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.
First R Program and Data Types
Vectors in R
Decision Making in R
Functions , Matrices and Factors in R
Data Frames in R
Competitive Programming using R
Data Manipulation and Accessing Multiple Data Sources in R
Data Visualization using R
Assignment 1
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35Creating Pie chart in R
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36Analyzing Employee Data
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37Creating Bar Plot
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38Stacked Bar Plot in R
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39What is Boxplot?
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40Drawing Boxplot using mtcars dataset in R
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41Boxplot with notch
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42What is Histogram?
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43Drawing Histogram in R using hist() function
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44Using breaks xlim ylim in histogram
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45Time Series using ggplot2 : Basic Line Chart for Time Series
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46Scatter Plot and plot matrices in R
Data Science Fundamentals
Statistics for Machine Learning in R
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48Introduction
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49What is Data Science
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50Why and Where to use Data Science
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51Difference Between Data Science and Machine Learning
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52Data Science and Machine Learning Workflow
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53Elements of Data Science
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54Process of Data Science
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55Tools for Data Science
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56Key Issues in Data Science
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57Data Science Applications and Conclusion
Project : Imputing Missing Values using R Packages
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58Finding Mean in R and how to remove missing values
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59Finding Median and Writing Custom Function to find Mode
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60What is Linear Regression?
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61Prediction using Linear Regression Model
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62Reading CSV file, Creating LR model and Prediction
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63What is Multiple Regression?
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64Predicting Car Mileage using Multiple Regression in R
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65Logistic Regression
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66Normal Distribution
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67Normal Distribution using dnorm() and pnorm() function
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68Normal Distribution using qnorm() and prnorm() function