The Data Scientist’s Toolbox

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

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About 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.

Course Syllabus

The Week 3 lectures focus on conceptual issues behind study design and turning data into knowledge. If you have trouble or want to explore issues in more depth, please seek out answers on the forums. They are a great resource! If you happen to be a superstar who already gets it, please take the time to help your classmates by answering their questions as well. This is one of the best ways to practice using and explaining your skills to others. These are two of the key characteristics of excellent data scientists.

Deep Learning Specialization on Coursera

Course Introduction

The Data Scientist’s Toolbox Once you’ve identified a problem to solve, you’ll need to make use of statistical analysis and exploration to solve the problem. This course is the ideal place to start if you’ve never taken a course on statistical analysis or data engineering before. Our faculty bring an extensive body of statistical knowledge to the classroom and we’ll start you off on the right foot. This course will introduce you to basic statistical concepts and you’ll be working through a series of problems using Python that are designed to showcase the use of pandas data analysis tools within the pandas library. We’ll cover the basics of data analysis, including data normalization, data cleaning, and using regression models to explore the problem at hand. We’ll also cover some of the most important statistical exploratory techniques used in the pandas data analysis pipeline, like plotting and visualizing data. You’ll also get a head start on using the tools to make sense of your data and interpreting results for better decision making. This is the second course in the Data Engineer’s Toolbox specialization. The Data Engineer focuses on creating a more advanced data engineering toolkit by integrating the design goals of the Data Scientist with the expertise of data scientists and data engineers. You’ll learn how to build a pipeline of data scientists working on a project’s design, and you’ll also gain a head start on using the pipeline to make

Course Tag

Data Science Github R Programming Rstudio

Related Wiki Topic

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