Statistics for Data Science and Business Analysis

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Saira Sadiq

5:21:38

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1 - Introduction

1 - Introduction.mp4

01:08

2 - What you will learn in this tutorial.html

2 - R Programming

3 - Why Do We Need R.mp4

00:48

4 - What Is R and R Studio.mp4

01:01

5 - R Installation.mp4

01:46

6 - RStudio Installation.mp4

02:11

7 - R Studio Interface Console and Help Tab.mp4

02:30

8 - R Studio Interface File Packages Plot and Environment Tabs.mp4

01:07

9 - Create a New Project in R.mp4

01:28

10 - Download Code and Data Files for This Project.mp4

01:55

10 - RScriptAndData.rar

11 - R Packages.mp4

02:13

12 - Install a Package in R.mp4

03:57

13 - Load a Package in R.mp4

01:11

14 - R Data Sets.mp4

04:55

15 - Broom in R Studio.mp4

00:53

16 - Get the Feel of the Data.mp4

01:20

17 - Get the Feel of the Data View.mp4

01:18

18 - Get the Feel of the Data glimpse.mp4

01:19

19 - Types of Variables in R.mp4

00:55

20 - Types of Variables in R Integers.mp4

00:44

21 - Types of Variables in R Numerics.mp4

02:39

22 - Types of Variables in R Factors or Categorical Variables.mp4

01:43

23 - Types of Variables in R Types of Factors or Categorical Variables.mp4

04:10

24 - Errors Warnings and Messages.mp4

00:27

25 - Error Warning and Messages Errors.mp4

01:59

26 - Error Warning and Messages Information Messages.mp4

00:40

27 - Error Warning and Messages Warning Messages.mp4

01:15

28 - Error Warning and Messages A Quick Recap.mp4

00:59

3 - Subjects in the Population

29 - Subjects in a Population.mp4

01:34

4 - Statistical Questions

30 - Statistical Questions.mp4

00:36

31 - Types of Statistical Questions.mp4

00:47

32 - Descriptive Questions.mp4

01:52

33 - Comparative Questions.mp4

01:04

34 - Relationship Questions.mp4

01:14

35 - Causal Questions.mp4

00:53

36 - Predictive Questions.mp4

01:49

5 - Types of Data

37 - Types of Data.mp4

01:28

38 - Categorical Data.mp4

03:37

39 - Nominal Categorical Data.mp4

02:08

40 - Ordinal Categorical Data.mp4

01:58

41 - Dichotomous Categorical Data.mp4

00:26

42 - Quantitative Data.mp4

00:52

43 - Discrete Quantitative Data.mp4

01:46

44 - Continuous Quantitative Data.mp4

01:58

45 - Why It Is Important to Classify Variables.mp4

01:31

6 - Descriptive and Inferential Statistics

46 - Descriptive and Inferential Statistics.mp4

00:46

47 - Descriptive Statistics.mp4

01:14

48 - General Social Survey.mp4

01:19

49 - Real World Example of Descriptive Statistics GSS Survey.mp4

04:43

50 - Descriptive Statistics for the Entire Population.mp4

02:17

51 - Inferential Statistics.mp4

06:39

52 - Sample Statistics and Population Parameters.html

7 - Distribution of a Variable and Frequency Table

53 - Distribution of a variable and Frequency table.mp4

01:01

54 - Distribution of a Categorical Variable.mp4

02:16

55 - Frequency Table for Categorical Variables.mp4

01:50

56 - Understanding Relative Frequency Proportions and Percentages.mp4

00:58

57 - Distribution of Quantitative Variables.mp4

02:22

58 - Frequency Table for Discrete Quantitative Variables.mp4

00:36

59 - Frequency table for discrete Var Hours Per Day Watching TV Limited Outcomes.mp4

01:37

60 - Frequency table for discrete Var Ideal Number of Kids Limited Outcomes.mp4

00:58

61 - Frequency table for discrete Var Math Exam Scores Wide Range of Outcomes.mp4

01:48

62 - Frequency Table Continuous Quantitative Variables.mp4

00:45

63 - Frequency Table for Continuous Variables Age in Census.mp4

04:55

64 - Calculate Proportion and Percentages in Excel.mp4

06:33

65 - Task 1 Frequency Table for Discrete Quantitative Variable in Excel.mp4

01:03

66 - Task 2 Frequency Table for Categorical Variable in Excel.mp4

00:57

67 - Titanic Dataset.mp4

01:58

68 - Loading Titanic Data Set from Excel into R.mp4

04:08

69 - Getting to Know the Titanic Dataset Exploring Variables.mp4

01:41

70 - Frequency Table for Categorical Variables in R.mp4

04:39

71 - Proportions and Percentages in Frequency Table in R.mp4

02:07

72 - Pipe Operator.mp4

02:36

73 - Discrete Data.mp4

00:54

74 - General Social Survey Number of Kids.mp4

01:51

75 - Recreating GSS Survey Data in Excel Ideal Number of Kids.mp4

02:27

76 - Frequency Table for Discrete Data 1 Loading Number of Kids Data in R.mp4

02:02

77 - Frequency Table for Discrete Data 2 Creating a Frequency Table.mp4

03:32

78 - Frequency Table for Continuous Data 1 Loading Titanic Data In R.mp4

01:29

79 - Frequency Table for Continuous Data 2 Calculating Range.mp4

04:43

80 - Frequency Table for Continuous Data 3 Grouping Passengers by Age Group.mp4

03:49

81 - Frequency Table for Continuous Data 4 LeftClosed and RightOpen Intervals.mp4

03:35

82 - Frequency Table for Continuous Data 5 Creating a Frequency Table.mp4

01:14

83 - Frequency Table for Continuous Data 6 Missing Values NAs.mp4

01:50

8 - Visualizing the Distribution of a Variable Using Graphs

84 - Graphs.mp4

00:39

85 - Bar Graph.mp4

00:23

86 - Pie Chart.mp4

00:31

87 - Pie Chart in R.mp4

04:46

88 - Bar graph or Pie chart Article.html

89 - Histogram for a Discrete Variable.mp4

00:39

90 - Histogram for a Discrete Variable in R.mp4

04:26

91 - Histogram for a Continuous Variable.mp4

00:53

92 - Histogram for a Continuous Variable.mp4

05:02

93 - Histogram for a Continuous Var LeftClosed RightOpen Adjusting Intervals.mp4

00:59

94 - Histogram for a Continuous Var Dealing with Missing Values.mp4

01:14

95 - Histogram for a Continuous Var Setting Interval Lengths in a Histogram.mp4

01:09

9 - The Shape of the Distribution

96 - The Shape of a Distribution.mp4

01:01

97 - Unimodal Distribution.mp4

00:22

98 - Symmetric Distribution.mp4

01:07

99 - Symmetric Distribution of Male Height An Example.mp4

01:03

100 - Simulating Hypothetical Normal Distribution in R Male Heights.mp4

02:29

101 - Symmetric Distribution Histogram in R.mp4

02:37

102 - Skewed Distributions.mp4

00:50

103 - LeftSkewed Distribution.mp4

01:43

104 - LeftSkewed Distribution Histogram in R.mp4

01:36

105 - RightSkewed Distribution.mp4

01:31

106 - RightSkewed Distribution Histogram in R.mp4

02:17

107 - Bimodal Distributions.mp4

01:40

108 - Histogram for Bimodal Distribution.mp4

02:45

109 - Another Example of Bimodal Distribution.mp4

01:10

110 - Histogram for Bimodal Distribution 2.mp4

00:47

111 - Uniform Distribution.mp4

01:27

112 - Simulating Hypothetical Uniform Distribution in R Dice Output.mp4

02:14

113 - Histogram for Uniform Distribution.mp4

01:03

10 - Center of Quantitative Data

114 - Center of Quantitative Data.mp4

02:15

115 - Mode.mp4

00:39

116 - Mode in Symmetric Discrete Distribution.mp4

02:38

117 - Mode in Left Skewed Discrete Distribution.mp4

01:33

118 - Mode in Right Skewed Discrete Distribution.mp4

01:44

119 - Mode in Uniform Discrete Distribution.mp4

01:51

120 - Mode in Categorical Variables.mp4

02:01

121 - Mean.mp4

01:10

122 - Calculating Mean in Excel.mp4

02:08

123 - Impact of Outliers on the Mean.mp4

01:20

124 - Formula for the MeanArticle.html

125 - Compute Population Mean in R.mp4

02:00

126 - Compute Sample Mean in R Part 1.mp4

03:24

127 - Compute Sample Mean in R Part 2.mp4

01:41

128 - Median.mp4

03:03

129 - Impact of Outliers on the Median.mp4

00:35

130 - Formula for Median Article.html

131 - Compute Population Median in R.mp4

01:08

132 - Compute Sample Median in R.mp4

01:17

133 - Outliers and Skewed Distributions Article.html

134 - Mean and Median in Symmetric Distribution.mp4

00:59

135 - Mean and Median in Symmetric Distribution In R.mp4

03:37

136 - Mean Median in Skewed Distribution Article.html

137 - Mean Median in Right Skewed Distribution In R.mp4

02:12

138 - Mean Median in Left Skewed Distribution In R.mp4

01:57

139 - The Mean or Median Article.html

11 - Measuring the Variability of Quantitative Data

140 - What is the Variability.mp4

03:05

141 - Range.mp4

02:09

142 - Standard Deviation.mp4

01:07

143 - Compute Standard Deviation Manually in ExcelPart 1.mp4

01:01

144 - Compute Standard Deviation Manually in ExcelPart 2.mp4

02:43

145 - Formula for Population Standard Deviation Article.html

146 - Formula for the Sample Standard Deviation Article.html

147 - Population Standard Deviation In R.mp4

01:13

148 - Sample Standard Deviation In R.mp4

02:22

149 - Sample Standard Deviation Formula Recap.mp4

00:41

150 - Calculate Standard Deviation Manually in R Using n1 Part 1.mp4

02:08

151 - Calculate Standard Deviation Manually in R Using n1 Part 2.mp4

01:47

152 - Sample Standard Deviation vs Population Standard Deviation Article.html

153 - n versus n1 Mathematically Article.html

154 - Hypothetical Normal Distribution.mp4

01:28

155 - Hypothetical Normal Distribution In R.mp4

02:59

156 - ZScore Article.html

12 - Empirical Rule

157 - Empirical Rule.mp4

01:25

158 - Empirical Rule in R Part 1.mp4

02:22

159 - Empirical Rule in R Part 2.mp4

01:41

160 - Understanding Data Distribution with the Empirical Rule.mp4

02:07

161 - Outliers in Normal Distributions Article.html

162 - Plausible Value of Standard Deviation Article.html

13 - Percentiles and Quartiles

163 - Percentiles and Quartiles.mp4

02:06

164 - Percentiles and Quartiles in R.mp4

01:24

165 - RealLife Usage of Percentiles Growth Charts.mp4

03:54

166 - RealLife Usage of Percentiles SAT Exams.mp4

01:43

167 - Making Sense of SAT Scores The Scaling Process Simplified Article.html

168 - 5Number Summary Using Quartiles Article.html

169 - Measuring Variability Using Interquartile Range IQR.mp4

00:40

170 - Identifying Outliers Using Interquartile Range IQR.html

171 - Boxplot in R.mp4

04:09

172 - Creating SidebySide Box Plots.mp4

03:39

173 - Comparing Two Distributions Using Box Plots.mp4

01:32

14 - Relationship Between Variables

174 - Relationship Between Variables.mp4

02:11

175 - Response and Explanatory Variables Article.html

176 - Types of Relationship.mp4

00:24

177 - Relationship Between Two Categorical Variables.mp4

01:34

178 - Contingency Table.mp4

03:13

179 - Contingency Table In R.mp4

04:27

180 - Stacked Bar Plot Article.html

181 - Stacked Bar Plot In R.mp4

02:45

182 - Relationship between Categorical and Quantitative Variables.mp4

02:29

183 - Relationship between Two Quantitative Variables.mp4

01:22

184 - Positive Relationship Article.html

185 - Negative Relationship Article.html

186 - No Relationship Article.html

187 - Scatterplot in R.mp4

03:02

188 - Correlation Article.html

189 - Correlation In R.mp4

01:55

15 - Summary

190 - Summary.html

Description

Mastering Statistics Fundamentals using R Programming

What You'll Learn?

Learn the essentials of R programming, including installation, setup, and exploring datasets for effective data analysis.
Understand the concept of subjects within a population and their relevance in statistical analysis.
Explore five types of statistical questions and their applications in summarizing, comparing, and predicting data.
Differentiate between categorical and quantitative data and understand their significance in statistical analysis.
Explore variable distribution and frequency tables to gain insights into data patterns.
Gain insights into both descriptive and inferential statistics and their usage in analyzing sample and population data.
Learn to visualize categorical and quantitative data distributions using various graphical representations.
Understand the different shapes of distributions for quantitative variables and their implications.
Learn methods to describe the center of quantitative data, including mean, median, and mode.
Explore measures of variability, including range and standard deviation, to understand data spread.
Gain insights into the empirical rule for understanding data distribution and identifying outliers.
Understand percentiles and quartiles and their significance in summarizing data variability.
Explore the relationship between different variables, including categorical and quantitative variables, and understand correlation analysis.
Learn predictive analysis techniques to make informed predictions based on data patterns and trends.

Who is this for?

This course is suitable for anyone interested in learning statistical analysis techniques using R programming. Whether you're a beginner looking to acquire new skills or someone already familiar with statistical concepts seeking to deepen your knowledge, this course provides valuable insights and practical guidance.

What You Need to Know?

The prerequisites for taking this course include a willingness to learn. No prior experience with statistical analysis or programming is required. Whether you're a beginner or seeking to enhance your skills, this course offers a solid foundation in statistical analysis techniques using R programming.

More details

Description
Welcome to our comprehensive course on statistical analysis! This course is designed to equip you with the essential skills and knowledge needed to excel in statistical analysis, whether you're a beginner or seeking to enhance your expertise.

Through a series of engaging modules, we'll guide you through the fundamentals of statistics using the powerful R programming language. From understanding the basics of R programming to exploring descriptive and inferential statistics, data types, visualization techniques, and more, this course covers a wide range of topics essential for effective statistical analysis in various fields.

Each module is carefully crafted with practical examples and explanations, ensuring you grasp each concept thoroughly. By the end of the course, you'll have the confidence and skills to apply statistical analysis techniques in real-world scenarios, making better-informed decisions and driving impactful outcomes.

Key concepts taught in the course are:
1. R Programming
2. Subjects in the Population
3. Statistical Questions
4. Types of Data
5. Descriptive and Inferential Statistics
6. Distribution of a Variable and Frequency Table
7. Visualizing Distribution with Graphs
8. Shape of Distribution
9. Center of Quantitative Data
10. Measuring Variability of Quantitative Data
11. Empirical Rule
12. Percentiles and Quartiles
13. Relationship Between Variables

Congratulations on taking the first step towards mastering statistical analysis! Dive in and let's embark on this exciting journey together.
Who this course is for:
This course is suitable for anyone interested in learning statistical analysis techniques using R programming. Whether you're a beginner looking to acquire new skills or someone already familiar with statistical concepts seeking to deepen your knowledge, this course provides valuable insights and practical guidance.

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Saira Sadiq

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Saira Sadiq is a seasoned software engineer with over a decade of hands-on experience in PHP and Java development. Throughout her career journey, she has immersed herself in crafting efficient and reliable software solutions, harnessing the capabilities of these powerful programming languages. Beyond her professional pursuits, Saira has discovered a deep passion for teaching and knowledge-sharing. This drive has led her to create two comprehensive courses on Udemy, where she has distilled her extensive expertise into engaging and accessible learning experiences. Join Saira on this journey of growth and discovery as we unlock the potential of technology together.

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