What is data science & why we need it?
Data science deals with the procurement & processing of meaningful data & patterns extracted from a large collection of data belonging to an enterprise, which helps in generating knowledge of business value.
The need for data science arose with the growth of big data & its challenges. In traditional systems, data is structured & easy for enterprises to analyze using BI (Business Intelligence) tools. But as the amount of data collected by an organization grows in size, the data largely remain unstructured & BI tools are not sufficient anymore to process the data. That’s where Data Science comes into the picture. We use more efficient tools of Data Science such as Weka, BigML, R & RapidMiner to process data. Learn Data Science Course Malaysia
Some common areas where data science is employed are:
- Search engines like Google, DuckDuckGo etc. make use of Data science algorithms to search & display results related to our queries.
- Speech recognition technologies such as Cortana, Siri, Amazon’s Alexa employ data science in which voices are picked up by a microphone, transcribed into text & respective actions are taken by the machine.
- Self-driving cars, drones & autopilot mode in aircrafts.
- In social media forums to gather information like detecting the devices you use to access your accounts, whom you interact with & the location from where your account has been accessed.
- Spam filters used by e-mail services.
- Prediction of diseases in medicine field & gathering information on heart rate, stress levels & blood glucose in humans through wearables such as Fitbit.
- Data acquisition
- Predictive analytics
- Machine Learning algorithms
- Data Mining, Data Structures & data manipulation
- Big Data & Hadoop integration with R
- Data scientist roles & responsibilities
Data Science has learners from diverse educational backgrounds, and all that is needed to understand the concepts well are, a good command over mathematical & statistical concepts such as algebra, calculus & probability.
Technologies used in Data Science
Some common data science technologies, tools & languages used are:
- Python, R, Java TensorFlow and SQL.
- Spark/MLib, Amazon Machine Learning, Hadoop.
- Amazon Web Services, Julia, Google Cloud Compute.
Career opportunities in data science
There’s a big demand for data scientists & it is going to further surge in near future. There are 4 kinds of data science roles:
A data analyst retrieves & gathers data, organizes it and uses it to arrive at important & meaningful conclusions.
A data engineer is an infrastructure engineer who builds, controls and maintains software infrastructure. A data engineer has good knowledge of distributed systems.
Machine Learning engineer:
A machine learning engineer builds, optimizes & deploys machine learning models. These models are treated as components or APIs and plugged into hardwares & apps.
Data science generalist:
A generalist does everything starting from retrieval of data, processing it to the final analysis.
But it takes many years of experience to get hired as a data science generalist.
Data science is the need of the hour & finds its use in every major field. According to IBM predictions, the demand for Data Scientists will surge to 28% by 2020. Learning data science may not be a walk in the park, but it definitely is thrilling & challenging. So here’s your chance to master the art! Join data science course in and become expert.