Data science has been a trending fieldof study in recent times this is because of the amount of data that we createconstantly and the computing power that is available with advancements in technology .
but what is data science think about what happens when you book aride on uber you open the uber app on your phone and tell the app where you want to go uber tries to find the nearest cab since then the directions tocome pick you up and take you to your destination that was simple but in thebackground the seemingly simple task is carried out by collecting mountains ofdata from various sources like the phones the map and historic trends oftraffic and demand for rides with this data modern-day computers are programmedto calculate the nearest driver to you the best route to your location and destination.
The time it will take and what you should pay in other words thisis made possible with data science data science has countless other applicationsas well and is at the intersection of statistics data analysis and machinelearning it is a combination of scientific methods models and algorithmsworking together to extract actionable business insights from data the u.s.faces a shortage of 140,000 to 190,000 people with deep analytical skills and1.5 million managers who can analyze big data to make effective decisions theaverage salary of a data scientist is around 118 thousand dollars .
so still interested in data science as a profession continue on to learn moreabout who can become a data scientist why data scientists matter what is thedata science lifecycle how big data is driving the data science revolution thecareer prospects for data science data is the oil of our generation betascience is becoming indispensable in today’s digitally driven world helpingbusinesses understand consumer behavior fine tune its messaging and capture newmarket share to become a data scientist you don’t need to have a technical background to be a data scientist what you do need is in-depth knowledge inmathematics analytical reasoning .
The ability to work with large amounts ofdata it would also help to have a strong intellectual quest knowledge of dataengineering visualization ability and excellent business acumen if you do comefrom a non-technical background you will likely use are if you are from atechnical background then you could use Python and R it is all aboutunderstanding the possibilities and asking the right questions all in thesearch for the best answers every company is flooded with data and theyhave more data than they know what to do with so regardless of the industryvertical data science is likely to play a key role in your organization’s futuresuccess data scientists help find new ways of reducing costs entering newmarkets and customer demographics and launching new products or services data science also has found social and medical applications such as childwelfare and predictive diagnosis as well .
So what does the typical data sciencelifecycle look like the data discovery step includes the search for differentsources of relevant data structured or unstructured data then you make adecision to include specific datasets into your analysis the data preparationincludes converting data from different sources into a common format you willstandardize the data look for anomalies and make it more appropriate to workwith the data science models are built using statistics logistic and linearregression differential and integral calculus among other mathematicaltechniques you could use tools like R Python SAS SQL tableau and .
so on gettingthings in action phase includes checking the data models for its effectivenessand ability to deliver the results you will have to verify the model works ifnot you have to rework on your model a data scientist needs to liaison with thevarious teams and be able to seamlessly communicate hisfindings to key stakeholders and decision makers in the organizationanother critical element of data science are algorithms which are a process ofset of rules to solve a certain problem some of the important data sciencealgorithms include regression classification and clustering techniquesdecision trees and random forests machine learning techniques likesupervised unsupervised and reinforcement learning in addition tothese there are many algorithms that organizations develop to serve theirunique needs big data is driven by the data science revolution big data is theengine propelling the rise of data science hadoop is a popular big dataframework used by most organizations .
Hadoop works in a distributed mannerwhere in both the processing and storing of data is distributed on commodityhardware Hadoop is easily scalable highly economical fault tolerant andsecure Hadoop consists of Hadoop distributed file system or HDFS forstoring data and uses MapReduce for processing data another emergingframework is Apache spark which is touted to be up to 100 times faster thanMapReduce spark stores the data in the RAM so iterative processing is fast andefficient it also deploys direct acyclic graph or daj for processing of datathere is a huge demand and supply mismatch when it comes to datascientists due to this salaries of data scientists are among the best in theindustry top companies like Amazon Google Facebook Microsoft in the techspace to others like ExxonMobil Visa Boeing General Electric and Bank ofAmerica are actively hiring data scientists now that you have learnedabout data science why data science is indispensable the data science lifecyclehow it relates to big data it is time you start your journey in this promisingdomain and see your career soar