How To Become A Data Scientist
What is and what does a data scientist do?
The Data Scientist is one of the jobs with the greatest growth potential that has managed to capture the interest of professional profiles from countless fields of work: marketinians, economists, financial analysts, researchers, etc.Thank you for reading this post, don't forget to subscribe!
However, being a job role in full bloom, it is inevitable that more than reasonable doubts arise about this profession. What is a Data Scientist? What does a Data Scientist do?
Let’s solve them!
What is a Data Scientist?
To solve this question we must first answer another question. Why did this profession arise and what need does it solve?
The use of the Internet on a global scale has led to an explosion of data that is increasing exponentially every day. A good starting point for finding answers is knowing what happens on the Internet in a single minute.
As you can see…
The amount of information that is generated on a global scale is enormous!
The information circulating on the Internet per minute reaches millions of terabytes per second.
This dizzying pace of digital information creation is also taking place within the institutional and business sphere.
Maintaining competitiveness and enhancing the efficiency of organizations involves extracting knowledge from the torrent of information that is generated every day both internally and externally.
This is essential to survive in the ultra-competitive market of the next decades.
Hence the need to transform all this data into knowledge, a process requires data analysis and the skills provided by a specialized professional profile such as Data Scientist.
Data Scientists, also known as Data Scientists, encompass a broad set of professional competencies but they all have similar knowledge and training that we will discuss later.
They work both for the private and public sectors and not only in large multinationals such as Google, Amazon or in macro institutions such as the Tax Agency or Social Security. They are also present in medium-sized organizations.
It is not essential to have a huge volume of data such as that which a multinational can generate to exploit the potential of the information generated in an organization.
Currently, most companies have a website that generates information traffic that can be analyzed by a Data Scientist.
Any company, regardless of size, generates data continuously and constantly that can be analyzed to turn information into knowledge.
What does a Data Scientist do?
The Data Scientist or Data Scientist today is one of the most demanded professionals.
Professional competencies of the Data Scientist
Because in the face of this incessant flow of information, it has 4 fundamental professional skills:
- Analyze large volumes of data.
- Know and apply the available technology to understand them.
- Extract the valuable information that is in them.
- It provides solutions for the substantial improvement of processes.
Data Science is a discipline that solves one of the key needs of 21st century organizations: basing decision making on objective data with the minimum margin of error.
The Data Scientist or Data Scientist is a specialist in big data analysis and his main objective is to put the valuable information that resides in the data at the service of organizations.
Data is its raw material and its ability is to define reliable indicators that help in decision-making and help to propose corrective measures in organizations.
But what exactly does a Data Scientist do ? What is its role?
As noted by famed technology consultant and speaker Bernard Marr in Forbes magazine, the 3 key skills that a Data Scientist must develop are:
1. Understand the data.
Understanding what these large data sets are and what they represent is his main ability.
Neither the best technology nor the most advanced algorithms will be of any use if the data to be analyzed are not understood a priori.
This skill is trained with practice, but as a starting point, it requires specific training.
Simple and obvious solutions may sometimes be found, but most of the time they will be complex, because the problems facing a Data Scientist are almost always new.
So understanding data requires equal parts technical knowledge and creative thinking.
2. Understand the problem to be solved.
Understanding the problem to be solved supposes answering the following question: What knowledge can be extracted from the available data?
To respond, the Data Scientist must develop its work in 2 phases:
Phase 1. Obtain a data description model> for this, you must use the appropriate statistical method that allows you to extract knowledge from the data (data mining).
Phase 2. Predicting the behavior of the data> a problem that must be solved by applying techniques based on the “bootstrap” and the “assembly of models”.
3. Understand the technology available.
Knowing the available technology and knowing how to use it to solve problems implies having:
- The right resources and infrastructure.
- Give solutions in a timely manner.
If the available resources are expensive or do not allow you to analyze the information as it is needed, the solution will arrive late and the costs for the organization can be a serious problem.
Personal qualities of the Data Scientist
In addition to these 3 key capabilities that Bernard Marr told us about, every professional who wants to develop their career as a Data Scientist must have 2 qualities:
Curiosity has led the human being towards progress.
The Data Scientist must show curiosity about the data, which will motivate him to study it and analyze it to make new discoveries.
The mind of a data analyst should be driven by curiosity, not the conviction of taking the facts for granted.
Ability to communicate knowledge
The Data Scientist must be a good communicator since they must make their conclusions understandable to the rest of the team.
In Data Science, the premise is also fulfilled that the message transmitted by the sender is as important as the message decoded by the receiver.
Therefore, you must not only interpret the data and draw conclusions, but also know how to communicate them clearly and concisely and support your arguments in formats that guarantee better understanding, such as graphics.
How to become a data scientist
If you want to dedicate yourself to Data Science, in this new post we address two interesting questions to give you even more insight about this profession so in demand today:
How do you become a Data Scientist?
Do you know Moore’s Law? This hypothesis formulated by Intel co-founder Gordon Earl Moore predicted in the 1960s that:
- Computer processing speed would double every 2 years.
- The processing capacity would do it every 18 months.
- And the amount of digital information would multiply by 10 every 5 years.
These predictions, in addition to being fulfilled, have been exceeded.
Today the volume of data is increasing at a faster rate than the capacity to process it.
What is the role of a Data Science professional?
Within this ecosystem and at the dawn of 5G, its function is both simple and extremely complex:
The Data Scientist is the one who must know and transmit the value that resides in the data.
Slack’s Director of Data Engineering, Josh Wills, raises a curious definition of the role of the Data Scientist, as “the person who knows more about statistics than any programmer and who at the same time knows more about programming than any statistician.”
What differentiates a Data Scientist from a Statistician
The performance of the Data Scientist can be confusing with that of a Statistician.
Although both professionals are dedicated to analyzing, solving problems and interpreting large databases, the big difference is that the Data Scientist must perform 4 complementary roles:
Apply the available technology to provide the best solution to the problem, select the infrastructures, resources and program the algorithms that will solve the problem.
Select the best methodology to solve a specific problem with the available data.
Its objective is to find creative solutions to analyze data sets, propose new working hypotheses and innovative methods to face problems never imagined until now.
You must extract performance and added value from the data to give you an advantage over your competitors.
“Finding patterns is very easy in a data-rich environment and, in fact, that’s exactly what mediocre bettors do. The key is knowing how to decide if these patterns are noise or signal ”. Nate Silver (American statistician and writer).
What training and knowledge do I need?
As a common base, performance as a Data Scientist requires a solid knowledge in Applied Statistics and a set of specific knowledge divided into areas.
We highlight the following:
Mathematics and programming
- Machine learning
- Statistical modeling
- Experimental design
- Bayesian inference
- Decision trees
- Machine learning
- Data optimization
Programming and databases
- Computer science
- R language proficiency and packages
- SQL and NoSQL databases
- Relational algebra
- Parallel databases
- Parallel query processing
Communication and visualization
- Story telling skills
- High-quality visual representation
- Domain of R packages like ggplot2
- Ability to transfer the information that resides in the data into decisions and actions
- Graphic display tools
To practice as a Data Scientist it is necessary to be trained in analytical skills, in software management, in appropriate communication strategies and in new measurement theories and applications, with the aim of:
- Perform data analysis with maximum reliability.
- Anticipate the difficulties that may be encountered in the process.
- Select advanced tools to meet these challenges.
- Provide maximum value with the interpretation of results.
Only if you have this knowledge, and also the skills to put it into practice, is it possible to exercise a qualified and solvent performance in this profession.
A good strategy to know how to choose the training you need to work as a Data Scientist is to know the opinion of other professionals like you about their training experience and to know the problems that they have been able to solve in their professional performance.