Por Fernando Pérez y David Manero | Instructores de Udemy
Con motivo del día del trabajo, no está de más recordar que las actuales y futuras generaciones deberán prepararse para competir por empleos que aún no existen pero que sabemos estarán fuertemente ligados al conocimiento de las llamadas STEM (Science, Technology, Engineering and Mathematics).
Sin embargo, la realidad va por otro lado; según el reporte del Banco Mundial “At a Crossroads: Higher Education in Latin America and the Caribbean”, 44.7% de las personas que se integran actualmente a la educación superior lo hacen en estudios relacionados con ciencias sociales, leyes y negocios, en contraste con un 5.5% en ciencia.
Para este pequeño porcentaje de estudiantes o para aquellos que quieren explorar otras opciones profesionales, existen dos disciplinas cuyas perspectivas de empleo y crecimiento son atractivas: ciencia de datos y desarrollo de inteligencia artificial. Para ellos, Udemy comparte una guía general de los conocimientos y habilidades que deben aprender.
Ciencia de datos: tu futuro “data driven”
Su objetivo es identificar, procesar y convertir grandes volúmenes de datos en información valiosa para la toma de decisiones en cualquier ámbito del quehacer humano. Para desarrollarla y aplicarla se necesita:
Matemáticas, resaltando el dominio de algebra lineal para trabajar matrices, vectores directores, factores, cálculo diferencial, derivadas, etc.
Estadística básica para conocer una media, mediana, desviación, máximo-mínimo, teoría de la probabilidad y teorema de valles, entre otros.
Arquitectura de big data mediante el uso de software como Hadoop, bases de datos relacionales y no relacionales, empleando programas como Cassandra y Mongodb, y MySQL, PostgreSQL, respectivamente.
Lenguajes de programación como R, Python, S, C, SAS.
Manejo de bases de datos como SQL y programación en HIVE.
Programas de visualización de datos con softwares como Kibana, Tableau, Clip View, o incluso Excel.
Una habilidad fundamental para ser científico de datos, y que es considerada “blanda”, es tener curiosidad para buscar relaciones entre datos que en apariencia no están conectados o tienen lógica entre sí.
Actualmente en nuestro país no existe una licenciatura en Ciencia de Datos, sin embargo, sí existen posgrados, diplomados y cursos para iniciarse en este ámbito. Y en términos de ingreso, según el Observatorio Laboral del gobierno federal, el sueldo promedio va de 40 mil hasta 90 mil pesos mensuales.
Para estudiarla resultará más fácil si cuentas con una ingeniería, o cursaste alguna carrera asociada a matemáticas, estadística o si te desarrollas en un área científica.
Inteligencia artificial, cada vez más importante
La Inteligencia Artificial (IA) permite realizar sistemas capaces de aprender y predecir hechos o acciones a partir de la lectura de datos de otros sistemas o directamente del entorno, procesándolos y guardándolos en forma de conocimiento que posteriormente se usa para tomar decisiones.
La IA está creciendo aceleradamente, para 2017, 61% de las iniciativas empresariales de Estados Unidos habían utilizado Inteligencia Artificial y 71% tenía una estrategia de innovación para acercarse a nuevas tecnologías como esta.
Para desarrollarla y aplicarla es necesario contar con las mismas bases de conocimiento que un científico de datos, además de:
Procesamiento de datos. Fundamentos para comprender el ABC en la manera en que se “traduce” información en acciones.
Tener un buen dominio del desarrollo de aplicaciones y lenguajes de programación como R, Python, C#, C++, entre otros. A diferencia del software tradicional, centrado en una serie de tareas específicas, el que se utiliza en IA se centra en el aprendizaje constante.
Aprendizaje artificial o machine learning basado en big data para crear sistemas de aprendizaje guiado, no guiado y con base en reforzamiento.
Las posibilidades de desarrollo de IA podemos agruparlas en:
Específica: enfocada en la lectura de información de un solo tipo y brinda soluciones sobre la base de un propósito puntual
General: busca copiar las múltiples formas de pensar y actuar de un ser humano y decide sobre sus patrones de aprendizaje y decisiones; aunque todavía no está del todo desarrollada al ser amplia y compleja, al mismo tiempo que requiere de soluciones tecnológicas más robustas, ya se está trabajando para hacerla una realidad.
Para entrar en el mundo de la IA es recomendable haber estudiado alguna ingeniería en software, o contar con un alto dominio de matemáticas, estadística y programación.
Erich Wetzel, líder de la Amti, participó en el 9° Foro Nacional IMEF de Infraestructura, en el que se ventilaron acciones clave encaminadas a ofrecer respuestas a los retos de la demanda de servicios de punta.
Superar los cuellos de botella en la cadena de suministro requiere un enfoque integral. Desde la identificación de problemas hasta la implementación de tecnologías y la contratación de personal adicional, las estrategias deben adaptarse a las necesidades específicas de cada empresa. La inversión en tecnologías innovadoras y la colaboración con expertos en logística son pasos clave hacia una cadena de suministro fluida y eficiente. Con un enfoque proactivo, las empresas pueden anticipar desafíos y garantizar un flujo continuo de productos hacia sus clientes.
Los minoristas deben conocer la composición demográfica de sus clientes para tomar mejores decisiones sobre publicidad, colocación de productos y diversificación de mercancías.
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Por Fernando Pérez y David Manero | Instructores de Udemy