How to Use Data Science in Marketing
April 4, 2024
Article
Analyze Data, Build Models, and Present Insights. Transform data into actionable insights through data analysis, predictive modeling, data visualization, and communication of results.
Instructor: Brandon Krakowsky
Included with
Recommended experience
Beginner level
Familiarity with Python programming, such as completion of Penn's Introduction to Programming with Python and Java Specialization.
Recommended experience
Beginner level
Familiarity with Python programming, such as completion of Penn's Introduction to Programming with Python and Java Specialization.
Master the process of data wrangling, including data storage, access, and manipulation using SQL.
Perform exploratory data analysis (EDA) using Python, focusing on data inspection, cleaning, visualization, and summarization.
Apply predictive analytics techniques to make data-driven predictions using Python.
Create compelling data visualizations with Tableau for decision-making and storytelling.
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“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
Applied Learning Project
This specialization presents a variety of graded and practice assignments, both in the form of learning checks with multiple attempts, and in the form of programming assignments via Codio platform. Practice assignments in this course don't count towards the Final Grade. All of the other assignmetns are automatically graded, and provide instant feedback to the Learners. Please feel free to post in Discussion Forums located in every Module if you have any questions about the assignments or the instructions. Google Chrome is the recommended browser for completing coding assignments.
Classifying and analyzing data.
Executing SQL queries to work with data.
Using Python and pandas library to manipulate and explore data.
Implement data preprocessing and model training procedures for regression.
Interpret feature importance in decision trees and random forests.
Explain the difference between supervised and unsupervised learning.
Explore and interpret data through various visualization techniques, identifying trends, outliers, and patterns.
Create interactive and informative visualizations that enable data-driven decision-making and storytelling.
Analyze engaging and persuasive data presentations that captivate and inform stakeholders.
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
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This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.