Acerca de este curso: The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.
Para quién es esta clase: This course is primarily for individuals who are passionate about the field of data science and who are aspiring to become data scientists.
Creada por: IBM
Enseñado por: Alex Aklson, Data Scientist
IBM Cognitive Class
| Información básica | Curso 1 de 4 en Introduction to Applied Data Science Specialization |
| Nivel | Beginner |
| Compromiso | 3 weeks of study, 2-3 hours/week |
| Idioma | English |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 1
Defining Data Science and What Data Scientists Do
In this module, you will go over the course syllabus to learn what will be taught in this course. Also, you will hear from data science professionals to learn what data science is, what data scientists do, and what tools and algorithms data scientists use on a daily basis. Finally, you will be required to complete a reading assignment to learn why data science is considered the sexiest job in the 21st century.
6 videos, 3 readings
- Leyendo: Course Syllabus
- Vídeo: The Many Paths to Data Science
- Vídeo: Advice for New Data Scientists
- Leyendo: Data Science: The Sexiest Job in the 21st Century
- Vídeo: A day in the Life of a Data Scientist
- Vídeo: Data Science Topics and Algorithms
- Vídeo: What is the cloud?
- Leyendo: What Makes Someone a Data Scientist?
Calificado: Data Science: The Sexiest Job in the 21st Century
Calificado: What Makes Someone a Data Scientist?
SEMANA 2
Data Science Topics
In this module, you will hear from Norman White, the Faculty Director of the Stern Centre for Research Computing, at New York University, as he talks about data science and what skills are required for anyone interested in pursuing a career in this field and as he gives advice to those who are looking to start a career in data science. Finally, you will be required to complete reading assignments to learn about the process of mining a given dataset and about regression analysis.
7 videos, 2 readings
- Vídeo: Data Scientists at New York University
- Vídeo: What is Hadoop?
- Leyendo: Data Mining
- Vídeo: Neural Networks and Deep Learning
- Vídeo: How Can Someone Become a Data Scientist?
- Vídeo: High School Students and Data Science Careers
- Vídeo: Applications of Machine Learning
- Leyendo: Regression
Calificado: Data Mining
Calificado: Regression
SEMANA 3
Data Science in Business
In this module, you will learn about what companies need to do in order to start with data science. You will also learn about some of the qualities that differentiate data scientists from other professionals. In addition, you will learn about analytics and what important role data scientists play in this process, and about story-telling and the importance of an effective final deliverable. Finally, you will be required to apply what you learned about data science by answering open-ended questions.
3 videos, 2 readings
- Vídeo: Recruiting for Data Science
- Vídeo: Applications of Data Science
- Leyendo: The Final Deliverable
- Leyendo: The Report Structure
Calificado: The Final Deliverable
Calificado: The Report Structure
Calificado: Final Assignment
Preguntas Frecuentes
Cómo funciona
Trabajo del curso
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Ayuda de tus compañeros
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Certificados
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
Creadores
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Calificaciones y revisiones
The course is perfect for a beginner to get introduced to the concept of data science. The videos of Dr. Haider in particular are exceptional and his choice of words to describe components of DS makes it easy for the student to
The course was a helpful introduction to the field of data science. It does a great job of motivating the students to tackle some of the tougher concepts associated with this exciting field!
I had better understanding about data science after joined this course.
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RN
I did not learn much from this course. I did not enjoy seeing the young data scientists talking about their jobs. I was not too impressed by Dr. Haider or the professor from NYU.
Dr. Haider misused some words, such as "judgmental" and "argumentative". Without any evidence or examples for support, he claimed that it was more important for a job applicant to have a sense of humor than technical skills.
This course should have named specific techniques used in data science, and how to acquire the knowledge. Regression was mentioned, but the explanation was inadequate. Perhaps the explanation should have been omitted. K-nearest neighbor was mentioned.
Many students want to know what courses to take next, what computer languages to study, etc. What are the computer programming languages of the future?
Students cannot learn everything. Would it make sense for someone to skip some things, and to focus on others? Should everyone learn Python? Does everyone need to learn SQL? What about Tableau? Is that worthless?
How should students set their learning priorities in order to achieve a basic or minimal skill set within 3 or 6 or 12 months?
Remember that most Coursera students already have a college degree.
The course was created several years ago, so I think it needs to be updated regarding developments of the last three years.