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# Data Science With R

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Course type: Online Instructor led Course

Course ID: 43724

Course type: Online Instructor led Course

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Course Content:

1. Data Science Introduction
1.1 Data Science Brief
1.2 Data Statistics - Descriptive and Inferential
1.3 Data Visualization
1.4 Machine Learning Algorithm in Detail
- Supervised Learning Algorithm
- Unsupervised Learning Algorithm
- Reinforcement Learning Algorithm
1.5 Data Science Importance and Key challenges
1.6 Life of Data Scientist
1.7 Data Science Application

2. Measurement and Scaling
1.8 Measurement and Scaling Introduction
1.9 Primary Scales of Measurement
1.9.1. Nominal Scale and Ordinal Scale
1.9.2. Interval Scale and Ratio Scale
1.10 Comparative Scaling Techniques
1.10.1. Paired Comparison Scaling
1.10.2. Rank Order Scaling
1.10.3. Constant Sum Scaling
1.10.4. Q-Sort and Other Procedures
1.11 Non Comparative Scaling Techniques
1.11.1. Continuous Rating Scale
1.11.2. Itemized Rating Scale
1.11.2.1. Likert Scale
1.11.2.2. Sematic Differential Scale
1.11.2.3. Stapel Scale

3. R Introduction
â?¢ R â?? Overview and R Environmental Setup
â?¢ R â?? Basic Syntax
â?¢ R â?? Data Types and Variables
â?¢ R â?? Operators and Loops and If else Statements

4. R â?? Data Structures
â?¢ R â?? Vectors and Lists
â?¢ R â?? Strings and Matrices
â?¢ R â?? Arrays and Factors
â?¢ R â?? Data Frames
â?¢ R â?? Packages

5. R â?? Data Interfaces
â?¢ R- CSV files Read and Write and analyse the data
â?¢ R- Excel files Read and Write and analyse the data
â?¢ R- Text files Read and Write and analyse the data

6. Data Visualization Using SPSS, R and Excel
â?¢ Bar Graph and Line Graph
â?¢ Area Chart and Pie Chart
â?¢ Scatter Diagram and Histogram
â?¢ High-Low Graph
â?¢ Box Plot and Dual Axis Graph

7. Questionnaire Design
7.1 Questionnaire Definition
7.2 Objectives of a Questionnaire
7.3 Questionnaire Design Process
7.3.1 Specify the Information Needed
7.3.2 Type of Interviewing Method
7.3.3 Individual Question Content
7.3.6 Choosing Question Structure
7.3.7 Choosing Question Wording
7.3.8 Determining the Order of Question
7.3.9 Form and Layout
7.3.10 Reproduction of the Questionnaire
7.3.11 Pretesting

8. Descriptive Statistics using R, SPSS and Excel
8.1 Central Tendency
8.1.1 Mean and Weighted Mean and Geometric Mean
8.1.2 Median, Mode, Percentiles and Quartiles
8.2 Dispersion
8.2.1 Variance, Standard Deviation and Range
8.2.2 Interquartile Range and Coefficient of Variation
8.3 Numerical Measures: Z-Scores, Chebyshevâ??s Theorem, Empirical Rule and Detecting
Outliers
8.4 Exploratory Data Analysis â?? Five â?? Number Summary, Box Plot
8.5 Measures of Association: Covariance and Correlation Coefficient

9. Inferential Statistics: Introduction to Probability
9.1 Probability and Statistical Experiment
9.2 Decision Tree Diagram
9.3 Counting Rule â?? Permutation and Combination
9.4 Assigning Probabilities â?? Classical, Frequency and Subjective method
9.5 Events and Their Probabilities
9.6 Relationships of Probability â?? Union, Intersection, Compliments and Mutually
Exclusive events
9.7 Conditional Probability and Bayesâ?? Theorem

10. Discrete Probability Distribution
10.1 Discrete Probability Distribution
10.2 Random Variable â?? Discrete and Continuous
10.3 Binomial Probability Distribution
10.3.1 Evans Electronics Real time example using Binomial Probability
10.4 Poisson Probability Distribution
10.4.1 Mercy Hospital Real time example using Binomial Probability distribution
10.5 Hyper geometric Probability Distribution
10.5.1 Nevereadyâ??s Hospital Real time example using Binomial Probability

11. Continuous Probability Distribution
11.1 Continuous Probability distribution
11.2 Uniform Probability Distribution
11.2.1 Slaterâ??s Buffet Real time example using Uniform Probability Distribution
11.3 Normal Probability Distribution
11.3.1 Pep Zone Real time example using Normal Probability distribution
11.4 Exponential Probability Distribution
11.4.1 Real time example using Exponential Probability distribution

12. Data Preparation
12.1 Data Preparation Process
12.2 Coding and Transcribing
12.3 Data Cleaning
12.5 Selecting a Data Analysis Strategy
12.6 Classification of Statistical Technique â?? Uni variant and Multi Variant

13. Primary and Secondary Data
13.1 Primary Data Collection
13.2 Secondary Data Collection
13.3 Comparison of Primary and Secondary Data
13.4 Classification of Secondary Data

14. Experimental Design
14.2 Pre Experimental Design
14.2.1 One-Shot Case Study
14.2.2 One Group Pre-test - Post-test Design
14.2.3 Static Group Design
14.3 True Experimental Designs
14.3.1 Pre-test - Post-test Control Group Design
14.3.2 Post-test Only Control Group Design
14.4 Quasi Experimental Designs
14.4.1 Time Series Design and Multiple Time Series Design
14.5 Statistical Design
14.6 Randomized Block Design
14.7 Latin Square Design
14.8 Factorial Design

15. Hypothesis Test
15.1 Introduction of Hypothesis Testing
15.2 Types of Hypothesis Test
15.3 Formulation of Hypothesis Testing
15.4 Type I and Type II Error
15.5 Calculation of Test Statistic
15.6 Mapping Hypothesis Test with Real time Example

16. Correlation using R, SPSS and Excel
16.1 Correlation Analysis
16.2 Formulation of Correlation Matrix
16.2.1 Product Moment Correlation
16.2.2 Partial Correlation
16.2.3 Non metric Correlation
16.3 Mapping Correlation concept with Real Time Example

17. Regression using R, SPSS and Excel
17.1 Regression Analysis
17.2 Formulation of Regression Model
17.3 Bivariate Regression
17.4 Statistics Associated with Bivariate Regression Analysis
17.5 Conducting Bivariate Regression Analysis
17.6 Multiple Regressions
17.7 Conducting Multiple Regression
17.8 Mapping Bivariate Regression with Real Time Example

18. ANOVA â?? Analysis of Variance using R, SPSS and Excel
18.1 One way ANOVA
18.1.1 Statistics associated with ANOVA
18.1.2 Conducting One-Way Analysis of Variance
18.1.3 Identification of Dependent & Independent Variables
18.1.4 Decomposition of the Total Variation
18.1.5 Measurement of Effects
18.1.6 Significance Testing
18.1.7 Interpretation of Results
18.1.8 Mapping One-Way with real time example
18.2 Two - Way ANOVA
18.3 N â?? Way ANOVA

19. ANCOVA â?? Analysis of Covariance using R, SPSS and Excel
19.1 ANCOVA Introduction
19.2 Conducting ANCOVA
19.3 Mapping ANCOVA with Real time example

20. Factor Analysis
20.1 Factor Analysis Introduction
20.2 Factor Analysis Model
20.3 Statistics associated with Factor Analysis
20.4 Conducting Factor Analysis
20.5 Construction of Factor Analysis
20.6 Factor Analysis Method
20.6.1 Principal Component Analysis
20.6.2 Rotation Method
20.7 Mapping Factor Analysis with Real Time Example

21. Discriminant Analysis
21.1 Relationship between ANOVA, Regression and Discriminant Analysis
21.2 Discriminant Analysis Model
21.3 Statistics associated with Discriminant Analysis
21.4 Conducting Discriminant Analysis
21.5 Multiple Discriminant Analysis
21.6 Mapping Discriminant Analysis with Real Time Example

22. Cluster Analysis using R, SPSS and Excel
22.1 Cluster Analysis Introduction
22.2 Statistics associated with Cluster Analysis
22.3 Conducting Cluster Analysis
22.4 Classification of Clustering Procedure
22.4.1 Hierarchical Clustering
22.4.2 Non Hierarchical Clustering

23. Logistic Regression using R, SPSS and Excel
23.1 Logistic Function
23.2 Single Predictor Model
23.3 Determine Logistic Cut off
23.4 Estimated Equation for Logistic Regression

24. NaÃ¯ve Bayes
24.1 NaÃ¯ve Rule
24.2 Characteristics of NaÃ¯ve Bayes
24.3 Mapping NaÃ¯ve Bayes with Real Time example
24.4 Advantage and Shortcoming of NaÃ¯ve Bayes

25. Association
25.1 Association Rule
25.2 Apriori Algorithm
25.3 Multiple Association Rules

26. Inferential Statistics using R, SPSS and Excel
26.1 Non â?? Parametric Test
26.1.1 Wilcoxon Sign Test
26.1.2 Friedman Test
26.1.3 Mann â?? Whitney Test
26.1.4 Kruskal â?? Wallis Test
26.1.5 Chi-Square Test
26.2 Parametric Test
26.2.1 T â?? test (One and Two Sample)
26.2.2 Z - test (One and Two Sample)
26.2.3 F â?? Test (One and Two Sample)

27. Data Warehousing
27.1 Data Warehouse Introduction
27.2 Online Transaction Processing (OLTP)
27.3 Online Analytical Processing (OLAP)
27.4 Data Warehousing Modelling
27.4.1 Star Schema
27.4.2 Snowflake Schema
27.4.3 Fact Constellations

28. Decision Trees:
28.1 Decision Tree Introduction
28.2 Measuring Impurity: Gini Index and Entropy
28.3 Decision Tree Structure
28.4 CHAID

29. Real Time Project â?? Primary Research
29.1 Scope of the Project
29.2 Economic Industry Analysis
29.3 Company Analysis
29.4 Competitor Analysis
29.5 Project Specific Analysis
29.6 Theoretical Framework
29.7 Limitations and Findings
29.8 Recommendation

30. Real Time Project â?? Secondary Research
30.1 Scope of the Project
30.2 Industry wise Analysis and Sector Wise Analysis
30.3 Individual Company Analysis
30.4 Findings for the Secondary Research

â?¢ Part of this course, I will share the Live project along with Case studies.
â?¢ Participant should have:
â?¢ Personal Computer with Internet Connection to access Internet.

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Shanavas

MCA, BSC [ Mathematics with Statistics]

10 plus years of experience with various technologies.

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