Introduction to Data Science Specialization

开始时间: 07/16/2018 持续时间: Unknown

所在平台: Coursera专项课程

课程类别: 其他类别

大学或机构: CourseraNew



Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.


第一个写评论        关注课程


In this Specialization learners will develop foundational Data Science skills to prepare them for a career or further learning that involves more advanced topics in Data Science. The specialization entails understanding what is Data Science and the various kinds of activities that a Data Scientist performs. It will familiarize learners with various open source tools, like Jupyter notebooks, used by Data Scientists. It will teach them about methodology involved in tackling data science problems. The specialization also provides knowledge of relational database concepts and the use of SQL to query databases. Learners will complete hands-on labs and projects to apply their newly acquired skills and knowledge.


4 courses

What is Data Science?
Current session: Jul 16
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.

Open Source tools for Data Science
Current session: Jul 16
What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Cognitive Class Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Data Science Experience and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

Data Science Methodology
Current session: Jul 16
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. Accordingly, in this course, you will learn: - The major steps involved in tackling a data science problem. - The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. - How data scientists think!

Databases and SQL for Data Science
Current session: Jul 16
SQL is a powerful language which is used for communicating with databases. Before you step into the field of data science, it is vitally important that you set yourself apart by mastering the foundations of SQL. The purpose of this course is to introduce relational database concepts and help you learn and apply knowledge of the SQL language. Learners will also practice building and running SQL queries hands-on using a cloud-based environment as well as learn how to access databases from Jupyter notebooks using Python. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.


Deep Learning Specialization on Coursera


Introduction to Data Science Specialization-