❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [September 15th, 2023]

What does this course tell?
(Please note that the following overview content is from the original platform)
Why should a company invest in data analytics and deploy big data analytics for example today? The reason is that analytics and decisions based on data analytics and data insights drive business value and enhance firms marketing and financial performance Firms that have developed capabilities related to data analytics perform better in the marketplace They outperform their competitorsAnd this should be a major reason why todays companies should invest in data analytics Whats the focus of this course? It is our job - or your job as a data analyst - to extract insights from data This course introduces learners to predictive analytics applied to management and business administration so that managers can deliver more relevant and meaningful customer experiences at all customer touchpoints throughout the customer life cycle boosting customer loyalty and revenues In particular predictive analytics is a set of tools and algorithms used to make predictive marketing and customer analytics possible In this course we will cover the core principles of predictive data analytics We will cover the core principles of predictive data analytics through the discussion of the different steps in the Cross-Industry Standard Process Model for Data Mining (CRISP-DM) that links business understanding data and methods to business valueDuring the course you will develop and acquire abilities to:Understand the art and science of predictive analytics to define clear actions that result in improved business results;Describe the core principles of predictive customer analytics;Embracing the Cross-Industry Standard Process Model for Data Mining (CRISP-DM) steps to building predictive models

We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)

What skills and knowledge will you acquire during this course?
During this course, learners will acquire the skills and knowledge necessary to understand and apply predictive data analytics in a management and business administration context. The focus of the course is on extracting insights from data to deliver more relevant and meaningful customer experiences, ultimately boosting customer loyalty and revenues.

Learners will learn about the core principles of predictive data analytics, including the different steps in the Cross-Industry Standard Process Model for Data Mining (CRISP-DM). This model links business understanding, data, and methods to business value, providing a framework for building predictive models.

By the end of the course, learners will develop the ability to define clear actions based on predictive analytics that lead to improved business results. They will also be able to describe the core principles of predictive customer analytics and understand how to embrace the CRISP-DM steps in building predictive models.

Overall, this course will equip learners with the necessary skills and knowledge to effectively apply predictive data analytics in a business context, enabling them to make data-driven decisions that drive business value and enhance marketing and financial performance.

How does this course contribute to professional growth?
This course on Introduction to Predictive Data Analytics contributes significantly to professional growth. By taking this course, individuals can enhance their skills and knowledge in the field of data analytics, which is highly valued in today's business world.

The course focuses on predictive analytics applied to management and business administration. As a data analyst, the learner will be able to extract valuable insights from data, enabling them to make informed decisions and drive business value. This skill is crucial in enhancing a company's marketing and financial performance.

One of the key benefits of this course is its emphasis on delivering more relevant and meaningful customer experiences. By utilizing predictive analytics, managers can understand customer behavior and preferences, allowing them to tailor their strategies and offerings accordingly. This, in turn, boosts customer loyalty and revenues.

Throughout the course, learners will be introduced to the core principles of predictive data analytics. They will gain a comprehensive understanding of the different steps in the Cross-Industry Standard Process Model for Data Mining (CRISP-DM), which links business understanding, data, methods, and ultimately, business value. This knowledge will enable them to effectively build predictive models and make data-driven decisions.

By completing this course, individuals will develop and acquire abilities that are highly sought after in the professional world. They will be able to define clear actions that result in improved business results, describe the core principles of predictive customer analytics, and embrace the CRISP-DM steps to building predictive models. These skills will not only enhance their professional growth but also make them valuable assets to any organization that invests in data analytics.

Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education. It introduces learners to predictive analytics applied to management and business administration, and covers the core principles of predictive data analytics. Learners will develop and acquire abilities to understand the art and science of predictive analytics, describe the core principles of predictive customer analytics, and embrace the Cross-Industry Standard Process Model for Data Mining (CRISP-DM) steps to building predictive models.

Course Syllabus

Introduction

Fundamentals: Machine Learning and Models for Predictive Analytics

From Data to Value: The CRISP-DM Process for Predictive Data Analytics