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Introduction to Business Analytics with R

Ronald Guymon and Ashish Khandelwal

4.6

Based on 278 ratings

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Ronald Guymon and Ashish Khandelwal

Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings.

In this course you will use a data analytic language, R, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow.

As you learn how to use R to prepare data for analysis you will gain experience using RStudio, a powerful integrated development environment (IDE), that has many built-in features that simplify coding with R.

As you learn about the business analytic workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.

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What's inside

Syllabus

Course Overview and Module 1: How Do I Get Started Using a Data Analytic Language to Solve Business Problem?

In this module you will be introduced to (1) the role of data analytics in business domains, and (2) R and RStudio.

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Read about what's good

what should give you pause

and possible dealbreakers

Covers data analytics workflow, which is a skill required almost in every business area

Develops data processing skills and knowledge applicable to many business settings

Teaches how to use R, a data analytic language, and RStudio, an integrated development environment (IDE)

Focuses on data preparation and workflow principles to help learners understand the interplay between business and analytics

Involves hands-on experience in cleaning, transforming, aggregating, and reshaping data using R

May require additional software or hardware, which could pose financial barriers for some learners

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Reviews summary

A solid introduction to business analytics with r

According to students, this course offers a solid introduction to business analytics using R, effectively bridging data processing skills with business principles. Learners particularly appreciate the hands-on approach to data cleaning, transformation, and aggregation using RStudio and the Tidyverse packages. It provides a strong foundational understanding for applying analytics in various business settings and communicating results effectively, making it highly relevant for professionals.

Teaches methods for presenting analytical results.

"The methods for communicating data analytic results without manual copying were a game-changer for me."

"I learned valuable strategies for sharing data insights efficiently."

"This aspect helps me bridge the gap between analysis and stakeholder understanding."

Hands-on experience with RStudio and Tidyverse.

"Working with RStudio made coding in R much more intuitive and efficient."

"The focus on Tidyverse packages for data transformation was incredibly useful for practical application."

"I enjoyed the practical examples demonstrating how to prepare data for visualizations and algorithms."

Connects data analytics with key business principles.

"I appreciated how the course emphasized the interplay between business principles and data analytics."

"Understanding delegation, control, and feasibility in data processing was a unique and valuable insight."

"It's great to see data skills taught with a direct link to solving real business problems."

Provides essential skills in R for data preparation.

"I found the introduction to R and RStudio very helpful for getting started with business analytics."

"This course equipped me with a foundational set of data processing skills using R that I can apply immediately."

"Learning how to effectively clean, transform, and aggregate data with R was incredibly practical."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Business Analytics with R with these activities:

Create a comprehensive notes repository

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Organize and consolidate your course materials, making them easily accessible for review and future reference.

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Gather notes, assignments, quizzes, and exams from all course modules.
Categorize and arrange the materials logically, using a note-taking app or physical folders.
Review and supplement your notes regularly to reinforce your understanding and identify areas for further study.

Review statistics concepts for data analysis

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Enhance your understanding of statistical concepts to strengthen your foundation for data analysis.

Browse courses on Statistics

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Review basic statistics concepts such as probability distributions, hypothesis testing, and regression analysis.
Apply statistical techniques to real-world data analysis scenarios.
Use statistical software or online tools to perform data analysis and interpretation.

Join a study group

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Deepen your understanding of data analytic concepts and RStudio by engaging in discussions and problem-solving with peers.

Browse courses on Data Analytics

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Find or create a study group with fellow learners who share similar interests and goals.
Meet regularly to discuss course materials, share insights, and work on projects together.
Engage in active listening, asking questions, and seeking clarification to enhance your understanding.

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Practice data cleaning and transformation using R

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Gain proficiency in data cleaning and transformation techniques to prepare data for analysis.

Browse courses on Data Cleaning

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Obtain datasets with varying levels of complexity and data quality.
Use R functions and packages to clean and transform the data, such as removing duplicates, handling missing values, and standardizing formats.
Validate the cleaned and transformed data to ensure its integrity and readiness for analysis.

Solve coding challenges on R coding platforms

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Sharpen your R programming skills and deepen your understanding of data manipulation and algorithmic techniques.

Browse courses on R Programming

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Identify relevant coding challenges on platforms like HackerRank or CodeChef that align with course topics.
Attempt to solve the challenges independently, and use debugging tools to identify and correct errors.
Review solutions and compare your approach with others to learn alternative strategies.

Explore advanced R packages for data visualization

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Expand your data visualization skills by exploring advanced R packages to create visually appealing and informative data representations.

Browse courses on Data Visualization

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Identify R packages for data visualization, such as ggplot2, plotly, and dplyr.
Follow online tutorials and documentation to learn the syntax and capabilities of these packages.
Create interactive and dynamic data visualizations using the learned techniques.

Build a portfolio of data analytics projects

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Showcase your data analytics skills by creating a portfolio of projects that demonstrate your ability to solve real-world business problems.

Browse courses on Data Analysis

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Identify real-world business problems that can be addressed using data analytics.
Gather and analyze data using appropriate techniques and tools.
Present your findings and recommendations in a clear and concise manner.
Document your projects, including the problem statement, approach, and results.

Contribute to R open-source projects

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Gain practical experience in R programming and contribute to the open-source community by participating in R projects.

Browse courses on Open Source

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Identify R open-source projects that align with your interests and skills.
Review the project documentation and codebase to understand the project's goals and structure.
Propose and implement improvements or bug fixes to the project.
Collaborate with other contributors and maintainers to ensure code quality and project progress.

Career center

Learners who complete Introduction to Business Analytics with R will develop knowledge and skills that may be useful to these careers:

Business Analyst

Business Analysts use data analysis to help businesses improve their operations, plan for the future, and make better decisions. This course can help you develop the skills you need to become a Business Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Business Analyst

Data Analyst

Data Analysts use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course can help you develop the skills you need to become a Data Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use data to build models and algorithms that can solve business problems. They use a variety of statistical and machine learning techniques to extract insights from data. This course can help you develop the skills you need to become a Data Scientist, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians use data to understand the world around us. They collect, analyze, and interpret data to draw conclusions about the world. This course can help you develop the skills you need to become a Statistician, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Statistician

Operations Research Analyst

Operations Research Analysts use data to optimize business processes. They use a variety of mathematical and modeling techniques to identify inefficiencies and improve operations. This course can help you develop the skills you need to become an Operations Research Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use data to analyze financial markets and make investment recommendations. They use a variety of financial models and techniques to forecast future financial performance. This course can help you develop the skills you need to become a Financial Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

Marketing Analysts use data to understand consumer behavior and develop marketing campaigns. They use a variety of market research techniques to collect and analyze data about consumers. This course can help you develop the skills you need to become a Marketing Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Marketing Analyst

Product Manager

Product Managers use data to develop and manage products. They work with engineers, designers, and marketers to bring products to market. This course can help you develop the skills you need to become a Product Manager, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Product Manager

Project Manager

Project Managers use data to plan, execute, and track projects. They work with stakeholders to define project goals, develop project plans, and manage project risks. This course can help you develop the skills you need to become a Project Manager, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Project Manager

Business Intelligence Analyst

Business Intelligence Analysts use data to identify trends and opportunities for businesses. They work with business leaders to develop data-driven insights and make better decisions. This course can help you develop the skills you need to become a Business Intelligence Analyst, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Business Intelligence Analyst

Data Visualization Specialist

Data Visualization Specialists use data to create visual representations of data. They work with designers and engineers to create dashboards, charts, and other visual aids that help people understand data. This course can help you develop the skills you need to become a Data Visualization Specialist, including data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Data Visualization Specialist

Software Engineer

Software Engineers use data to develop and maintain software applications. They work with designers and other engineers to create software that meets the needs of users. This course may be useful for Software Engineers who want to learn more about data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Software Engineer

Web Developer

Web Developers use data to create and maintain websites. They work with designers and other developers to create websites that are user-friendly and efficient. This course may be useful for Web Developers who want to learn more about data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Web Developer

Database Administrator

Database Administrators use data to manage databases. They work with engineers and other IT professionals to ensure that databases are running smoothly and efficiently. This course may be useful for Database Administrators who want to learn more about data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Database Administrator

Information Technology Specialist

Information Technology Specialists use data to manage IT systems. They work with engineers and other IT professionals to ensure that IT systems are running smoothly and efficiently. This course may be useful for Information Technology Specialists who want to learn more about data cleaning, transformation, and aggregation. You will also learn about the business analytic workflow and how to communicate data analytic results effectively.

See salaries and explore the career path for Information Technology Specialist

Reading list

We've selected 16 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introduction to Business Analytics with R.