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Start Your Free Trial Today >>Take Your R & R Studio Skills To The Next Level. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2
Ready to take your R Programming skills to the next level?
Want to truly become proficient at Data Science and Analytics with R?
This course is for you!
Professional R Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for Analytics of the REAL WORLD.
In this course, you will learn:
How to prepare data for analysis in R
How to perform the median imputation method in R
How to work with date-times in R
What Lists are and how to use them
What the Apply family of functions is
How to use apply(), lapply() and sapply() instead of loops
How to nest your own functions within apply-type functions
How to nest apply(), lapply() and sapply() functions within each other
And much, much more!
The more you learn, the better you will get. After every module, you will have a robust set of skills to take with you into your Data Science career.
The course is divided into three main sections based on real-life case studies:
Section 1: Data Preparation
In the first section, you will be working with financial data, cleaning it up, and preparing for analysis. You were asked to create charts showing revenue, expenses, and profit for various industries.
Section 2: Lists in R
In the second section, you will be helping Coal Terminal understand what machines are underutilized by preparing various data analysis tasks.
Section 3: "Apply" Family of Functions
In the third section, you are heading to the meteorology bureau. They want to understand better weather patterns and requested your assistance on that.
Why Choose This Course?
Hands-On Learning: Engage with practical exercises and real-world case studies to apply your knowledge.
Flexibility: Whether you are starting from scratch or looking to update your skills, this course is structured to cater to your learning pace and style.
Community and Support: Join a community of like-minded learners and professionals, with access to expert support throughout your learning journey.
$35
/ MonthAccelerate your Career and boost your Earning Potential with our Expert Instructors & Community! What Youāll Unlock: - š 40+ Courses (over 200 hours!) - š 17 Specialized Career Paths - š Quizzes and Practice Activities - š Certificates for Courses & Career Paths - š Prizes for Learning - š§Ŗ Weekly Live Labs (plus recordings!) - šÆ Monthly Missions to practice even more - š¼ Monthly Career Booster Events - š¬ Full access to the SDS Community - ā” Monthly Speed Networking - š„ Monthly Resume Clinics - š„ Group Mentorship Program "In just a few months of learning at SDS, I landed a Data Analyst job!" ā Sanaz Afshar, California
$157
/ MonthAccelerate your Career and boost your Earning Potential with our Expert Instructors & Community! What Youāll Unlock: - š 40+ Courses (over 200 hours!) - š 17 Specialized Career Paths - š Quizzes and Practice Activities - š Certificates for Courses & Career Paths - š Prizes for Learning - š§Ŗ Weekly Live Labs (plus recordings!) - šÆ Monthly Missions to practice even more - š¼ Monthly Career Booster Events - š¬ Full access to the SDS Community - ā” Monthly Speed Networking - š„ Monthly Resume Clinics - š„ Group Mentorship Program Plus, Pro Plan perks just for you: - š A Personalized Career Path built around your goals - š§āš« 1-on-1 Mentoring Sessions every month - āļø Personalized Resume Reviews "In just a few months of learning at SDS, I landed a Data Analyst job!" ā Sanaz Afshar, California
Course content
Welcome to This Section. This Is What You Will Learn!
02:43
Project Brief: Financial Review
02:49
Import Data Into R
05:11
What are Factors (Refresher)
07:37
The Factor Variable Trap
10:09
FVT Example
06:35
gsub() and sub()
09:45
Dealing With Missing Data
09:32
What is an NA?
05:15
An Elegant Way To Locate Missing Data
10:01
Data Filters: which() for Non-Missing Data
08:58
Data Filters: is.na() for Missing Data
05:52
Removing Records With Missing Data
04:48
Reseting the Dataframe Index
05:04
Replacing Missing Data: Factual Analysis Method
06:49
Replacing Missing Data: Median Imputation Method (Part 1)
13:09
Replacing Missing Data: Median Imputation Method (Part 2)
04:30
Replacing Missing Data: Median Imputation Method (Part 3)
06:14
Replacing Missing Data: Deriving Values Method
04:34
Visualizing Results
10:50
Section Recap
05:49
Quiz: Data Preparation
Welcome to This Section. This Is What You Will Learn!
01:44
Project Brief: Machine Utilization
17:44
Import Data Into R
05:58
Handling Date-Times in R
10:18
R Programming: What is a List?
11:19
Naming Components of a List
04:36
Extracting Components of Lists: [] vs [[]] vs $
06:46
Adding and Deleting Components
09:36
Subsetting a List
08:06
Creating a Timeseries Plot
09:12
Section Recap
03:32
Quiz: Lists in R
Welcome to This Section. This Is What You Will Learn!
02:41
Project Brief: Weather Patterns
08:50
Import Data Into R
09:46
R Programming: What is the Apply Family?
07:34
Using apply()
08:34
Recreating the apply Function With Loops (Advanced Topic)
07:39
Using lapply()
11:02
Combining lapply() With []
07:11
Adding Your Own Functions
09:33
Using sapply()
10:58
Nesting apply() Functions
08:19
which.max() and which.min() (Advanced Topic)
11:33
Section Recap
05:12
Quiz: "Apply" Family of Functions