Data Scientist

The Data Scientist is the expert in data science or Data Science within an organization. That is, he is the professional who is in charge of extracting and interpreting data to obtain relevant information that helps improve company performance. Next, we are going to see in more detail what his functions are, what skills and knowledge he needs and why he is an increasingly valuable figure within companies.

What are the functions of a Data Scientist?

The figure of the Data Scientist may vary from one company to another. Still, broadly speaking, these are their most notable functions:

  • Data extraction: Organizations collect and have at their disposal a large amount of data that they can use to their benefit, and Data Science must be able to obtain all the useful information.
  • Data purging or cleaning: Once the data has been collected, it is necessary to filter it and discard those that are neither necessary nor significant and that, in addition, can distort the results. This aspect is key to being able to move on to data processing.
  • Data processing: It consists of data processing to obtain valuable information. At this point, different disciplines come into play, with the aim of understanding the data, identifying patterns and relationships between them and, finally, validating them. For all this, statistical and analytical techniques, specialized software, automation systems, Machine Learning tools, etc. can be used.
  • Data visualization: In order for the conclusions and information to be understandable to everyone, Data Science must also know how to present the data effectively.

What knowledge and skills should a Data Scientist have?

Generally, the profiles that companies usually look for in a Data Scientist role are computer or telecommunications engineers. Still, they may also require people trained in statistics, mathematics, economics, business management or with some specialized training. And to work as a Data Scientist it is important to have programming knowledge, analytical skills and business vision. Let’s look in more detail at the technical knowledge and skills that are usually required.

1. Programming: Be proficient in programming languages ​​such as Python, R or SQL to manipulate databases, implement automated learning systems, do integrations with applications and websites, perform statistical analysis, etc.

2. Statistics and Mathematics – This provides:

  • The theoretical framework for research.
  • Understanding patterns.
  • Processing data.
  • Forming hypotheses, among many other things.

Some of the statistical techniques that are usually asked of a Data Scientist are data modelling, clustering and predictive analysis.

3. Databases: knowing how to extract and manage large amounts of data and cross-reference them with other sources of information.

4. Machine Learning and Deep Learning: Generally, the Data Scientist is usually asked to understand, master and know how to develop algorithms and models that allow computers to learn patterns from data, perform tasks and solve complex problems.

5. Big Data: Be familiar with tools such asHadooporSpark, which allows for handling large volumes of data.

6. Privacy and data processing regulations: Be aware of the ethical implications and regulations related to data management and protection.

In addition to all this, the Data Scientist must also be able to identify and improve processes to be more efficient and obtain higher quality data, as well as collaborate with other teams such as business analysts or developers. To do this, it is also necessary to have the following skills or soft skills:

  • Problem-solving ability
  • Continuous learning
  • Analytical thinking
  • Effective communication
  • Business vision

Why should marketing companies have Data Scientists?

The Data Science profile is one of the most in-demand by companies currently since it can provide a lot of value in making tactical decisions and help focus marketing and sales strategies, among others. These are some of the reasons why organizations need the figure of the data scientist:

  1. Decision-making based on real data: the information provided by the Data Scientist allows you to support and make strategic decisions based on data and not only on intuition or experience.
  2. Improving efficiency: Data science can help identify process improvements, leading to greater efficiency and, often, reduced costs. Furthermore, the automation of tasks and the implementation of systems based on algorithms encourage employees to focus on more strategic and less routine tasks.
  3. Customer satisfaction and retention: Customer clustering, or the analysis of customer behaviour and preferences, allows us to offer them more personalized solutions, and this translates into greater satisfaction and retention. In addition, data science allows you to know the churn or customer cancellation rate and their lifetime value, which is very useful for predicting your decisions and anticipating them.
  4. Improving the customer experience: In relation to the above, analyzing the interaction of customers with the company will allow us to have a clear customer experience map, anticipate their needs, understand what we can offer them at each step and solve customer problems. More effective way.
  5. More successful marketing strategies: Data science can help analyze the market and the company’s position in relation to the competition and identify patterns and trends that often go unnoticed. It is possible, for example, to predict changes in demand for a product or service or to analyze risks and cost overruns in unexpected situations. All of this allows us to create more effective marketing and positioning strategies, anticipate possible changes in the market and take the necessary measures.
  6. Product and service development: In-depth knowledge of the market, competition and customers helps companies develop products and services that are in line with their customer’s expectations and establish, among other things, more effective pricing strategies.

How to train in Data Science

Currently, there are a multitude of training offers to become a Data Scientist, from university degrees, postgraduate degrees and master’s degrees to specialized courses and certifications.

The growing need for companies to collect, manage and interpret data makes this figure decisive for decision-making. That is why they usually incorporate it into their templates or have partners who offer them this service, as we do at Cyberclick, where we have a team specialized in data science that can help you identify trends, risks and opportunities, as well as improve your marketing strategies and boost your results.

Also Read: Real-Time Data in Business Agility


Please enter your comment!
Please enter your name here