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Data analysis and data science are two professions that make it possible to explore and interpret a multitude of data, “Big Data”. But what are the differences between the profession Data scientist and Data analyst who nevertheless have very distinct objectives.

What is a data analyst, a data scientist and how much do they earn? What are the missions of a data scientist and what does a data analyst do? What training for Data analyst and Data scientist? What is the difference between a data scientist and a data analyst?

What is Big Data?

Before comparing these two professions related to big data analysis, it is important to understand what big data is in IT and what are the challenges of Big Data.

Big data, or big data, refers to a large set of data that cannot be exploited by a traditional data management tool and which, if used well, can help solve complex problems for a company. This revolution was accompanied by the deployment of cloud computing for data storage and the arrival of the development of new databases adapted to unstructured data (Hadoop).

Big data developers are highly sought after by companies that struggle to recruit good profiles.

Read our article and complete guide on Big-Data.

What is Data Science and why becoming a data scientist?

Data science is a mix of thousands of raw data, algorithm development and statistics. Data science, via a Data scientist, makes it possible to explore and analyze raw data to transform it into valuable information to solve business problems.

For example, Netflix uses its data to understand its users and build prediction models to suggest series that will be adapted to their users. These predictions, thanks to data analysis, also make it possible to predict what type of series to produce, it becomes a Data Product.

Today, this field is becoming popular thanks to the profusions of data created by the evolution of the means of communication. The person who works around data science is called “data scientist”.

A Data scientist is the expert around the thousands of data of a company, he must be able to understand the problem to be solved and to formulate it in a mathematical and algorithmic way and to design a suitable solution.

The missions of the Data scientist will be to:

  • Define statistical modeling;
  • Identify the analysis tools to be used to collect data, sometimes build them;
  • Study the data;
  • Synthesize the results obtained and make them easily usable.

Pros:

  • Project yourself into a profession of the future given that companies are increasingly exploiting their data
  • An attractive salary
  • Actor of the digital transformation of the company
  • An intellectually stimulating profession
  • Few qualified profiles

Cons:

  • Position of responsibility therefore significant pressure
  • Few specialized courses
  • Requires advanced skills
  • English proficiency

What is a Data Analyst and why becoming a data analyst?

Given that data has become a mine of useful information as soon as it is studied, the profession of Data analyst was born in order to have a better knowledge of the business environment.

The role of the data analyst is to process the data in order to extract any information that could change the company’s strategy. This data will allow the company to understand the behavior of their consumers via their purchasing and consumption trends, or by defining the customer’s profile (persona) and their expectations.

The data analyst is therefore an essential element of the company because he will provide the concrete means to develop the commercial strategy based on his interpretation of data. The main missions of the Data analyst will be to:

Data AnalystData Scientist
Extraction and interpretation of big data data gathered by the data scientist

Programming skills lighter than a Data scientist

A hybrid profile between technology and marketing

Brings new light to the scientist’s data and will take part in the company’s strategic marketing and commercial decisions
Strong programming skills (Python, MySqL, Java)

Knowledge of statistical and mathematical predictive models

Data mining knowledge

Gathers, aggregates, makes sense of and extracts data that is useful and often informs the work of marketing, IT and sales professionals

Important player in the digital transformation of a company

How to become a data scientist and what training for data scientist?

To become a business-friendly data scientist, you need to have the following skills:

  • Programming
  • Statistics, mathematics, modelling, data analysis
  • machine-learning
  • Data preparation
  • Rigor and good stress management

Long studies are necessary to become a credible data scientist in the eyes of companies. There are very few specialized courses in big data engineering.

How to become a data analyst and what training for data analyst?

To become a data analyst you must:

  • Obtain expertise in mathematics and statistics
  • Mastering databases
  • Have a certain rigor and good stress management
  • Develop analytical skills
  • Understand business issues to be able to interpret data with a marketing and commercial vision
  • To obtain a Data Analyst position, a Masters degree level diploma is essential. This may be a degree in the field of computer engineering, marketing, or statistical studies.

What salary for a data scientist and how much does a data analyst earn?

After graduation, a junior data scientist earns an average of $60,000/year. With some experience, you will be able to claim a higher salary. On average, a senior Data scientist earns an average of $70,000/year and can go up to $100,000/year.

A data analyst can earn as a junior, a salary of $50,000/year, slightly below the data scientist even if it remains very attractive for a junior profile. With experience, a senior data analyst earns an average of €75,000/year. The data analyst can also evolve and take on more responsibilities in order to become a data scientist.

Source: Glassdoor

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