The advent of Big Data means the production and collection of billions of pieces of data every second. For businesses, this information is a godsend, enabling them to make more informed decisions based on verifiable facts. Provided they know how to interpret the available data. This is precisely the role of the Data Analyst. Find out more about this profession, its missions, its skills, the tools it uses, its salary, and of course, how to become a Data Analyst.
What is a Data Analyst?
The Data Analyst is a data expert. Thanks to their understanding of the data universe and its tools, they are able to interpret thousands of pieces of data and extract relevant insights. They work alongside the Data Scientist, who provides them with the tools to analyze or create new data.
The information analyzed may concern different aspects of the organization, such as customers or prospects, products, performance, financial situation, competitors… And very often, the data comes from a wide variety of sources (CRM, social networks, reports…).
Whatever the source, the role of the Data Analyst is to process this information to help decision-makers make the best strategic choices. For example, to identify purchasing and consumption trends, determine the profile of the ideal customer and his expectations, or anticipate the risks associated with the arrival of a new competitor on the market.
What does a Data Analyst do?
While the role of the Data Analyst is to analyze data, the tasks involved in achieving this objective are more or less varied:
- Data preparation: the data used by a company comes from a multitude of sources, and often presents different formats, or even inconsistencies. The Data Analyst must therefore bring all this raw data together in one place. Above all, they must clean it up (removing duplicates, false, obsolete or irrelevant data, etc.) and reformat it.
- Database modeling: databases must facilitate data collection and processing. They must also be easy to understand and read.
- Data exploitation: to this end, algorithms can be developed to facilitate analysis.
- Updating: to help companies improve their performance and strategy, the Data Analyst must use up-to-date data. This means regularly updating the database to facilitate real-time decision-making.
- Dashboard creation: using the KPIs defined by managers, the Data Analyst creates reporting tables, with various graphs. This simplified approach enables non-experts to quickly visualize the trends that emerge.
- Data presentation: all the data management work carried out upstream must enable decision-makers to adopt the right strategy. The data analyst must then present his findings in a readable and comprehensible way.
- Technology watch: the idea is always to use the best tools for data exploitation. Whether it’s to automate the preparation phase, optimize the frequency of data availability, improve data quality... The tools used enable the company to benefit from a competitive advantage (either through productivity gains, or through more relevant decisions).
What skills does a Data Analyst need?
As a data expert, the Data Analyst must first master a series of essential skills:
- Statistics: if they have to analyze thousands of items of information, Data Analysts generally have to deal with numerical data. This means mastering mathematical and statistical tools to identify trends.
- Databases: this is where you’ll find all the information you need to analyze. As such, they need to be able to use these IT tools and the programming languages that allow them to be manipulated.
- Data visualization: this involves simplifying the interpretation of data through visual elements, such as graphs, curves, data stories, maps… The idea is to help decision-makers better understand the analyses (even if they have no data skills). Data analysts need to be at ease with the various data viz tools and the use of dashboards.
- An analytical mindset: a data analyst must be extremely rigorous, with a strong analytical mind and unfailing organization.
- They must also have a basic knowledge of English and the legal system, so they can adapt to any sector or data management standard, depending on the country in which they work.
What tools does a data analyst need?
To analyze data and help organizations make the right decisions, the Data Analyst uses an ultra-complete toolbox:
- Accessing data sources: To access data, the data analyst can use tools such as BigQuery, MySQL, Amazon Redshift, PostgreSQL, ORACLE, SQL Server.Tools like BigQuery enable massive interactive analysis of large datasets in collaboration with cloud storage spaces. As for SQL, it enables you to write queries to access information.
- Processing and displaying results: The Data Analyst must be able to provide answers to the company’s questions, understand its needs and study the market in which it operates in greater depth. To do this, there are a number of tools available to answer these questions, such as Excel, Python or R. Other, more specific tools offer more in-depth capabilities to answer more complex problems. These include Anaconda, a distribution of the Python and R programming languages for scientific computing, which aims to simplify package management and deployment. Or Pandas, an open source Python library that is most widely used for data science, data analysis and machine learning tasks.
- Visualization tools: so that the rest of the team can follow the progress of an analysis, the Data Analyst can be asked to create dashboards. Through various spreadsheet and business intelligence tools such as Kibana or Amazon QuickSight, Power BI is an interactive reporting platform. It can easily handle a wide variety of data types and massive amounts of data. Kibana is a data visualization extension for Elasticsearch. It enables you to search and visualize data indexed in Elasticsearch. Amazon QuickSight lets users query data in natural language to generate visualizations in just a few seconds.
- Be efficient with low/no code: Low/no code platforms provide a development environment used to create software applications through a graphical user interface. Among these tools, Bubble makes it possible to program complex applications and websites without coding, while the Microsoft Power Platform range of software applications enables application development and application connectivity.
What are the differences between Data Analyst and Data Scientist?
Like the Data Analyst, the Data Scientist helps organizations make better decisions using data. But the method is somewhat different, as the Data Scientist will perform predictive analyses and solve complex problems. To do this, they model massive volumes of data, create algorithms, automation systems and data frameworks, and use Artificial Intelligence, Machine Learning and Deep Learning.
The idea is to create new data models to improve predictive analysis, while the Data Analyst extracts insights from pre-existing information.
How much does a Data Analyst earn in France?
At the start of a career, the average salary for a Data Analyst is around €2,800 net per month (or €45,000 gross per year). After several years’ experience, they can expect a net monthly salary of €3,100 (between €50 and €55K per year).
For more details, you can find all Data Analyst salary estimates according to different criteria or locations on our article dedicated to Data Analyst salaries.
However, the Data Analyst is a highly sought-after profession in France, and one of the 10 most sought-after, with no end in sight. Companies today all need a Data Analyst to make more informed decisions and stay competitive. Market leaders, for their part, will need to recruit several Data Analysts if they are to analyze the ever-increasing daily flow of data.
How do I become a Data Analyst?
Between the technical skills you need to acquire, your analytical skills and your business appetite, becoming a data analyst is not something you can improvise. It’s essential to undergo training to become a data analyst.
What studies to become a Data Analyst?
As the data analyst occupies a central strategic position within organizations, it is preferable to have a baccalaureate + 5 in continuing education.
More specifically, a master’s degree in computer science, statistics, mathematics or marketing. There are also a number of masters degrees specializing in Artificial Intelligence and Big Data. But these are still too few and far between.
So, to increase your chances of finding a job once you’ve finished your studies, don’t hesitate to complement your curriculum with specialized training in data analysis. The idea is to learn everything you need to know about this promising profession, from both a theoretical and practical point of view.
This is precisely what we offer at DataScientest. You’ll learn how to use all the tools required for the job of Data Analyst, and how to apply the right working methods.
How can I gain professional experience?
Although data analysts are widely sought after by companies in all sectors, it’s best to have some professional experience before entering the job market. This is possible with sandwich courses. You combine :
- weeks at school to discover new concepts and master new tools ;
- with weeks on the job, where you put everything you’ve learned into practice.
This type of apprenticeship doesn’t just give you work experience. It can also offer you a job. At the end of your training, the company that hires you as a work-study student can convert your work-study contract into an employment contract.
How do you build a portfolio and a network?
While the CV is still indispensable when looking for a job, it’s no longer the only differentiator. And yes, companies don’t just want to know what school you went to. They want proof of your skills. That’s where the portfolio comes in. It’s a digital space where you present all your achievements.
You can add individual projects or group work carried out during your training. If you’ve completed a work-study program, don’t hesitate to include all the projects you’ve worked on.
As for your network, you can start developing it as soon as you start your training. At DataScientest, courses are online, but you’ll also benefit from a community space where you can exchange ideas with other students on the course. Start there.
Then there’s the team of professors and lecturers who already have solid experience in the world of data.
Finally, get in direct contact with professionals, whether through your work-study program, social networks (especially LinkedIn) or data events.
What are the prospects for development?
The data analyst profession is evolving rapidly, and offers a wide range of prospects. After several years’ experience, you can take your career in a new direction. There are many ways to do this:
- Towards managerial positions: such as lead data analyst, data manager, data security manager or chief data officer.
- Towards more technical professions: as a data scientist or data engineer, for example.
- Towards specializations: such as pricing, revenue management, customer relationship management (CRM), data marketing, etc.
Whatever your desires, the data analyst profession opens many doors for you.
