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The Art of Reading Data: How Statistics Now Run The World

The pandemic has taught a thing or two about the importance of figures.

We hear about mortality rates, positivity rates, infection rates, and disease peaks. We’ve grown so used to interpreting these figures that we forget the critical role which statistics play in generating meaningful insights out of numbers.

What’s Statistics got to do with it?

For starters, statistics is the discipline which deals with the collection and analysis of data in order to understand why things work the way they do. It helps to systematically collect, analyse, predict and represent data that is otherwise unruly and vague.

We see data all around us, in all sorts of forms (fun fact#1: We generate the same amount of recorded data in a week today, that we did in a year back in the 2000s). From the number of customers in your local supermarket to the number of pepperoni that makes a pizza slice perfect, it’s all data. This data can be analysed to predict the way things work.

What then is the role of data science?

Statistics that lack scientific evidence are usually made-up figures to substitute shoddy data collection and analysis methods. To overcome such challenges, the interdisciplinary field known as “data science” was created. The scientific study of making predictions and providing insights based on raw data which has been collected through different sources is known as data science. Prediction, classification, optimization, ranking, segmentation, recommendation are all concepts that are covered by data science.

It’s the number one application of statistics in the world as of today.

The applications of statistics

Statistics is an indispensable part of our lives and is the true definition of something that lies hidden in plain sight (fun fact#2: 70% of statistics is made up). The application of statistics is numerous and vast. Listed below are a few fields where statistics has been used extensively.

a. Healthcare

Diseases can be linked to the causal factors so that its source can be traced. The use of IoT (Internet of Things) and BASN (Body Area Sensor Networks) have helped in developing devices that are capable of constantly monitoring the wearers of smart devices. By analysing the  data generated by these mobile devices, it is possible to identify patterns from abnormalities and to predict ailments. Statistics plays an important role in healthcare by identifying areas that require more assistance, allocating medical equipment whenever and wherever required etc. Oncora, a revolutionary ML based data analysis tool, works on cancer diagnoses, treatment plans and outcomes to help fight cancer.

b. Economics

Through data analysis, market value predictions can be made. If meticulously used, this data can help investors in buying and selling stocks at the best rates. Statistics have helped in understanding how the economy of nations work and to predict where their GDPs are headed in order to make invaluable decisions. It can help in analysing the percentage of a particular populace that is unemployed or are headed towards unemployment based on how the job market behaves.

c. Lifestyle

Many large organizations resort to using data mining techniques to identify relevant patterns when it comes to consumers buying products. Many customers might prefer a particular brand due to its quality while some may buy from another due to smart-pricing. Identifying correlations between the products and consumer behaviour can help in improving the sales figures, while also providing information about which products to stock and which ones to be destocked. Data analysis can be used to predict routes based on the behaviour of vehicles. By using traffic patterns, the most optimal routes can be predicted. UPS has been making use of data analysis to predict the best routes to send packages on so that they can reach the customers within the shortest possible time. When it comes to using applications on smartphones, most of them customise feeds based on the interests of the user. Music preferences, movie titles, and advertisements are generated from what a user likes and dislikes.

d. Sports

Data scientists have helped many sports teams to make the right decisions in order to emerge victorious. In certain scenarios, analysis has helped to pick the right players even when there are budget constraints. In cricket, based on different plays, it is possible to identify the best possible position for the fielders to be positioned while playing a particularly dangerous batsman whose strong side is to his leg-side.

e. Corporate

Data analytics has been used to recruit new employees by HR professionals. By using analytics, recruiters can easily identify specific traits in potential employees and earmark them based on the requirements of the organization or team. Wondering how this works? For starters, intelligent systems capable of segregating impressive resumes from the not so impressive ones are all the rage in the corporate world. This not only reduces the amount of time an HR has to put in to go through all the entries, it also helps to easily separate candidates based on their expertise. Data analysis can help in assigning members to a project based on different traits. Product placement is another area where analysis helps.

f. Administration/Government

The amount of data collected by governments is literally unimaginable. This can be used to predict the growth rates of particular regions and their populace based on the economy, literacy, employment indices, etc. Crime statistics can be used to predict how victims are targeted and what the most common modus operand is. Based on such information, it is possible to assign extra personnel to areas that have a higher ratio of criminal activities.

Roles that make use of statistics

Analytics makes use of statistics and data to generate patterns in daily life. One of the most popular examples when it comes to showing how analytics works is how men who went to a store to pick up diapers also picked up beer. A popular chain store worked out the correlation between beer and diapers through data analysis and realised that men who picked up diapers on a weekend were more likely to be motivated to buy beer as a reward for the trouble they went through. This correlation would have remained an unexplained phenomenon, had it not been for data analytics doing its job. These days we have many analytical roles in different fields. Data analyst, technical analyst, banking analyst… the list is vast. Analytical skills include being creative, possessing great communication skills, having the ability to think critically and logically, and the ability to research about things that are relatively unheard of or are uncommon.

Data architects are those people who are responsible for creating maps that help in connecting decentralized entities. The architect is responsible for generating workflows that often link the different modules of data management systems and dictate how a database must be organized in order to easily access it. Data engineers are the ones who implement the system that is designed by the architects and the data scientists. They have to take care of the maintenance of the database and are responsible to understand how data responds to the algorithms put in place by the data scientists.

Who is a data scientist?

Dubbed the sexiest job of the 21st century, data scientists are among one of the most highly sought-after specimens in today’s job market. One of the best examples of data scientists revolutionizing the way we connect is how LinkedIn’s community was utilized to make a well-connected network. This network made use of the members’ information to generate personalized feeds that showed job opportunities, possible new connections that a member might know, available online courses, etc. Data science can identify patterns that usually evade us. Data scientists are required to, poetically put, shine light in the darkness. They are comfortable working with chaotically unstructured data and strive to bring out meaningful insights from available data. This not only requires good computing skills, but also requires one to possess great observational and logical skills.

What’s in Store?

As of now, the gap between the supply and demand for roles that work with statistics is growing wider and has shown no signs of slowing down. There is a lot of potential for data scientist/analyst roles and most of the people with such skills are literally being chased after. This is because of the large amount of both structured and unstructured data being generated everyday, which is known as “Big Data”. Due to the extensive usage of social media applications and the infinitesimal data exchange that’s taking place in today’s world, the number of people required to maintain this large amount of data is vastly increasing. The demand for graduates with such skills has taken a sharp upturn. Most organizations sit on a pile of information without having the right personnel to manage data. Meltem Ballan, Data Science and Analytics Manager at Ernst and Young opines that industry leaders and advocates who can converge and come up with “best practices, standards and training rules” make the best use of data scientists. It makes no sense to amass data without working out a way through which this data can be analysed. Statistically speaking, more than 70% of the jobs in the future will have to do with data and the art of reading it. If one is good with statistics and logic, data science is definitely a viable option. There are quite a few textbooks that are available in the market right now which will help to brush up the fundamentals of data science.

Most of us might have used Microsoft Excel at least once in their lives. The seemingly humble Excel is a voracious tool capable of powerful operations that require the manipulation of data. The software can behave as a data visualisation tool where graphs can be generated. It also utilizes macros to substitute patterns and criteria to perform operations related to how an output should behave. Another tool that provides insightful takes on data is Tableau. From generating varieties of graphs to generating insightful reports from legacy data, Tableau is an all-powerful data analysis tool. Many data scientists make use of tools such as Tensorflow, Plotly, RStudio among others to collect data and to add sense to the uncharted data. In order to be a good data scientist or an analyst, one must be familiar with quite a few of these tools. These tools not only make your lives easier, they also possess great power to convert raw data into information.

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