Photo from the Unsplash website . By Christina @ wocintechchat.com
I have worked as both a professional data analyst (Data Analyst) and a data scientist (Data Scientist). I think it would be helpful to share experiences for each position, highlighting the key differences in day to day tasks. I hope my article will help you decide which is right for you. And those who are already working may, after reading it, want to change their position. Some start out as data analysts and then progress to researchers. Not so popular, but no less interesting is the path from a researcher in low positions to an analyst in a senior position. Both positions have their own characteristics and require certain skills that you need to know about before taking the next big step in professional development.
Below, based on my experience, I will tell you what it means to be a data analyst and data scientist, and I will answer in detail the most common questions about each position.
Data Analyst
If you want to describe data for the past period or the current moment and present key search results to stakeholders, a complete visualization of changes and trends, then the position of a data analyst is right for you. The positions mentioned have similarities that I have described in another article covering the similarities and differences between the skills required for these positions. Now I want to show how the role of data analyst versus the role of data scientist is felt. It is very important to understand what to expect for these specialists in their daily work. The analyst will interact with different people, communicate a lot, and maintain a high pace of completion of tasks - higher than is required of the data scientist.
Therefore, the impressions received in each of the positions can vary greatly.
Below you will find answers to the most common questions about what data analysts face.
- Who will you have to work with?
Mostly with the company's stakeholders who request data summarization, visualization of findings and reporting on results. Communication is usually verbal or digital channels: email, Slack and Jira. In my experience, you have to work closely with the human and analytical side of the business, not the engineering and manufacturing.
- Who are the results provided to?
Most likely to the aforementioned stakeholders. However, if you have a manager, you report to him, and he already transfers the data to the stakeholders. It is also possible that you collect a pool of requests, compile a report on them and present them to stakeholders. For reporting purposes, you may have tools like Tableau, Google Data Studio, Power BI, and Salesforce that provide easy access to data such as CSV files. Other tools require more technical effort - building advanced database queries using SQL.
- What will be the pace of work on the project?
Significantly higher than data scientists. You can prepare multiple data pools (queries) or reports daily and large presentations with outputs weekly. Since you are not building models or making predictions (usually), and the results are rather descriptive and ad-hoc, things go faster.
Data scientist
Data scientists are quite different from data analysts. They may use the same tools and languages, but the researcher has to work with other people on larger projects (such as building and implementing a machine learning model) and spend more time on it. Data analysts usually work on their projects on their own: for example, one person can use the Tableau dashboard to present the results. Data Scientists have the power to employ multiple engineers and product managers to efficiently accomplish business tasks with the right tools and quality solutions.
- Who will you have to work with?
Unlike a data analyst, you will only interact with stakeholders on some issues, while for other issues related to models and results of their use, you will contact data engineers, software engineers and product managers.
- Who are the results provided to?
You can share them with stakeholders, as well as with engineers who need to have an idea of the finished product in order, for example, to develop a UI (user interface) in accordance with your predictions.
- What will be the pace of work on the project?
Probably the biggest difference in the perception and functioning of these positions is the amount of time for each project. The speed of data analysts is quite high, and data scientists can take weeks or even months to complete a project. Modeling and preparing data scientist projects are time-consuming processes as they involve data collection, exploratory data analysis, master model creation, iteration, model tuning, and results extraction.
Conclusion
Photo from the Unsplash website . By Markus Winkler
Analysts and data scientists use the same tools like Tableau, SQL, and even Python, but their professional tasks can be very different. The day-to-day activities of a data analyst include more meetings and face-to-face interactions, requires advanced soft skills and fast project execution. The work of a researcher involves longer processes, communication with engineers and product managers, as well as building predictive models that comprehend new data or phenomena in their development, while analysts focus on the past and current state.
I hope the article was interesting and helpful. Thanks for your attention!