About the Course
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.
What you will learn?
- Use R to clean, analyze, and visualize data.
- Navigate the entire data science pipeline from data acquisition to publication.
- Use GitHub to manage data science projects.
- Perform regression analysis, least squares and inference using regression models.
Skills you will gain
- Machine Learning
- R Programming
- Regression Analysis
- Data Science
- Data Analysis
- Data Manipulation
- Regular Expression (REGEX)
- Data Cleansing
- Cluster Analysis
- The Data Scientist’s Toolbox: 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.
- R Programming: 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.
- Getting & Cleaning Data: 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.
- Exploratory Data Analysis: 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.
- Reproducible Research: 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.
- Statistical Inference: 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 strategies and explicit use of designs and randomization in analyses.
- Regression Models: This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well.
- Practical Machine Learning: This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications.
- Developing Data Products: This course covers the basics of creating data products using Shiny, R packages, and interactive graphics.
- Data Science Capstone: The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers.
To enroll for this course, click the link below.
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