How to Pass the Introduction to Analytics WGU D491 OA

The Analytics course D491 therefore serves as your entry point to explore how data and statistical models can be leveraged in a creative manner to tell the story that gets executives to act and the story that ends up in business outcomes. The topics covered in this course will include an analysis of the tasks and roles performed by data analytics as a field. It will generally help to build a base level of thinking statistically, analyzing, problem-solving, and even programming to some level.

This module is built on key materials from influential books such as:

• Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data
• Build a Career in Data Science
• Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management

The following sources of information will offer you the right backbone of data analytics by imparting the requisite knowledge and skills.

Section 1: Careers and Goals

First, in this section here, you will learn the meaning of data analytics and how it can be distinguished from data science. You will learn about the different positions and responsibilities of the position of a data analyst and the relation of those positions to key stakeholders of the project. You are also going to comprehend how to recognize and define the data analytics projects knowledge which is significant for practical usage. After the completion of this section, you should be able to have a very clear picture of what kind of skills are needed for each of the positions that are involved in a data analytics project, as well as how those skills are used in the project.

Section 2: The Data Analytics Lifecycle

The heavy lifting of any such presentation happens in this section. The course will present the entire process of conducting data analytics where during the initiation phase you’ll learn the critical questions and sources for the project. From the next steps, you will be working on data preparation, model planning, model execution, and result communication, and last but not least, model findings’ implementation. Every phase comes with a tool, activity, and deliverable that you will arrive at. This makes each phase distinct. By following the above-mentioned steps, one will be well equipped with not only the general knowledge of how data analytical projects should be run from scratch to end but also specific knowledge about the particular steps required for the completion of the particular project on hand.

Section 3: Defining Values for Success

The final area will involve looking more specifically at how business questions can be defined, and answered, through various forms of analytics: descriptive, diagnostic, predictive, and prescriptive analytics. You will also acquire knowledge of data collection techniques, data quality specifications, and many data analysis ways. Moreover, you will go deeper into the subject and study topics such as regression, clustering, and ARIMA models, as well as points of using specific software, such as Hadoop and DS.js. Familiarizing yourself with these concepts will assist with choosing the right methods and representations for your data analytics issues, which will lead you to successful project outcomes.

In general, the purpose of this course is to give you a broad understanding of the subject of data analytics and equip you with all the tools you need to succeed in the world of data analysis. Solely or if you want to strengthen your knowledge of group work this module will provide you with the necessary tools and knowledge.