Data is a collection of facts, such as numbers, words, measurements, observations, or just descriptions of things. For example, a written text, numbers, information, and facts we store in our minds or files in memory represent data.
What is Data Science?
Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to extract useful information effectively. Data science aims to gain insights and knowledge from any type of data — both structured and unstructured.
Processes of extracting useful information from data: Data Sources, Data Analytics, Information, Action.
Types of data analytics:
1- Descriptive Analytics: Descriptive analytics answers the question of what happened. It is a statistical method used to search and summarize historical data to identify patterns or meaning. You can see what happened in the past when you examine any data set, such as sales rates.
2- Diagnostic Analytics: Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?” Diagnostic analytics gives in-depth insights into a particular problem.
3- Predictive Analytics: Predictive Analytics looks for an answer to the question of what will happen. You can think of Predictive Analytics as then using this historical data to develop statistical models that will forecast future possibilities.
4- Prescriptive Analytics: It is also known as Normative. It looks for an answer to the question of what will be consequences. Prescriptive Analytics takes Predictive Analytics a step further and takes the possible forecasted outcomes, and predicts consequences for these outcomes.
Who is a Data Scientist?
A data scientist is a person who manages the process of extracting useful information from data using his technical and social skills. The data scientist role is an offshoot of several traditional technical roles, including mathematician, scientist, statistician, and computer professional.
Data Scientist Role and Responsibilities:
1. Ask the right questions to begin the discovery process
2. Acquire data
3. Process and clean the data
4. Integrate and store data
5. Initial data investigation and exploratory data analysis
6. Choose one or more potential models and algorithms
7. Apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence
8. Measure and improve results
9. Present final result to stakeholders
10. Make adjustments based on feedback
11. Repeat the process to solve a new problem
And here are some articles I found particularly interesting:
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