The degree of proficiency in using data is what separates a data scientist from a data analyst. A data scientist needs to have greater experience with sophisticated programming methods and computer equipment than the other. A data scientist should also be more skilled in creating algorithms and data models. For a data analyst, the profile is primarily exploratory in contrast to an experimental work profile of a data scientist.
Having a better understanding of the many ways that organizations use data can also help to clarify the functions that they play:
- Descriptive Analytics – This category of analytical solutions addresses the What and Why aspects of business problems. By recognizing patterns, trends, and anomalies in previous data, they offer insights that can be put into practice. Descriptive analytics can be used, for instance, to determine how customer involvement has changed over time and the factors that have contributed to that shift.
- Predictive Analytics – This class of solutions uses historical data trends and machine learning algorithms to predict the future. Predictive analytics is used, for instance, to determine the likelihood of cross-selling a product to a customer based on their prior engagement.
Data Analysts are more focused towards descriptive analysis by addressing the theoretical parts of the business, along with some predictive analysis but as a whole, predictive analysis is mastered by Data Scientists who need to play more with data and coding to predict the outcomes.
Now, let’s dive deeper into how the project life cycle of a data analyst differs from that of a data scientist.
Data Analyst
- Data Extraction
- Data Cleaning and Preparation – Tidy up the data and carry out data manipulation in order to create useful results and visualizations.
- Data Exploration – Exploration of reasons behind trends observed in the business.
- Data Visualization and Reporting- An analyst creates user-friendly dashboards and management reports with business commentary, which they then distribute to the relevant parties. For this, a number of self-serving business intelligence technologies, such Tableau and Power BI, are frequently employed.
Data Scientist
- Data Extraction
- Data Cleaning
- Feature Engineering – The practice of using cleansed data to build variables predicted to have a stronger predictive power on the desired outcome is known as feature engineering. For instance, BMI can be included as an extra input to a predictive model in addition to height and weight to determine a person’s chance of developing diabetes. In this case, weight and height are used to calculate BMI, an engineered feature.
- Model development using AI/ML techniques – A machine-learning system is fed the data to generate predictions.
- Model Testing – Here, data scientists check for whether the model they produced are accurate or not. When the results are not satisfactory, the scientist goes back to the model’s parameters and adjusts them to get the desired outcome.
- Productionalizing the Model – Build model pipelines that are ready for production. This makes it possible to integrate with the data flow seamlessly. They next concentrate on formalizing the code and putting it into the production environment as a last step.
Salaries for Data Analysts typically range from $80,000 to $135,000 whereas for Data Scientists, they range from $95,000 to $160,000 on average.
While they handle data differently, data scientists and analysts share the commonality of working with large amounts of data. Some of the most important factors that set them apart are the amount of data they work with and how much programming they do. A data scientist is likely to receive a better income, but there are more demands and obligations in this position. To help you select which method of dealing with data you prefer, you might wish to initially try one or two courses from each path if you’re still not sure which one to take.