Fundamentals of Data Analysis

Overview

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This course is the first of a series that aims to prepare you for a role working in data analytics. In this course, you’ll be introduced to many of the primary types of data analytics and core concepts. You’ll learn about the tools and skills required to conduct data analysis. We’ll go through some of the foundational math and statistics used in data analysis and workflows for conducting efficient and effective data analytics. This course covers a wide variety of topics that are critical for working in data analytics and are designed to give you an introduction and overview as you begin to build relevant knowledge and skills.

Syllabus

Types of Data Analysis

In the first module of the course, we'll learn about the primary types of data analysis including, descriptive, predictive, diagnostic, and exploratory. We will also learn about some advanced data analytic types including mechanistic, causal, and inferential. By the end of this module, you will know how to identify the different types of data analysis and their use cases. So let's get started!

The Phases of Data Analysis

In the second module of this course, we'll learn about the phases of the data analysis process including identifying data, defining scope, and level of detail. We'll learn about the data collection process, from gathering targeted information to evaluating outcomes. We'll discover the importance of data cleaning and how removing, modifying, and formatting data is a priority, as well as the benefits of visualizing data.

Data Analytics Tools and Skills

In the third module of this course, we'll learn about the tools and skills essential for data analysis. We'll learn about using spreadsheets and databases for analyzing and managing the data. We'll discover the power of query languages and multidimensional expressions. We’ll also describe the fundamental programming languages used in data analytics.

Foundational Data Analytics Math and Stats

In the fourth module of this course, we'll learn about the fundamental math and stats used for data analysis. We’ll also describe some advanced data analytic algorithms and their use cases, including linear regression and clustering.