Question

Part 1: Theory

Answer each question in no more than two hundred and fifty words.

Type your answers into a Word document or a notepad, name the file using your last name and submit it.

What is Big Data? Why is it important? Provide specific examples of how big data can be used in business.

Differentiate between structured and unstructured data.

Explain data visualization - benefits and dangers.

What are the benefits of using R to manage large quantities of data for analytics. Contrast with spreadsheets and also discuss potential challenges of using R (including R-Studio /other integrated IDEs).

Part 2: Exploratory Analytics

Go to finance.yahoo.com?Use R-Studio & quantmod package - Download 3 year data till 03/14/2018 for citi, mfg, gs, wfc, bpmx

Perform exploratory analysis -complete?Create Charts

Prepare a dataframe in R covering the following stock (lookup data on finance.yahoo.com):

citi, mfg, gs, wfc, bpmx

Data must contain the following variables for each of the stock above: Symbol, Closing Price as of 03/14/2018, market cap, beta, Dividend, Yield, 1y Target Est.

Conduct exploratory analysis – which stock looks interesting and why?

Comment /share your own thoughts based on your analysis – about 50 to 90 words max.

Where would you put your money?

Answer 1

What is Big Data?

Big Data refers to extremely large data sets that can be analysed to reveal patterns and trends.

The 3V’s of Big Data

Much of the tech industry follows Gartner’s ‘3Vs’ model to define Big Data. Data that is high in:

Volume
Velocity
Variety

The volume of data organisations handle can progress from megabytes through to terabytes and even petabytes. In terms of velocity, data has gone from being handled in batches and periodically to having to be processed in real time. The variety of data has also diversified from simple tables and databases through to photo, web, mobile and social data, and the most challenging: unstructured data.

Every day, we create 2.5 quintillion bytes of data – so much, that 90% of data in the world today has been created in the last two years alone.

When data sets get so big that they cannot be analysed by traditional data processing application tools, it becomes known as ‘Big Data’.

As different companies have varied ceilings on how much data they can handle, depending on their database management tools, there is no set level where data becomes ‘big’.

This means that Big Data and analytics tend to go hand-in-hand, as without being able to analyse the data it becomes meaningless.

Why Is Big Data Important?

The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:

• Determining root causes of failures, issues and defects in near-real time.
• Generating coupons at the point of sale based on the customer’s buying habits.
• Recalculating entire risk portfolios in minutes.
• Detecting fraudulent behavior before it affects your organization.

Provide specific examples of how big data can be used in business.

Healthcare

Big data in healthcare is transforming the way we identify and treat illnesses, improve quality of life and avoid preventable deaths. The drive now is to understand as much about a patient as possible, as early in their life as possible – hopefully picking up warning signs of serious illness at an early enough stage that treatment is far simpler, and less expensive.

For example, in one specialist premature and sick baby unit, Big Data techniques have been used to monitor the babies’ heartbeats and breathing patterns. Using this data, the unit was able to develop algorithms that predict infections 24 hours before any physical symptoms occur.

Retail

The way we buy and sell is evolving fast. Both online and offline, those retailers that are embracing a data-first strategy towards understanding their customers, matching them to products and parting them from their cash are reaping huge rewards.

This means that data analytics is now being applied at every stage of the retail process – working out what the popular products will be by predicting trends, forecasting where the demand will be for those products, optimising pricing for a competitive edge, identifying the customers likely to be interested in certain products, working out the best way to approach those customers, taking their money and, finally, working out what to sell them next.

Manufacturing

Data plays a hugely important role in modern manufacturing processes. Advances in robotics and increasing levels of automation are dramatically changing the face of manufacturing. Adidas is one big name investing heavily in automated factories, for example.

Even in a more traditional manufacturing environment, data is still making its mark. By embedding sensors into their equipment, manufacturers are capturing valuable data that helps them monitor the health and efficiency of those machines. Sensors are also increasingly being installed into a wide range of products, from jet engines to yoga mats, allowing manufacturers to gather valuable data on how those products are performing and being used.

Financial services, banking and insurance

The applications of data go far beyond high-tech, big-money trading. For example, data is helping credit card companies like American Express detect fraudulent transactions and expand into trend analysis services for businesses.

In insurance, data is already being used to help insurers set fairer and more accurate policy premiums, identify fraudulent claims and improve their marketing efforts. Companies like Progressive and Aviva are taking data collection a step further by offering discounts to drivers who allow them to monitor their driving via smart phone apps and in-car devices, allowing the insurer to observe how safe a person’s driving really is.

Education

Increasingly large amounts of data are being generated about how we learn, and education establishments are now beginning to turn this data into insights that can identify better teaching strategies, highlight areas where students may not be learning efficiently, and transform the delivery of education. In one example, in Wisconsin’s Menomonee Falls School District, data has been put to use for everything from improving classroom cleanliness to planning school bus routes.

Of course, these days, not all education takes place in the classroom. The boom in online courses is providing a wealth of insights into the ways that people learn, and leading to huge advances in personalised, adaptive learning.