• Professional certificate consisting of an 8-course series to build in-demand skills, gain career credentials, and experience

• 7 months approximate course period, learning data analytics and it's applications in marketing

The Importance of Analytics in Marketing

1. Introduction to the Facebook Marketing Analytics Certificate program

2. The role of a marketer

• The role of a marketer, basic marketing principles

3. The use of analytics in marketing

• Uses of marketing analytics, data in future of marketing

4. Analytics in marketing: application

• Analytics for marketing, finding audience

• Planning and forecasting, evaluating advertising effectiveness

• Optimizing sales funnel and marketing strategy

Marketing Data Sources

1. What data do marketers use?

• Online and offline behavior data, sampled vs. non-sampled data

• First-party, second-party and third-party data, marketing dataset sources

2. Sources of digital data

• Sources of digital data, data from online interactions

• Use of Browser Cookies, Tags or Pixels, mobile SDKs, APIs, and UIDs

3. Collecting data for marketing: application

• Implementing the Facebook Pixel, SDK and API

Marketing measurement and Analytics Tools

1. Analyzing and visualizing data

• Measurement and analytics tools, spreadsheets & visualization tools

2. Tools to evaluate digital data

• Digital & website data evaluation tools, web measurement terminology

• Google Analytics data evaluations, GA demo account

3. Tools for evaluating marketing success

• Facebook Ads Manager, Google Ads reports, other common platforms

Data and Privacy

1. Data and privacy from the consumer perspective

• Consumer data for online experience, data collection settings in Facebook

2. Data and privacy from the advertiser perspective

• Responsible data collection and ownership in advertising ecosystem

3. Protection and regulations

• Need for data protection and regulations to protect user data, industry organizations and privacy

Working with Data

1. Introduction to the program

2. What is data analytics

• Data analytics vs. data science, data analyst's work

3. The OSEMN framework

• OSEMN framework tool, starting with a goal and understanding KPIs, OSEMN example

Obtaining and Scrubbing Data

1. Obtaining data

• Common data formats, sampled data, first and third party data

• Validity of data sources, free data sources

2. Scrubbing data

• Remove duplicates, missing or wrong values, and formatting records

3. Example obtaining and scrubbing data

• Real world example, data obtaining & scrubbing case studies

 Exploring and Modeling Data

1. Exploring data

• Understanding data, creating visualizations

• Examining data distributions & relationships

2. Modeling data

• Predictions with data modeling, working of data models, different types

3. Example: exploring and modeling data

• Real world example, data exploring & modeling case studies

Working with Data in Spreadsheets

1. Introduction to the program

2. Spreadsheet fundamentals

• Spreadsheets features and use, commonly used tools

3. The OSEMN framework and spreadsheets

• OSEMN recap, using spreadsheets for data analysis

Data Analysis with Spreadsheets

1. Scrubbing data

• Accessing, labeling, filtering, sorting, cleaning, & formatting data

2. Exploring data

• Sheet functions: total, average, median, minimum & maximum, evaluating correlations

3. Exploring data visually

• Comparing variables with charts, evaluating trends, inspecting relationships between variables

• Scrubbing, exploring, & visualizing data in Google Sheets

4. Modeling data with spreadsheets

• Modeling using spreadsheet functions, limitations and example

 Extracting Data with SQL

1. SQL fundamentals

• SQL uses, examples, using SQL in Google Sheets

2. Writing SQL queries

• Practice: SELECT statements, AND, OR, and WHERE clauses, ORDER BY and LIMIT clauses, writing SQL queries

3. Functions in SQL

• Practice: writing Arithmetic, Aggregate, & Group by functions in SQL, extracting data with SQL

Data Visualization

1. The power of data visualization

• Need for visualization, different tools, common chart types

2. Data visualization in Google Sheets

• Practice: bar charts, pie charts, trend chart/lines, & scatter Plots

3. Data visualization with Tableau

• Tableau Public installation, account setup, interface

• Practice: connecting data, creating first chart, creating complex charts

Introduction to Python

1. Introduction to the program

2. Why a programming language for data analysis

• Data analysis with OSEMN framework, why Python

3. Introducing Python with Jupyter Notebooks

• Basics of using Jupyter Notebook, notebook example on Coursera

4. Python concepts: variables

• Variable meaning, variable types, working with types in Python

5. Python concepts: data structures

• Using lists, tuples & dictionaries, other data structures

6. Python concepts: conditional statements

• Using booleans & conditional statements

7. Python concepts: control flow with iteration

• Using for loops, iterators, & control flow with data structures

8. Python concepts: control flow with functions

• Built-in Python functions, common functions, writing functions