In this ever-evolving world of IoT, the makers and developers have to keep their information log updated to stay relevant in the field. Having said that, it is not easy because of the lack of time and a one-stop site to get all the information.

Understanding this need, we have come up with a weekly post, “IoT NEWS packets,” which gives you snippets of what’s new in the field of IoT.

Here are the latest developments in the field of IoT this week:

Samsung Family Hub 2.0 at Samsung Forum 2017

The Samsung Family Hub 2.0 comes in the form of a smart refrigerator. It has a 21.5-inch LED touchscreen and is driven by a Tizen OS that manages up to five family members’ food, cooking, artistic, music, video, and memo needs. It has S Voice recognition technology and a wide range of commands to help you with so many things, from shopping lists to the weather. It is can even find recipes using what is in the fridge.

Global IoT security market is expectedto grow steadily at a CAGR of 47.91% by 2021

According to a study by Market Research Hub, the global IoT security market is all set to grow at a remarkable CAGR of 47.91% during the period 2017–2021. In terms of geography, the APAC region is projected to lead the global market in the coming years; one of the major reasons for this growth is the high adoption of IoT security solutions among enterprises.

Download the report

Target’s Internet of Things Store Gets a Renovation

https://www.youtube.com/watch?v=mRHtgzkFjUM

Target Open House is back after seven long weeks of renovation. The connected device concept store gives the guests hands-on interaction with new products and services. It also gives the entrepreneur community a place to meet and learn from one another and consumers.

Target Open House makes it easier for startups to get their products in front of thousands of guests much before they’re available for sale.

Along with Open House, Target is launching a new tool called Mission Control. This software will help startups submit products to be showcased at the Open House, and it also offers a real-time dashboard for exhibitors to look at guest interactions, collect feedback, and understand traffic and sales for their products.

Micro Mote: An energy-efficient computer with deep learning capabilities

David Blaauw and Dennis Sylvester, computer scientists at the University of Michigan, have developed the “Micro Mote” computer to make smarter, energy efficient, and smaller sensors for medical devices and the Internet of Things. They have also used deep learning technologies for enhancing face and voice recognition capabilities. It incorporates a deep-learning processor that can operate a neural network while using just 288 micro watts.

IBM and Visa: Turn any connected device into a point of sale with Watson IoT

A new partnership between Visa and IBM Watson gives Visa access to as many as 6,000 IoT client companies. Visa allows them to provision Visa tokenization into their devices and effectively turn them into point-of-sale (POS) terminals that allow users to pay on-the-go.

For example, a pair of smart shoes might monitor a user’s running distance and after a certain number of miles remind him to buy a new pair, which he or she could do on the spot through an activity tracker or an app.

Azure IoT comes with new Azure Stream Analytics features

IoT solutions must monitor real-time data coming from various devices and take action when troubling patterns are found. This capability is referred to as “stream processing.” At the scale of IoT, customers need a robust and scalable solution.

Microsoft Azure Stream Analytics will meet these needs with the following features:

• Native support for geospatial functions
• Custom code with JavaScript
• Low-latency dashboards
• Job diagnostics logs
• Visual Studio integration

French national railway company accelerates innovation with Watson IoT

IBM announced that French Railways operator SNCF is using Watson IoT on IBM Cloud to deliver superior customer experiences, greater operational efficiency, and enhanced rail safety to its 13.5 million daily commuters.

Now the mass transit Parisian lines and new generation trains are equipped with 2,000 sensors, which grab 70,000 data points per month. Rather than having to manually examine each train, SNCF engineers can remotely monitor up to 200 trains at a time for potential issues including door failures or air conditioning, all the while they are in transit.

Qualcomm announced 802.11ax WiFi technology for IoT gadgets

The technology called 802.11ax is the next evolutionary step of the WiFi technology improvements. Qualcomm is the first company to announce 802.11ax chips.

According to Qualcomm, the latest WiFi technology delivers four times more capacity than the current top WiFi routers, along with boosting speeds and wider coverage area.

Qualcomm’s 802.11ax chip employs techniques used in cellular communications to enhance WiFi efficiency without asking for more spectrum.

FluoWiFi – A Wireless Development Board for IoT

FluoWiFi has been designed to provide a powerful yet versatile IoT prototyping board that anyone can use and easily program using the Arduino IDE. It is a microcontroller board based on the ATmega644p by Atmel and the ESP32 module. ESP32 is a 2.4 GHz Wi-Fi and Bluetooth low power combo chip. It supports IPv4 and IPv6, Secure HTTP, CoAP, REST, and MQTT protocols ready to go.

50% of organizations in the US and Europe lag in IoT adoption

HCL released the findings of a first-of-its-kind survey of senior business and technology decision-makers in IoT in the major global enterprises.

A survey was conducted in 263 organisations in Europe and the U.S. and here are a few key findings:

• 50% of respondents said their organizations are already “behind the curve on IoT.”
• 49% of organizations are still struggling to get off the ground with IoT, “due to an uncoordinated and siloed approach.”
• 38% of respondents agree that the biggest barrier to IoT adoption is security concerns.
• On average, only 48 percent of data collected from the IoT is analyzed, while IoT adapters take five days to turn data into insight.

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Introduction

Data classification is a very important task in machine learning.Support Vector Machines (SVMs) are widely applied in the field of pattern classifications and nonlinear regressions. The original form of the SVM algorithm was introduced by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1963. Since then, SVMs have been transformed tremendously to be used successfully in many real-world problemssuch as text (and hypertext) categorization,image classification,bioinformatics (Protein classification,Cancer classification), handwritten character recognition, etc.

Table of Contents

1. What is a Support Vector Machine?
2. How does it work?
3. Derivation of SVM Equations
4. Pros and Cons of SVMs
5. Python and R implementation

What is a Support Vector Machine(SVM)?

A Support Vector Machine is a supervised machine learning algorithm which can be used for both classification and regression problems. It follows a technique called the kernel trick to transform the data and based on these transformations, it finds an optimal boundary between the possible outputs.

In simple words, it does some extremely complex data transformations to figure out how to separate the data based on the labels or outputs defined.We will be looking only at the SVM classification algorithm in this article.

How does it work?

The main idea is to identify the optimal separating hyperplane which maximizes the margin of the training data. Let us understand this objective term by term.

What is a separating hyperplane?

We can see that it is possible to separate the data given in the plot above. For instance, we can draw a line in which all the points above the line are green and the ones below the line are red. Such a line is said to be a separating hyperplane.

Now the obvious confusion, why is it called a hyperplane if it is a line?

In the diagram above, we have considered the simplest of examples, i.e., the dataset lies in the 2-dimensional plane(R2). But the support vector machine can work for a general n-dimensional dataset too. And in the case of higher dimensions, thehyperplane is the generalization of a plane.

More formally, it is an n-1 dimensional subspace of an n-dimensional Euclidean space. So for a

• 1D dataset, a single point represents the hyperplane.
• 2D dataset, a line is a hyperplane.
• 3D dataset, a plane is a hyperplane.
• And in the higher dimension, it is called a hyperplane.

We have said that the objective of an SVM is to find the optimal separating hyperplane. When is a separating hyperplane said to be optimal?

The fact that there exists a hyperplane separating the dataset doesn’t mean that it is the best one.

Let us understand the optimal hyperplane through a set of diagrams.

1. Multiple hyperplanes
   There are multiple hyperplanes, but which one of them is a separating hyperplane? It can be easily seen that line B is the one which best separates the two classes.

1. Multiple separating hyperplanes
   There can be multiple separating as well. How do wefind the optimal one? Intuitively, if we select a hyperplane which is close to the data points of one class, then it might not generalize well. So the aim is to choose the hyperplane which is as far as possible from the data points of each category.

1. In the diagram above, the hyperplane that meets the specified criteria for the optimal hyperplane is B.

Therefore, maximizing the distance between the nearest points of each class and the hyperplane would result in an optimal separating hyperplane. This distance is called the margin.

The goal of SVMs is to find the optimal hyperplane because it not only classifies the existing dataset but also helps predict the class of the unseen data. And the optimal hyperplane is the one which has the biggest margin.

Mathematical Setup

Now that we have understood the basic setup of this algorithm, let us dive straight into the mathematical technicalities of SVMs.

I will be assuming you are familiar withbasic mathematical concepts such as vectors, vector arithmetic(addition, subtraction, dot product) and the orthogonal projection. Some of these concepts can also be found in the article, Prerequisites of linear algebra for machine learning.

Equation of Hyperplane

You musthave come across the equation of a straight line as y=mx+c, where m is the slope and cis the y-intercept of the line.

The generalized equation of a hyperplane is as follows:

wTx=0

Here w and x are the vectors and wTx represents the dot product of the two vectors. The vector w is often called as the weight vector.

Consider the equation of the line as y−mx−c=0.In this case,

w=⎛⎝⎜−c−m1⎞⎠⎟ and x=⎛⎝⎜1xy⎞⎠⎟

wTx=−c×1−m×x+y=y−mx−c=0

It is just two different ways of representing the same thing. So why do we use wTx=0? Simply because it is easier to deal with this representation in thecase of higher dimensional dataset and w represents the vector which is normal to the hyperplane. This property will be useful once we start computing the distance from a point to the hyperplane.

Understanding the constraints

The training data in our classification problem is of the form {(x1,y1),(x2,y2),…,(xn,yn)}∈Rn×−1,1. This means that the training dataset is a pair of xi, an n-dimensional feature vector and yi, the label of xi. When yi=1 implies that the sample with the feature vector xi belongs to class 1 and if yi=−1 implies that the sample belongs to class -1.

In a classification problem, we thus try to find out a function, y=f(x):Rn⟶{−1,1}. f(x) learns from the training data set and then applies its knowledge to classify the unseen data.

There are an infinite number of functions, f(x) that can exist, so we have to restrict the class of functions that we are dealing with. In thecase of SVM’s, this class of functions is that of the hyperplanerepresented as wTx=0.

It can also be represented as w⃗ .x⃗ +b=0;w⃗ ∈Rn and b∈R

This divides the input space into two parts, one containing vectors of class ?1 and the other containing vectors of class +1.

For the rest of this article, we will consider 2-dimensional vectors. Let H0 be a hyperplane separating the dataset and satisfying the following:

w⃗ .x⃗ +b=0

Along with H0, we can select two others hyperplanes H1 and H2 such that they also separate the data and have the following equations:

w⃗ .x⃗ +b=δ and w⃗ .x⃗ +b=-δ

This makes Ho equidistant from H1 as well as H2.

The variable ? is not necessary so we can set ?=1 to simplify the problem as w⃗ .x⃗ +b=1 and w⃗ .x⃗ +b=-1

Next, we want to ensure that there is no point between them. So for this, we will select only those hyperplanes which satisfy the following constraints:

For every vector xieither:

1. w⃗ .x⃗ +b≤-1 for xi having the class ?1 or
2. w⃗ .x⃗ +b≥1 for xi having the class 1

Combining the constraints

Both the constraints stated above can be combined into a single constraint.

Constraint 1:

For xi having the class -1, w⃗ .x⃗ +b≤-1
Multiplying both sides by yi (which is always -1 for this equation)
yi(w⃗ .x⃗ +b)≥yi(−1) which implies yi(w⃗ .x⃗ +b)≥1 for xi having the class?1.

Constraint 2:yi=1

yi(w⃗ .x⃗ +b)≥1 for xi having the class 1

Combining both the above equations, we get yi(w⃗ .x⃗ +b)≥1 for all 1≤i≤n

This leads to a unique constraint instead of two which are mathematically equivalent. The combined new constraint also has the same effect, i.e., no points between the two hyperplanes.

Maximize the margin

For the sake of simplicity, we will skip the derivation of the formula for calculating the margin, m which is

m=2||w⃗ ||

The only variable in this formula is w, which is indirectly proportional to m, hence to maximize the margin we will have to minimize ||w⃗ ||. This leads to the following optimization problem:

Minimize in (w⃗ ,b){||w⃗ ||22 subject to yi(w⃗ .x⃗ +b)≥1 for any i=1,…,n

The above is the case when our data is linearly separable. There are many cases where the data can not be perfectly classified through linear separation. In such cases, Support Vector Machine looks for the hyperplane that maximizes the margin and minimizes the misclassifications.

For this, we introduce the slack variable,ζi which allows some objects to fall off the margin but it penalizes them.

In this scenario, the algorithm tries to maintain the slack variable to zero while maximizing the margin. However, it minimizes the sum of distances of the misclassification from the margin hyperplanes and not the number of misclassifications.

Constraints now changes to yi(w⃗ .xi→+b)≥1−ζi for all 1≤i≤n,ζi≥0

and the optimization problem changes to

Minimize in (w⃗ ,b){||w⃗ ||22+C∑iζi subject to yi(w⃗ .x⃗ +b)≥1−ζi for any i=1,…,n

Here, the parameter C is the regularization parameter that controls the trade-off between the slack variable penalty (misclassifications) and width of the margin.

• Small C makes the constraints easy to ignore which leads to a large margin.
• Large C allows the constraints hard to be ignored which leads to a small margin.
• For C=inf, all the constraints are enforced.

The easiest way to separate two classes of data is a line in case of 2D data and a plane in case of 3D data. But it is not always possible to use lines or planes and one requires a nonlinear region to separate these classes. Support Vector Machines handle such situations by using a kernel function which maps the data to a different space where a linear hyperplane can be used to separate classes. This is known as thekernel trick where the kernel function transforms the data into the higher dimensional feature space so that a linear separation is possible.

If ϕ is the kernel function which maps xito ϕ(xi), the constraints change toyi(w⃗ .ϕ(xi)+b)≥1−ζi for all 1≤i≤n,ζi≥0

And the optimization problem is

Minimize in (w⃗ ,b){||w⃗ ||22+C∑iζi subject to yi(w⃗ .ϕ(xi)+b)≥1−ζi  for all 1≤i≤n,ζi≥0

We will not get into the solution of these optimization problems. The most common method used to solve these optimization problems is Convex Optimization.

Pros and Cons of Support Vector Machines

Every classification algorithm has its own advantages and disadvantages that are come into play according to the dataset being analyzed. Some of the advantages of SVMs are as follows:

• The very nature of the Convex Optimization method ensures guaranteed optimality. The solution is guaranteed to be a global minimum and not a local minimum.
• SVMis an algorithm which is suitable for both linearly and nonlinearly separable data (using kernel trick). The only thing to do is to come up with the regularization term, C.
• SVMswork well on small as well as high dimensional data spaces. It works effectively for high-dimensional datasets because of the fact that the complexity of the training dataset in SVM is generally characterized by the number of support vectors rather than the dimensionality. Even if all other training examples are removed and the training is repeated, we will get the same optimal separating hyperplane.
• SVMscan work effectively on smaller training datasets as they don’trely on the entire data.

Disadvantages of SVMs are as follows:

• Theyarenot suitable for larger datasets because the training time with SVMs can be high and much more computationally intensive.
• They areless effective on noisier datasets that have overlapping classes.

SVM with Python and R

Let us look at the libraries and functions used to implement SVM in Python and R.

Python Implementation

The most widely used library for implementing machine learning algorithms in Python is scikit-learn. The class used for SVMclassification in scikit-learn issvm.SVC()

sklearn.svm.SVC (C=1.0, kernel=’rbf’, degree=3, gamma=’auto’)

Parameters are as follows:

• C: It is the regularization parameter, C, of the error term.
• kernel: It specifies the kernel type to be used in the algorithm. It can be ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’, or a callable. The default value is ‘rbf’.
• degree: It is the degree of the polynomial kernel function (‘poly’) and is ignored by all other kernels. The default valueis 3.
• gamma: It is the kernel coefficient for ‘rbf’, ‘poly’, and ‘sigmoid’. If gamma is ‘auto’, then 1/n_features will be used instead.

There are many advanced parameters too which I have not discussed here. You can check them outhere.

https://gist.github.com/HackerEarthBlog/07492b3da67a2eb0ee8308da60bf40d9

One can tune the SVM by changing the parameters C,γ and the kernel function. The function for tuning the parameters available in scikit-learn is called gridSearchCV().

sklearn.model_selection.GridSearchCV(estimator, param_grid)

Parameters of this function are defined as:

• estimator: It is theestimator object which is svm.SVC() in our case.
• param_grid: It is the dictionary or list with parameters names (string) as keys and lists of parameter settings to try as values.

To know more about other parameters of GridSearch.CV(), click here.

https://gist.github.com/HackerEarthBlog/a84a446810494d4ca0c178e864ab2391

In the above code, the parameters we have considered for tuning are kernel, C, and gamma. The values from which the best value is to be are the ones written in the bracket. Here, we have only given a few values to be considered but a whole range of values can be given for tuning but it will take a longer time for execution.

R Implementation

The package that we will use for implementing SVM algorithm in R is e1071. The function used will be svm().

https://gist.github.com/HackerEarthBlog/0336338c5d93dc3d724a8edb67ad0a05

Summary

Inthis article, Ihave gone through a very basic explanation of SVM classification algorithm. I have left outa few mathematical complications such as calculating distances and solving the optimization problem. But I hope this gives you enough know-how abouthow a machine learning algorithm, that is,SVM, can be modified based on the type of dataset provided.

Introduction

We certainly have some interesting times to look forward to. All ed tech and career forecasts for this decade talk about artificial intelligence (AI) technologies, including machine learning, deep learning, and natural language processing, enabling digital transformation in ways that are quite “out there.”

To stay relevant in this economy, the brightest minds, naturally, want to stay ahead of the pack by specialising in these exciting fields.

Going back to school may not be a feasible or attractive route when looking for new career options for people who are already equipped with degrees in computer science, engineering, math, or statistics. So, they typically get certified from edX, Coursera, and Udacity. Read more top free courses from these ed platforms here.

In the U.S., many premier universities offer offline and online graduate programs in data science and only a few in machine learning. Some universities such as Johns Hopkins, Princeton, Rutgers, and University of Wisconsin–Madison offers machine learning/AI courses designed for data science, computer science, math, or stats graduate students.

But for students who can’t wait to learn on the job, we’ve put together a list of universities that offer graduate and/or PhD programs on the campus in the US and India.

Table of Contents

1. Universities / Colleges in the US
   • Carnegie Mellon University, Pennsylvania
   • University of Washington, Washington
   • Colombia University, New York
   • Stanford University, California
   • Texas A & M University, Texas
   • New York University, New York
   • Georgia Tech, Georgia
   • North Carolina State University, North Carolina
   • Northwestern University, Illionis
   • UC Berkley, California
2. Universities / Colleges in India
   • Great Lakes Institute of Management, Gurgaon / Chennai / Bengaluru
   • SP Jain School of Global Management, Pune
   • Narsee Monjee Institute of Management Studies, Mumbai
   • MISB Bocconi, Mumbai
   • Indian School of Business (ISB), Bengaluru
   • IIM Bangalore
   • Institute of Finance and International Management (IFIM), Bengaluru

Universities / Colleges in the US

1. Carnegie Mellon University, Pennsylvania

Situated in Pittsburgh, CMU has seven colleges and independent schools and is among the top 25 universities in the U.S. The Machine Learning Department offers three courses to introduce students to the concept of data-driven decision making:

• Master of Science in Machine Learning which focuses on data mining.For information about the application procedure and deadlines, go here.
• Secondary Master’s in Machine Learning which is open only to its PhD students, faculty, and staff.For information about admission requirements and application, go here.
• Fifth Year Master’s in Machine Learning for its undergraduate students to get an MS by earning credits in ML courses.For information about program requirements and application, go here.
• The Language Technologies Department offers a Master of Computational Data ScienceDegree.

2. University of Washington, Washington

UW’s Master of Science in Data Science degree teaches students to manage, model, and visualize big data. Expert faculty from six of the university’s departments who teach this fee-based course expect the students to have “a solid background mathematics, computer programming and communication.” The course is designed for working professionals, with evening classes on the campus, who can enroll as part-time or full-time students.

• For information about the application procedure and deadlines, go here.
• For information about financial aid and cost of study, go here.

UW’s Certificate in Data Science teaches basic math, computer science, and analytics to aspiring data scientists. Professionals are expected to know some SQL, programming, and statistics. Data storage and manipulation tools (e.g. Hadoop, MapReduce), core machine learning concepts, types of databases, and real-life data science applications are part of the curriculum.

3. Columbia University, New York

Its Master of Science in Data Science is a great option for careerists who want to switch to data science. Students need to earn 30 credits, 21 by taking the core courses, including machine learning, and 9 credits by working on an elective (Foundations of Data Science, Cybersecurity, Financial and Business Analytics, Health Analytics, New Media Sense, Collect and Move Data, Smart Cities) from the Data Science Institute. The university offers both part-time and full-time options.

• For more course information, go here.

The department also has an online Certification of Professional Achievement in Data Sciences course. The Computer Science Department has a Machine Learning Track as a part of the MS degree in CS.

4. Stanford University, California

The Department of Statistics and Institute for Computational and Mathematical Engineering (ICME) offer an M.S. in Data Science, where it is a terminal degree for the former and a specialized track for ICME. There are several electives that range from machine learning to human neuroimaging methods for students, but strong math (linear algebra, numerical methods, probabilities, PDE, stats, etc.) and programming skills (C++, R) form the core of the course. Go to the homepage for more information about prerequisites and requirements.

• For information about admissions and financial aid, go here.

5. Texas A&M University, Texas

The Houston-based university has a Master of Science in Analytics degree offered by the Department of Statistics. The course is tailored for “working professionals with strong quantitative skills.” What’s more, students can access Mays Business School courses as well. The part-time course, with evening classes, takes two years to complete. The program, which focuses on statistical modeling and predictive analysis, does have an online option.

• For information on course requirements, go here.

6. New York University, New York

The Master of Science in Data Science is for students with a strong programming and mathematical background. The Center for Urban Science and Progress and the Center for the Promotion of Research Involving Innovative Statistical Methodology work closely with the Center for Data Science. The university offers full-time and part-time options; students have to earn 36 credits and also have six electives to choose from. Tuition scholarships are available although not for university fees.

• For more information about the course, go here.

7. Georgia Tech, Georgia

The on-campus Master of Science in Analytics program Georgia Tech offers opportunities to strengthen your skills in statistics, computing, operations research, and business. The instructors include experts from the College of Engineering, the College of Computing, and Scheller College of Business. Applicants to this premium tuition program are expected to be proficient in basic mathematical concepts such as calculus, statistics, and high-level computing languages such as C++ and Python. Depending on what their career goals are, students can choose from one of these tracks: Analytical Tools, Business Analytics, and Computational Data Analytics.

What’s great for the students is that the college has dedicated job placement assistance and chances to network with influencers in the data science industry.

• For more information on how to apply, go here.

The College of Computing has courses in artificial intelligence (AI) and machine learning (ML) at the undergraduate and graduate levels; they do not award degrees in these.

8. North Carolina State University

The Institute for Advanced Analytics offers a 10-month long Master of Science in Analytics degree. The program is “innovative, practical, and relevant.” The Summer session includes Statistics primer and Analytics tools and foundation. The Practicum, which last eight months in the fall and spring, teaches you a range of topics including data mining, machine learning, optimization, simulation & risk, web analytics, financial analytics, data visualization, and business concepts such as project management.

• For information about application requirements and procedures, go here.

• For information about the tuition and fees, go here.

9. Northwestern University, Illinois

McCormick School of Engineering and Applied Science offers a 15-month full-time MS in Analytics degree. The faculty “combines mathematical and statistical studies with instruction in advanced information technology and data management.” The course has an 8-month practicum project, 3-month summer internship, and a 10-week capstone project. Scholarships that cover up to 50% of the tuition are available on merit basis.

• For information about admission requirements and procedures, go here.
• For information about the tuition and funding, go here.

10. UC Berkeley, California

Although the Master of Information and Data Science is an online course, students have to attend a week on campus. The curriculum covers areas in social science, policy research, statistics, computer science, and engineering. The full-time option takes 12 to 20 months; the university lets you complete the course part time as well.

• For more information about the course, go here.

Universities / Colleges in India

1. Great Lakes Institute of Management

Great Lakes’ Post Graduate Program in Business Analytics and Business Intelligence has been ranked the best analytics course in the country by Analytics India Magazine. The course is designed for working professionals and is offered in its Chennai, Gurgaon, and Bengaluru campuses. The curriculum combines business management skills and analytics, including case studies and hands-on training in relevant tools such as Tableau, R, and SAS. Students have to attend 230 hours of classroom sessions and 110 hours of online sessions.

• For more information about the program, go here.

2. SP Jain School of Global Management

Students can opt for the full-time or part-time options of the Big Data & Analytics program offered by the Mumbai-based institute. People with prior work experience are given preference. The program has 10 core courses including cutting-edge topics such as machine learning, data mining, predictive modeling, natural language processing, visualization techniques, and statistics. Industry experts and academicians focus on application-based learning, teaching students how to apply current tools and technologies to extract valuable insights from big data.

• For more information about the program, go here.

3. Narsee Monjee Institute of Management Studies

It offers a 1-year Postgraduate Certificate Program in Business Analytics in partnership with University of South Florida. The course conducted in its Mumbai campus combines classroom training with online sessions. NMIMS will take 12 hours and USF Muma College of Business faculty will take 20 hours to instruct students on the current Business Analytical tools, methodologies, and technologies. Course covers topics such as introduction to statistics, database management, business intelligence and visualization, machine learning, big data analytics, data mining, financial analytics, and optimization. Students will learn how to tackle real-world business issues through the capstone project.

• For more information about the program, go here.

4. MISB Bocconi

The 12-month Executive Program in Business Analytics is taught by renowned faculty from SDA Bocconi (Milan) and Jigsaw Academy at the Mumbai International School of Business Bocconi (MISB) campus in Mumbai. The course content comprises web analytics, statistics, visualization, R, time series, text mining, SAS, machine learning, Big Data (Sqoop, Flume, Pig, HBASE, Hive, Oozie, and SPARK), and digital marketing. Students learn core concepts of business analytics and its application across various domains.

• For more information about the course curriculum, go here.

5. Indian School of Business (ISB)

ISB offers a Certificate Program in Business Analytics on its Hyderabad campus. The course is designed for working professionals (with at least 3 years of work experience) who have to spend 18 days at the institute during the 12-month program; a technology-aided learning platform takes over the rest of the time. The rigorous course is chock-full with lectures, projects, and assignments. The comprehensive curriculum also includes preparatory pre-term courses and a capstone project.

• For more information about the course curriculum, go here.

6. IIM Bangalore

The year-long Certificate Program on Business Analytics and Intelligence comprises six modules and a project. The course content includes Data Visualization and Interpretation, Data Preprocessing and Imputation, Predictive Analytics: Supervised Learning Algorithms, Optimization Analytics, Stochastic Models, Data Reduction, Advanced Forecasting and Operations Analytics, Machine Learning Algorithms, Big Data Analytics,and Analytics in Finance and Marketing. The Institute would like the applicants to have a minimum of 3 years of work experience. Online classes are open to a limited number of participants, who must attend on-campus sessions as well.

• For information about eligibility criteria, go here.
• For information about the program fees, go here.

7. Institute of Finance and International Management (IFIM)

The Institute of Finance and International Management, Bangalore, offers a 15-month full-time Business Analytics program for working executives. Program features include live streaming and classroom sessions, opportunity to work with relevant IBM, OpenSource, and Microsoft software, and convenient weekend classes.

• For more information about this program, go here.

Conclusion

With the huge amounts of data pouring in and the need to apply analytical solutions to address business challenges, the future looks brighter than ever for data scientists and machine learning experts. Salaries are naturally high for these much sought-after skills.

For programmers and statisticians, getting certified is the next step. For students looking to distinguish themselves, these are great career opportunities.

In this post, we have put together a list of graduate programs offered by highly ranked institutes and universities in the US and India. On-campus courses are interactive; nothing can beat face-to-face contact with the faculty and peers, the friends you make, and the easy access to relevant resources.