Whether being used to customize advertising to millions of website visitors or streamline inventory ordering at a small restaurant, data is becoming more integral to success. Too often, we’re not sure how use data to find answers to the questions that will make us more successful in what we do. In this course, you will discover what data is and think about what questions you have that can be answered by the data – even if you’ve never thought about data before. Based on existing data, you will learn to develop a research question, describe the variables and their relationships, calculate basic statistics, and present your results clearly. By the end of the course, you will be able to use powerful data analysis tools – either SAS or Python – to manage and visualize your data, including how to deal with missing data, variable groups, and graphs. Throughout the course, you will share your progress with others to gain valuable feedback, while also learning how your peers use data to answer their own questions.

In this 1-hour 40-minutes long project-based course, you will learn how to design a database system by identifying the entities and their attributes as well as the relations between these entities. Furthermore, you will get to implement the database system that you have designed using Microsoft SQL Server through SQL Server Management Studio. This project will have you explore key concepts of database design and will have you get introduced to the building blocks of the world of databases. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this 1-hour long project-based course, you will learn how to build your own Machine Learning Image Classifier using Python and Colab. You will be able to easily load the data, preview it, process and normalize it, then train and test your model! I hope you enjoy the experience! Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material. Topics include: ~Set theory, including Venn diagrams ~Properties of the real number line ~Interval notation and algebra with inequalities ~Uses for summation and Sigma notation ~Math on the Cartesian (x,y) plane, slope and distance formulas ~Graphing and describing functions and their inverses on the x-y plane, ~The concept of instantaneous rate of change and tangent lines to a curve ~Exponents, logarithms, and the natural log function. ~Probability theory, including Bayes’ theorem. While this course is intended as a general introduction to the math skills needed for data science, it can be considered a prerequisite for learners interested in the course, "Mastering Data Analysis in Excel," which is part of the Excel to MySQL Data Science Specialization. Learners who master Data Science Math Skills will be fully prepared for success with the more advanced math concepts introduced in "Mastering Data Analysis in Excel." Good luck and we hope you enjoy the course!

Ignite your career with Geodesign! The magnitude of challenges before us exceeds the reach of conventional approaches to planning and design. The pandemic has spawned urgent needs for new design approaches and solutions. Also at the doorstep is climate change: altering community design approaches; addressing infrastructure types and locations; as well as the need to protect carbon-sequestering environs. Geodesign provides a revolutionary way forward. It leverages information systems to foster collaborations that result in geographically specific, adaptive, and resilient strategies to wicked problems across scales of the natural and built environment. Geodesign builds confidence through dynamic real-time feedback, which empowers engaged collaborations for meaningful plans. With Geodesign, you can change your world – for the better! This course includes well-illustrated lectures by the instructor, as well as guest lectures each week to ensure you are hearing a variety of viewpoints. Each week you will also be able to examine what Geodesign is through interactive mapping that showcases real-word Case Study examples of Geodesign from around the globe. As you move along in the course, you will discover the interrelationships of both the physical and human aspects that contribute to how Geodesign strategies are composed. The course concludes with you outlining your own Geodesign Challenge, and receiving feedback about that from your peers

Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. This course will focus on understanding key analytics concepts and the breadth of analytic possibilities. Together, the class will explore dozens of real-world analytics problems and solutions across most major industries and business functions. The course will also touch on analytic technologies, architectures, and roles from business intelligence to data science, and from data warehouses to data lakes. And the course will wrap up with a discussion of analytics trends and futures.

In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately. At the end of each week, learners will apply what they’ve learned using Python within the course environment. During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

Data never arrive in the condition that you need them in order to do effective data analysis. Data need to be re-shaped, re-arranged, and re-formatted, so that they can be visualized or be inputted into a machine learning algorithm. This course addresses the problem of wrangling your data so that you can bring them under control and analyze them effectively. The key goal in data wrangling is transforming non-tidy data into tidy data. This course covers many of the critical details about handling tidy and non-tidy data in R such as converting from wide to long formats, manipulating tables with the dplyr package, understanding different R data types, processing text data with regular expressions, and conducting basic exploratory data analyses. Investing the time to learn these data wrangling techniques will make your analyses more efficient, more reproducible, and more understandable to your data science team. In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.

An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for researchers who encounter large datasets in their own research. The course presents software tools developed by the Ma’ayan Laboratory (http://labs.icahn.mssm.edu/maayanlab/) from the Icahn School of Medicine at Mount Sinai, but also other freely available data analysis and visualization tools. The ultimate aim of the course is to enable participants to utilize the methods presented in this course for analyzing their own data for their own projects. For those participants that do not work in the field, the course introduces the current research challenges faced in the field of computational systems biology.

In this course you’ll learn how to effectively retrieve data from a relational database table using the SQL language. We all know that most computer systems rely on at least one database to store data. Your tax information is stored in the database used by the Internal Revenue Service. Your phone stores your contacts’ names, addresses, email addresses, and phone numbers in a database. If you shop online, you’re viewing photos, descriptions, and prices of products that are stored in a database. Database designers go to great lengths to design databases so that the data can be stored securely and in an organized format. It’s important to note that the main reason they go to all that work is so that we can get the data back out again when we need it! That’s called “data retrieval”. Data is retrieved or read from a relational database by using a language called SQL to query (or question) the database. SQL is referred to as “the language of relational databases”. It can be used by itself or embedded in programs to retrieve data. Once the data is retrieved, it can be displayed on a web page or PC application, or even printed on paper. You’ll be practicing writing SQL queries using SQLiteStudio. Next time you go online and look up the daily special at your favorite restaurant, you can think about the fact that it’s likely that an SQL query was used behind the scenes to fetch that data and pop it up on your screen. By the end of this course, you’ll even have a pretty good idea what the query might have looked like! Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.