메타 마케팅 분석 전문 자격증

This course lays the foundation of marketing analytics. You’ll learn the basic principles of marketing. You’ll learn the role analytics plays in digital marketing and how data is collected and managed for marketing. You will also learn basic privacy regulations that govern the online marketing space as well as common challenges when working with marketing data.

By the end of this course you will be able to: • Describe how data and measurement inform a marketing action • Describe the basic principles of marketing • Identify why measurement and analytics matter in digital marketing • Describe how data is collected and related to digital marketing • Explain the significance of the privacy regulations that govern the online marketing space • Describe the Meta pixel and how it is created on the Meta platform • Describe how information is recorded on mobile devices • Explain how an API connects data captured offline to an online platform • Describe common platforms for online data management and evaluation • Navigate Google Analytics and Meta Ads Manager reports Regardless of your current marketing and analytics experience, this course will help you build a solid foundation for incorporating data into your marketing efforts. Learners don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate.

This course equips you with a practical understanding and a framework to guide the execution of basic analytics tasks such as pulling, cleaning, manipulating and analyzing data by introducing you to the OSEMN cycle for analytics projects. You’ll learn to perform data analytics tasks using spreadsheet and SQL queries. You will also be introduced to using the Python programming language to manipulate datasets as an alternative to spreadsheets. You will learn foundational programming concepts and how they apply to marketing. You will also learn how to use Tableau to create data visualizations and dashboards.

By the end of this course, you will be able to: • State business goals, KPIs and associated metrics • Apply a Data Analysis Process: OSEMN • Identify and define the relevant data to be collected for marketing • Compare and contrast the different formats and use cases of different kinds of data • Identify gaps in data collected and describe the strengths and weaknesses • Demonstrate proficiency in Python with variables, control flow, loops, and basic data structures • Sort, query and structure data in spreadsheets and with Python libraries • Write basic SQL statements to select, group and filter data • Visualize data patterns and trends with spreadsheets • Utilize Tableau to visualize data patterns and trends This course is designed for people who want to learn the basics of data analytics including using spreadsheets and Python to sort and structure data and using Tableau to visualize data patterns. Learners don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Learners also need access to a computer with strong internet connection. Ideally learners have already completed course 1 (Marketing Analytics Foundation) in this program.

This course takes a deep dive into the statistical foundation upon which Marketing Analytics is built. The first part of this course is all about getting a thorough understanding of a dataset and gaining insight into what the data actually means. The second part of this course goes into sampling and how to ask specific questions about your data. Finally, the third part is about answering those questions with analyses. Many of the mistakes made by Marketing Analysts today are caused by not understanding the concepts behind the analytics they run, which causes them to run the wrong test or misinterpret the results. This course is specifically designed to give you the background you need to understand what you are doing and why you are doing it on a practical level.

By the end of this course you will be able to: • Understand the concept of dependent and independent variables • Identify variables to test • Understand the Null Hypothesis, P-Values, and their role in testing hypotheses • Formulate a hypothesis and align hypotheses with business goals • Identify actions based on hypothesis validation/invalidation • Explain Descriptive Statistics (mean, median, standard deviation, distribution) and their use cases • Understand basic concepts from Inferential Statistics • Explain the different levels of analytics (descriptive, predictive, prescriptive) in the context of marketing • Create basic statistical models for regression using data • Create time-series forecasts using historical data and basic statistical models • Understand the basic assumptions, use cases, and limitations of Linear Regression • Fit a linear regression model to a dataset and interpret the output using Tableau and statsmodels • Explain the difference between linear and multivariate regression • Run a segmentation (cluster) analysis • Describe the difference between observational methods and experiments This course is designed for people who want to learn the basics of descriptive and inferential statistics and analytics in marketing. Learners don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally learners have already completed course 1 (Marketing Analytics Foundation) and course 2 (Introduction to Data Analytics) in this program.

This course explores common analytics methods used by marketers. You’ll learn how to define a target audience using segmentation with K-means clustering. You’ll also explore how linear regression can help marketers plan and forecast. You’ll learn to evaluate the effectiveness of advertising using experiments as well as observational methods and you’ll explore methods to optimize your marketing mix; marketing mix modeling and attribution. Finally, you’ll learn to evaluate sales funnel shapes, visualize and optimize them.

By the end of this course you will be able to: • Describe when analytics is most commonly used in marketing • Understand your audience using analytics and variable descriptions • Segment a population into different audiences using cluster analysis • Use historical data to plan your marketing across different channels • Use linear regression to forecast marketing outcomes • Describe marketing mix modeling • Describe attribution modeling • Apply different attribution models • Evaluate advertising effectiveness and describe the shortcomings • Describe the use of experiments to evaluate advertising effectiveness • Explain how A/B testing works and how you can use it to optimize ads • Evaluate results of an experiment and assess the strength of the experiment • Evaluate and optimize your sales funnel This course is for people who want to learn how to plan and forecast marketing efforts as well as evaluate marketing methods and sales funnels for optimization. Learners don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally learners have already completed course 1 (Marketing Analytics Foundation), course 2 (Introduction to Data Analytics), and course 3 (Statistics for Marketing) in this program.