R Programming for Data Science and Machine Learning
- Description
- Curriculum
- FAQ
- Reviews
R is one of the most popular and widely used tools for statistical programming.
It is a powerful, versatile, and easy to use tool for data analytics, and data visualization.
It is the first choice for thousands of data analysts working in both companies and academia.
This course will help you master R programming, as a first step to become a skilled R data scientist.
Got the R knowledge and not sure what to do with it?
This is your chance to finally get your hands dirty with the R and data science.
Each lecture will you give you the tools to achieve the following two goals:
Analyze your data using R.
Find the right visualization for your data using various R tools and packages taught in the course.
Draw some useful visualization and find some useful predictions based on the outcome.
It will take just a few lines of code to get you started.
For an added challenge, repeat the same analysis for your own data.
Share your progress for a chance to receive feedback and suggestions.
That way, this feedback can come not just from me but also from other students in the course.
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35
Creating Pie chart in R -
36
Analyzing Employee Data
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37
Creating Bar Plot
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38
Stacked Bar Plot in R
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39
What is Boxplot?
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40
Drawing Boxplot using mtcars dataset in R
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41
Boxplot with notch
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42
What is Histogram?
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43
Drawing Histogram in R using hist() function
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44
Using breaks xlim ylim in histogram
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45
Time Series using ggplot2 : Basic Line Chart for Time Series
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46
Scatter Plot and plot matrices in R
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48
Introduction
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49
What is Data Science
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50
Why and Where to use Data Science
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51
Difference Between Data Science and Machine Learning
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52
Data Science and Machine Learning Workflow
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53
Elements of Data Science
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54
Process of Data Science
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55
Tools for Data Science
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56
Key Issues in Data Science
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57
Data Science Applications and Conclusion
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58
Finding Mean in R and how to remove missing values
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59
Finding Median and Writing Custom Function to find Mode
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60
What is Linear Regression?
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61
Prediction using Linear Regression Model
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62
Reading CSV file, Creating LR model and Prediction
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63
What is Multiple Regression?
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64
Predicting Car Mileage using Multiple Regression in R
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65
Logistic Regression
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66
Normal Distribution
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67
Normal Distribution using dnorm() and pnorm() function
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68
Normal Distribution using qnorm() and prnorm() function
