The Difference between Data analytics and Data science
Since, data science, analytics, and machine learning are growing and the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. By the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet, which makes it extremely important to know the basics of the field at least. After all, here is where our future lies.
What is Data Science?
Data science is a concept used to tackle big data and includes data cleansing, preparation, and analysis. A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets. They understand data from a business point of view and can provide accurate predictions and insights that can be used to power critical business decisions.
Skills Required to Become a Data Scientist
- Strong knowledge of Python, SAS, R, Scala
- Structured and unstructured database understanding
- Understand multiple analytical functions
- Knowledge of machine learning
What is a Data Analyst?
A data analyst is usually the person who can do basic descriptive statistics, visualize data, and communicate data points for conclusions. They must have a basic understanding of statistics, a perfect sense of databases, the ability to create new views, and the perception to visualize the data. Data analytics can be referred to as the necessary level of data science.
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