Data Science Courses

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STATISTICAL ANALYSIS SYSTTEM (SAS)

  • Introduction to SAS
  • Types of Libraries and Variables
  • Data –Reading ,Writing ,Importing and Exporting
  • Functions and Options
  • Conditional Statements and Logical Operators
  • Datasets –Introduction ,Appending ,Merging and Sorting
  • Report Generation ,Data set Manipulation
  • Introduction to Databases ,RDBMS Concepts
  • Structured Query Lanauage

Statistics Theory:

  • Introduction to Statistics
  • Graphical and Tabular Descriptive Statistics
  • Probability
  • Probability Distribution
  • Hypothesis Testing
  • Statistical Tests(Z-Test, Chi-Square, T-Tests,etc.)

R Programming, Data Handling and Basic Statistics

1. Introduction Analytics Tool(R)

  • Introduction to Data Analysis
  • Introduction to R programming
  • R Environment and Basic Commands

2. Data Handling in R

  • Importing data
  • Sampling
  • Data Exploration
  • Creating calculated fields
  • Sorting & removing duplicates

3. Basic Descriptive Statistics

  • Population and Sample
  • Measures of Central tendency
  • Measures of dispersion

4. Reporting and Data Validation

  • Percentiles & Quartiles
  • Box plots and outlier detection
  • Creating Graphs and Reporting

Data Exploration, Validation and Cleaning Project

  • Project on Data handling
  • Data exploration
  • Data validation
  • Missing values identification
  • Outliers identification
  • Data Cleaning
  • Basic Descriptive statistics

Regression Analysis & Logistic Regression Model Building

1. Regression Analysis

  • Correlation
  • Simple Regression models
  • R-Square
  • Multiple regression
  • Multicollinearity
  • Individual Variable Impact

2. Logistic Regression

  • Need of logistic Regression
  • Logistic regression models
  • Validation of logistic regression models
  • Multicollinearity in logistic regression
  • Individual Impact of variables
  • Confusion Matrix

Decision Trees & Model Selection

1. Decision Trees

  • Segmentation
  • Entropy
  • Building Decision Trees
  • Validation of Trees
  • Fine tuning and Prediction using Trees
  • Model Selection and Cross validation
  • How to validate a model?
  • What is a best model?
  • Types of data d. Types of errors
  • The problem of over fitting
  • The problem of under fitting
  • Bias Variance Tradeoff
  • Cross validation
  • Boot strapping

Predictive Modelling Project

  • Objective
  •  Model building-1
  • Model building-2
  • Model validation
  • Variable selection
  • Model calibration
  • Out of time validation

Neural Network, SVM and Random Forest

1.Neural Networks

  • Neural network Intuition
  • Neural network and vocabulary
  • Neural network algorithm
  • Math behind neural network algorithm
  • Building the neural networks
  • Validating the neural network model
  • Neural network applications
  • Image recognition using neural networks

 

2. SVM

  • Introduction
  • The decision boundary with largest margin
  • SVM- The large margin classifier
  • SVM algorithm
  • The kernel trick
  • Building SVM model
  • Conclusion

 

3. Random Forest and Boosting

  • Introduction
  • The decision boundary with largest margin
  • SVM- The large margin classifier
  • SVM algorithm
  • The kernel trick
  • Building SVM model
  • Conclusion

 

Machine Learning Project

  • Objective
  • ML Model-1
  • ML Model-2

Python Introduction & Project

Python Introduction

  • What is Python & History?
  • Installing Python & Python Environment
  • Basic commands in Python
  • Data Types and Operations
  • Python packages
  • Loops
  • My first python program
  • If-then- else statement

2. Data Handling in Python

  • Data importing
  • Working with datasets
  • Manipulating the datasets
  • Creating new variables
  • Exporting the datasets into external files
  • Data Merging

3. Python Basic Statistics

  • Taking a random sample from data
  • Descriptive statistics
  • Central Tendency
  • Variance e. Quartiles, Percentiles
  • Box Plots
  • Graphs

 

4. Python Data Handling project

  • Project on Data handling
  • Data exploration
  • Data validation
  • Missing values identification
  • Outliers identification
  • Data Cleaning
  • Basic Descriptive statistics

 

Python Predictive Modeling & Project

Regression Analysis

  • Correlation
  • Simple Regression models
  • R-Square
  • Multiple regression
  • Multicollinearity
  • Individual Variable Impact

2. Logistic Regression

  • Need of logistic Regression
  • Logistic regression models
  • Validation of logistic regression models
  • Multicollinearity in logistic regression
  • Individual Impact of variables
  • Confusion Matrix

3. Decision Trees

  • Segmentation
  • Entropy
  • Building Decision Trees
  • Validation of Trees
  • Fine tuning and Prediction using Trees

4. Model Selection and Cross validation

  • How to validate a model?
  • What is a best model?
  • Types of data
  • Types of errors
  • The problem of over fitting
  • The problem of under fitting
  • Bias Variance Tradeoff
  • Cross validation
  • Boot strapping

Python Machine Learning

-Neural Network, SVM and Random Forest

  • Neural Networks
  • Neural network Intuition
  • Neural network and vocabulary
  • Neural network algorithm
  • Math behind neural network algorithm
  • Building the neural networks
  • Validating the neural network model
  • Neural network applications
  • Image recognition using neural networks

B. SVM

  • Introduction
  • The decision boundary with largest margin
  • SVM- The large margin classifier
  • SVM algorithm
  • The kernel trick
  • Building SVM model
  • Conclusion

 

c. Random Forest and Boosting

  • Introduction
  • The decision boundary with largest margin
  • SVM- The large margin classifier
  • SVM algorithm
  • The kernel trick
  • Building SVM model
  • Conclusion

Machine Learning Project

  • Objective
  • ML Model-1
  • ML Model-2

Data Science Hackathon / Competition

Enroll to data online science completion

  • Data exploration
  • Model building
  • Testing the score and rank
  • Variable selection
  • Future reengineering
  • Checking the score and rank
  • Final Submission