Hello, my name is Naomi, and I’m a Data Scientist. I have been an enthusiast and a technology student since 2018, and when people become interested in technology, they realize that there are countless areas of expertise, including the field of Data Science. That’s what I’m going to talk about today.
When we talk about Data Science, we are describing the process of turning data into knowledge (LOUKIDES, 2016). This tells us that merely using data — collecting, storing, and publishing — is not enough, but rather adding value to them, enabling new ways of usage.
“A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.”
— Josh Wills
Well, according to Wills, software engineering and statistics are somehow related to this field. That’s because the values aggregated from analyses have base on statistical concepts that are applied in codes - whether by data processing, visualization or modeling. Therefore, we can say that Data Science is composed by some steps as follow:
Data Science is generally composed of the following steps:
- Data collection — gathering raw data from various sources
- Data cleaning — handling missing values, outliers, and inconsistencies
- Exploratory analysis — understanding patterns and distributions
- Modeling — building predictive or descriptive models
- Communication — presenting insights to stakeholders
Note: Not all steps are always required. In a more exploratory analysis, modeling can be discarded, for example.
Related occupations
Now that you understand what Data Science is, you might be interested in exploring some related roles:
- Data Engineer
- Data Analyst
- Data Scientist
- Data Manager
Thanks for reading, see you in the next one ⭐