À propos de ce cours : 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.
Les destinataires de ce cours : This course is primarily for individuals who are passionate about the field of data science and who are aspiring to become data scientists.
Créé par : IBM
Enseigné par : Alex Aklson, Data Scientist
IBM Cognitive Class
| Informations de base | Cours 1 de 4 dans Introduction to Applied Data Science Specialization |
| Niveau | Beginner |
| Engagement | 3 weeks of study, 2-3 hours/week |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 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 vidéos, 3 lectures
- Reading: Course Syllabus
- Vidéo: The Many Paths to Data Science
- Vidéo: Advice for New Data Scientists
- Reading: Data Science: The Sexiest Job in the 21st Century
- Vidéo: A day in the Life of a Data Scientist
- Vidéo: Data Science Topics and Algorithms
- Vidéo: What is the cloud?
- Reading: What Makes Someone a Data Scientist?
Noté: Data Science: The Sexiest Job in the 21st Century
Noté: What Makes Someone a Data Scientist?
SEMAINE 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 vidéos, 2 lectures
- Vidéo: Data Scientists at New York University
- Vidéo: What is Hadoop?
- Reading: Data Mining
- Vidéo: Neural Networks and Deep Learning
- Vidéo: How Can Someone Become a Data Scientist?
- Vidéo: High School Students and Data Science Careers
- Vidéo: Applications of Machine Learning
- Reading: Regression
Noté: Data Mining
Noté: Regression
SEMAINE 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 vidéos, 2 lectures
- Vidéo: Recruiting for Data Science
- Vidéo: Applications of Data Science
- Reading: The Final Deliverable
- Reading: The Report Structure
Noté: The Final Deliverable
Noté: The Report Structure
Noté: Final Assignment
FAQ
Comment cela fonctionne
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
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.