Index
- What is data science?
- What kind of problems can be solved with data science?
- The data science workflow
- Data science related roles
1. What is data science?
Data science is a set of methodologies for processing and studying data. The goal is to use this data to draw meaningful conclusions. For instance, thanks to insights coming from data transactions, we could make an effective detection and prevention of fraud involving credit cards.
2. What kind of problems can be solved with data science?
More precisely, data scientists can use data to:
- Describe the current state of an organization or process
- Detect anomalous events, such as fraudulent transactions
- Diagnose the causes of observed events and behaviors
- Predict future events
Data science is about discovering and communicating insights from data, which is coming from very different sources, so in order to exploit it, data scientists need to adopt a specific workflow.
3. The data science workflow
Problem Statement. Establish a well defined question.
Data collection and storage. Fist we collect data from many sources, such as surveys and financial transactions and then, we store that data in a safe and accessible way.
Data preparation. This step includes the cleaning, for instance finding missing or duplicate values and converting data into a more organized format.
Exploration and visualization. This can involve building dashboards, track how the data changes over time or performing comparisons.
Experimentation and predictions. This could include building forecasting systems and trying different machine learning algorithms in order to compare their effectiveness on the data.
4. Data science related roles
There is not a single job within the data science world. Generally, there are four main roles:
- Data engineer
Data engineers control the flow of data: they build custom data pipelines and storage systems. They design infrastructure so that data is not only collected, but easy to obtain and process.
Data engineers are proficient in SQL, which they use to store and organize data. They need to be comfortable with cloud computing to ingest and store large amounts of data.
- Data analyst
Data analysts perform simpler analysis that describe data. They do this by exploring the data and creating visualizations and dashboards.
Analyst also use Business Intelligence tools such as Tableau and Power BI, to create dashboards and share their analyses.
- Data scientist
Data scientist have a strong background in statistics, enabling them to find new insights from data, rather than solely describing data. The also use traditional machine learning for prediction and forecasting. Data scientist must be proficient in Python or R.
- Machine Learning scientist
They go beyond traditional machine learning with deep learning. Machine learning scientist use either R or Python to create their predictive models.
