What are the key functions used in Data Science?

Question

What are the key functions commonly used in Data Science? Could you explain the important ones or how they contribute to the field?

Answer 1

Sanjeet Singh ,

your question covers a very wide range of information. so i took chatGPT and pasted your question as it is.

you can try it by yourself. the results are well-structured explanations that will give you a good overview for further researching. this is the final summery:

... different stages of the data science workflow: data acquisition, cleaning, exploration, modeling, and communication.

Answer 2

Sanjeet Singh as Lothar has mentioned. In addition to that you can do thorough research using websites like medium, towards data science and geeksforgeeks for further in detail about some data science key functions and their uses. You can even checkout kaggle for the datasets, models and many more for enhancing the data science field. Every function has its own pros and cons we can't say this is good that is good. It completely depends upon your interest. So choose it wisely. Happy coding!!

Answer 3

Key functions in data science include data collection, data cleaning, and data exploration. Data collection involves gathering relevant datasets from various sources. Data cleaning ensures the data is accurate, consistent, and free from errors or missing values. Data exploration helps in understanding patterns, relationships, and trends through statistical analysis and visualization techniques. Feature engineering is crucial for creating meaningful input variables. Data modeling applies machine learning algorithms to make predictions or classifications. Evaluation of models using metrics like accuracy or precision ensures their effectiveness. Finally, data visualization presents insights in a comprehensible way for decision-making.

Answer 4

archi jain ,

your post looks like it is coming from an `ai` ?

Answer 5

Key functions used in data science include data collection (gathering raw data from various sources), data cleaning (preparing and handling missing or inconsistent data), data analysis (exploring and interpreting data patterns), and data modeling (building predictive models using machine learning or statistical techniques). These functions work together to extract meaningful insights and drive informed decision-making.