Data science is a field that focuses on extracting useful information from data.
The aim of data science is to get predictive or useful information from data.
Data science has become a buzzword that can be broadly used to represent business analytics, business intelligence and predictive modeling.
3 Concepts of Data Science
Data science combines the fields of strategy, statistics and programming.
Since the aim of data science is to extract useful information for a certain goal, data scientists need to understand the goal well.
Examples of such goals are to:
- Improve business revenue
- Lower business costs
- Find trading opportunities in the markets
- Solve engineering tasks
- Create self-driving cars
Once the data scientist understands the goal and its underlying mechanics, he or she will be able to devise an appropriate strategy to analyze and extract information that will be useful for that goal.
The data scientist needs good knowledge of statistics in order to analyze the data in an appropriate way.
Misusing statistics might lead to results that are misleading or erroneous.
Machine learning and big data management are complementary skills here.
Programming skills are needed for the data scientist to apply their statistical skill to the data.
Examples of Data Science
- Google uses its vast amount of data to determine which search results are the most relevant.
- Netflix applies machine learning to its users’ data to determine what shows are they more likely keen on.
- Paypal analyzes its users and their transactions to spot possible fraud.
Links to Complicated Explanations
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