Este curso forma parte de Programa especializado: Data Science for Investment Professionals

4.7
estrellas
26 calificaciones

Shreenivas Kunte, CFA, CIPM

2235 ya inscrito

Ofrecido Por

Programa especializado: Data Science for Investment ProfessionalsInstituto CFA

Acerca de este Curso

7595 vistas recientes

One of the biggest changes in the past decade is the rapid adoption of machine learning, AI, and big data in investment decision making. This course introduces learners with knowledge of the investment industry to foundational statistical concepts underpinning machine learning as well as advanced AI techniques. This course demonstrates core modeling frameworks along with carefully selected real-world investment practice examples. The course seeks to familiarize learners with two important programming languages — Python and R (no prior knowledge of Python or R necessary). The motivation is to demonstrate the elegance — and speed — simple programming brings to the investment decision-making process. The reading material in this course offers in-practice insights curated from the blogs of CFA Institute as well as other leading publications.
After taking this course you will be able to:
-	Describe the importance of identifying information patterns for building models
-	Explain probability concepts for solving investing problems
-	Explain the use of linear regression and interpret related Python and R code
-	Describe gradient descent, explain logistic regression, and interpret Python and R code 
-	Describe the characteristics and uses of time-series models

This course is part of the Data Science for Investment Professionals Specialization offered by CFA Institute.

Fechas límite flexibles

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Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

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Laboratorios de Coursera

Incluye proyectos de aprendizaje prácticos.

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Curso 2 de 3 en

Programa especializado: Data Science for Investment Professionals

Nivel principiante

Background in investment products and firms

Aprox. 18 horas para completar

Inglés (English)

Qué aprenderás

Understand the importance of information patterns and the concept of causality in decision making models
Assess and apply probability concepts to real-world investing scenarios
Describe and use linear and logistic regression modeling for investment decision making
Compare simple time-series models and understand their limitations

Habilidades que obtendrás

Data Science
Logistic Regression Modeling
Linear Regression Modeling
Python Programming
Statistical Concepts

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Laboratorios de Coursera

Incluye proyectos de aprendizaje prácticos.

Obtén más información sobre los laboratorios de Coursera

Curso 2 de 3 en

Programa especializado: Data Science for Investment Professionals

Nivel principiante

Background in investment products and firms

Aprox. 18 horas para completar

Inglés (English)

Instructor

Calificación del instructor

4,63/5 (11 calificaciones)

Shreenivas Kunte, CFA, CIPM

Director, Continuing Education and Advocacy

2235 alumnos

1 curso

Ofrecido por

Instituto CFA

Programa - Qué aprenderás en este curso

Semana1

Semana 1

24 minutos para completar

Welcome to Statistics for Machine Learning for Investment Professionals

24 minutos para completar

1 video (Total 4 minutos), 1 lectura

2 horas para completar

Data and Patterns

2 horas para completar

5 videos (Total 35 minutos), 2 lecturas, 3 cuestionarios

5 videos

Data and Patterns1mData and the Importance of Patterns10mHow Do We Model Relationships between Variables? Part 111mHow Do We Model Relationships between Variables? Part 24mExpert Insights: Data and Patterns7m

2 lecturas

Data and Alternative Sources of Alpha10mFinancial Models10m

3 ejercicios de práctica

Data and the Importance of Patterns15mModeling Relationships between Variables20mData and Patterns Graded Assessment20m

Semana2

Semana 2

2 horas para completar

Randomness and Probability

2 horas para completar

6 videos (Total 52 minutos), 2 lecturas, 3 cuestionarios

6 videos

Randomness and Probability1mRandom Processes and Probability Distributions, Part 112mRandom Processes and Probability Distributions, Part 27mProbability Concepts, Part 112mProbability Concepts, Part 29mExpert Insights: Probability Concepts7m

2 lecturas

Real-Life Case Study: Common Probability Distributions10mReal-Life Example: Bayes Theorem10m

3 ejercicios de práctica

Random Processes and Probability Distributions15mProbability Concepts15mRandomness and Probability Graded Assessment20m

Semana3

Semana 3

6 horas para completar

Linear Regression

6 horas para completar

15 videos (Total 113 minutos), 2 lecturas, 12 cuestionarios

15 videos

Linear Regression2mFrom Patterns to Regressions, Part 18mFrom Patterns to Regressions, Part 27mRegression with Python and R, Part 310mFrom Patterns to Regressions, Part 46mLinear Regression Assumptions and Interpreting Output, Part 16mLinear Regression Assumptions and Interpreting Output, Part 211mLinear Regression Assumptions and Interpreting Output, Part 38mLinear Regression Assumptions and Interpreting Output, Part 46mLinear Regression Dummies, Part 17mLinear Regression Dummies, Part 28mLinear Regression Assumption Violations, Part 18mLinear Regression Assumption Violations, Part 24mLinear Regression Assumption Violations, Part 33mExpert Insights: Linear Regression10m

2 lecturas

Installing Python & Anaconda5mAn R-Squared Chart Taxonomy: Seeing Is Not Believing10m

5 ejercicios de práctica

From Patterns to Regressions15mLinear Regression Assumptions15mLinear Regression: Using Dummy Variables15mLinear Regression Assumption Violations30mLinear Regression Graded Assessment30m

Semana4

Semana 4

5 horas para completar

Advanced Regression Concepts

5 horas para completar

10 videos (Total 70 minutos), 2 lecturas, 10 cuestionarios

10 videos

Advanced Regression Concepts3mGradient Descent, Part 110mGradient Descent, Part 23mBias–Variance and Linear Regression Limitations9mBias–Variance: Python Case Study8mBias and Variance: In-Practice Examples3mLogistic Regression10mLogistic Regression: Using Python6mLogistic Regression: Real-World Examples6mExpert Insights: Logistic Regression7m

2 lecturas

Quick Snapshot: Linear vs. Logistic Regression10mDetecting Earnings Manipulation and Fraud: A Light Tutorial on Probit Analysis10m

7 ejercicios de práctica

Gradient Descent Use Case15mGradient Descent5mBias–Variance and Linear Regression Limitations15mBias and Variance: In-Practice Examples15mLogistic Regression30mLogistic Regression: Real-World Examples30mAdvanced Regression Concepts Graded Assessment30m

Reseñas

4.7

4 reseña

5 stars
74,07 %
4 stars
18,51 %
2 stars
3,70 %
1 star
3,70 %

Principales reseñas sobre STATISTICS FOR MACHINE LEARNING FOR INVESTMENT PROFESSIONALS

por CL28 de sep. de 2022

Its a course that serves as a good refresher on statistical analysis. Real applications makes statistical concepts easier to relate

Ver todas las reseñas

Acerca de Programa especializado: Data Science for Investment Professionals

This Specialization is uniquely tailored to the needs of investment professionals or those with investment industry knowledge who want to develop a basic, practical understanding of machine learning techniques and how they are used in the investment process. Through the three courses, you will learn techniques for presenting data and importance of the “data story”, produce data visualizations using Python, assess and apply probability concepts to investing scenarios, compare simple time-series models and understand their limitations, discover how machine learning applications can address investment problems, and understand how to apply the CFA Institute Ethical Decision-Making Framework to machine learning dilemmas. All that you learn in this Specialization will give you the knowledge and confidence to explain clearly and “translate” machine learning concepts and their application to real-world investment problems to a non-expert audience and clients.

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.
Does completing this course give me credit in any CFA Institute Programs?
No. Completing this course or any other courses offered by CFA Institute on Coursera does not count as credit in the CFA Program, CIPM Program, or any other professional certificate or credential offered by CFA Institute.

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Statistics for Machine Learning for Investment Professionals

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás