We are currently living in a world full of data. Regarded as the age of Big Data, the present 21st century is dominated by Big Data technologies. Data Science, Data Analytics, and Machine Learning are among the most in-demand Big Data technologies that are making a huge impact in the present day. Many companies across the industry verticals like IT, Healthcare, Banking, Finance, etc are relying heavily on the use of these Big Data technologies to grow at scale.
Data Science, Analytics, and Machine Learning technologies have become lucrative career options for people coming from both technical and non-technical backgrounds. There’s a surge in the demand for professionals who are capable of playing with Big Data at the tip of their fingers and support enterprises in making swift business decisions. As Big Data analysis has become the hottest job skill, those who possess Data Science, Data Analytics, and Machine Learning skills gain a strong foothold in this competitive world.
Now let’s understand how Data Science, Data Analytics and Machine Learning technologies differ from each other.
What is Data Analytics?
We can look at the following Venn diagram presented by Hugh Conway in 2010 to get a clear idea of Data Science.
From the Venn diagram, we can see that Data Science incorporates Math, Statistics, and hacking skills. If you have expertise in all these three fields then you can master Data Science without much hassle.
Data Science incorporates different techniques like Data Cleansing, Preparation, and Analysis to make accurate interpretations out of Big Data. It is the responsibility of a Data Scientist to gather relevant data from different sources and to apply Machine Learning, Predictive Analytics, and Sentiment Analysis to achieve the desired objectives. Data Science can play a crucial role in the business decision-making process. You can master the skills in Data Science with our Data Science Training in Hyderabad course.
What is Data Science?
A Data Analyst is a professional who has skills sets involving Descriptive Statistics, Data Visualization, and communicate data points for relevant conclusions. Data Analysts must have good knowledge of statistics, and the perception to visualize the data. Data analytics can be considered a beginner level of Data Science.
What is Machine Learning?
Machine Learning can be interpreted as an advanced concept of Data Science that helps in making accurate predictions from Big Data. Machine Learning algorithms can be used to analyze data to accurately forecast future trends for that topic. The best example of Machine Learning in use is Facebook’s Machine Learning algorithm. It analyzes the users historical data and recommends articles and notifications on the news feed that perfectly align with users interests.