Welcome to Analytical Methods for Effective Data Analysis. This course is designed to provide you with a comprehensive understanding of data analytics by breaking it down into four main components: marketing analytics, social media analytics, predictive analytics, and prescriptive analytics.

In this course, you will learn how these different types of analytics fit together seamlessly. We'll start by exploring the customer-centric world of marketing analytics, covering topics such as customer life cycles and various marketing models, including customer segmentation, acquisition, RFM, market basket analysis, and more. You'll discover how these models can help retain and engage customers effectively.

Moving on, we will dive into social media analytics, where you'll gain insights into measuring, collecting, and analyzing data from social media platforms. You'll also explore sentiment analysis, a crucial tool for understanding public opinions and sentiments expressed in textual content.

The course's third section focuses on predictive analytics, using statistics, machine learning, and data mining to predict future events. Additionally, we'll delve into prescriptive analytics, which guides decision-making by optimizing key metrics based on past performance and trends.

By the end of this course, you will possess the skills and knowledge needed to excel in the world of data analytics. Whether you're a marketing professional, business analyst, or anyone interested in harnessing the power of data, this course will help equip you with practical tools and insights to make informed decisions and drive success in your field. Don't miss this opportunity to master analytical methods for effective data analysis.

In this course, students will learn how to:

• Analyze customer data using segmentation models for targeted marketing campaigns.

• Implement Recency Frequency Monetary (RFM) models to optimize customer engagement.

• Evaluate the success of social media campaigns by measuring brand mentions and sentiment.

• Apply predictive analytics techniques to make informed predictions about future events.

• Utilize prescriptive analytics to develop strategies for achieving specific business goals.

• Create revenue optimization models to optimize profit through data-driven decisions.

• Demonstrate an understanding of dynamic pricing and its role in revenue management.

• Develop practical skills in setting up and utilizing sentiment analysis for text data.