Mastering Data Science

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Why this course ?

  • 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

Duration

N/A

Price

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ADVANCED SAS

Description

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

PYTHON ANALYTICS

Description

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
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
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
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

PYTHON PROGRAMMING

Description

Installation of jupyter notebook
Python package Numpy for numerical computation
Python package matplotlib for visualization
Python package pandas for input and output

R ANALYTICS

Description

Concept of Hypothesis Test- I
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

R PROGRAMMING

Description

Introduction to R
Installation of R for Windows
Installation of R Packages

SAS PREDICTIVE MODELING

Description

Description

Dimensionality and its problem. Eigen Value Decomposition
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
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
Vector
Lists & Matrix
Array
Data Frame
Vectors
Logical Operator
Conditional Control Statement
Loops
While loop
Functions
Create your own function
Create matrices
colnames() rownames()
Matrix Operations
Subset matrices
Import Data
Operations on data frame
Application on data frame
Filter your data
Array and data manipulation
Concept of ANOVA
Hands-On session on ANOVA
Concept of Linear Regression
Assumption of Linear Regression
Multicolinearity & Autocorrelation
Hands-On Session on Linear Regression
Concept of Logistic Regression
Hands-On session on Logistic Regression
Components of Time Series
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
Text Mining 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
Inner Join
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
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]
Central Tendency
Measures of Variance
Practical for central tendency
Discrete random variable
Binomial Distribution
Poisson Distribution
Hyper-geometric Distribution
Normal Distribution
Exponential Distribution
Normality Practical
Sampling
Sampling Distribution
Sampling Practical
One Sample t- test Practical
Two Sample t-test Practical
Chi-Square Distribution Practical
One 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
Case Study of Logistic Regression
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
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
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Course Name: Mastering Data Science