Analytical Methods for Effective Data Analysis
Master data analytics and explore marketing, social media, predictive, and prescriptive analytics.
Welcome to our course, "Analytical Methods for Effective Data Analysis." This program is meticulously crafted to provide you with a profound grasp of data analytics, dissecting it into four key components: marketing analytics, social media analytics, predictive analytics, and prescriptive analytics.
Throughout your journey, you will seamlessly connect these diverse facets of analytics. We'll embark by immersing you in the customer-centric universe of marketing analytics. We'll cover crucial topics, such as customer life cycles and various marketing models, including customer segmentation, acquisition, RFM, market basket analysis, and more. You'll unlock the potential of these models to bolster customer retention and engagement strategies.
Next, we'll venture into the dynamic sphere of social media analytics. Here, you'll acquire the skills to gather, measure, and analyze data from various social media platforms. Dive deep into sentiment analysis, an invaluable tool for comprehending public opinions and emotions expressed in textual content.
Our course's third section delves into predictive analytics, harnessing statistics, machine learning, and data mining to forecast future events. Additionally, we'll explore prescriptive analytics, which guides decision-making by optimizing key metrics based on historical trends.
Upon course completion, you should have the skills and knowledge needed to excel in data analytics. Whether you're a marketing professional, a business analyst, or simply someone eager to harness data's power, this course equips you with practical tools and insights to make informed decisions and thrive in your field. Seize this opportunity to master analytical methods for effective data analysis and elevate your career prospects.
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.
Course Format
- This is a video-led course. You can preview some videos below.
- This course includes practice exercises.
- This course is aimed at all users.
Course Certificate Included
This course includes an optional online quiz that will generate a course certificate upon completion.
14-Day Guarantee
We offer an unconditional, 14-day, full money-back guarantee, no questions asked. This is our way of ensuring you have complete confidence in your Stream Skill purchase. If you don’t learn, you don’t pay.
Why Trust Us?
Stream Skill is part of the Simon Sez IT family and has been in business 14+ years and taught over 1 million students in that time. We’ve created over 130 software training courses, 8,000+ video tutorials, and we’ve helped thousands of people across the world learn how to use Microsoft Office. We’ve now created Microsoft Excel courses for the last five versions and continue to help people get to grips with this essential tool.
Your Instructor
Course Curriculum
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Free PreviewIntroduction (6:22)
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Free PreviewFour Types of Analytics (11:09)
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Free PreviewCustomer Life Cycle: Part 1 (10:26)
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StartCustomer Life Cycle: Part 2 (9:21)
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StartMarketing Model Types: Part 1 (10:32)
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StartMarketing Model Types: Part 2 (8:50)
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StartMarketing Model Types: Part 3 (9:45)
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StartMarketing Model Types: Part 4 (10:20)
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StartMarketing Model Types: Part 5 (6:54)
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StartMarketing Model Types: Part 6 (9:23)
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StartMarketing Model Types: Part 7 (10:57)