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Updated in [February 21st, 2023]

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 or IBM Data Analytics with Excel and R Professional Certificate Programs.

In this capstone project, you will take on the role of a data scientist who has recently joined an organization and is 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.

(Please note that we obtained the following content based on information that users may want to know, such as skills, applicable scenarios, future development, etc., combined with AI tools, and have been manually reviewed)

This course provides learners with the opportunity to apply their data science skills and techniques to a real-world problem. Learners will be able to collect and understand data from multiple sources, conduct data wrangling and preparation, perform exploratory data analysis, model data with linear regression, create charts and plots to visualize the data, and build an interactive dashboard. At the end of the course, learners will present their data analysis report with an executive summary for the various stakeholders in the organization.

Possible Development Paths:
Completing this course can open up a variety of career paths for learners. They can become data scientists, data analysts, data engineers, or data visualization specialists. They can also pursue further education in data science, analytics, or related fields.

Learning Suggestions:
To get the most out of this course, learners should have a basic understanding of data science, analytics, and programming. They should also be familiar with the R programming language and the Tidyverse, SQL, and ggplot2 libraries. Additionally, learners should have some experience with data wrangling, exploratory data analysis, and linear regression.

[Applications]
After completing this course, students should be able to apply the data science skills and techniques they have learned to real-world datasets. They should be able to 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. Additionally, students should be able to present their data analysis report with an executive summary for the various stakeholders in the organization.

[Career Paths]
1. Data Scientist: Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data to identify trends and patterns. They use a variety of tools and techniques, such as machine learning, natural language processing, and statistical analysis, to uncover insights from data. Data Scientists are in high demand as organizations increasingly rely on data-driven decision making.

2. Business Intelligence Analyst: Business Intelligence Analysts are responsible for gathering, analyzing, and interpreting data to help organizations make informed decisions. They use a variety of tools and techniques, such as data mining, predictive analytics, and data visualization, to uncover insights from data. Business Intelligence Analysts are in high demand as organizations increasingly rely on data-driven decision making.

3. Data Engineer: Data Engineers are responsible for designing, building, and maintaining data pipelines and data warehouses. They use a variety of tools and techniques, such as ETL (Extract, Transform, Load) processes, data modeling, and data warehousing, to ensure that data is stored and accessed efficiently. Data Engineers are in high demand as organizations increasingly rely on data-driven decision making.

4. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use a variety of tools and techniques, such as deep learning, natural language processing, and reinforcement learning, to create models that can make predictions and decisions. Machine Learning Engineers are in high demand as organizations increasingly rely on data-driven decision making.