Business Analytics and AI
Data drives virtually all companies. If it isn’t, then the company is not at it’s full potential. Data driven decisions are always the well informed one. Take a simple example of how you quickly get search suggestions while trying to search for your favourite food or while browsing for a movie to watch on your preferred streaming service. These suggestions that you get are based on large collections of data that was acquired from other people who use the same service. These services employ the use of various data analysis techniques to infer patterns based on various factors to suggest the right choice to you.
Data analytic techniques when applied to businesses help identify the right choice based on the future trend determined from past data. To achieve this, a combination of disciplines like statistics, computer science and business is used. All one needs is a strong desire to learn such techniques. This can then be applied to many industries such as Healthcare, Automotive,IT, Manufacturing, Banking, Education etc.
Keeping in mind, the requirements for various industries, we bring to you, this certification program from Virginia Tech. The Certificate Program in Business Analytics and AI from Virginia Tech is a 4 month online program which brings together academia and industry in a self paced manner. The opportunity to interact with industry leaders along with the professors from Virginia Tech help bring real world applications to those who enroll in this course.
Hear from Dr. Tarun Sen-Professor Emeritus and Managing Director about Data Science and it's importance in today's connected world
Dr. Guru Ghosh – Vice President Outreach and International Affairs speaks about the Virginia
Tech programs and the reason for reaching out globally
This Certificate Program in Business Analytics and AI from Virginia Tech enables the student to learn the concepts of data analytics to prepare them for a role as a data scientist. We start with the very basics of understanding the types of data that you would have to deal with. With data growing each day, it is beneficial for you, as a student, to equip yourself with an understanding of how to apply machine learning techniques to automate the process of going through your data with little intervention.Our course is structured around a unique pedagogy of DATA , DOMAIN , KNOWLEDGE AND TECHNOLOGY enabling the students to understand clearly the way to approach and apply the knowledge acquired.
When it comes to “Big Data”, there are other approaches that you need to be aware of such as NOSQL Databases and Hadoop Framework. Without applying what you went through, the entire process of learning is not complete. Various challenges and capstone projects will be made available to you to test your knowledge and further enhance your capabilities with real world situations
Data analytics deals with two types of data: Structured and Unstructured data. Structured data is easier to deal with while unstructured is not. The majority of data that a data analyst deals with will be of the unstructured form. In this module you will
Machine learning, a subset of artificial intelligence, enables machines, or to be more specific, computers to learn from data without being explicitly programmed to. By using this ability of the computer, the data scientist or business analyst can analyse the large chunks of data that they have to work on with minimal intervention. In this module you will be introduced to Machine learning and AI. In specific, you will learn about :
Python is a popular programming language used in Data Science. Along with other tools , it is used in various stages of analysing the data. Python is used to pull data from the databases and sort them to prepare them for analysis. Using the various libraries available, you can start exploring your data. You can then build a predictive model to understand future trends using Python. With such an essential role, it’s quintessential that Python is in your arsenal as a data scientist. In this module, you will get:
Both “Classification” and “Clustering” are used for organizing your data. The major difference is that classification is based on a predefined set of information and clustering basically tries to find similarities between the data for organizing them. As a Data scientist, you need to know
Which is what we will cover in this module
As time progresses, the amount of data available for a particular purpose or application increases exponentially. This leaves the data scientist with the largest version of the 3 V’s of data science. In order to deal with the large volume, velocity and variety associated with big data, you need the right type of database that is capable of doing so. NOSQL which stands for “Not Only SQL” has this capability and Hadoop is the framework that processes the data stored in the NOSQL Database In this module, you will learn about
After learning about the theoretical aspects of the concepts and technologies required by a data scientist, this module helps to understand the applications of what you learnt in the previous modules. We will cover
For further understanding the applications through a hands-on approach, we will focus on projects covering
And then finalise with a Capstone Project
A majority of the companies today employ "Data analytics" to their daily routine to optimise their decisions and outcomes. Learning the theoretical aspects of data analytics is the first step, but learning from real world scenarios and applications enforces the content that is conveyed to you in this program. This is part of the "Experiential learning" that you will encounter in this course. The real world applications are taught by eminent members of the industry whose cumulative years of experience will help you surmount any problems that you may face in your career as a Business analyst or Data Scientist. As part of the industry based learning of this program, we have professionals from firms that engage in :
The teaching faculty of Virginia Tech also takes part in this Certification Program to work alongside the industry professionals to help deliver the content to you in the most effective manner. The syllabus will be periodically reviewed by the faculty advisors along with the industry professionals to keep the program up to date. Our advisors are from different domains such as Mechanical Engineering, Information Technology, Finance, Accounting and Economics. This program ensures a wide range of applications covered because of the diversity of the faculty. Students of this program can be of the following disciplines:
mongoDB is a popular database program which is open source. It is classified as a NOSQL database. It deviates from the traditional approach of representing data in the form of rows and columns
Amazon Web Services brings to you a plethora of services that will enable you to gather data, process it, analyse it and then display it to make sense of the data that you need to work on.
MySQL is a "Structured Query Language" that can be used to access and sort the data that is stored in databases that collect a large amount of data. It is particularly popular for dealing with "Big Data"
Hadoop is well known to be a reliable framework that employs tools that helps work with "Big Data"
NVIDIA provides solutions for running GPU accelerated data analytics on large sets of data. Without having to rely entirely on the CPU of your system, it enables processing of data at a much faster pace.
Automobile manufacturers have started to employ data scientists to deal with the large amount of data that comes in from their connected car experiences. With manufacturers looking to develop autonomous cars, data scientists play a vital role in dealing with the data that needs to be dealt with to develop these autonomous capabilities. In India, we have Mercedes Benz R&D, Hyundai R&D to name a few who are actively looking for such candidates.
Retailers need to rely on a large amount of data from their customers to make informative decisions on products that they need to provide. A wrong or delayed decision can leave them in the dust with their competitors.
The Finance sector uses Data Science for managing customer data, analyzing risks, fraud detection and for providing personalized services in the form of virtual assistants.
Insurance sectors use data analytics to make informed decision on driving their business. Some of the most prominent cases where data science is applied in the insurance sector is Customer classification, Fraud Detection, Call Center Optimization.
With billions of people using internet services, prominent sectors like e-commerce and social media have their hands full with data. This is where data scientists play a pivotal role in helping these firms make an informed decision.
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