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Data Science Specialization
Starts Nov 21.
Enroll
Launch Your Career in Data Science
A nine-course introduction to data science, developed and taught by leading professors.
About This Specialization
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Created by:
Industry Partners

10 courses

Projects

Certificates
Courses

Beginner Specialization. No prior experience required.
COURSE 1
The Data Scientist’s Toolbox
Current session: Nov 21 — Dec 26.
Commitment
1-4 hours/week

About the Course
In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

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COURSE 2
R Programming
Current session: Nov 21 — Dec 26.
About the Course
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

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COURSE 3
Getting and Cleaning Data
Current session: Nov 21 — Dec 26.
About the Course
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

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COURSE 4
Exploratory Data Analysis
Current session: Nov 21 — Dec 26.
About the Course
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

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COURSE 5
Reproducible Research
Current session: Nov 21 — Dec 26.
Commitment
4-9 hours/week

About the Course
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

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COURSE 6
Statistical Inference
Current session: Nov 21 — Dec 26.
About the Course
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strateg

Kết quả (Việt) 1:

Toggle navigationNavigation openNavigation closedData Science SpecializationStarts Nov 21. EnrollLaunch Your Career in Data ScienceA nine-course introduction to data science, developed and taught by leading professors.About This SpecializationAsk the right questions, manipulate data sets, and create visualizations to communicate results.This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material. Created by:Industry Partners10 coursesProjectsCertificatesCoursesBeginner Specialization. No prior experience required.COURSE 1The Data Scientist’s ToolboxCurrent session: Nov 21 — Dec 26.Commitment1-4 hours/weekAbout the CourseIn this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.More DetailsCOURSE 2R ProgrammingCurrent session: Nov 21 — Dec 26.About the CourseIn this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.More DetailsCOURSE 3Getting and Cleaning DataCurrent session: Nov 21 — Dec 26.About the CourseBefore you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.More DetailsCOURSE 4Phân tích dữ liệu thăm dòPhiên làm việc hiện tại: 21 tháng mười một-tháng mười hai 26.Về các khóa họcKhóa học này bao gồm các kỹ thuật thăm dò cần thiết cho tổng kết dữ liệu. Các kỹ thuật này thường được áp dụng trước khi chính thức mô hình bắt đầu và có thể giúp thông báo cho sự phát triển của các mô hình thống kê phức tạp hơn. Thăm dò kỹ thuật cũng rất quan trọng cho việc loại bỏ hoặc mài tiềm năng giả thuyết về thế giới mà có thể được giải quyết bởi các dữ liệu. Chúng tôi sẽ bao gồm chi tiết các hệ thống plotting R cũng như một số nguyên tắc cơ bản xây dựng dữ liệu đồ họa. Chúng tôi cũng sẽ bao gồm một số kỹ thuật thống kê đa biến thường được sử dụng để hình dung dữ liệu chiều cao.Biết thêm chi tiếtKHÓA HỌC 5Nghiên cứu thể sanh sản nhiềuPhiên làm việc hiện tại: 21 tháng mười một-tháng mười hai 26.Cam kết4-9 giờ/tuầnVề các khóa họcThis course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.More DetailsCOURSE 6Statistical InferenceCurrent session: Nov 21 — Dec 26.About the CourseStatistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strateg