Data Analyst
HOW TO BECOME A DATA ANALYST
The world of big data has been booming for several years now. The massive amounts of digital data collected provide key information for fine-tuning the various strategies of companies, regardless of their specialization field. Decoding a key job in the digital age.
What is a data analyst?
Also known as data scientists, they are responsible for processing the data of a company. This can include customer data as well as product data or data relating to company performance. The resulting predictive trends are passed on to senior management to facilitate strategic decision-making. In the world of sports, data analysts analyze the results and actions during a game to produce statistics which can then be used by players, their coaches or any professional involved.
What are the duties of a data analyst?
There are several stages in a data analyst’s job prior to handing over their report. Initially, they receive raw data. Using various IT tools, such as a CRM or Hadoop, they define segmentation criteria to best study the figures to process. For instance, computer languages such as Python provide tools to create a model that renders a database usable. All that remains to do then is to present a marketing strategy based on their analysis.
When working for a betting company such as La Française des Jeux, data analysts collect information such as the number of injuries, penalties or points scored by a player, the price of club transfers for the year, bonuses paid… This information arrives as raw data and must be sorted into various usable categories. These are then put together, using different tools and segmentation criteria, to identify trends and statistics. This helps draw up performance indicators with forecast values for potential winners.
Horse betting is a good example, because the bets are placed based on these estimates. However, marketing strategy uses these forecasts well beyond the mere sale of predictive analysis. Specifically, they help track profit and loss margins that emerge from bets, while estimating how many people and how much money will be committed to one value.
The role of data scientists is therefore to coordinate a set of data to give managers a readable picture and help them deliver the most accurate diagnosis possible or implement a strategy. Upon request or on their own initiative, they may also create machine learning. This technique, which can be found in many of the tools we use every day, such as our smartphones, offers real-time analyses of data flows by considering a variety of preset criteria. These instinctive programs help to fine-tune decisions at a moment’s notice in case of major changes in preset criteria, such as an unexpected comeback.
Key qualities and skills required of a data analyst
These IT and more specifically business intelligence careers are, justifiably, associated with high-level expertise in statistics, business intelligence (BI) and business analysis (BA). Examples include such tools as ETLs, APIs, data warehouses, data lakes or the use of algorithms. However, companies also greatly appreciate candidates who demonstrate:
- Good writing skills for drawing up quality reports
- Diligence, which is essential for day-to-day database monitoring and accuracy
- Fluency in English when working for a global corporation
- Extensive knowledge of the sports industry to better understand the company’s needs
Salary range and career prospects
Data analyst salaries are variable. Junior professionals can expect to earn around €3,000 monthly whereas a data analyst with some years of experience (data scientist) is paid around €5,000 gross per month.
The exponential all-digital market opens a wide choice of business areas for Big Data. The sports industry is no exception. In addition to soccer and rugby clubs, data analysts are in high demand by TV channels, as well as the betting sector with La Française des Jeux.
This is where data such as the number of injuries, penalties or points scored by a player, transfer prices for the year, and bonuses paid will be used. However, federations and clubs won’t use this data in the same way.
Indeed, clubs or federations will use it to identify tomorrow’s new champion and invest in him or her by recruiting them before they develop their full potential, to secure capital gains. If the investment is reasonable and the athlete becomes number 1 in the competitions played, then the ensuing contracts, sponsorships and merchandising sales, such as jerseys bearing his or her name, will provide a handsome return for the initial investor.
After several years working for a federation or a sports equipment manufacturer, data analysts may move on to the position of data scientist or take over the management of a department in a large organization and thereby become chief data officer.
What are the qualifications required to become a data analyst in the sports industry?
The future Data Analyst must follow a course of study in computer engineering, where he or she will learn how to create algorithms and use servers such as the sqi server. All the analytical and technical aspects of their future profession will be taught to them during their higher education studies.
In fact, once you’ve obtained your engineering diploma, it’s also advisable to take a master’s degree in marketing or statistics. A dual skill set is therefore required. For this second course, a business school is ideal. These programs offer courses in data management, for example.
However, if you’d like to specialize straight out of high school, or after a first 5-year course in the sports sector, a bachelor’s degree specializing in sports professions is just what you’re looking for. The fundamentals of general culture, economics and finance are taught through the prism of sport.
To acquire more advanced skills in the data sector, it is advisable to continue on to a Master’s degree. What’s more, the possibility of doing internships during this curriculum enables students to gain initial professional experience, which will facilitate their integration into the workforce upon graduation.
Also known as data scientists, they are responsible for processing the data of a company. This can include customer data as well as product data or data relating to company performance. The resulting predictive trends are passed on to senior management to facilitate strategic decision-making. In the world of sports, data analysts analyze the results and actions during a game to produce statistics which can then be used by players, their coaches or any professional involved.
There are several stages in a data analyst’s job prior to handing over their report. Initially, they receive raw data. Using various IT tools, such as a CRM or Hadoop, they define segmentation criteria to best study the figures to process. For instance, computer languages such as Python provide tools to create a model that renders a database usable. All that remains to do then is to present a marketing strategy based on their analysis.
When working for a betting company such as La Française des Jeux, data analysts collect information such as the number of injuries, penalties or points scored by a player, the price of club transfers for the year, bonuses paid… This information arrives as raw data and must be sorted into various usable categories. These are then put together, using different tools and segmentation criteria, to identify trends and statistics. This helps draw up performance indicators with forecast values for potential winners.
Horse betting is a good example, because the bets are placed based on these estimates. However, marketing strategy uses these forecasts well beyond the mere sale of predictive analysis. Specifically, they help track profit and loss margins that emerge from bets, while estimating how many people and how much money will be committed to one value.
The role of data scientists is therefore to coordinate a set of data to give managers a readable picture and help them deliver the most accurate diagnosis possible or implement a strategy. Upon request or on their own initiative, they may also create machine learning. This technique, which can be found in many of the tools we use every day, such as our smartphones, offers real-time analyses of data flows by considering a variety of preset criteria. These instinctive programs help to fine-tune decisions at a moment’s notice in case of major changes in preset criteria, such as an unexpected comeback.
These IT and more specifically business intelligence careers are, justifiably, associated with high-level expertise in statistics, business intelligence (BI) and business analysis (BA). Examples include such tools as ETLs, APIs, data warehouses, data lakes or the use of algorithms. However, companies also greatly appreciate candidates who demonstrate:
- Good writing skills for drawing up quality reports
- Diligence, which is essential for day-to-day database monitoring and accuracy
- Fluency in English when working for a global corporation
- Extensive knowledge of the sports industry to better understand the company’s needs
Data analyst salaries are variable. Junior professionals can expect to earn around €3,000 monthly whereas a data analyst with some years of experience (data scientist) is paid around €5,000 gross per month.
The exponential all-digital market opens a wide choice of business areas for Big Data. The sports industry is no exception. In addition to soccer and rugby clubs, data analysts are in high demand by TV channels, as well as the betting sector with La Française des Jeux.
This is where data such as the number of injuries, penalties or points scored by a player, transfer prices for the year, and bonuses paid will be used. However, federations and clubs won’t use this data in the same way.
Indeed, clubs or federations will use it to identify tomorrow’s new champion and invest in him or her by recruiting them before they develop their full potential, to secure capital gains. If the investment is reasonable and the athlete becomes number 1 in the competitions played, then the ensuing contracts, sponsorships and merchandising sales, such as jerseys bearing his or her name, will provide a handsome return for the initial investor.
After several years working for a federation or a sports equipment manufacturer, data analysts may move on to the position of data scientist or take over the management of a department in a large organization and thereby become chief data officer.
The future Data Analyst must follow a course of study in computer engineering, where he or she will learn how to create algorithms and use servers such as the sqi server. All the analytical and technical aspects of their future profession will be taught to them during their higher education studies.
In fact, once you’ve obtained your engineering diploma, it’s also advisable to take a master’s degree in marketing or statistics. A dual skill set is therefore required. For this second course, a business school is ideal. These programs offer courses in data management, for example.
However, if you’d like to specialize straight out of high school, or after a first 5-year course in the sports sector, a bachelor’s degree specializing in sports professions is just what you’re looking for. The fundamentals of general culture, economics and finance are taught through the prism of sport.
To acquire more advanced skills in the data sector, it is advisable to continue on to a Master’s degree. What’s more, the possibility of doing internships during this curriculum enables students to gain initial professional experience, which will facilitate their integration into the workforce upon graduation.
