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In this course you will be introduced to basic concepts of information security. You will also get to understand some of the areas and domains where information security is being used and also be exposed to new advancements in the field and areas of cutting edge research such as quantum computing, what it means to conventional information security.
Today, we’re surrounded by data. People upload videos, take pictures on their cell phones, text friends, update their Facebook status, leave comments around the web, click on ads, and so forth. Machines, too, are generating and keeping more and more data. To process such large datasets, there is a need for specialized tools.
Microsoft has made big strides in this domain , and it is no surprise as to why they are one of the leaders when it comes to Cloud Computing. This course is designed to help you get familiar with Microsoft’s Cloud platform , Microsoft Azure.
Cloud Computing on AWS is designed to help you learn the absolute basic concepts that you must understand to work with any cloud platform (and not just AWS). This course covers Compute, Storage, Network, Identity & Access Management and Cloud Organization.
Deep learning allows machines to solve relatively complex problems even when using data that is diverse, less structured or interdependent. Deep learning is a form of machine learning that is inspired and modeled on how the human brain works. In this course you will be introduced to the basics of deep learning and learn how it compares to other techniques.
Computer vision (CV) is a set of techniques that are used to help machines to “see” and comprehend what is contained in digital imagery such as photos and videos. It has varied applications such as in medical imaging, motion capture, surveillance, automating retail checkouts, and in optical character recognition. This course starts with the basic steps of digitizing images,
This beginner course explains some basic terminologies used in marketing and discusses an application of analytics in retail. Marketing analytics is the process of measuring, managing and analyzing marketing performance to maximize effectiveness and to optimize return on investment.
In this highly competitive global environment, any professional and progressive organization that aims to grow significantly cannot depend solely on qualitative methods of prediction. Instead, robust quantitative methods that model large amounts of data can be used for more accurate prediction.
The financial services industry is increasingly utilizing data and information to help drive decision-making and to help assess and manage risk. This course provides an introduction to Financial Risk Analytics and will help you understand how to assess credit risk, how to model credit risk and also look at methods of optimizing risk.
All of us in our day-to-day life use numbers in our calculations. Organizations today are inundated with numerical data and information. In business, it is essential for managers to carry out data analysis and be able to interpret their results for effective decision-making.
In this course, you will be introduced to the basic concepts of cloud computing and become familiar with the history and evolution of cloud computing. Further in the course, you will also get introduced to virtualization and its correlation with cloud computing.
A lot of people lack the ability to understand data in its purest form and that is where data visualization comes in. Visualization plays an important role in the representation of both small and large scale datasets. In this course, you will learn to create easy to read and understand graphs, charts and other visual representations of data.
R is a comprehensive statistical and graphical programming language which is fast gaining popularity among data analysts. It is free and runs on a variety of platforms including Windows, Unix, and macOS. It provides an unparalleled platform for programming new statistical methods in an easy and straightforward manner.
Machine learning uses two types of techniques: one is supervised learning which trains a model on known input and output data so that it can predict future outputs. The second called unsupervised learning finds hidden patterns or intrinsic structures in input data.
Python is an easy to learn, powerful programming language. You can use Python when your data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database.