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- A huge amount of data that is being generated and the evolution in the field of Analytics, Data Science has turned out to be a necessity for all the companies. To make most out of their data, companies from all domains, be it Finance, Marketing, Retail, IT or Bank. All are looking for Data Scientists. This has led to a huge demand for Data Scientists all over the globe. A good amount of salary that a company has to offer and it has been declared as trending job of 21st century, it is a lucrative job for many. This field is such that anyone from any background can make a career as a Data Scientist. R&Python both are open source leading data science tool now. So learning Hands-On Data Science using R&Python will help you to get a job in industry.

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1. What is analytics & Data Science?

2. Common Terms in Analytics

3. Analytics vs. Data warehousing, OLAP, MIS Reporting

4. Relevance in industry and need of the hour

5. Types of problems and business objectives in various industries

6. Overview of analytics tools & their popularity

7. List of steps in Analytics projects

8. Identify the most appropriate solution design for the given problem statement

9. Project plan for Analytics project & key milestones based on effort estimates

10. Build Resource plan for analytics project

11. Why R and Python for data science?

2. Common Terms in Analytics

3. Analytics vs. Data warehousing, OLAP, MIS Reporting

4. Relevance in industry and need of the hour

5. Types of problems and business objectives in various industries

6. Overview of analytics tools & their popularity

7. List of steps in Analytics projects

8. Identify the most appropriate solution design for the given problem statement

9. Project plan for Analytics project & key milestones based on effort estimates

10. Build Resource plan for analytics project

11. Why R and Python for data science?

1. What is R

2. What is S

3. History of R

4. Features of R

5. SAS versus R

2. What is S

3. History of R

4. Features of R

5. SAS versus R

1. Installing R

2. Packages

3. Input/output

4. R interfaces

5. R Library

2. Packages

3. Input/output

4. R interfaces

5. R Library

1. Basic operations in R

2. Different data types and data structures in R

3. Sub setting in R

4. Additional topics on data structures

5. Importing data sets in R

6. R loops and special functions

7. Calculation of commission and simple interest

8. Plots and charts in R

9. Merging and sorting functions in R

10. Summarising Data

11. Calculations of the measures of central tendency and measures of variability

2. Different data types and data structures in R

3. Sub setting in R

4. Additional topics on data structures

5. Importing data sets in R

6. R loops and special functions

7. Calculation of commission and simple interest

8. Plots and charts in R

9. Merging and sorting functions in R

10. Summarising Data

11. Calculations of the measures of central tendency and measures of variability

1. Concept of hypothesis

2. Null hypothesis

3. Alternative hypothesis

4. Type-I error

5. Type-II error

6. Level of Significance

7. Confidence Interval

8. Parametric Tests and Non Parametric Tests

9. One Sample T test

10. Two independent sample T test

11. Paired Sample T test

12. Chi square Test for Independence of Attributes.

2. Null hypothesis

3. Alternative hypothesis

4. Type-I error

5. Type-II error

6. Level of Significance

7. Confidence Interval

8. Parametric Tests and Non Parametric Tests

9. One Sample T test

10. Two independent sample T test

11. Paired Sample T test

12. Chi square Test for Independence of Attributes.

1. One Way Anova

2. Two Way Anova

2. Two Way Anova

1. Principal Component Analysis

2. Estimating the Initial Communalities

3. Eigen Values and Eigen Vectors

4. Correlation Matrix check and KMO-MSA check

5. Factor loading Matrix

6. Diagrammatic Representation of Factors

7. Problems of Factor Loadings and Solutions

2. Estimating the Initial Communalities

3. Eigen Values and Eigen Vectors

4. Correlation Matrix check and KMO-MSA check

5. Factor loading Matrix

6. Diagrammatic Representation of Factors

7. Problems of Factor Loadings and Solutions

1. Types of Clusters

2. Metric and linkage

3. Wardâ€™s Minimum Variance Criteria

4. Semi-Partial R-Square and R-Square

5. Diagrammatic Representation of clusters

6. Problems of Cluster Analysis

2. Metric and linkage

3. Wardâ€™s Minimum Variance Criteria

4. Semi-Partial R-Square and R-Square

5. Diagrammatic Representation of clusters

6. Problems of Cluster Analysis

1. Concept of Regression and features of Linear line.

2. Assumptions of Classical Linear Model

3. Method of Least Squares

4. Understanding the Goodness of Fit

5. Test of Significance of The Estimated Parameters

6. Multiple linear Regression with their Assumptions

7. Concept of Multocollinearity

8. Signs of Multicollinearity

9. The Idea Of Autocorrelation

2. Assumptions of Classical Linear Model

3. Method of Least Squares

4. Understanding the Goodness of Fit

5. Test of Significance of The Estimated Parameters

6. Multiple linear Regression with their Assumptions

7. Concept of Multocollinearity

8. Signs of Multicollinearity

9. The Idea Of Autocorrelation

1. Concept and Applications of Logistic Regression

2. Principles Behind Logistic Regression

3. Comparison between Linear probability Model and Logistic Regression

4. Mathematical Concepts related to Logistic Regression

5. Concordant Pairs, Discordant Pairs and Tied Pairs

6. Classification Table

7. Graphical Representation Related to logistic Regression.

2. Principles Behind Logistic Regression

3. Comparison between Linear probability Model and Logistic Regression

4. Mathematical Concepts related to Logistic Regression

5. Concordant Pairs, Discordant Pairs and Tied Pairs

6. Classification Table

7. Graphical Representation Related to logistic Regression.

1. Decision Tree

2. Concept of Time Series and its Applications

3. Assumptions of Time Series Analysis

4. Components of Time Series

5. Smoothening techniques

6. Stationarity

7. Random Walk

8. ARIMA Forecasting

9. Box Jenkins Technology

10. Merits and Demerits of BJ Technology

2. Concept of Time Series and its Applications

3. Assumptions of Time Series Analysis

4. Components of Time Series

5. Smoothening techniques

6. Stationarity

7. Random Walk

8. ARIMA Forecasting

9. Box Jenkins Technology

10. Merits and Demerits of BJ Technology

1. Python string functions

2. Python Numeric function

2. Python Numeric function

1. Introduction to Loop

2. FOR loop for Sum

3. FOR loop for multiplication

4. While loop

2. FOR loop for Sum

3. FOR loop for multiplication

4. While loop

1. Introduction to python

2. Installation of Anaconda

2. Installation of Anaconda

1. Conditional statement(if elif else)

2. More of if else elif

3. Introduction to python list

4. Indexing of Python List

5. slicing of list

6. modifying a list

7. List methods

2. More of if else elif

3. Introduction to python list

4. Indexing of Python List

5. slicing of list

6. modifying a list

7. List methods

1. Python Variable

2. Understanding more about variable

3. User input variable

4. Calculate discounted price

5. Conditional Statements

2. Understanding more about variable

3. User input variable

4. Calculate discounted price

5. Conditional Statements

1. Python Tuple

2. Functions on Tuple

3 Python Dictionary

4 Fuctions on dictionary

5 Methods of Dictionary

2. Functions on Tuple

3 Python Dictionary

4 Fuctions on dictionary

5 Methods of Dictionary

1. Python Set

2. Create your own functions

3. Classes in python

4. Inheritance in Python

5. Time management function

2. Create your own functions

3. Classes in python

4. Inheritance in Python

5. Time management function

1. Introduction to numpy

2. Array in Numpy

3. Matrices using Numpy

4. Mathematical fuctions using numpy

2. Array in Numpy

3. Matrices using Numpy

4. Mathematical fuctions using numpy

1. Introduction to panda

2. Data frame in pandas

3. Group-by in pandas

2. Data frame in pandas

3. Group-by in pandas

1. Recap of linear regression theory

2. Application of linear regression using Python Library

2. Application of linear regression using Python Library

1. Recap of Logistic regression theory

2. Application of logistic regression using Python Library

2. Application of logistic regression using Python Library

1. SVM theory: Linear, Dual SVM and Kernel Trick

2. SVM code along

2. SVM code along

1. Curse of dimensionality

2. Principal Component Analysis

3. Singular Value Decomposition

4. Independent Component Analysis

5. Fisher Linear Discriminate Analysis

2. Principal Component Analysis

3. Singular Value Decomposition

4. Independent Component Analysis

5. Fisher Linear Discriminate Analysis

1. k-Means Clustering: Theory and Code-along

2. Hierarchical Clustering: Theory and Code-along

3. Gaussian Mixture Model: Theory and Code-along

2. Hierarchical Clustering: Theory and Code-along

3. Gaussian Mixture Model: Theory and Code-along

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