factors to optimize its performance and address the complexity of binary search. Firstly, understanding what is the time complexity of the binary search algorithm is essential. With a time complexity of O(log n), it excels in scenarios with large datasets. To mitigate the binary search worst case time complexity, developers can implement early exit strategies, breaking out of the search loop when conditions indicate that the target is not present. This avoids unnecessary iterations and enhances efficiency.

Considering data characteristics is crucial. For sorted datasets, binary search is optimal. Developers should ensure that the dataset remains sorted, or consider alternative search algorithms for unsorted data. Practical tips for space complexity of binary search include favoring the iterative method, which maintains a space complexity of O(1) compared to the recursive method’s O(log n).

Incorporating boundary checks and validations can prevent common errors and enhance the algorithm’s robustness. Testing the implementation on diverse datasets, including edge cases, provides insights into its real-world performance. By adhering to these practical tips, developers can harness the strengths of the binary search algorithm while minimizing complexities and ensuring efficient outcomes in various scenarios.

Practical Tips for Implementation

Implementing the binary search algorithm effectively involves considering key factors to optimize its performance and address the complexity of binary search. Firstly, understanding what is the time complexity of the binary search algorithm is essential. With a time complexity of O(log n), it excels in scenarios with large datasets. To mitigate the binary search worst case time complexity, developers can implement early exit strategies, breaking out of the search loop when conditions indicate that the target is not present. This avoids unnecessary iterations and enhances efficiency.

Considering data characteristics is crucial. For sorted datasets, binary search is optimal. Developers should ensure that the dataset remains sorted, or consider alternative search algorithms for unsorted data. Practical tips for space complexity of binary search include favoring the iterative method, which maintains a space complexity of O(1) compared to the recursive method’s O(log n).

Incorporating boundary checks and validations can prevent common errors and enhance the algorithm’s robustness. Testing the implementation on diverse datasets, including edge cases, provides insights into its real-world performance. By adhering to these practical tips, developers can harness the strengths of the binary search algorithm while minimizing complexities and ensuring efficient outcomes in various scenarios.

Time and Space complexity

People often do not have an understanding of binary search worst case and best case. The time complexity of the binary search algorithm is O(log n). The best-case time complexity would be O(1) when the central index would directly match the desired value. Binary search algorithm time complexity worst case differs from that. The worst-case scenario could be the values at either the extremity of the list or those not on the list. 

In the worse case binary search algorithm complexity, the values are present in such a way that either they are at the extremity of the list or are not present in the list at all. Below is a brief description of how to find worse case complexity of the binary search. 

The equation T(n)= T(n/2)+1 is known as the recurrence relation for binary search. 

To perform time complexity of binary search analysis, we apply the master theorem to the equation and get O(log n).

Worse case complexity of the binary search is often easier to compute but carries the drawback of being too much pessimistic. 

On the other hand, another type of  time complexity of binary search analysis, which is binary search algorithm average complexity, is a rarely chosen measure. As it is harder to compute and requires an in-depth knowledge of how much input has been distributed, people tend to avoid binary search algorithm average complexity.

Below are the basic steps to performing Binary Search.

  1. Find the mid element of the whole array, as it would be the search key.
  2. Look at whether or not the search key is equivalent to the item in the middle of the interval and return an index of that search key. 
  3. If the value of the middle item in the interval is more than the search key, reduce the interval’s lower half.
  4. If the opposite, then lower the upper half.
  5. Repeat from point 2, until the value is found or the interval gets empty.

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The space complexity of the binary search algorithm depends on the implementation of the algorithm. There are two ways of implementing it:

  1. Iterative method
  2. Recursive method

Both methods are quite the same, with two differences in implementation. First, there is no loop in the recursive method. Second, rather than passing the new values to the next iteration of the loop, it passes them to the next recursion. In the iterative method, the iterations can be controlled through the looping conditions, while in the recursive method, the maximum and minimum are used as the boundary condition. 

In the iterative method, the space complexity would be O(1). While in the recursive method, the space complexity would be O(log n). 

Limitations and Edge Cases

While the binary search algorithm is highly efficient in many scenarios, it does have limitations, particularly concerning its worst-case time complexity. The binary search worst case time complexity occurs when the target element is either located at an extremity of the sorted list or is absent altogether. In such situations, the algorithm performs suboptimally, approaching a linear search-like time complexity.

Edge cases also reveal certain limitations. For instance, when dealing with datasets that are frequently changing or unsorted, the overhead of maintaining a sorted order can outweigh the benefits of binary search. Additionally, the algorithm may exhibit unexpected behavior when handling duplicate elements. Depending on the implementation, it may return the first, last, or any arbitrary occurrence of a duplicate value, which can impact the reliability of search results.

Understanding these limitations and edge cases is crucial for selecting the appropriate search algorithm based on the specific characteristics of the dataset. While binary search excels in sorted datasets, consideration of its constraints is necessary to make informed algorithmic choices in various real-world scenarios.

Evolution of Binary Search

The evolution of the binary search algorithm reflects a journey from ancient manual techniques to its formalization in computer science, showcasing its adaptability and continued relevance. The basic concept of binary search, dividing a sorted dataset to locate a target efficiently, has ancient roots in manual search methods, like those used in manuscripts or books. In the mid-20th century, binary search found its place in electronic computing. 

Notably, John Mauchly employed binary search on the ENIAC in 1946, marking an early application in computing history. Derrick Henry Lehmer’s 1948 paper further formalized the algorithm’s description in the context of electronic computers. As computing advanced, binary search became integral to sorting and searching libraries and found its way into standard programming languages. The algorithm’s logarithmic time complexity made it invaluable for efficient searches in large datasets. 

Ongoing research and analysis have led to a deeper understanding of its complexities, with adaptations and optimizations addressing specific use cases. Today, the evolution of binary search continues, with ongoing research exploring improvements, adaptations, and its integration into emerging technologies. Its enduring presence in computer science underscores its foundational role in algorithmic solutions and highlights its capacity to evolve with the changing landscape of technology.

Interactive Examples or Visualizations

Improve understanding of the binary search algorithm with interactive examples or visualizations. These aids provide an intuitive understanding of its step-by-step process, aiding users in understanding the algorithm’s intricacies. Visualize the dataset, highlighting how binary search divides it in half during each iteration. Incorporate interactive elements, allowing users to input target values and witness the algorithm’s path to the solution. Such visual aids not only make the learning experience engaging but also reinforce the principles of binary search in a dynamic and accessible manner, promoting a deeper understanding of its functionality.

Benefits 

  • A binary search algorithm is a fairly simple search algorithm to implement. 
  • It is a significant improvement over linear search and performs almost the same in comparison to some of the harder to implement search algorithms.
  • The binary search algorithm breaks the list down in half on every iteration, rather than sequentially combing through the list. On large lists, this method can be really useful.

Checkout: Decision Tree Classification: Everything You Need to Know

Conclusion

A binary search algorithm is a widely used algorithm in the computational domain. It is a fat and accurate search algorithm that can work well on both big and small datasets. A binary search algorithm is a simple and reliable algorithm to implement. With time and space analysis, the benefits of using this particular technique are evident. 

If you are curious to learn about 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.

Frequently Asked Questions (FAQs)

1. Is it true that linear search is superior to binary search?

If you just need to search once, linear search will surely be faster than sorting followed by binary search if the data is originally unsorted. Binary search, on the other hand, is recognized to be a considerably quicker method of searching than linear search. Binary search allows you to remove half of the remaining items at a time, whereas linear search would go through each element one by one.

2. What distinguishes interpolation search from binary search?

Interpolation search is a binary search-like technique for finding a specified target value in a sorted array. It's similar to how people search through a phone book for a certain name, with the target value used to sort the book's contents. To check, binary search always travels to the center element. Interpolation searching, on the other hand, may lead to various places depending on the value of the key being searched for. If the key's value is closer to the final element, for example, interpolation search is more likely to begin at the end.

3. Is it better to do a recursive binary search or an iterative binary search?

The recursive version of Binary Search has a space complexity of O(log N), but the iterative version has a space complexity of O(log N) (1). As a result, while the recursive version is simple to build, the iterative form is more efficient.

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Rohit Sharma

Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

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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. Our learners also read: Top Python Free Courses 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. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses What Kind of Salaries do Data Scientists and Analysts Demand? Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses “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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by Apoorva Shankar

07 Dec'16
Decoding Easy vs. Not-So-Easy Data Analytics

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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. Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications 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!) upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 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. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Unleashing the Power of Data Analytics Our learners also read: Free Python Course with Certification Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?   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!” Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.  
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by Prof. S. Sadagopan

14 Dec'16
Launching UpGrad’s Data Analytics Roadshow – Are You Game?

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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.  Also Read: Analytics Vidhya article on the UpGrad Data Analytics Roadshow 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. Featured Data Science program for you: Master of Science in Data Science from from IIIT-B 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.  Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications 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?” Our learners also read: Learn Python Online for Free Mr. S. Anand, CEO of Gramener, wonders aloud. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4   Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
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by Apoorva Shankar

15 Dec'16
What’s Cooking in Data Analytics? Team Data at UpGrad Speaks Up!

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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. Our learners also read: Learn Python Online for Free 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). Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses 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. Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications 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. Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?   Where are the best places for data professionals to find mentors? upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 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.  
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by Rohit Sharma

23 Dec'16