Data Scientist
A data scientist is a professional responsible for collecting, analyzing and interpreting large volumes of data. They are often tasked with discovering patterns and trends in datasets to help organizations make decisions.
What is a data scientist?
Data scientists use various techniques and technologies to uncover insights, such as machine learning, natural language processing, statistical analysis and data manipulation. They also create visualizations to present their findings in a meaningful way.
Ultimately, the goal of a data scientist is to use data to help organizations better understand their business, their customers and their environment.
With the rise of large-scale data collection and the need for analysis, data scientists have become highly sought after in a wide range of companies and industries...
Data science as a career incorporates a range of skills in mathematics, statistics and computer programming.
Skills needed to be a Data Scientist
The specific skills required for data science positions depend on each company, in general what is sought is:
- Skills in software, statistical programming languages and database.
- Learning methods and development of new data-based products.
- Knowledge of data virtualization tools.
How to become a data scientist
Today there are different viable options for becoming a data scientist, from the academic offerings provided by graduate schools offering a master's in data, there are also informal options and online options offered by global educational platforms.
To be a data scientist you need solid business knowledge, real experience in data management, communication skills and an understanding of the management of the problems you are trying to solve.
What is data science?
A data scientist has a true passion for analysis. Extensive knowledge in business analysis, statistics, computing and science, with experience in fields such as experiment design, algorithms, dashboards and data visualization.
- Ability to quickly identify a simple, robust and scalable solution to a problem:
- Ability to convince and push management in the right direction, sometimes against its will, for the benefit of the company, its users and shareholders:
- Knowledge of data architecture:
- Data collection and cleaning techniques;
- Good knowledge of algorithms.
Some data scientists are also data strategists and develop a strategy that generates impact. This requires creativity to develop business-based analytical solutions.
Data science lies at the intersection of computer science, business engineering and statistics, data mining and automation.
Maria's story, Data Scientist
Maria woke up that morning with a feeling of excitement. She worked at Cinema Lam as a data scientist for the Technology division. Every day was a new opportunity to dive into data and extract valuable insights about the company's clients. Maria graduated in Mathematics and Psychology from the University in 2011 and completed her Master's in Applied Statistics a year later. With six years of work experience behind her, she was at an early but exciting stage of her career.
No two days were the same for Maria. Her role focused on understanding who Cinema Lam's clients were and what their preferences were. Recently, she worked on designing and implementing a movie recommendation engine and personalized experiences for viewers. The project involved everything from defining the problem to coding the necessary algorithms.
Maria valued her training in mathematics as a vital tool to back up with data her recommendations and answers to business problems. She used methods such as time series analysis, logistic regression and non-parametric hypothesis testing to reach solutions grounded in reason and logic.Â
The most rewarding thing for her was when she delivered something new that allowed a business partner to move forward with their project or campaign. Computer programming was a crucial tool in her arsenal, allowing her to bring solutions into production efficiently. However, not everything was perfect; what she liked least about her job was the routine reporting tasks.
MarÃa started her professional career as a data solutions engineer, a job she got through her university job fair. But she soon realized that she wanted to do work more grounded in statistics. That's why she moved to Cinema Lam as a statistical analyst. After some time in that position, she felt that her responsibilities were leaning back toward report generation, which led her to move to a more research-oriented data science team with longer-term projects.
MarÃa feels that her professional path is still evolving. Each new role gives her additional skills and passions, and allows her to increasingly define her professional path. Her advice for those just starting out is not to worry too much about finding the perfect job right away; the key is to constantly learn and adapt.
And so, MarÃa headed to her office, ready to face the challenges and opportunities awaiting her that day, knowing that each project was a new adventure in her ongoing journey of discovery in the world of data.
