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Data and Visual Analytics is a growing field that specialises in analysing, modelling, and visualising complex high dimensional data.

You will learn to handle complex real-world data. The course illustrates several case studies and hands-on work with the R programming language. At first, you will learn how to work on R programming software. Then the course shows you the process of data analysis covering Data Preprocessing, Data Processing and Data Visualization.

Finally, the course covers Regression including Logistic Regression, Linear Regression, and Regularization.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

Having an Official Edukite Certification is a great way to celebrate and share your success. You can:

  • Add the certificate to your CV or resume and brighten up your career
  • Show it to prove your success

Course Credit: Georgia Institute of Technology

Course Curriculum

Module: 1
Data is Ubiquitous – Data Analysis with R 1 00:02:00
Data is Ubiquitous – Data Analysis with R 2 00:01:00
Go Exploring – Data Analysis with R 3 00:01:00
Why learn EDA? – Data Analysis with R 4 00:03:00
Aude’s Interest in Data – Data Analysis with R 5 00:01:00
Goals of EDA – Data Analysis with R 6 00:02:00
The Growth of Televisions – Data Analysis with R 7 00:01:00
The Growth of Televisions – Data Analysis with R 8 00:02:00
Our Approach for This Course – Data Analysis with R 9 00:01:00
Aude Explores Coordinated Migration – Data Analysis with R 10 00:03:00
Module: 2
Course Overview – Data Analysis with R 11 00:01:00
The Power of R – Data Analysis with R 12 00:01:00
The Power of R – Data Analysis with R 13 00:01:00
Why R – Data Analysis with R 14 00:01:00
Install RStudio on Windows – Data Analysis with R 15 00:02:00
Install RStudio on a Mac – Data Analysis with R 16 00:01:00
RStudio Layout – Data Analysis with R 17 00:02:00
RStudio Layout – Data Analysis with R 18 00:01:00
Demystifying R – Data Analysis with R 19 00:02:00
Demystifying R – Data Analysis with R 20 00:01:00
Module: 3
Getting Help – Data Analysis with R 21 00:01:00
Read and Subset Data – Data Analysis with R 22 00:04:00
R Markdown Documents – Data Analysis with R 23 00:02:00
R Markdown Documents – Data Analysis with R 24 00:01:00
Factor Variables – Data Analysis with R 25 00:02:00
Ordered Factors – Data Analysis with R 26 00:02:00
Ordered Factors – Data Analysis with R 27 00:01:00
Setting Levels of Ordered Factors – Data Analysis with R 28 00:01:00
Setting Levels of Ordered Factors – Data Analysis with R 29 00:01:00
Data Munging – Data Analysis with R 30 00:01:00
Module: 4
Advice for Data Scientists – Data Analysis with R 31 00:01:00
Congratulations – Data Analysis with R 32 00:01:00
Welcome! – Data Analysis with R 33 00:01:00
What to Do First? – Data Analysis with R 34 00:01:00
Pseudo-Facebook User Data – Data Analysis with R 35 00:02:00
Histogram of Users’ Birthdays – Data Analysis with R 36 00:02:00
Histogram of Users’ Birthdays – Data Analysis with R 37 00:01:00
Moira’s Investigation – Data Analysis with R 38 00:01:00
Estimating Your Audience Size – Data Analysis with R 39 00:01:00
Perceived Audience Size – Data Analysis with R 40 00:01:00
Module: 5
Faceting – Data Analysis with R 41 00:02:00
Faceting – Data Analysis with R 42 00:01:00
Be Skeptical Outliers and Anomalies – Data Analysis with R 43 00:02:00
Moira’s Outlier – Data Analysis with R 44 00:01:00
Moira’s Outlier – Data Analysis with R 45 00:01:00
Friend Count – Data Analysis with R 46 00:01:00
Friend Count – Data Analysis with R 47 00:01:00
Limiting the Axes – Data Analysis with R 48 00:01:00
Exploring with Bin Width – Data Analysis with R 49 00:01:00
Adjusting the Bin Width – Data Analysis with R 50 00:01:00
Module: 6
Faceting Friend Count – Data Analysis with R 51 00:01:00
Omitting NA Observations – Data Analysis with R 52 00:01:00
Statistics ‘by’ Gender – Data Analysis with R 53 00:01:00
Statistics ‘by’ Gender – Data Analysis with R 54 00:01:00
Labeling Plots – Data Analysis with R 55 00:01:00
Tenure Histogram By Years – Data Analysis with R 56 00:01:00
Labeling Plots – Data Analysis with R 57 00:01:00
User Ages – Data Analysis with R 58 00:01:00
User Ages – Data Analysis with R 59 00:02:00
The Spread of Memes – Data Analysis with R 60 00:01:00
Module: 7
Lada’s Money Bag Meme – Data Analysis with R 61 00:03:00
Transforming Data – Data Analysis with R 62 00:03:00
Transforming Data – Data Analysis with R 63 00:03:00
Add a Scaling Layer – Data Analysis with R 64 00:01:00
Frequency Polygons – Data Analysis with R 65 00:03:00
Frequency Polygons – Data Analysis with R 66 00:01:00
Likes on the Web – Data Analysis with R 67 00:01:00
Likes on the Web – Data Analysis with R 68 00:01:00
Box Plots – Data Analysis with R 69 00:02:00
Box Plots – Data Analysis with R 70 00:01:00
Module: 8
“Box Plots 71 00:02:00
“Box Plots 72 00:02:00
Getting Logical – Data Analysis with R 73 00:02:00
Getting Logical – Data Analysis with R 74 00:01:00
Analyzing One Variable – Data Analysis with R 75 00:01:00
Analyzing One Variable – Data Analysis with R 76 00:01:00
Welcome! – Data Analysis with R 77 00:01:00
Scatterplots and Perceived Audience Size – Data Analysis with R 78 00:01:00
Scatterplots – Data Analysis with R 79 00:01:00
Scatterplots – Data Analysis with R 80 00:01:00
Module: 9
ggplot Syntax – Data Analysis with R 81 00:02:00
Overplotting – Data Analysis with R 82 00:01:00
Overplotting – Data Analysis with R 83 00:01:00
coord_trans() – Data Analysis with R 84 00:01:00
coord_trans() – Data Analysis with R 85 00:01:00
Alpha and Jitter – Data Analysis with R 86 00:01:00
Alpha and Jitter – Data Analysis with R 87 00:02:00
Overplotting and Domain Knowledge – Data Analysis with R 88 00:02:00
Conditional Means – Data Analysis with R 89 00:05:00
Conditional Means – Data Analysis with R 90 00:01:00
Module: 10
Overlaying Summaries with Raw Data – Data Analysis with R 91 00:03:00
Overlaying Summaries Solution – Data Analysis with R 92 00:01:00
Moira: Histogram Summary & Scatterplots – Data Analysis with R 93 00:02:00
Correlation – Data Analysis with R 94 00:01:00
Correlation – Data Analysis with R 95 00:01:00
Correlation on Subsets – Data Analysis with R 96 00:01:00
Correlation on Subsets – Data Analysis with R 97 00:01:00
Correlation Methods – Data Analysis with R 98 00:01:00
Create Scatterplots – Data Analysis with R 99 00:01:00
Create Scatterplots – Data Analysis with R 100 00:01:00
Module: 11
Strong Correlations – Data Analysis with R 101 00:01:00
Strong Correlations – Data Analysis with R102 00:01:00
Moira on Correlation – Data Analysis with R 103 00:01:00
More Caution with Correlation – Data Analysis with R 104 00:01:00
More Caution with Correlation – Data Analysis with R 105 00:01:00
Noisy Scatterplots – Data Analysis with R 106 00:01:00
Noisy Scatterplots – Data Analysis with R 107 00:01:00
Making Sense of Data – Data Analysis with R 108 00:01:00
Making Sense of Data – Data Analysis with R 109 00:01:00
A New Perspective – Data Analysis with R 110 00:01:00
Module: 12
A New Perspective – Data Analysis with R 111 00:01:00
Understanding Noise: Age to Age Months – Data Analysis with R 112 00:02:00
Understanding Noise: Age to Age Months – Data Analysis with R 113 00:01:00
Age with Months Means – Data Analysis with R 114 00:01:00
Age with Months Means – Data Analysis with R 115 00:03:00
Noise in Conditional Means – Data Analysis with R 116 00:01:00
Noise in Conditional Means – Data Analysis with R 117 00:01:00
Smoothing Conditional Means – Data Analysis with R 118 00:03:00
Which Plot to Choose? – Data Analysis with R 119 00:01:00
Analyzing Two Variables – Data Analysis with R 120 00:01:00
Module: 13
Analyzing Two Variables – Data Analysis with R 121 00:01:00
Multivariate Data – Data Analysis with R 122 00:01:00
Perceived Audience Size by Age – Data Analysis with R 123 00:01:00
Third Qualitative Variable – Data Analysis with R 124 00:02:00
Third Qualitative Variable – Data Analysis with R 125 00:01:00
Plotting Conditional Summaries – Data Analysis with R 126 00:01:00
Plotting Conditional Summaries – Data Analysis with R 127 00:01:00
Thinking in Ratios – Data Analysis with R 128 00:01:00
Wide and Long Format – Data Analysis with R 129 00:01:00
Reshaping Data – Data Analysis with R 130 00:02:00
Module: 14
Ratio Plot – Data Analysis with R 131 00:01:00
Ratio Plot – Data Analysis with R 132 00:01:00
Third Quantitative Variable – Data Analysis with R 133 00:01:00
Third Quantitative Variable – Data Analysis with R 134 00:01:00
Cut a Variable – Data Analysis with R 135 00:01:00
Cut a Variable – Data Analysis with R 136 00:01:00
Plotting It All Together – Data Analysis with R 137 00:01:00
Plotting It All Together – Data Analysis with R 138 00:01:00
Plot the Grand Mean – Data Analysis with R 139 00:01:00
Plot the Grand Mean – Data Analysis with R 140 00:01:00
Module: 15
Friending Rate – Data Analysis with R 141 00:01:00
Friending Rate – Data Analysis with R 142 00:01:00
Friendships Initiated – Data Analysis with R 143 00:01:00
Friendships Initiated – Data Analysis with R 144 00:01:00
Bias Variance Trade off Revisited – Data Analysis with R 145 00:01:00
Bias Variance Trade off Revisited – Data Analysis with R 146 00:01:00
Sean’s NFL Fan Sentiment Study – Data Analysis with R 147 00:01:00
Introducing the Yogurt Dataset – Data Analysis with R 148 00:02:00
Histograms Revisited – Data Analysis with R 149 00:01:00
Histograms Revisited – Data Analysis with R 150 00:01:00
Module: 16
Number of Purchases – Data Analysis with R 151 00:01:00
Number of Purchases – Data Analysis with R 152 00:01:00
Prices Over Time – Data Analysis with R 153 00:01:00
Prices Over Time – Data Analysis with R 154 00:01:00
Sampling Observations – Data Analysis with R 155 00:01:00
Looking at Samples of Households – Data Analysis with R 156 00:02:00
The Limits of Cross Sectional Data – Data Analysis with R 157 00:01:00
Many Variables – Data Analysis with R 158 00:02:00
Scatterplot Matrices – Data Analysis with R 159 00:02:00
Scatterplot Matrices – Data Analysis with R 160 00:01:00
Module: 17
Even More Variables – Data Analysis with R 161 00:01:00
Heat Maps – Data Analysis with R 162 00:02:00
Analyzing Three or More Variables – Data Analysis with R 163 00:01:00
Analyzing Three or More Variables – Data Analysis with R 164 00:01:00
Welcome! – Data Analysis with R 165 00:01:00
Scatterplot Review – Data Analysis with R 166 00:01:00
Scatterplot Review – Data Analysis with R 167 00:01:00
Price and Carat Relationship – Data Analysis with R 168 00:01:00
Price and Carat Relationship – Data Analysis with R 169 00:01:00
Frances Gerety – Data Analysis with R 170 00:02:00
Module: 18
Frances Gerety – Data Analysis with R 171 00:01:00
The Rise of Diamonds – Data Analysis with R 172 00:01:00
ggpairs Function – Data Analysis with R 173 00:01:00
ggpairs Function – Data Analysis with R 174 00:01:00
The Demand of Diamonds – Data Analysis with R 175 00:01:00
The Demand of Diamonds – Data Analysis with R 176 00:01:00
Connecting Demand and Price Distribution – Data Analysis with R 177 00:01:00
Connecting Demand and Price Distribution – Data Analysis with R 178 00:01:00
Scatterplot Transformation – Data Analysis with R 179 00:01:00
Overplotting Revisited – Data Analysis with R 180 00:01:00
Module: 19
Overplotting Revisited – Data Analysis with R 181 00:01:00
Plot Colors for Qualitative Factors – Data Analysis with R 182 00:01:00
Price vs. Carat and Clarity – Data Analysis with R 183 00:01:00
Price vs. Carat and Clarity – Data Analysis with R 184 00:01:00
Clarity and Price – Data Analysis with R 185 00:01:00
Clarity and Price – Data Analysis with R 186 00:01:00
Price vs Carat and Cut – Data Analysis with R 187 00:01:00
Price vs Carat and Cut – Data Analysis with R 188 00:01:00
Cut and Price – Data Analysis with R 189 00:01:00
Cut and Price – Data Analysis with R 190 00:01:00
Module: 20
Price vs Carat and Color – Data Analysis with R 191 00:01:00
Price vs Carat and Color – Data Analysis with R 192 00:01:00
Color and Price – Data Analysis with R 193 00:01:00
Color and Price – Data Analysis with R 194 00:01:00
Linear Models in R – Data Analysis with R 195 00:01:00
Linear Models in R – Data Analysis with R 196 00:01:00
Building the Linear Model – Data Analysis with R 197 00:01:00
Model Problems – Data Analysis with R 198 00:01:00
Model Problems – Data Analysis with R 199 00:01:00
“A Bigger 200 00:01:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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