Prepare for the in-demand field of data analytics. In this program, you’ll learn high valued skills like Excel, Cognos Analytics, and R programming language to get job-ready in less than 3 months, with no prior experience or degree needed.

Data analytics is a strategy-based science where data is analyzed to find trends, answer questions, shape business processes, and aid decision-making. This Professional Certificate focuses on data analysis using Microsoft Excel and R programming language. If you’re interested in using Python, please explore the IBM Data Analyst PC.

This program will teach you the foundational data skills employers are seeking for entry level data analytics roles and will provide a portfolio of projects and a Professional Certificate from IBM to showcase your expertise to potential employers.

You’ll learn the latest skills and tools used by professional data analysts and upon successful completion of this program, you will be able to work with Excel spreadsheets, Jupyter Notebooks, and R Studio to analyze data and create visualizations. You will also use the R programming language to complete the entire data analysis process, including data preparation, statistical analysis, data visualization, predictive modeling and creating interactive dashboards. Lastly, you’ll learn how to communicate your data findings and prepare a summary report.

This program is ACE® recommended—when you complete, you can earn up to 15 college credits.

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!

Excel Basics for Data Analysis

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.

SQL for Data Science with R

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.

Data Science with R - Capstone Project

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.

Data Visualization and Dashboards with Excel and Cognos

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.

Introduction to R Programming for Data Science

This course is the final step in the Data Analysis and Visualization Foundations Specialization. It contains a graded final examination that covers content from three courses: Introduction to Data Analytics, Excel Basics for Data Analysis, and Data Visualization and Dashboards with Excel and Cognos.  

From the Introduction to Data Analytics course, your understanding will be assessed on topics like the data ecosystem and the fundamentals of data analysis, covering tools for data gathering and data mining. Moving on to the Excel Basics for Data Analysis course, expect questions focusing on the use of Excel spreadsheets in data analytics, proficiency in data cleansing and wrangling, and skills in working with pivot tables. Finally, from the Data Visualization and Dashboards with Excel and Cognos course, you will demonstrate your knowledge of IBM Cognos basics and your ability to use Excel for effective data visualization.

Assessment for Data Analysis and Visualization Foundations

Ready to start a career in Data Analysis but don’t know where to begin? This course presents you with a gentle introduction to Data Analysis, the role of a Data Analyst, and the tools used in this job. You will learn about the skills and responsibilities of a data analyst and hear from several data experts sharing their tips & advice to start a career. This course will help you to differentiate between the roles of Data Analysts, Data Scientists, and Data Engineers. 

You will familiarize yourself with the data ecosystem, alongside Databases, Data Warehouses, Data Marts, Data Lakes and Data Pipelines. Continue this exciting journey and discover Big Data platforms such as Hadoop, Hive, and Spark.  

By the end of this course you’ll be able to understand the fundamentals of the data analysis process including gathering, cleaning, analyzing and sharing data and communicating your insights with the use of visualizations and dashboard tools. 

This all comes together in the final project where it will test your knowledge of the course material, and provide a real-world scenario of data analysis tasks.  

This course does not require any prior data analysis, spreadsheet, or computer science experience.

Introduction to Data Analytics

The R programming language is purpose-built for data analysis. R is the key that opens the door between the problems that you want to solve with data and the answers you need to meet your objectives.  This course starts with a question and then walks you through the process of answering it through data. You will first learn important techniques for preparing (or wrangling) your data for analysis. You will then learn how to gain a better understanding of your data through exploratory data analysis, helping you to summarize your data and identify relevant relationships between variables that can lead to insights. Once your data is ready to analyze, you will learn how to develop your model and evaluate and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, and you can have confidence in the results. 

You will build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best model. 

Watch the videos, work through the labs, and add to your portfolio. Good luck!

Note: The pre-requisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM.

Data Analysis with R

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.

IBM Data Analytics with Excel and R

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