Overview
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Prepare for a career in the high-growth field of data analytics. In this program, you’ll build in-demand technical skills like Python, Statistics, and SQL in spreadsheets to get job-ready in 5 months or less, no prior experience needed. You'll also have the option to learn how generative AI tools and techniques are used in data analytics.
Data analysis involves collecting, processing, and analyzing data to extract insights that can inform decision-making and strategy across an organization.
In this program, you’ll learn basic data analysis principles, how data informs decisions, and how to apply the OSEMN framework to approach common analytics questions. You’ll also learn how to use essential tools like SQL, Python, and Tableau to collect, connect, visualize, and analyze relevant data.
You’ll learn how to apply common statistical methods to writing hypotheses through project scenarios to gain practical experience with designing experiments and analyzing results.
When you complete this full program, you’ll have a portfolio of hands-on projects and a Professional Certificate from Meta to showcase your expertise.
Syllabus
Course 1: Introduction to Data Analytics
- Offered by Meta. This course provides a practical understanding and framework for basic analytics tasks, including data extraction, ... Enroll for free.
Course 2: Data Analysis with Spreadsheets and SQL
- Offered by Meta. This course introduces you to how to use spreadsheets and SQL queries to analyze and extract data. You will learn how to ... Enroll for free.
Course 3: Python Data Analytics
- Offered by Meta. This course introduces the use of the Python programming language to manipulate datasets as an alternative to spreadsheets. ... Enroll for free.
Course 4: Statistics Foundations
- Offered by Meta. This course takes a deep dive into the statistical foundation upon which data analytics is built. The first part of this ... Enroll for free.
Course 5: Introduction to Data Management
- Offered by Meta. This course explores the basics of data management. You will learn the role that data plays in making decisions and the ... Enroll for free.
- Offered by Meta. This course provides a practical understanding and framework for basic analytics tasks, including data extraction, ... Enroll for free.
Course 2: Data Analysis with Spreadsheets and SQL
- Offered by Meta. This course introduces you to how to use spreadsheets and SQL queries to analyze and extract data. You will learn how to ... Enroll for free.
Course 3: Python Data Analytics
- Offered by Meta. This course introduces the use of the Python programming language to manipulate datasets as an alternative to spreadsheets. ... Enroll for free.
Course 4: Statistics Foundations
- Offered by Meta. This course takes a deep dive into the statistical foundation upon which data analytics is built. The first part of this ... Enroll for free.
Course 5: Introduction to Data Management
- Offered by Meta. This course explores the basics of data management. You will learn the role that data plays in making decisions and the ... Enroll for free.
Courses
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This course provides a practical understanding and framework for basic analytics tasks, including data extraction, cleaning, manipulation, and analysis. It introduces the OSEMN cycle for managing analytics projects and you'll examine real-world examples of how companies use data insights to improve decision-making. By the end of this course you will be able to: • Formulate business goals, KPIs and associated metrics • Apply a data analysis process using the OSEMN framework • Identify and define the relevant data to be collected for marketing • Compare and contrast various data formats and their applications across different scenarios • Identify data gaps and articulate the strengths and weaknesses of collected data You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally you have already completed course 1: Marketing Analytics Foundation in this program.
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This course takes a deep dive into the statistical foundation upon which data analytics is built. The first part of this course will help you to thoroughly understand your dataset and what the data actually means. Then, it will go into sampling including how to ask specific questions about your data and how to conduct analysis to answer those questions. Many of the mistakes made by data analysts today are due to a lack of understanding the concepts behind the tests they run, leading to incorrect tests or misinterpreting the results. This course is tailored to provide you with the necessary background knowledge to comprehend the "what" and "why" of your actions in a practical sense. 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 it to 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 • 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.
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This course introduces you to how to use spreadsheets and SQL queries to analyze and extract data. You will learn how to practically apply the OSEMN data analysis framework and spreadsheet functions to clean data, calculate summary statistics, evaluate correlations, and more. You’ll also dive into common data visualization techniques and learn how to use dashboards to tell a story with your data. By the end of this course you will be able to: • Clean data with spreadsheets • Use common spreadsheet formulas to calculate summary statistics • Identify data trends and patterns • Write foundational SQL statements and queries to extract data in spreadsheets • Create charts in Google Sheets and use Tableau to visualize data • Use dashboards to create data visualizations You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally you have already completed course 1: Marketing Analytics Foundation and course 2: Introduction to Data Analytics in this program.
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This course introduces the use of the Python programming language to manipulate datasets as an alternative to spreadsheets. You will follow the OSEMN framework of data analysis to pull, clean, manipulate, and interpret data all while learning foundational programming principles and basic Python functions. You will be introduced to the Python library, Pandas, and how you can use it to obtain, scrub, explore, and visualize data. By the end of this course you will be able to: • Use Python to construct loops and basic data structures • Sort, query, and structure data in Pandas, the Python library • Create data visualizations with Python libraries • Model and interpret data using Python This course is designed for people who want to learn the basics of using Python to sort and structure data for data analysis. You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate.
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This course explores the basics of data management. You will learn the role that data plays in making decisions and the standard methods of data collection and quality management. You will gain an understanding of data storage systems and data storage architecture. You will examine the fundamentals of data privacy and compliance, as well as the basics of machine learning. By the end of this course, you will be able to: • Describe the fundamentals of data collection and data quality management • Explain data storage solutions and architectures, including big data management • Define data security, privacy and governance principles This course is for learners who want to gain foundational knowledge of data management.
Taught by
Anke Audenaert, Brandon Larkin, Cameron Dodd and Victor Geislinger