Coursera

Data Analysis with Spreadsheets and SQL (Coursera)

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 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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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.
This course is part of the Meta Marketing Analytics Professional Certificate.

Syllabus

Working with Data in Spreadsheets
Module 1
This week you learn the basics of spreadsheets and their usefulness in data analysis. You will also identify how to apply the OSEMN framework when working with data in spreadsheets.

Data Analysis with Spreadsheets
Module 2
This week you will learn how to clean data in spreadsheets using foundational spreadsheet functions. You will also learn how to calculate summary statistics in spreadsheets as well as how to identify data trends and relationships between variables.

Extracting Data with SQL
Module 3
This week you will be introduced to SQL and how it is used in spreadsheets. You will utilize basic queries and functions for handling data with SQL.

Data Visualization
Module 4
This week you will learn about common chart types and how to determine the best type of chart for the dataset you want to present. You will also learn how to create these charts using both Google Sheets and Tableau.

Creating Dashboards
Module 5
In the final week you will be introduced to dashboards and how to use them to report on progress within a business setting. You will also learn how to create dashboards in Tableau and use these to tell a story when presenting your data findings.

Go to Class

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Coursera

University of Washington

Machine Learning Foundations: A Case Study Approach (Coursera)

Statistics & Data Analysis Data Science

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies.

Jun 8th 2026

5-12 Weeks

Python Machine Learning Clustering

Coursera

University of Illinois at Urbana-Champaign

Data Visualization (Coursera)

Statistics & Data Analysis Data Science

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

Jun 8th 2026

4 Weeks

Data Mining Numerical Analysis Data Visualization

Coursera

University of Washington

Data Manipulation at Scale: Systems and Algorithms (Coursera)

Statistics & Data Analysis Data Science

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.

Jun 8th 2026

4 Weeks

Algebra Algorithms Databases

Coursera

ESSEC Business School

Foundations of strategic business analytics (Coursera)

Management & Leadership Business

Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering.

Jun 8th 2026

4 Weeks

Data Analysis Analytics Presentations

Coursera

University of Colorado Boulder

Business Analytics for Decision Making (Coursera)

Statistics & Data Analysis Data Science

In this course you will learn how to create models for decision making. We will start with cluster analysis, a technique for data reduction that is very useful in market segmentation. You will then learn the basics of Monte Carlo simulation that will help you model the uncertainty that is prevalent in many business decisions.

Jun 8th 2026

4 Weeks

Monte Carlo Method Data Analysis Decision Making

Coursera

Johns Hopkins University

Statistical Inference (Coursera)

Statistics & Data Analysis Data Science

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference.

Jun 8th 2026

4 Weeks

Statistics Probability Data Analysis

Coursera

Universidad Nacional Autónoma de México

Introducción a Data Science: Programación Estadística con R (Coursera)

Statistics & Data Analysis Data Science

Este curso te proporcionará las bases del lenguaje de programación estadística R, la lengua franca de la estadística, el cual te permitirá escribir programas que lean, manipulen y analicen datos cuantitativos. Te explicaremos la instalación del lenguaje; también verás una introducción a los sistemas base de gráficos y al paquete para graficar ggplot2, para visualizar estos datos. Además también abordarás la utilización de uno de los IDEs más populares entre la comunidad de usuarios de R, llamado RStudio.

Jun 8th 2026

4 Weeks

Data Analysis Data Science R Language

Coursera

University of Colorado Boulder

Communicating Business Analytics Results (Coursera)

Statistics & Data Analysis Data Science

The analytical process does not end with models than can predict with accuracy or prescribe the best solution to business problems. Developing these models and gaining insights from data do not necessarily lead to successful implementations. This depends on the ability to communicate results to those who make decisions.

Jun 8th 2026

4 Weeks

Business Data Selling

Coursera

University of Michigan

Introduction to Data Science in Python (Coursera)

CS: Software Engineering Statistics & Data Analysis

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

Jun 8th 2026

4 Weeks

Python Data Analysis Data Science

Coursera

University of Colorado Boulder

Introduction to Data Analytics for Business (Coursera)

Statistics & Data Analysis Data Science

This course will expose you to the data analytics practices executed in the business. We will explore such key areas of data analytics as the analytical process, how data is created, stored, and accessed, and how the organization works with data and creates the environment in which analytics can flourish.

Jun 8th 2026

4 Weeks

Business SQL Data Modeling

Coursera

Johns Hopkins University

Exploratory Data Analysis (Coursera)

Statistics & Data Analysis Data Science

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data.

Jun 8th 2026

4 Weeks

Statistics Data Analysis Data Science

Coursera

University of California, San Diego

Big Data Modeling and Management Systems (Coursera)

Statistics & Data Analysis Data Science

Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools.

Jun 8th 2026

5-12 Weeks

Big Data Data Management Data Modeling