This Professional Certificate is intended for anyone who is seeking to develop the job-ready skills, tools, and portfolio for an entry-level data analyst or data scientist position. Through these eight online courses, you will dive into the role of a data analyst or data scientist and develop the essential skills you need work with a range of data sources and apply powerful tools, including Excel, Cognos Analytics, and the R programming language, towards becoming a data driven practitioner, and gaining a competitive edge in the job market.
By the end of this Professional Certificate, you will be able to explain the data analyst and data scientist roles. You will work with Excel spreadsheets and utilize them for data analysis to create charts and plots. You will utilize Cognos Analytics to create interactive dashboards. You will work with relational databases and query data using SQL statements. You will use the R programming language to complete the entire data analysis process - including data preparation, statistical analysis, data visualization, predictive modeling, and creating interactive data applications. You will also communicate your data findings and learn to prepare a report for stakeholders.
This program does not require any prior data analysis, statistics, or programming experience.
This program is ACE® recommended—when you complete, you can earn up to 15 college credits.
Course 1: Introduction to Data Analytics - Offered by IBM Skills Network. This course presents a gentle introduction into the concepts of data analysis, the role of a Data Analyst, ... Enroll for free.
Course 2: Excel Basics for Data Analysis - Offered by IBM Skills Network. This course is designed to provide you with basic working knowledge for using Excel spreadsheets for Data ... Enroll for free.
Course 3: Data Visualization and Dashboards with Excel and Cognos - Offered by IBM Skills Network. This course covers some of the first steps in the development of data visualizations using spreadsheets and ... Enroll for free.
Course 4: Assessment for Data Analysis and Visualization Foundations - Offered by IBM Skills Network. This is the final course in the Data Analysis and Visualization Foundations Specialization. It contains a ... Enroll for free.
Course 5: Introduction to R Programming for Data Science - Offered by IBM Skills Network. When working in the data science field you will definitely become acquainted with the R language and the role ... Enroll for free.
Course 6: SQL for Data Science with R - Offered by IBM Skills Network. Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language ... Enroll for free.
Course 7: Data Analysis with R - Offered by IBM Skills Network. The R programming language is purpose-built for data analysis. R is the key that opens the door between the ... Enroll for free.
Course 8: Data Visualization with R - Offered by IBM Skills Network. In this course, you will learn the Grammar of Graphics, a system for describing and building graphs, and how ... Enroll for free.
Course 9: Data Science with R - Capstone Project - Offered by IBM Skills Network. In this capstone course, you will apply various data science skills and techniques that you have learned as ... Enroll for free.
This course is designed to provide you with basic working knowledge for using Excel spreadsheets for Data Analysis. It covers some of the first steps for working with spreadsheets and their usage in the process of analyzing data. It includes plenty of videos, demos, and examples for you to learn, followed by step-by-step instructions for you to apply and practice on a live spreadsheet.
Excel is an essential tool for working with data - whether for business, marketing, data analytics, or research. This course is suitable for those aspiring to take up Data Analysis or Data Science as a profession, as well as those who just want to use Excel for data analysis in their own domains. You will gain valuable experience in cleansing and wrangling data using functions and then analyze your data using techniques like filtering, sorting and creating pivot tables.
This course starts with an introduction to spreadsheets like Microsoft Excel and Google Sheets and loading data from multiple formats. With this introduction you will then learn to perform some basic level data wrangling and cleansing tasks and continue to expand your knowledge of analyzing data through the use of filtering, sorting, and using pivot tables within the spreadsheet. By performing these tasks throughout the course, it will give you an understanding of how spreadsheets can be used as a data analysis tool and understand its limitations.
There is a strong focus on practice and applied learning in this course. With each lab, you will gain hands-on experience in manipulating data and begin to understand the important role of spreadsheets. Clean and analyze your data faster by understanding functions in the formatting of data. You will then convert your data to a pivot table and learn its features to make your data organized and readable. The final project enables you to show off your newly acquired data analysis skills. By the end of this course you will have worked with several data sets and spreadsheets and demonstrated the basics of cleaning and analyzing data all without having to learn any code.
Getting started with Excel is made easy in this course. It does not require any prior experience with spreadsheets or coding. Nor does it require downloads or installation of any software. All you need is a device with a modern web browser, and ability to create a Microsoft account to access Excel online at no-cost. However if you already have a desktop version of Excel, you can follow along quite easily as well.
This course equips you with a practical understanding and a framework to guide the execution of basic analytics tasks such as pulling, cleaning, manipulating and analyzing data by introducing you to the OSEMN cycle for analytics projects. You’ll learn to perform data analytics tasks using spreadsheet and SQL queries. You will also be introduced to using the Python programming language to manipulate datasets as an alternative to spreadsheets. You will learn foundational programming concepts and how they apply to marketing. You will also learn how to use Tableau to create data visualizations and dashboards.
By the end of this course, you will be able to:
• State business goals, KPIs and associated metrics
• Apply a Data Analysis Process: OSEMN
• Identify and define the relevant data to be collected for marketing
• Compare and contrast the different formats and use cases of different kinds of data
• Identify gaps in data collected and describe the strengths and weaknesses
• Demonstrate proficiency in Python with variables, control flow, loops, and basic data structures
• Sort, query and structure data in spreadsheets and with Python libraries
• Write basic SQL statements to select, group and filter data
• Visualize data patterns and trends with spreadsheets
• Utilize Tableau to visualize data patterns and trends
This course is designed for people who want to learn the basics of data analytics including using spreadsheets and Python to sort and structure data and using Tableau to visualize data patterns.
Learners don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Learners also need access to a computer with strong internet connection. Ideally learners have already completed course 1 (Marketing Analytics Foundation) in this program.
This course covers some of the first steps in the development of data visualizations using spreadsheets and dashboards. Begin the process of telling a story with your data by creating the many types of charts that are available in spreadsheets like Excel. Explore the different tools of a spreadsheet, such as the important pivot function and the ability to create dashboards and learn how each one has its own unique property to transform your data. Continue to gain valuable experience by becoming familiar with the popular analytics tool - IBM Cognos Analytics - to create interactive dashboards.
By completing this course, you will have a basic understanding of using spreadsheets as a data visualization tool. You will gain the ability to effectively create data visualizations, such as charts or graphs, and will begin to see how they play a key role in communicating your data analysis findings. All of this can be accomplished by learning the basics of data analysis with Excel and IBM Cognos Analytics, without having to write any code. By the end of this course you will be able to describe common dashboarding tools used by a data analyst, design and create a dashboard in a cloud platform, and begin to elevate your confidence level in creating intermediate level data visualizations.
Throughout this course you will encounter numerous hands-on labs and a final project. With each lab, gain hands-on experience with creating basic and advanced charts, then continue through the course and begin creating dashboards with spreadsheets and IBM Cognos Analytics. You will then end this course by creating a set of data visualizations with IBM Cognos Analytics and creating an interactive dashboard that can be shared with peers, professional communities or prospective employers.
This course does not require any prior data analysis, or computer science experience. All you need to get started is basic computer literacy, high school level math, access to a modern web browser such as Chrome or Firefox, the ability to create a Microsoft account to access Excel for the Web, and a basic understanding of Excel spreadsheets.
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.
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.
Gabriela de Queiroz, Kevin McFaul, Rav Ahuja, Saishruthi Swaminathan, Sandip Saha Joy, Steve Ryan, Yan Luo and Yiwen Li