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[sharwareeshinde11-blog]

Future Of Data Science

What is data science?

Data science is a field that uses scientific method, process, algorithms and system to extract knowledge from data in various forms, structural, unstructured it is similar to data mining.

Data science is a concept to unify statistic, data analysis, machine learning and their related method in order to understand actual phenomena with data .It employs theory and techniques drawn from many field within the context of mathematics, statistics, information science and computer science

How to learn data science

Data science is a very practical field, and so it’s so important to apply theoretical knowledge that you have gathered. For example, when you learn starts, don’t just sit and read about it .see how it can be done with a programming language such as python, which a very popular language for beginners, find a data set and apply these concept on the data. Including other language like

1 linear algebra and calculus –for these refer to the book called advanced engineering mathematics by kreyszig. The various topics that you need to brush up your understanding in are hyper parameters, regularization function, cluster analyses are few topics under this domain and which is important for machine learning

2 vector calculus

3 Statistics—go on learning statistics on various sites like udacity, khan academy, courser. They provide detailed overview on this topic which is a vital topic under data science.

4 programming language---The most widely preferred open source statistical tools are ‘R’ and ‘Python’.

FOR R--start learning libraries like dplyr, tidyr, data table for data manipulation and ggplot2 for visualization which has the same syntax as R.in fact the visualization in ggplot2 is way better than that of matplotib

   Go to kaggle and download data sets so that you can practice the concept that you have studied and build project and put up on your Git profile  

How To Prepare for the Future of Data Science

There are many ways companies can and should prepare for the future of data science. These include creating a culture for using machine learning models and their output, standardize and digitize processes, experimenting with a cloud infrastructure solution, have an agile approach to data science projects and creating dedicated data science units. Being able to execute on some of these points will increase the likelihood of succeeding in a highly digitized world.

A Data Science Unit

In my previous role I was working as a data scientist for an insurance company. One of the smart moves they made was to create an analytics unit, which worked across company verticals.

This made it easier for us to reuse our skills and models on a variety of data sets. It was also a signal to the rest of the company that we had a focus on data science and that this was a prioritized issue. If a company has a certain size, creating a dedicated data science unit is definitely the right move to make.

Standardization

Standardization of processes is also important. This will make it easier to digitize and perhaps automate these processes in the future. Automation is a key driver for growth, making it much easier to scale. An added bonus is that the data collected from automated processes is usually a lot less messy and less error prone than data collected from manual processes. Since an important enabler of data science models is access to good data, this will help make the models better.

Adoption of Data Science                                  

There should also be a culture in the company for adopting the use of machine learning algorithms and using their output in business decisions. This is of course often easier said than done since many employees might fear that the algorithms are making them obsolete.

It is therefore critical that there be a strong focus on how employees can use their existing skill set alongside algorithms to make more high-level and tactical business decisions, as this combination of human and machine is likely to be the future of work in many occupations. It will probably be more than a few years before the machine learning algorithms are able to navigate alone and make superhuman decisions in an open world setting, meaning mass unemployment due to the rise of the machines is not a likely scenario in the near future.

Always Experiment

With new data being generated from IoT sources, it is important to explore new data sets and see how they can be used to augment your existing models. There is a constant flow of new data waiting to be discovered.

Perhaps including two new variables from an obscure data set into your model will increase the precision of the leads generating model by 5% — and perhaps not. The point is to always experiment and not be afraid to fail. Like all other scientific inquires, failed attempts abound, and the winners are those who keep on trying.

Create an environment that promotes experimentation and that tries to make incremental improvements to existing business processes. This will make it easier for data scientist to introduce new models and will also set the focus on the smaller improvements, which are a lot less risky than the larger grand visions. Remember, data science is still a lot like software development and the more complex the project becomes the more likely it is to fail.

Try building an app that your customers or suppliers can use to interact with your services. This will make it easier to gather relevant data. Create incentives to promote usage of the app which will increase the amount of data being generated. It is also imperative that the UX of the app be appealing and promotes use.

We might need to venture outside of our comfort zones to take on the opportunities and challenges that this digital gold brings. As the amount of data continues to grow, machine learning algorithms get smarter and our computational abilities improve, we will need to adapt. Hopefully, by creating a strong environment for using data science your company will be better prepared for what the future will bring.

Applications / Uses of Data Science

Using data science, companies have become intelligent enough to push & sell products as per customer’s purchasing power & interest. Here’s how they are ruling our hearts and minds:

Internet Search

When we speak of search, we think ‘Google’. Right? But there are many other search engines like Yahoo, Bing, Ask, AOL, Duckduckgo etc. All these search engines (including Google) make use of data science algorithms to deliver the best result for our searched query in fraction of seconds. Considering the fact that, Google processes more than 20 petabytes of data every day. Had there been no data science, Google wouldn’t have been the ‘Google’ we know today.

Digital Advertisements (Targeted Advertising and re-targeting)

If you thought Search would have been the biggest application of data science and machine learning, here is a challenger – the entire digital marketing spectrum. Starting from the display banners on various websites to the digital bill boards at the airports – almost all of them are decided by using data science algorithms.

This is the reason why digital ads have been able to get a lot higher CTR than traditional advertisements. They can be targeted based on users past behavior. 

Speech Recognition

Some of the best example of speech recognition products are Google Voice, Siri, Cortana etc. Using speech recognition feature, even if you are not in position to type a message, your life wouldn’t stop. Simply speak out the message and it will be converted to text. However, at times, you would realize, speech recognition doesn’t perform accurately.

Image Recognition

You upload your image with friends on Facebook and you start getting suggestions to tag your friends. This automatic tag suggestion feature uses face recognition algorithm. Similarly, while using whats app web, you scan a bar code in your web browser using your mobile phone. In addition, Google provides you the option to search for images by uploading them. It uses image recognition and provides related search results.

Gaming

EA Sports, Zynga, Sony, Nintendo, Activision-Blizzard have led gaming experience to the next level using data science. Games are now designed using machine learning algorithms which improve / upgrade themselves as the player moves up to a higher level. In motion gaming also, your opponent (computer) analyzes your previous moves and accordingly shapes up its game.

Fraud and Risk Detection

 One of the first applications of data science originated from Finance discipline. Companies were fed up of bad debts and losses every year. However, they had a lot of data which use to get collected during the initial paper work while sanctioning loans. They decided to bring in data science practices in order to rescue them out of losses. Over the years, banking companies learned to divide and conquer data via customer profiling, past expenditures and other essential variables to analyze the probabilities of risk and default. Moreover, it also helped them to push their banking products based on customer’s purchasing power

Airline Route Planning

Airline Industry across the world is known to bear heavy losses. Except a few airline service providers, companies are struggling to maintain their occupancy ratio and operating profits. With high rise in air fuel prices and need to offer heavy discounts to customers has further made the situation worse. It wasn’t for long when airlines companies started using data science to identify the strategic areas of improvements. Now using data science, the airline companies can:

1.   Predict flight delay

2.   Decide which class of airplanes to buy

3.   Whether to directly land at the destination, or take a halt in between (For example: A flight can have a direct route from New Delhi to New York. Alternatively, it can also choose to halt in any country.)

4.   Effectively drive customer loyalty programs

Southwest Airlines, Alaska Airlines are among the top companies who’ve embraced data science to bring changes in their way of working

Coming Up In Future

Though, not much has been reveled about them except the prototypes, and neither I know when they would be available for a common man’s disposal. Hence,  We need to wait and watch how far Google can become successful in their self driving cars project. 

Self Driving Cars