What are the main differences between a data scientist and a data analyst?
Data scientist and data analyst roles are among the most sought after in the IT field and more specifically in big data. These data experts manage the collection, extraction, modelling and analysis of information gathered. Both roles also have good programming skills. Their similarities mean that they are often mistaken for each other, not just by students, but also by IT professionals and companies. However, despite their similarities, these two data specialists have very specific expertise, missions, responsibilities and remuneration. To help you distinguish between them, here are the main differences between a data analyst and a data scientist.
The main difference between data analyst and data scientist
The major difference between a data scientist and a data analyst can be identified from their title: the former is a scientist, the latter an analyst.
The data scientist will manage the data from the moment it is collected until it is made available to other team members (including the data analyst). In concrete terms, the data scientist will be the first to work on the raw data sets. They will sort them, manage storage and above all design modelling tools to facilitate analysis of the data. They might spend up to 60% of their time just cleaning data using programming languages such as Python or R.
The data analyst will receive the information which has already been processed by the data scientist. They will make greater use of their mathematical and programming skills (particularly in query languages such as SQL) to generate reports and identify trends. This discipline focuses on statistical analysis to address problems, anticipate developments and consequently define strategies (mainly commercial).
Although the work of the data analyst usually comes in after the data scientist, there is no hierarchical link between them. Often, they belong to different departments of their companies. Data scientists are generally part of the R&D, IT or technical sectors.
Data analysts are more likely to be linked to strategic and commercial departments such as sales, marketing and communication. The difference in their affiliation is also explained by the fact that their project tasks are very different.
The different tasks undertaken by a data analyst and data scientist
The data analyst's tasks
The main tasks of the data analyst are:
exploration and analysis of the data prepared by the data scientist;
designing SQL queries to answer functional questions;
definition of new metrics to understand the development of the organisation's business;
identifying similarities and correlations to discover trends;
detection of possible data quality issues;
creation of statistical reports using reporting tools.
The data scientist's tasks
The tasks of the data scientist consist of:
discovery and exploitation of raw data;
identifying issues and considerations from the data;
data cleansing, sorting and classification;
development of new analytical methods and machine learning models;
conducting consistency tests;
designing reports and visualisations to facilitate secondary data analysis.
The skills of a data analyst and a data scientist
The common skills of the data analyst and the data scientist
The two data specialists have overlapping tasks, particularly in terms of information analysis and exploitation. They therefore have common competences in :
mathematics and statistics expertise;
algorithms and visualisation techniques;
software engineering expertise;
written and oral communication to share their reports and analyses with their colleagues.
The skills of the data analyst
In addition to the above skills, the data analyst is expert in the use of:
querying data using query languages, in particular SQL;
data analysis and forecasting using Excel (visualisation tools, pivot tables, etc.);
creating dashboards with business intelligence software;
various types of predictive or prescriptive analysis.
The skills of the data scientist
The data scientist is a data expert who:
uses languages and platforms such as R, Python, MatLab;
manages data warehouses such as Apache Hive and Pig;
is expert in machine learning algorithms, automatic learning and natural language processing (e.g. with tools such as Tensorflow);
Explores data using APIs or ETL pipeline creation.
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