IBM Data Analytics with Excel and R

Spreadsheet tools like Excel are an essential tool for working with data - whether for data analytics, business, marketing, or research. This course is designed to give you a basic working knowledge of Excel and how to use it for analyzing data. This course is suitable for those who are interested in pursuing a career in data analysis or data science, as well as anyone looking to use Excel for data analysis in their own domain. No prior experience with spreadsheets or coding is required - all you need is a device with a modern web browser and the ability to create a Microsoft account to access Excel online at no cost. If you have a desktop version of Excel, you can also easily follow along with the course. Throughout this course, you'll gain valuable experience working with data sets and spreadsheets. We'll start by introducing you to spreadsheets like Microsoft Excel and Google Sheets, and show you how to load data from multiple formats. From there, you'll learn how to perform basic data wrangling and cleansing tasks using functions, and expand your knowledge of data analysis through the use of filtering, sorting, and pivot tables. There is a strong focus on practice and applied learning in this course. With each lab, you'll have the opportunity to manipulate data and gain hands-on experience using Excel. You'll learn how to clean and format your data efficiently, and convert it into a pivot table to make it more organized and readable. The final project will allow you to showcase your newly acquired data analysis skills by working with real data sets and spreadsheets. By the end of this course, you'll have a solid foundation in using Excel for data analysis. You'll have worked with multiple data sets and spreadsheets, and will have the skills and knowledge needed to effectively clean and analyze data without having to learn any code. So let's get started!

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.

Learn how to create data visualizations and dashboards using spreadsheets and analytics tools. This course covers some of the first steps for telling a compelling story with your data using various types of charts and graphs. You'll learn the basics of visualizing data with Excel and IBM Cognos Analytics without having to write any code. You'll start by creating simple charts in Excel such as line, pie and bar charts. You will then create more advanced visualizations with Treemaps, Scatter Charts, Histograms, Filled Map Charts, and Sparklines. Next you’ll also work with the Excel PivotChart feature as well as assemble several visualizations in an Excel dashboard. This course also teaches you how to use business intelligence (BI) tools like Cognos Analytics to create interactive dashboards. By the end of the course you will have an appreciation for the key role that data visualizations play in communicating your data analysis findings, and the ability to effectively create them. Throughout this course there will be numerous hands-on labs to help you develop practical experience for working with Excel and Cognos. There is also a final project in which you’ll create a set of data visualizations and an interactive dashboard to add to your portfolio, which you can share with peers, professional communities or prospective employers.

In this course, you will learn the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will also learn how to further customize your charts and plots using themes and other techniques. You will then learn how to use another data visualization package for R called Leaflet to create map plots, a unique way to plot data based on geolocation data. Finally, you will be introduced to creating interactive dashboards using the R Shiny package. You will learn how to create and customize Shiny apps, alter the appearance of the apps by adding HTML and image components, and deploy your interactive data apps on the web. You will practice what you learn and build hands-on experience by completing labs in each module and a final project at the end of the course. Watch the videos, work through the labs, and watch your data science skill grow. Good luck! NOTE: This course requires knowledge of working with R and data. If you do not have these skills, it is highly recommended that you first take the Introduction to R Programming for Data Science as well as the Data Analysis with R courses from IBM prior to starting this course. Note: The pre-requisite for this course is basic R programming skills.

When working in the data science field you will definitely become acquainted with the R language and the role it plays in data analysis. This course introduces you to the basics of the R language such as data types, techniques for manipulation, and how to implement fundamental programming tasks. You will begin the process of understanding common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language. The emphasis in this course is hands-on and practical learning . You will write a simple program using RStudio, manipulate data in a data frame or matrix, and complete a final project as a data analyst using Watson Studio and Jupyter notebooks to acquire and analyze data-driven insights. No prior knowledge of R, or programming is required.

In this capstone course, you will apply various data science skills and techniques that you have learned as part of the previous courses in the IBM Data Science with R Specialization or IBM Data Analytics with Excel and R Professional Certificate. For this project, you will assume the role of a Data Scientist who has recently joined an organization and be presented with a challenge that requires data collection, analysis, basic hypothesis testing, visualization, and modeling to be performed on real-world datasets. You will collect and understand data from multiple sources, conduct data wrangling and preparation with Tidyverse, perform exploratory data analysis with SQL, Tidyverse and ggplot2, model data with linear regression, create charts and plots to visualize the data, and build an interactive dashboard. The project will culminate with a presentation of your data analysis report, with an executive summary for the various stakeholders in the organization.

Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL and R languages. It is also intended to get you started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning. As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs, you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and R. No prior knowledge of databases, SQL, R, or programming is required. Anyone can audit this course at no charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course.