Currency:

- Hands-On Data science and Machine learning course designed to impart the training to understand the scientific techniques to extract meaning and insights from data.
- This course designed to introduce participant’s to this rapidly growing field and equip them with some of its basic principles and frequently used tools as well as its general mindset.
- In the field data science Python, R and SAS are the three most popular languages. This program will help to develop all the required skill to become a successful data scientist.
- To automate analytical model building we use Machine learning. Machine learning is a field of research that enable computers to learn from data.

Program Duration

and Fees

N/A

N/A

What is SQL and How it comes into the picture of SAS?

Brief introduction to Probability and Statistics

Understanding different types of data

Examining distribution of the variables

Examining relationship among variables

Exploratory data analysis using python

Understanding different types of data

Examining distribution of the variables

Examining relationship among variables

Exploratory data analysis using python

Linear Regression on bi-variate data

Python implementation of linear regression with bi-variate data

Multivariate regression and Polynomial regression

Python implementation of Gradient descent update rule for regression

Python implementation of linear regression with multivariate data and polynomial regression

Python implementation of linear regression with bi-variate data

Multivariate regression and Polynomial regression

Python implementation of Gradient descent update rule for regression

Python implementation of linear regression with multivariate data and polynomial regression

Classification problem. introduction to Logistic regression for binary classification problem

Logistic regression on Binary classification problem and multi-class classification problem. Metrics for classification.

Python program on GD update rule for logistic regression

Python implementation of LR with binary and multi-class classification problem

Logistic regression on Binary classification problem and multi-class classification problem. Metrics for classification.

Python program on GD update rule for logistic regression

Python implementation of LR with binary and multi-class classification problem

kNN classifier

Implementation of kNN classifier using python

Naïve Bayes Classifier

Implementation of Naïve bayes classifier using python

Decision Tree Classifier

Implementation of Decision tree classifier using python

Support vector machine

Implementation of SVM classifier using python

Random Forest Classifier

Implementation of RF classifier using python

Implementation of kNN classifier using python

Naïve Bayes Classifier

Implementation of Naïve bayes classifier using python

Decision Tree Classifier

Implementation of Decision tree classifier using python

Support vector machine

Implementation of SVM classifier using python

Random Forest Classifier

Implementation of RF classifier using python

Python package Numpy for numerical computation

Python package matplotlib for visualization

Python package pandas for input and output

Concept of Hypothesis Test-II

Hypothesis test for mean

Hands-On session on One Sample T-Test

Hands-On session on Two Sample T-Test

Concept of chi-square distribution

Hands-On session on Chi-Square Test

Introduction to R

Installation of R for Windows

Installation of R Packages

Installation of R for Windows

Installation of R Packages

Introduction to statistics

Dimensionality and its problem. Eigen Value Decomposition

Principal component analysis

Principal component analysis in python

Principal component analysis

Principal component analysis in python

k-Means clustering algorithm and its limitation

Implementation of k-means clustering

Hierarchical clustering

Implementation of hierarchical clustering in python

Implementation of k-means clustering

Hierarchical clustering

Implementation of hierarchical clustering in python

Perceptron and its learning rule, Limitations of perceptron

ANN: Multilayered perceptron architecture

ANN: Learning rule - Backprop

Build a ANN for hand digit recognition task in python

ANN: Multilayered perceptron architecture

ANN: Learning rule - Backprop

Build a ANN for hand digit recognition task in python

Vector

Lists & Matrix

Array

Data Frame

Lists & Matrix

Array

Data Frame

Vectors

Logical Operator

Conditional Control Statement

Loops

While loop

Functions

Create your own function

Logical Operator

Conditional Control Statement

Loops

While loop

Functions

Create your own function

Create matrices

colnames() rownames()

Matrix Operations

Subset matrices

colnames() rownames()

Matrix Operations

Subset matrices

Import Data

Operations on data frame

Application on data frame

Filter your data

Array and data manipulation

Operations on data frame

Application on data frame

Filter your data

Array and data manipulation

Concept of ANOVA

Hands-On session on ANOVA

Hands-On session on ANOVA

Concept of Linear Regression

Assumption of Linear Regression

Multicolinearity & Autocorrelation

Hands-On Session on Linear Regression

Assumption of Linear Regression

Multicolinearity & Autocorrelation

Hands-On Session on Linear Regression

Concept of Logistic Regression

Hands-On session on Logistic Regression

Hands-On session on Logistic Regression

Components of Time Series

ARIMA Modelling

Hands-On session on Time Series Analysis

ARIMA Modelling

Hands-On session on Time Series Analysis

Concept of Clustering

Distance Measurement / Linkage

K- Means Clustering

Hierarchical Clustering

Hands-On Session on Hierarchical Clustering

Distance Measurement / Linkage

K- Means Clustering

Hierarchical Clustering

Hands-On Session on Hierarchical Clustering

Text Mining Analysis

Decision Tree

Market Basket Analysis

Decision Tree

Market Basket Analysis

SQL Syntax

Where Clause

Select statement and Columns (Variables,Calculated Value,Formatted Value)

Case Logic

Summary Functions

A Small Assignment [Problem Statement Discussion]

Assignment Solution

Where Clause

Select statement and Columns (Variables,Calculated Value,Formatted Value)

Case Logic

Summary Functions

A Small Assignment [Problem Statement Discussion]

Assignment Solution

Inner Join

Joining Three Tables

Left/Right Join

Full Join (using coalesce function)

A Small Assignment on Joining [Problem Statement Discussion]

Assignment Solution

Joining Three Tables

Left/Right Join

Full Join (using coalesce function)

A Small Assignment on Joining [Problem Statement Discussion]

Assignment Solution

Creating A Table

Altering Columns (add, modify, delete + adding values to column)

Inserting Rows with a Query and SET statement + Deleting Rows

A Small Assignment on Tables [Problem Statement Discussion]

Assignment Solution

Altering Columns (add, modify, delete + adding values to column)

Inserting Rows with a Query and SET statement + Deleting Rows

A Small Assignment on Tables [Problem Statement Discussion]

Assignment Solution

Comparing Tables

Finding Duplicate Records

Assignment Only Displaying Unique records/rows[Problem Statement ]

Assignment Only Displaying Unique records/rows[Solution ]

Customize The Way You Sort

Already covered in the last chapter

Short Assignment [Problem]

Short Assignment [Solution]

Finding Duplicate Records

Assignment Only Displaying Unique records/rows[Problem Statement ]

Assignment Only Displaying Unique records/rows[Solution ]

Customize The Way You Sort

Already covered in the last chapter

Short Assignment [Problem]

Short Assignment [Solution]

Central Tendency

Measures of Variance

Practical for central tendency

Measures of Variance

Practical for central tendency

Discrete random variable

Binomial Distribution

Poisson Distribution

Hyper-geometric Distribution

Binomial Distribution

Poisson Distribution

Hyper-geometric Distribution

Normal Distribution

Exponential Distribution

Normality Practical

Exponential Distribution

Normality Practical

Sampling

Sampling Distribution

Sampling Practical

Sampling Distribution

Sampling Practical

One Sample t- test Practical

Two Sample t-test Practical

Chi-Square Distribution Practical

Two Sample t-test Practical

Chi-Square Distribution Practical

One Way ANOVA Practical

Two Way ANOVA Practical

Two Way ANOVA Practical

Case Study of Linear Regression

Linear Regression Practical Session - Part- 1

Linear Regression Practical Session - Part- 2

Linear Regression Practical Session - Part- 3

Linear Regression Practical Session - Part- 4

Linear Regression Practical Session - Part- 1

Linear Regression Practical Session - Part- 2

Linear Regression Practical Session - Part- 3

Linear Regression Practical Session - Part- 4

Case Study of Logistic Regression

Logistic Regression Practical - part – 1

Logistic Regression Practical - part – 2

Logistic Regression Practical - part – 3

Logistic Regression Practical - part – 1

Logistic Regression Practical - part – 2

Logistic Regression Practical - part – 3

Case Study Of Time Series

Time series Practical - Part – 1

Time series Practical - Part – 2

Time series Practical - Part – 1

Time series Practical - Part – 2

Application of Hierarchical Cluster analysis - Part – 1

Application of Hierarchical Cluster analysis - Part – 2

Application of Hierarchical Cluster analysis - Part – 3

Application of Factor analysis

Application of Hierarchical Cluster analysis - Part – 2

Application of Hierarchical Cluster analysis - Part – 3

Application of Factor analysis

×
**Checkout**