Course HighlightsCOURSE
Digital Analytics & Regression

Digital Analytics & Regression

Learn how to communicate data-driven results effectively. Get practical experience using Google Trends, R Studio, and other technologies. Discover how to formulate business objectives using data science tools.

Digital Analytics & Regression Highlights

  Course duration

Duration

  • 4 weeks, online
    2-4 hours/week
  Course Fee

Fee

US$ 99 - US$ 199

Course duration

Duration

  • 4 weeks, online
    2-4 hours/week
Course Fee

Fee

US$ 99 - US$ 199

Digital analytics helps organizations provide a better online experience to their clients and potential customers. Within this, regression analysis helps businesses understand the data points they have and enables them to improve their decision making.

During this course, you will work on a case study - CEO vs CMO - to decide upon an appropriate name for a new cloud-based technology. During this exercise, you’ll build your understanding of the business context, formulate the business objective, state the hypothesis, assess available data, and assign data for use. You will also learn about the R programming language and its suitability for data analysis, including how to load data into R-Studio and view the rows in the resulting dataset.

Additionally, you will access Google trends - a web tool - in R and analyze various Google search trends. You will also discover how to access Google Trends on R Studio, and view the output in the form of box plots, histograms, scatter plots and linear regression trends. In the final module, you will then summarize the data analytics process and focus on the conclusions and recommendations you would make to the relevant stakeholders.

Once you have completed this course, you will be able to define business objectives from big, medium or small data using data science tools and Google Trends. For individuals keen to learn how to communicate data-driven results to the relevant stakeholders, this course is an ideal place to start.

This course comprises five purposely designed modules that take you on a carefully defined learning journey.

It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 2-4 hours per week, you will complete the course in 4 weeks. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And don’t worry, you’re not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.

The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes and a final assignment.

Once you have successfully completed the course, you will earn your IBM Certificate.

As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services may vary package wise.

You will understand:

  • The business analytics cycle.
  • How to create a analytics solution.
  • How to articulate a recommendation.
  • Business analysts
  • Data science analysts
  • Corporate consultants
  • Statisticians
  • Individuals with basic knowledge of analytics and R programming language looking to upskill.

There are no prerequisites for this course, however, knowledge of basic statistics and basic R is beneficial.

Course Outline

General Information
Learning Objectives
Syllabus
Grading Scheme
Change Log
Copyrights and Trademarks
Learning Objectives
Case Study Assignment: 'CEO vs. CMO'
Formulating the Business Objective
A Data Driven Approach
Graded Review Questions
Learning Objectives
IBM Developer Skills Network-Labs
What is R
Lab - Exercise - Import Google Trends Data
Graded Review Questions
Learning Objectives
Regression and Google Trends Data in R
Box Plots & Histograms in R
Scatter Plots & Lines of Best Fit in R
Simple Linear Regression in R
Graded Review Questions
Learning Objectives
Using data to answer a business question
Summarizing the Data Analytics Process
Presenting Data Insights
Course Certificate

Earn your certificate

Once you have completed this course, you will earn your certificate.

Preview digital certificate
Digital Analytics & Regression

FAQs

Data science is a blend of programming tools, statistical analysis, algorithms, and machine learning principles that is becoming increasingly popular. It involves the application of a variety of disciplines, including statistics, scientific methodologies, artificial intelligence (AI), and data analysis.

A data analyst is someone who analyses data and develops insightful conclusions from their findings, which then help to clarify a company's market position. Typical responsibilities include:

  • Data extraction utilizing high-tech computer models.
  • Deleting damaged data, and other related tasks.
  • Performing preliminary analysis to determine the quality of the data.
  • Preparing presentations based on the data analysis.
  • Making presentations to senior management.

Data is an important firm asset, and data-driven business processes improve efficiency and spur innovation. Thus, the demand for data scientists with strong skills is growing, and firms are willing to offer very competitive packages to attract the most qualified individuals. Some well-known data science service providers include:

1. Oracle

2. Amazon

3. JP Morgan Chase

4. Teradata

5. Accenture

Data Analytics & Regression is a self-paced online course. As a result, you will require internet connectivity in order to use the course materials. When you register for this course, you will immediately have access to the course materials through the course link in your dashboard.

This course is completely self-paced.

A self-paced course does not follow a defined schedule for live sessions or webinars. Instead, you can work as swiftly or as slowly as you wish as long as you complete the modules and the course before the end of your enrollment.