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Arpan Chakraborty
Take Udacity's Data Analytics and Visualization course and learn state-of-the-art modeling, analysis and visualization techniques. Learn online with Udacity.
Register for this course and see more details by visiting:
OpenCourser.com/course/uct9cf/udacity
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UDACITY40
What's inside
Syllabus
Course Information
Install RStudio and packages, learn the layout and basic commands of R, practice writing basic R scripts, and inspect data sets.
Read more
Syllabus
Course Information
Install RStudio and packages, learn the layout and basic commands of R, practice writing basic R scripts, and inspect data sets.
Read more
Netflix Interview 1
R Programming Language
Netflix Interview 2
R Programming: Syntax and Data Structure
Data Visualization
Preprocessing Data
Netflix Interview 3
Logistic Regression
Netflix Interview 4
Linear Regression
Regularization
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores data modeling, analysis, and visualization techniques used in competitive industries
Teaches R programming language, an industry standard for data analysis
Develops skills in data preprocessing, logistic and linear regression, and regularization
Offers a mix of video lectures, interactive materials, and hands-on labs
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Save Data and Visual Analytics to your list so you can find it easily later:
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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 Data and Visual Analytics with these
activities:
Review basic statistics concepts
Show steps
Strengthen your foundation in statistics by reviewing key concepts such as descriptive statistics, probability, and hypothesis testing.
Browse courses on
Statistics
Show steps
-
Read through a textbook or online resources on basic statistics
-
Complete practice problems and exercises to test your understanding
Read 'R for Data Science'
Show steps
Gain a deeper understanding of R's capabilities for data science applications by reviewing this foundational text.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read through the book's chapters on data manipulation, visualization, and modeling
-
Complete the exercises and examples provided in the book
Explore R tutorials on DataCamp
Show steps
Supplement your understanding of R by following guided tutorials on DataCamp, covering topics ranging from data cleaning to visualization techniques.
Browse courses on
R Programming
Show steps
-
Create an account on DataCamp
-
Enroll in the 'Introduction to R' course
-
Complete the tutorials and quizzes
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice R programming
Show steps
Reinforce your understanding of R programming concepts by completing a series of practice problems.
Browse courses on
R Programming
Show steps
-
Install RStudio and necessary packages
-
Load a dataset into R
-
Write R code to clean and analyze the dataset
-
Visualize the results of your analysis
Join a study group for the course
Show steps
Enhance your learning experience by collaborating with peers in a study group, discussing course material and working together on assignments.
Show steps
-
Find classmates who are interested in forming a study group
-
Set up regular meetings
-
Review course material together and discuss concepts
-
Work on assignments collaboratively (optional)
Build a data visualization dashboard
Show steps
Enhance your data visualization skills by creating an interactive dashboard that showcases key insights from a dataset.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and identify key insights
-
Design the layout and structure of the dashboard
-
Select appropriate visualization techniques
-
Implement the dashboard using R or a visualization tool
-
Test and refine the dashboard for usability and effectiveness (optional)
Create a blog post on a data analysis project
Show steps
Solidify your understanding of data analysis and visualization techniques by creating a blog post that showcases your skills in these areas.
Browse courses on
Data Analysis
Show steps
-
Choose a dataset and define a research question
-
Perform data analysis and visualization using R
-
Write a blog post that explains your findings and insights
Review basic statistics concepts
Show steps
Strengthen your foundation in statistics by reviewing key concepts such as descriptive statistics, probability, and hypothesis testing.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Read through a textbook or online resources on basic statistics
- Complete practice problems and exercises to test your understanding
Read 'R for Data Science'
Show steps
Gain a deeper understanding of R's capabilities for data science applications by reviewing this foundational text.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read through the book's chapters on data manipulation, visualization, and modeling
- Complete the exercises and examples provided in the book
Explore R tutorials on DataCamp
Show steps
Supplement your understanding of R by following guided tutorials on DataCamp, covering topics ranging from data cleaning to visualization techniques.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Create an account on DataCamp
- Enroll in the 'Introduction to R' course
- Complete the tutorials and quizzes
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice R programming
Show steps
Reinforce your understanding of R programming concepts by completing a series of practice problems.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Install RStudio and necessary packages
- Load a dataset into R
- Write R code to clean and analyze the dataset
- Visualize the results of your analysis
Join a study group for the course
Show steps
Enhance your learning experience by collaborating with peers in a study group, discussing course material and working together on assignments.
Show steps
Show steps
Show steps
- Find classmates who are interested in forming a study group
- Set up regular meetings
- Review course material together and discuss concepts
- Work on assignments collaboratively (optional)
Build a data visualization dashboard
Show steps
Enhance your data visualization skills by creating an interactive dashboard that showcases key insights from a dataset.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and identify key insights
- Design the layout and structure of the dashboard
- Select appropriate visualization techniques
- Implement the dashboard using R or a visualization tool
- Test and refine the dashboard for usability and effectiveness (optional)
Create a blog post on a data analysis project
Show steps
Solidify your understanding of data analysis and visualization techniques by creating a blog post that showcases your skills in these areas.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a dataset and define a research question
- Perform data analysis and visualization using R
- Write a blog post that explains your findings and insights
Career center
Learners who complete Data and Visual Analytics will develop knowledge and skills
that may be useful to these careers:
Data Visualization Specialist
Data Visualization Specialist
A Data Visualization Specialist designs and develops data visualizations to communicate data insights. This course may be very helpful for a Data Visualization Specialist. The course provides a foundation in data visualization, which is a must-have skill for the role. Furthermore, it teaches R, which is a popular tool for data visualization among Data Visualization Specialists.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to extract meaningful insights. This course, starting from scratch, may be very helpful for a person who wants to transition into a Data Analyst role. The course provides a foundational knowledge in data analysis techniques, which is very useful for any data analyst. Furthermore, it will help build a foundation in data visualization, which is a must-have skill for Data Analysts.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to improve the efficiency of business operations. Data analysis and visualization are both very important skills for an Operations Research Analyst, and this course provides a foundation in both of these areas. Furthermore, R is a popular tool for data analysis and visualization among Operations Research Analysts, and this course provides a solid foundation in R.
Quality Assurance Analyst
Quality Assurance Analyst
A Quality Assurance Analyst evaluates the quality of software products and processes. Data analysis and visualization are both very important skills for a Quality Assurance Analyst, and this course provides a foundation in both of these areas. Furthermore, R is a popular tool for data analysis and visualization among Quality Assurance Analysts, and this course provides a solid foundation in R.
Business Analyst
Business Analyst
A Business Analyst uses data and analysis to help businesses make better decisions. This course can be helpful for a Business Analyst, as it provides a foundation in data analysis and visualization, which are both very important skills for the role. In particular, this course teaches R, which is a popular tool for data analysis and visualization among Business Analysts.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and infrastructure to support data analysis and machine learning initiatives. The main focus of this course is data analytics and visualization, which is a must-have skill for a job in this domain. The course teaches the basics of data analysis and visualization, with a focus on R, which are must-have skills for a Data Engineer.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze and forecast financial data. As someone who wants to build a career in this field, this course may be helpful for someone who wants to become a Quantitative Analyst. This course provides a foundational knowledge in data analysis techniques, which is very useful for a Quantitative Analyst. Furthermore, it will help build a foundation in data visualization, which is a must-have skill for Quantitative Analysts.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course may be helpful for someone who wants to become a Data Scientist as it provides a foundation in data analytics and visualization, with a focus on R. Furthermore, the course covers topics such as logistic regression, linear regression, and regularization, which are all very relevant to Data Science.
Information Security Analyst
Information Security Analyst
An Information Security Analyst plans and implements security measures to protect an organization's information systems and data. Data analysis and visualization are both very important skills for an Information Security Analyst, and this course provides a strong foundation in both of these areas. Furthermore, the course covers topics such as logistic regression, linear regression, and regularization, which are all very relevant to Information Security Analysis.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes and interprets financial data to make recommendations on investments. As someone who wants to build a career in this field, this course may be helpful. The course provides a foundational knowledge in data analysis techniques, which is very useful for a Financial Analyst. Furthermore, it will help build a foundation in data visualization, which is a must-have skill for Financial Analysts.
Risk Analyst
Risk Analyst
A Risk Analyst identifies, assesses, and mitigates risks to an organization. The main focus of this course is data analytics and visualization, which is a must-have skill for a job in this domain. Furthermore, it covers topics such as logistic regression, linear regression, and regularization, which are all very relevant to Risk Analysis.
Product Manager
Product Manager
A Product Manager leads the development and launch of new products and features. Data analysis and visualization are both very important skills for a Product Manager, and this course provides a foundation in both of these areas. Furthermore, R is a popular tool for data analysis and visualization among Product Managers, and this course provides a solid foundation in R.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course may be helpful for a Software Engineer, as it provides a foundation in data analysis and visualization, which are both very important skills for the role. In particular, this course teaches R, which is a popular tool for data analysis and visualization among Software Engineers.
Market Researcher
Market Researcher
A Market Researcher gathers, analyzes, and interprets market data to help businesses understand their customers and make better decisions. This course may be helpful for a Market Researcher as it provides a foundation in data analytics and visualization, with a focus on R. R is a popular tool for data analysis and visualization among Market Researchers, and this course can help build a strong foundation in it.
Statistician
Statistician
A Statistician studies data and develops statistical methods to solve problems. Understanding data visualization is key for a Statistician. This course teaches Data Visualization, a skill very useful for a job in this domain. Furthermore, learning R can be very useful to handle statistical models, and this course gives a solid foundation in it. Therefore, this course may be helpful for a Statistician.
For more career information including salaries, visit:
OpenCourser.com/course/uct9cf/udacity
Reading list
We've selected nine 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
Data and Visual Analytics.
Classic text on statistical learning, providing a comprehensive overview of the field. It covers a wide range of topics, including linear regression, logistic regression, decision trees, and support vector machines.
Provides a comprehensive introduction to statistical learning, including supervised and unsupervised learning, as well as model selection and evaluation.
Save
Provides a comprehensive introduction to data analytics using the R programming language. It covers a wide range of topics, including data preparation, data visualization, statistical modeling, and machine learning.
Provides a comprehensive introduction to data science for business. It covers a wide range of topics, from data mining to data-analytic thinking.
Comprehensive introduction to the R programming language for data scientists. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning.
Step-by-step guide to data science using the R programming language. It covers a wide range of topics, from data preparation and visualization to model building and evaluation.
Practical guide to data science using the R programming language. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning.
Comprehensive guide to the ggplot2 package in R, which powerful tool for creating data visualizations. It covers a wide range of topics, from basic plotting to advanced techniques.
Provides a practical introduction to machine learning using the R programming language. It covers a wide range of topics, including supervised and unsupervised learning, as well as model selection and evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uct9cf/udacity
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Effort
2 hours
Via
Udacity
Instructor
Arpan Chakraborty
Language
English
Good to know
Explores data modeling, analysis, and visualization techniques used in competitive industries
Teaches R programming language, an industry standard for data analysis
Develops skills in data preprocessing, logistic and linear regression, and regularization
Offers a mix of video lectures, interactive materials, and hands-on labs
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