Píldoras de conocimiento: Women Tech - Sesión 4

Introduction

The video starts with music playing in the background.

Introduction to Camila Araya

Camila Araya is introduced as a guest. She talks about her work at Banco de Chile and how she got into data science.

Camila's Background

• Camila introduces herself and talks about her studies.

• She discusses how she got interested in data science and reinvented her career.

• Camila shares her achievements, challenges, and benefits of working in data science.

Education

• Camila talks about her education, including studying finance and learning English abroad.

• She mentions taking a course called Financial Fox that introduced her to data science tools like Excel and SPSS.

• Camila explains that she had some programming courses during university, which helped her learn more about data science.

Reinventing Her Career

• After finishing school, Camila realized that she needed to learn more about data science tools like SQL and Power BI to be competitive in the job market.

• She started taking free online courses to learn the basics of different programming languages.

• Camila identified a gap in her knowledge of applying data science tools, so she continued taking courses to fill this gap.

Conclusion

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Reinventing My Career in Data Science

In this section, the speaker talks about how he reinvented his career and found a job in data science. He also discusses his experience working in a consultancy firm that provided services to financial institutions.

Finding a Job in Data Science

• The speaker reinvented his career to specialize in data science.

• He found a job at a consultancy firm that provided services to financial institutions.

• The firm validated models used by financial institutions and offered other related services.

Learning About Financial Institutions

• The speaker learned about how financial institutions estimate the probability of customer default using econometric models.

• He discovered that different financial institutions have varying volumes and granularity of data, making it challenging to manage everything with data science alone.

Working at Banco de Chile

• The speaker started working at Banco de Chile, where he worked on building models with his colleagues.

• His area of work involved connecting macroeconomics with the bank's capital by calculating how much capital shareholders should have under different macroeconomic scenarios.

• They used machine learning tools such as Python, R, SQL, and Microsoft Excel for their work.

Challenges Faced by Data Scientists

In this section, the speaker talks about some challenges faced by data scientists and emphasizes the importance of constant learning and standardizing processes.

Constant Learning

• Data scientists need to keep up with new updates, libraries, methodologies, and processes constantly.

• It is essential to learn from what others are doing around the world to create our processes.

Connecting Business with Data Science

• The speaker emphasizes the importance of connecting business with data science by using economic data to evaluate a customer's capacity to pay.

• He also mentions that presenting and documenting automated processes is challenging, and it is crucial to explain the process in simple terms.

• Standardizing processes is another significant challenge faced by data scientists.

La importancia de la adaptabilidad en la automatización

En este segmento, se discute la importancia de que los procesos automatizados sean adaptables a los cambios del mercado y las variables macroeconómicas.

Adaptabilidad en la automatización

• Los procesos automatizados deben ser adaptables debido a que el mercado es dinámico y las variables macroeconómicas cambian constantemente.

• La adaptabilidad eficiente permite ajustar el proceso a estos cambios de forma rápida y efectiva.

Beneficios de la ciencia de datos en el mundo empresarial

En esta sección, se discuten los beneficios que ofrece la ciencia de datos al mundo empresarial.

Automatización y productividad

• La ciencia de datos permite la automatización de procesos, lo que reduce significativamente el margen de error y aumenta la productividad.

• Una vez validado, el proceso puede integrarse en otros departamentos para su reutilización.

Eficiencia del rendimiento

• La coordinación entre programadores y analistas mejora gracias a la ciencia de datos, lo que permite una mejor comprensión del proceso y resultados más precisos.

Granularidad de los datos

• La granularidad proporcionada por la ciencia de datos permite un análisis más detallado e integral.

Fiabilidad en los resultados

• Gracias a la ciencia de datos, es posible garantizar una mayor fiabilidad en los resultados obtenidos.

Consejos para aprender ciencia de datos

En esta sección, se ofrecen consejos para aquellos que deseen aprender sobre ciencia de datos.

Ganas de aprender

• La actitud y las ganas de aprender son fundamentales para iniciarse en la ciencia de datos.

Material disponible

• Existe una gran cantidad de material disponible en línea, como cursos gratuitos en YouTube, LinkedIn y Udemy.

• No es necesario pagar grandes sumas por cursos especializados.

Cualquier persona puede aprender

• Cualquier persona puede aprender sobre ciencia de datos, independientemente de su formación académica o experiencia laboral previa.

Paciencia y perseverancia

• Es importante tener paciencia y no frustrarse ante los errores que puedan surgir durante el proceso de aprendizaje.

• Hay comunidades en línea donde se pueden encontrar soluciones a problemas específicos relacionados con la ciencia de datos, como Stack Overflow.

Introduction to Data Science

In this section, Cami introduces the concept of connecting business with data science and highlights the importance of integrating and interpreting data. She also emphasizes that learning about data science is a continuous process.

Connecting Business with Data Science

• Connecting business with data science is important.

• Integrating and interpreting data is crucial to understanding why we are doing something and how we are getting results.

• Learning about data science is a continuous process.

Q&A Session

In this section, Cami answers questions from the audience regarding her experience in data science, resources she used to learn more about it, and how she decided to specialize in banking.

Resources for Learning Data Science

• Cami took courses on Udemy and free courses offered by universities such as Universidad Católica and Universidad de Chile.

• She also talked to professionals on LinkedIn to get insights into what they use in their work.

Specializing in Banking

• Cami always had an interest in finance but learned that it was a broad field.

• She decided to enter consulting because it allowed her to learn how different financial institutions worked.

• She chose Banco de Chile because it was at the forefront of using new technologies like SQL and Python.

La importancia de la diversidad en el área de ciencia de datos

En esta sección, Camila comenta sobre su experiencia como la única mujer ingeniera comercial en un equipo conformado por estadísticos, matemáticos e ingenieros civiles. Además, destaca la importancia de la diversidad en el área de ciencia de datos.

La primera mujer en el equipo

• Camila fue la primera mujer en entrar al área donde trabaja.

• Todos sus compañeros son estadísticos, matemáticos e ingenieros civiles.

• Su jefe aceptó a una ingeniera comercial en el equipo porque necesitaba conectar el negocio con la ciencia de datos.

La importancia de la diversidad

• Mientras más diverso sea un equipo, menos sesgo habrá.

• Cada vez entran más mujeres a este mundo y eso es bueno para visibilizar a las mujeres.

• Camila agradece por la invitación y se despide.