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# Cluster Analysis in Data Mining: Applications, Methods & Requirements [With Examples]

Updated on 14 May, 2024

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Here we are going to discuss Cluster Analysis in Data Mining. So first let us know about what is clustering in data mining then its introduction and the need for clustering in data mining. The blogs cover how to define clustering in data mining, the different types of cluster in data mining and why clustering is so important. We are also going to discuss the algorithms and applications of cluster analysis in data science. Later we will learn about the different approaches in cluster analysis and data mining clustering methods.

## What is Clustering in Data Mining?

In clustering, a group of different data objects is classified as similar objects. One group means a cluster of data. Data sets are divided into different groups in the cluster analysis, which is based on the similarity of the data. After the classification of data into various groups, a label is assigned to the group. It helps in adapting to the changes by doing the classification.

The aim is to partition a set of data points into distinct, non-overlapping groups called clusters such that data points within a cluster have high similarity but are quite dissimilar from points in other clusters

The metric for assessing similarity/dissimilarity is typically based on distance measures like Euclidean or Manhattan distance. Data points are closer in the same cluster compared to points across clusters.

By labelling related groups with cluster identifiers, clustering facilitates the interpretation and evaluation of data distribution across various domains. It has extensive market segmentation, pattern recognition, image analysis and information retrieval applications.

What is clustering in data mining? Cluster data mining is an unsupervised learning technique frequently leveraged in data mining for explanatory data analysis as it reveals intrinsic data organization without prior training. The clusters and their characteristics signify interesting correlations.

So if we were to define clustering in data mining, then we can say that the process of cluster in data mining is basically comprising a set of abstract objects into groups of similar objects. The process of dividing and storing them in these groups is known as cluster analysis.

### What is Cluster Analysis in Data Mining?

Cluster Analysis in Data Mining means that to find out the group of objects which are similar to each other in the group but are different from the object in other groups. In the process of clustering in data analytics, the sets of data are divided into groups or classes based on data similarity. Then each of these classes is labelled according to their data types. Going through clustering in data mining example can help you understand the analysis more extensively.

Cluster data mining analysis is an unsupervised learning technique that groups data points based on their similarities. The goal is to create clusters where points within a cluster are more similar than points in other clusters. Some key aspects of cluster analysis:

• It can be used to discover patterns in data without prior knowledge of class labels. The algorithm explores the data and groups similar points together.
• Many clustering methods in data mining analysis algorithms include k-means, hierarchical clustering, density-based clustering, etc. Each has its approach for defining clusters.
• The number of classification and clustering in data mining must be determined beforehand for some algorithms like k-means. Other algorithms, like hierarchical clustering, can infer the number of clusters from the data.
• Choosing the appropriate clustering algorithm and tuning its parameters, like several clusters, is key for obtaining meaningful clusters from the data.
• Cluster evaluation in data mining analysis is commonly used for exploratory data analysis, market segmentation, social network analysis, and image segmentation.
• Cluster evaluation in data mining results requires domain knowledge and analyzing cluster characteristics like tightness, separation, etc. External evaluation measures can also be used if class labels are available.

So, in summary, clustering methods in data mining analysis groups unlabeled data based on similarity, revealing intrinsic patterns in the data. It is an important unsupervised learning technique for exploratory data mining.

Cluster analysis in data mining is a technique used to group a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. It is commonly used to discover structures within unlabelled data, categorize data into different groups, and identify relationships among data points. This method is useful in various applications, including market research, pattern recognition, data analysis, and image processing.

### Example of Cluster Analysis

An example of cluster backtracking analysis is in customer segmentation for a retail company. By analyzing purchasing behaviors and demographic data, the company can group customers into clusters that exhibit similar shopping patterns. This enables the company to tailor marketing strategies to each specific group, enhance customer engagement, and optimize product offerings. For instance, one cluster might consist of young, tech-savvy individuals who prefer online shopping, while another could be made up of older, price-sensitive customers who value in-store experiences.

## Applications of Data Mining Cluster Analysis

There are many uses of Data clustering analysis such as image processing, data analysis, pattern recognition, market research and many more. Using Data clustering, companies can discover new groups in the database of customers. They can also classify the existing customer base into distinct groups depending on the patterns of their purchases. Classification of data can also be done based on patterns of purchasing.

Taxonomy or the classification of animals with the help of cluster analysis is very common in the field of biology. Clustering can help identify and group species with similar genetic features and functionalities and also give us an understanding of some of the most commonly found inherent structures of specific populations or species.  Areas are identified using the clustering in data mining. In the database of earth observation, lands are identified which are similar to each other.

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Based on geographic location, value and house type, a group of houses are defined in the city. Clustering in data mining helps in the discovery of information by classifying the files on the internet. It is also used in detection applications. Fraud in a credit card can be easily detected using clustering in data mining which analyzes the pattern of deception. Read more about the applications of data science in finance industry.

If someone wanted to observe the characteristics of each data cluster, then cluster analysis can act as the tool to help them gain insight into the data clusters.

It helps in understanding each cluster and its characteristics. One can understand how the data is distributed, and it works as a tool in the function of data mining.

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## Requirements of Clustering in Data Mining

• Interpretability

Interpretability means that the clusters and their definitions should make intuitive sense and provide insight into the underlying patterns in the data. The clustering model and its results need to be transparent and understandable to users who may not be data scientists. The model is not very useful in practice if the clusters do not correspond to meaningful segments.

The types of data in cluster analysis in data mining of clustering should be usable, understandable and interpretable. The main aim of cluster analysis in data mining is to make sure haphazard data is stored in groups based on their characteristical similarity.

Some types of clustering in data mining to promote interpretability in cluster analysis:

• Use cluster analysis in data mining algorithms that produce clusters with understandable characteristics instead of being treated as black boxes. For example, k-means forms spherical, tightly grouped clusters.
• Visualize and explore the clusters to see if they match human intuition and domain expectations. Visual aids like data projections and heat maps can help.
• Examine each cluster and identify what common traits or attributes define that group. Attach meaningful labels or descriptions to each cluster.
• Evaluate cluster cohesion to ensure points within a cluster are tightly grouped, i.e. high intra-cluster similarity. Also, check for separation between clusters.
• Keep the number of clusters relatively low. Too many clusters reduce interpretability.
• Use domain expertise to validate that the clustering matches real-world segments.
• Helps in dealing with messed up data

Usually, the data is messed up and unstructured. It cannot be analyzed quickly, and that is why the clustering of information is so significant in data mining. Grouping can give some structure to the data by organizing it into groups of similar data objects.

It becomes more comfortable for the data expert in processing the data and also discover new things. Analyzing data that has already been classified and labelled through clustering is much easier than analyzing unstructured data. It also leaves less room for error.

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• High Dimensional

High dimensional data refers to data sets with many attributes, features or variables. For example, a customer dataset may contain hundreds of columns for attributes like demographics, purchase history, web activity, survey data, etc.

Data clustering is also able to handle the data of high dimension along with the data of small size. The clustering algorithms in data mining need to be able to handle any dimension of data.

Working with high dimensional data presents some key challenges for clustering algorithms:

• Curse of dimensionality – distance measures can become meaningless as the number of dimensions increases. Clusters become increasingly sparse and dissimilar.
• Computational complexity – processing a large number of dimensions requires more computing resources. The risk of overfitting also rises.
• Redundant attributes – many attributes may be correlated, leading to redundancy. Irrelevant attributes act as noise.

Some clustering techniques in data mining to handle high dimensional data for effective clustering:

• Feature selection – select subsets of the most relevant attributes and ignore redundant/irrelevant ones. Reduces noise and computational needs.
• Dimensionality reduction – use techniques like PCA to project data into lower dimensions while retaining most information. Makes distances more meaningful.
• Regularization – constrains model complexity to avoid overfitting on high dimensions.
• Distributed, parallel processing – to scale computation across clusters of machines.
• Using appropriate distance measures – certain measures like cosine similarity handle high dimensions better.
• Attribute shape clusters are discovered

Clustering algorithms in data mining should be able to detect arbitrarily shaped clusters. These algorithms should not be limited by only being able to find smaller, spherical clusters.

Real-world data may contain complex cluster shapes and densities. For example, the attributes of customers in a cluster may form an elongated, diagonal shape rather than a simple sphere. Shape limitations in clustering algorithms can lead to poor-quality clusters that do not fit the intrinsic patterns.

Some clustering techniques in data mining clustering algorithms can detect types of clustering in data mining shape clusters:

• Density-based methods like DBSCAN can find arbitrarily shaped clusters by looking at density reachability between points. Points with enough neighbours are grouped.
• Hierarchical clustering builds a dendrogram to capture non-convex shapes based on similarity levels.
• Probabilistic model-based clustering, like Expectation Maximization, can model complex cluster shapes based on probability density.
• Using appropriate similarity measures like Mahalanobis distance rather than Euclidean, which assumes spherical shapes.
• Allowing soft cluster assignments rather than hard assignments can better model overlapping clusters.
• Neural network-based deep clustering can learn feature representations to capture complex shapes.
• Using cluster validity measures to evaluate how well different shaped clusters are detected.
• Dealing with Erroneous Data

Usually, databases carry a lot of erroneous, noisy or absent data. If the algorithm being used during clustering is very sensitive to this type of anomaly, then it can lead to low-quality clusters. That is why it is very important that your clustering algorithm can handle this type of data without problems.

Real-world data often contains errors, outliers and missing values that can negatively impact the clustering process:

• Outliers can skew distance calculations and centroids during clustering. This affects cluster assignments.
• Missing or undefined values must be handled to avoid errors during similarity computations.
• Mislabeled data points act as noise that creates ambiguity about cluster boundaries.

Some applications of clustering in data mining to handle erroneous data for clustering:

• Data preprocessing to detect and remove/impute outliers and missing values.
• Using robust cluster algorithm in data mining such as density-based methods, which are less sensitive to outliers.
• Weighting attributes so noise in less important attributes does not dominate similarity measures.
• Using cluster ensembles and consensus clustering to be robust against data errors.
• Semi-supervised techniques that hint at how certain points should cluster can improve results.
• Post-processing, like pruning smaller clusters with low density, can handle mislabeled points.
• Algorithm Usability with multiple data kind

Many different kinds of data can be used with algorithms of clustering. The data can be like binary data, categorical and interval-based data.

Real-world data contains various types: continuous, categorical, ordinal, discrete, text, etc. A clustering algorithm needs to be flexible enough to handle different attribute types and data scales/ranges:

• Continuous numeric attributes – Require a suitable distance metric like Euclidean, Manhattan, Mahalanobis, etc. Values should be normalized before clustering.
• Binary or nominal categorical attributes – Hamming distance or simple matching coefficients can be used. Alternatively, encode categories into numeric.
• Ordinal attributes – Can use distance measures for numeric values after considering the order of categories.
• Text attributes – Require text embedding into numeric vectors before using distance measures.
• Mixed attributes – Gower’s similarity coefficient allows measuring proximity across different attribute types.
• Non-numeric scales – Standardization or normalization is required before applying distance calculations.
• Density data – This may need special treatment like using kernel density estimation.
In addition, the cluster algorithm in data mining should be able to operate on diverse data types natively without requiring extensive data conversion as a preprocessing step.

• Clustering Scalability

The database usually is enormous to deal with. The algorithm should be scalable to handle extensive database, so it needs to be scalable.

As real-world data continues to grow in size and complexity, clustering algorithms must be scalable to handle large datasets efficiently:

• They should be able to distribute computation across multiple CPUs and machines to parallelize cluster analysis on big data.
• Algorithms should have linear or near-linear time complexity rather than exponential growth. This ensures reasonable run times even for large inputs.
• Data sampling or dimensionality reduction is sometimes needed as a preprocessing step before clustering massive datasets.
• Core algorithms may need modification to make them more parallelizable through map-reduce style approaches.
• Cloud computing provides the infrastructure to scale clustering using distributed microservice architectures.
• Hardware innovations like GPUs and TPUs can accelerate some computations using parallelism.
• Complex models like deep neural networks require careful distribution of parameters and training data across clusters of machines.
• Model complexity may need to be reduced to avoid overfitting, which increases with more data.

## Data Mining Clustering Methods

Let’s take a look at different types of clustering in data mining!

### 1. Partitioning Clustering Method

In this method, let us say that “m” partition is done on the “p” objects of the database. A cluster will be represented by each partition and m < p. K is the number of groups after the classification of objects. There are some requirements which need to be satisfied with this Partitioning Clustering Method and they are: –

1. One objective should only belong to only one group.
2. There should be no group without even a single purpose.

There are some points which should be remembered in this type of Partitioning Clustering Method which are:

1. There will be an initial partitioning if we already give no. of a partition (say m).
2. There is one technique called iterative relocation, which means the object will be moved from one group to another to improve the partitioning.

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### 2. Hierarchical Clustering Methods

Among the many different types of clustering in data mining, In this hierarchical clustering method, the given set of an object of data is created into a kind of hierarchical decomposition. The formation of hierarchical decomposition will decide the purposes of classification. There are two types of approaches for the creation of hierarchical decomposition, which are: –

1. Divisive Approach

Another name for the Divisive approach is a top-down approach. At the beginning of this method, all the data objects are kept in the same cluster. Smaller clusters are created by splitting the group by using the continuous iteration. The constant iteration method will keep on going until the condition of termination is met. One cannot undo after the group is split or merged, and that is why this method is not so flexible.

2. Agglomerative Approach

Another name for this approach is the bottom-up approach. All the groups are separated in the beginning. Then it keeps on merging until all the groups are merged, or condition of termination is met.

There are two approaches which can be used to improve the Hierarchical Clustering Quality in Data Mining which are: –

1. One should carefully analyze the linkages of the object at every partitioning of hierarchical clustering.
2. One can use a hierarchical agglomerative algorithm for the integration of hierarchical agglomeration. In this approach, first, the objects are grouped into micro-clusters. After grouping data objects into microclusters, macro clustering is performed on the microcluster.

### 3. Density-Based Clustering Method

In this method of clustering in Data Mining, density is the main focus. The notion of mass is used as the basis for this clustering method. In this clustering method, the cluster will keep on growing continuously. At least one number of points should be there in the radius of the group for each point of data.

### 4. Grid-Based Clustering Method

In this type of Grid-Based Clustering Method, a grid is formed using the object together. A Grid Structure is formed by quantifying the object space into a finite number of cells.

Advantage of Grid-based clustering method: –

1. Faster time of processing: The processing time of this method is much quicker than another way, and thus it can save time.
2. This method depends on the no. of cells in the space of quantized each dimension.

### 5. Model-Based Clustering Methods

In this type of clustering method, every cluster is hypothesized so that it can find the data which is best suited for the model. The density function is clustered to locate the group in this method.

### 6. Constraint-Based Clustering Method

Application or user-oriented constraints are incorporated to perform the clustering. The expectation of the user is referred to as the constraint. In this process of grouping, communication is very interactive, which is provided by the restrictions.

## What kinds of classification is not considered a cluster analysis?

1. Graph Partitioning – The type of classification where areas are not the same and are only classified based on mutual synergy and relevance is not cluster analysis.
2. Results of a query – In this type of classification, the groups are created based on the specification given from external sources. It is not counted as a Cluster Analysis.
3. Simple Segmentation – Division of names into separate groups of registration based on the last name does not qualify as Cluster Analysis.
4. Supervised Classification – Those type of classification which is classified using label information cannot be said as Cluster Analysis because cluster analysis involves group based on the pattern.

## Importance of Cluster Analysis in Data Mining

• Pattern Recognition: Cluster analysis helps in identifying patterns and trends in large datasets, making it easier to understand complex data.
• Customer Segmentation: It enables businesses to segment customers based on behaviors and preferences, leading to more targeted marketing strategies.
• Anomaly Detection: Clustering can identify outliers or anomalies in data, crucial for fraud detection and maintaining data integrity.
• Efficient Resource Allocation: By grouping similar data points, cluster analysis aids in optimizing resource allocation in various applications like network traffic management.
• Data Summarization: It provides a compact representation of the data by grouping similar items, which simplifies large datasets and aids in quicker decision-making.

## Evaluation of clustering in data Mining

Evaluating clustering in data mining involves assessing the quality and effectiveness of the clustering algorithm used. Key metrics like silhouette score, Davies-Bouldin index, and the Dunn index are typically employed to measure the compactness and separation of the clusters formed. These metrics help determine how well each object lies within its cluster relative to other clusters, indicating the clarity and relevance of the grouping. Such evaluations are crucial as they ensure that the clusters generated are meaningful and useful in real-world applications, guiding improvements in algorithm selection and parameter tuning.

## Conclusion

Cluster analysis is a critical unsupervised learning technique for exploratory data mining. It involves using algorithms to group data points based on similarity, revealing underlying patterns. The major types of data in cluster analysis in data mining for effective clustering include interpretability, handling high dimensions, varied shapes, data errors and types, and scalability.

Many clustering algorithms range from k-means, hierarchical, density-based, model-based, etc. Each has its approach to forming clusters. The appropriate choice of algorithm and tuning parameters, like several clusters, is key to generating useful insights.

So now we have learned many things about Data Clustering such as the approaches and methods of Data Clustering and Cluster Analysis in Data mining. Going through diverse clustering in data mining example can further assist you to get an in-depth insight into the process.

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## Frequently Asked Questions (FAQs)

### 1. What are some of the drawbacks of cluster analysis?

Cluster analysis is a statistical approach that presupposes no prior knowledge of the market or customer behavior. Some cluster analysis methods produce somewhat different findings each time the statistical analysis is conducted. This can arise because there is no one-size-fits-all method to data analysis. Changing data outputs can be confusing and irritating for students who are new to the notion of cluster analysis.

### 2. How is cluster purity and cluster quality calculated?

We multiply the total number of data points by the number of accurate class labels in each cluster. Purity rises as the number of clusters rises in general. If we have a model that organizes each observation into its own cluster, for example, the purity becomes one. We may compute the average silhouette coefficient value of all objects in a cluster to determine its fitness inside a clustering. The average silhouette coefficient value of all objects in the data set may be used to assess the quality of a grouping.

### 3. What are the distinctions between K-means and K-medoids?

K-means tries to reduce total squared error, whereas k-medoids tries to reduce the sum of dissimilarities between points classified as being in a cluster and a point chosen as the cluster's center. Unlike the k-means method, the k-medoids algorithm picks data points as centers ( medoids or exemplars).

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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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(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” Learn data science courses from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 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”. 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 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. Announcing PG Diploma in Data Analytics with IIT Bangalore – To Know more about the Program Visit – PG Diploma in Data Analytics. 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 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 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? Our learners also read: Free Online Python Course for Beginners 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.
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11 May'16

5.18K+

Enlarge the analytics &amp; 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. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 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. 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 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. 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 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 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? Our learners also read: Free Python Course with Certification 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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by upGrad

14 May'16

5.68K+

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”. Explore our Popular Data Science Degrees 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 Degrees 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. Our learners also read: Free Online Python Course for Beginners 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 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. 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? 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. upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Check out our data science training to upskill yourself
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09 Oct'16

5.69K+

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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07 Dec'16

5.12K+

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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14 Dec'16

5.14K+

Launching UpGrad&#8217;s Data Analytics Roadshow &#8211; 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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15 Dec'16

5.22K+

What&#8217;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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23 Dec'16

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