Data Science

If you like new technologies and mathematics, and want to understand the development of intelligent machines and algorithms, Data Science is the discipline you are looking for.

To work in this rapidly evolving field, you need to be prepared to be constantly learning and willing to solve problems.

Why choose data science as a degree?

Today we have the ability to produce and store huge amounts of data from different sources; photographs, sales reports, numbers, images, video, text, etc and thus apply them in areas such as security, medicine, health, well-being, social relationships, politics, the environment, the economy and business.

Many companies today do not ask their candidates for requirements based on a university degree. Instead, they are looking for practical experience. Degrees are no longer so relevant in the industry, currently we can learn a lot without going to university. We have a more practical education ranging from massive courses to Bootcamps that can give you the technical skills required to build a career in Data.

For those who want to pursue a traditional education, a university degree (bachelor's) is also highly recommended, at the end of the text you will find a summary about the Degree in Data Science. You can learn many theoretical and important things, but keep in mind that you also have to develop your technical skills.

Why is ethics important in data science?

The fact that you can access and collect data does not mean that it is ethical to use it. Data scientists can work with very sensitive data and it is crucial to understand what the rights are.

Job field in data science

Salaries in data science-related positions will vary depending on the type of job, years of experience, and the country you live in.

As data grows, the field of data science also grows, so more roles are emerging for data experts. The types of roles will vary by company. The different roles in this field have changed many times over the years, from applied statistician to data mining, data scientist, machine learning engineer, artificial intelligence specialist, data architect, etc.

Data Science in English Data Science is the capture of knowledge from large volumes of structured or unstructured information. It is the process that follows “Data Mining” (which is the collection and organization of data).

Career opportunities

This professional can work in a wide variety of companies and institutions dedicated to: biotechnology, genomics, e-commerce, the automotive industry, finance, virtual reality, medicine, sports, energy, transportation, home automation, etc.

Data Analyst: responsible for collecting, processing and analyzing data to extract useful information from it. The analyst position is generally the starting point after finishing your studies. After a few years immersed in data, you can grow into a director or manager position.

The role of the data scientist can be to work with data models, ensure data quality, build analytical models and collaborate with the company's business teams. If you understand how the business works you know where you can make that change and add value with data.

Data Architect: responsible for the design, creation and optimization of the data management systems that integrate a company's data sources.

Data Engineer: They build the software infrastructure necessary to make data analysis possible in large-scale systems. They work with the latest Big Data technologies to scale solutions and create products. The data engineer focuses on data architecture. Data architecture designs how data should be stored in the different systems, what the structure should be like, and how data should be linked together. 

Business Analyst: data analyst with specialized knowledge in a company's business domain. Their purpose is to contribute to improving products and services and improving the profit and loss impact.  Business analysts do basic data analysis, looking for trends.

The data strategist focuses on business, determining what the Data strategy in the company should be. 

Data consultant: Works freelance and is suited for the most experienced professionals.

Other roles: researcher and lecturer at the University, Project manager, Business entrepreneur, R&D project analyst.

How to get your first job?

Internships are a good way to start because you will learn a lot and they are very hands-on. As for employers, larger companies and consultancies are usually more open to investing in new talent and helping them develop their career.

It is difficult to get your first job, but once you enter the job market, many doors will begin to open for you. In addition, the sooner you start building a professional network in the field, the sooner you will find opportunities. 

Challenges when applying for a job

A data scientist works with different ways of collecting data, from public data (for example, weather data), data from standardized systems to generating their own dataset.

Data is often messy when it does not come from standardized systems, so be prepared to spend 80% of your time working on cleaning and integrating data.

The job market wants practical (project) experience and not just theoretical knowledge. Therefore, it is important to have demonstrable programming experience/knowledge of open source tools.

As a professional you will be able to work in…

• Industrial companies of all kinds, manufacturing, energy or equipment.
• Data analysis in organization management.
• The marketing department for predicting product demand/supply. 
• In advertising and marketing companies.
• In production chain management, optimizing logistics processes through intelligent systems.
• In audio/video signal processing.
• In algorithm development.
• In research departments.
• In the management and development of artificial intelligence projects. 
• In the R&D department in artificial intelligence.
• In artificial intelligence consulting.
• In cybersecurity.

Professional environment 

Data scientists and engineers will be able to practice their career in fields as varied as health, manufacturing or product marketing, as well as choose to be an independent professional or within R&D&I in companies, Centros Tecnológicos, etc.
 
Programming languages and tools used in the sector 

Over the years, different programming languages became the most popular. Currently, the most popular programming language is Python, and tools are used to visualize data and make dashboards. An analyst can use tools for data visualization and to create more interactive dashboards, such as PowerBI, Tableau, Python Dash and R shiny.  Python is the most popular programming language now, R is popular for medical research while some large companies, especially banks, prefer to use Matlab.  SQL is very common. Hadoop and Spark are popular for storing and processing large amounts of data. For web development, Docker is used (an application for developers), Django (a high-level python web framework), Laravel (a custom software development tool), VueJS (also a great tool for building websites). Swift is a programming language developed by Apple and is also completely open source. 

What is the daily work like?

While it is a broad field and can vary from one company to another, many times, creating a simple data analysis and some data visualizations and in other cases, data science can involve complex mathematical and machine learning models.

On the company side, any project involving data will start with understanding the company, talking to the problem owner. Then the analysis and models to investigate the problem, and finally, they will present their solution and ways to solve it.
 
What are the requirements to enter the field of data science?

An excellent foundation in mathematics and statistics which are the basis of data science. In addition, as a data scientist you must understand the business and social skills.

How to choose a program and an institute?

To make the right choice of a program, the most appropriate thing is to start by choosing the program instead of the institute. 

Many courses focus on the analytics toolkit and Python which is the most common programming language. Although they are important and you have to learn them, keep in mind that tools can change and you won't use Python later,.

A program that integrates Social Skills

Social skills are important because you need to be able to communicate with your colleagues as well as explain your findings and be able to inspire your colleagues to work with data.

If you want to be a data scientist, in the sense that you want to build predictive models, then you might look for a degree that has very rigorous mathematics and statistics. On the other hand if you want to work more in data management or data engineering in this case, training focused more on computer science and specific tools.
 
Choose an institute

Location is a good thing to check before choosing an institute. It is better to study in the same region where you want to work since there you will start to build connections. In addition, many universities have mentoring programs that can help you connect with people in the industry.
 
A focused and hands-on program, six to nine weeks where they cover the tools used in the market and work hands-on with real business projects for candidates. In addition, they help with social, presentation and consulting skills. At the end of the program, they also help them find a job.

Degree or Bachelor's Degree in Data Science

The Degree in Data Science provides comprehensive training to process large volumes of data (numbers, images, video, text, etc.), as well as to apply it in areas such as healthcare, social relations, politics, the environment, economics and business.

Admission profile

In general, we look for an observant, analytical, creative, imaginative person, open to change, entrepreneurial, with the ability to work in a team and with a deep interest in mathematics, statistics, engineering and data processing.

Personal attitudes of initiative, teamwork, personal work organization, capacity for abstraction, critical thinking and responsibility and interest in the practical application of knowledge to solve real problems are valued, as well as a high level of competence in management skills and technology management.

Abilities

Good scientific and mathematical foundation, logical reasoning ability, ability to work with abstract models, good observation skills, attention and concentration.

The most comprehensive degree programs provide a solid knowledge base in mathematics, statistics, computer science, artificial intelligence, law, health and business, which make it possible to meet society's growing demand for making decisions based on the information provided by data.

Some subjects you can find in a degree program:

Calculus, Logic and discrete mathematics, Algorithmics and programming, Probability and statistics, Algebra and advanced calculus, Databases, Signals and systems, Information theory, Mathematical optimization, Data analysis, Machine learning, Information visualization, Information search and analysis, Image processing and computer vision, Mathematical analysis, Databases, algorithms, Probability and simulation, Data processing, Legal aspects of data, Data storage infrastructure, Linear models, Image processing, Data visualization, Statistics for omics data, Economics and data science, Spatial and geographic analysis, Audio and speech analysis and processing, Deep learning.

The Bachelor's degree in Data Science produces professionals capable of creating knowledge extracted from data and provides the knowledge necessary for professional development as a data analyst, big data specialist, artificial intelligence expert, business consultant, data architect and, in general, for all those occupations that require solid knowledge of data.

Professionals are able to design data collection from any environment (industrial, sociological, economic, political, business, etc.), and can process, analyze and combine data from different sources, to extract knowledge and effectively communicate how to manage strategic decision-making.

People with a degree in data science will be qualified to lead risk analysis projects, anticipating potential epidemics, analyzing drug resistance, managing clients, designing new products,

It combines the study of fundamental subjects such as mathematics or computer science, with new tools from digital information and communication technologies, including statistics, artificial intelligence or machine learning.

Why choose Data Science?

Because it is a degree with future prospects. In a world that generates more and more data and has more connected devices, it is necessary to train specialists in the intelligent analysis of all types of information.

Professionals are in high demand, especially in strategic sectors such as healthcare, banking, commerce, public sector, industry and communication. 

All socio-economic sectors require experts who analyze data from their environments, from industrial sectors to banking or administration. 

In the industrial and financial sector: almost all economic agents require intelligent algorithms that improve their production processes, their understanding of the market or that, directly, allow them to build innovative products such as the autonomous car, wind turbines or predictive risk or investment models, etc.

Also because it improves the competitiveness of companies and administrations through the strategic incorporation of data science and artificial intelligence technologies and mastering the technologies and technological trends in the field of information processing and task automation.