Virtual program of:Coursera |
This course was ranked in our selection of the best Digital Marketing courses, available on the main e-Learning platforms worldwide.
Anyone who wants to learn in-demand technical skills to start a career as a marketing analyst or improve their business analysis should enroll in this six-course Meta Marketing Analysis Certificate program. No previous knowledge is required.
The industry-relevant program was created by Aptly's marketing analytics specialists and Meta marketers to educate you for careers as a marketing analyst, marketing researcher, and other positions.
Learn the fundamentals of marketing, how data influences marketing decisions, and how to use OSEMN's data analytics framework to address common analytics questions.
You will learn to use key tools like Python and SQL to collect, connect and analyze relevant data. In addition, common statistical techniques are used to divide audiences into groups, evaluate campaign results, improve the marketing mix, and measure sales pressure.
A single ticket for exam 200-101: Meta Certified Marketing Science Professional will be awarded to students who successfully complete all 5 courses. In order to finish the Meta Marketing Analyst Certification Program and receive your Coursera certificate, you must pass this test.
Along the way, you'll learn how to use Tableau to display data and Meta Ads Manager to build campaigns, evaluate their effectiveness, and run tests. Plus, you'll be able to put your new skills to work with hands-on business-related assignments.
You prepare for the Meta Marketing Science Certification exam in the final course. You will receive the Coursera and Meta Marketing Science certifications upon successful completion of the program. Plus, you'll get exclusive access to the Meta Career Programs job board, a job search platform with over 200 leading organizations seeking qualified and certified staff.
Your results can be included in a portfolio that you show to a potential employer or applied to your own business.
Marketing analytics fundamentals
This course lays the foundation for marketing analysis. You will learn the basic principles of marketing. You will learn the role analytics plays in digital marketing and how data for marketing is collected and managed. You'll also learn the basic privacy rules that govern the online marketing space, as well as common challenges when working with marketing data.
At 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 are important in digital marketing
• Describe how data is collected and how it relates to digital marketing
• Explain the importance of the privacy rules 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 background, this course will help you build a strong foundation for incorporating data into your marketing efforts.
Students do not need experience in marketing or data analysis, but they should have basic Internet browsing skills and be eager to participate.
This course equips you with a practical understanding and framework to guide the execution of basic analytical tasks such as extracting, cleaning, manipulating, and analyzing data by introducing you to the OSEMN cycle for analytical projects. You will learn to perform data analysis tasks using spreadsheets and SQL queries. You will also be introduced to using the Python programming language to manipulate data sets as an alternative to spreadsheets. You will learn basic programming concepts and how they apply to marketing. You'll also learn how to use Tableau to create dashboards and data visualizations.
At the end of this course, you will be able to:
• Establish business goals, KPIs and associated metrics
• Apply a Data Analysis Process: OSEMN
• Identify and define relevant data to be collected for marketing
• Compare and contrast the different formats and use cases of different types of data
• Identify gaps in the data collected and describe 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
• Use Tableau to visualize trends and patterns in data
This course is designed for people who want to learn the basics of data analysis, including using spreadsheets and Python to sort and structure data, and using Tableau to visualize data patterns.
Students do not need experience in marketing or data analysis, but they should have basic Internet browsing skills and be eager to participate. Students also need access to a computer with a good Internet connection. Ideally, students have already completed Course 1 (Marketing Analytics Foundation) in this program.
This course delves into the statistical basis on which Marketing Analytics is built. The first part of this course is about gaining a deep understanding of a data set and getting a sense of what the data really means. The second part of this course is about sampling and how to ask specific questions about your data. Finally, the third part is about answering those questions with analysis.
Many of the mistakes marketing analysts make today are due to not understanding the concepts behind the analyzes they run, leading them to perform 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.
At 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 hypothesis testing
• Formulate an assumption and align assumptions with business goals
• Identify actions based on the validation/invalidation of hypotheses
• Explain descriptive statistics (mean, median, standard deviation, distribution) and their use cases
• Understand basic concepts of Inferential Statistics
• Explain the different levels of analysis (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 data set and interpret the result with Tableau and statsmodels.
• Explain the difference between linear and multivariate regression.
• Run a segmentation (cluster) analysis
• Describe the difference between observation methods and experiments.
Data analysis methods for marketing
This course explores common analytics methods used by marketers. You will learn how to define a target audience using K-means clustering segmentation. You'll also explore how linear regression can help marketers plan and forecast.
You'll learn how to evaluate the effectiveness of advertising through experiments and observational methods, and explore ways to optimize your marketing mix; modeling and attribution of the marketing mix. Finally, you will learn how to evaluate sales funnel shapes, visualize them, and optimize them.
At the end of this course you will be able to:
• Describe when analytics is most commonly used in marketing
• Understand your audience using analysis and variable descriptions
• Segment a population into different audiences through cluster analysis
• Use historical data to plan your marketing through different channels
• Use linear regression to forecast marketing results
• Describe the marketing mix model
• Describe the attribution model
• Apply different attribution models
• Assess the effectiveness of advertising and describe shortcomings
• Describe the use of experiments to evaluate the effectiveness of advertising
• Explain how A/B testing works and how you can use it to optimize your ads.
• Evaluate the results of an experiment and evaluate the strength of the experiment
• Evaluate and optimize your sales funnel
This course explores meta marketing analysis tools. You will learn how the advertising platform works and you will learn how to create ads using Meta Ads Manager. Then you will learn how Meta reports are obtained and how you can customize the reports to match your business goals.
You'll also learn how you can use meta-experiments to evaluate the effectiveness of your ad campaign. You'll learn how to optimize ads with A/B testing and explore how you can integrate target campaign data into marketing mix models. In this course, you will also find an overview of Meta's recommended approach to data analysis.
At the end of this course you will be able to:
• Describe how an ad is created and delivered in Meta Ads Manager
• Evaluate the results of the campaign
• Run an A/B test
• Evaluate advertising effectiveness with conversion lift tests
• Evaluate the effectiveness of advertising with Brand Lift tests.
• Choosing the best approach to assess the effectiveness of advertising in a given setting.
• Explain how and when to apply the marketing mix model.
• Choose the best approach to optimize your marketing mix given a given scenario.
• Implement a complete analysis process from formulating a hypothesis to recommending measurement solutions, conducting an analysis, generating insights, and presenting results and recommendations.
Meta Marketing Science Certification Exam
This course helps you prepare for the Meta Marketing Science Certification exam. You will be guided through scheduling and taking the exam through the Meta Blueprint. You'll get access to the study guide and other resources to help you prepare to take the test.
This course is only accessible to students who have successfully completed Course 1 (Fundamentals of Marketing Analysis), Course 2 (Introduction to Data Analysis), Course 3 (Statistics for Marketing), Course 4 (Marketing Analysis, data for marketing) and course 5 (Marketing analysis). with Facebook) in this program.
With a Coursera Professional Certificate you could start a new career or change your current career. Today's world is demanding skills and knowledge in technologies that more and more companies need. Learn at your own pace, when and where you want. In a period of less than a year you could acquire a professional certificate, which added to your current profession, could give you substantial added value to your work experience and attract the attention of "head hunters".
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Data science, business and personal development. You can enroll in multiple courses at once, earn unlimited certificates, and learn in-demand job skills to start, grow, and even change careers.
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CEO & Co-Founder Aptly, Adj. Professor, UCLA Anderson School of Management
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Marketing Leader & Entrepreneur
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