Data Science Training in Dilsukhnagar.☎ +91-7569649640 , Data Science course Training institute in Dilsukhnagar Hyderabad with Real time!!!
Course Contents
Week 0:-STATISTICAL ANALYSIS SYSTTEM (SAS)
Python Introduction
Regression Analysis
-Neural Network, SVM and Random Forest
Final Project: Enroll to data online science completion
Course Contents
Week 0:-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 Language
- Introduction to Statistics
- Graphical and Tabular Descriptive Statistics
- Probability
- Probability Distribution
- Hypothesis Testing
- Statistical Tests (Z-Test, Chi-Square, T-Tests, etc)
- Introduction Analytics Tool(R)
- Introduction to Data Analysis
- Introduction to R programming
- R Environment and Basic Commands
- Data Handling in R
- Importing data
- Sampling
- Data Exploration
- Creating calculated fields
- Sorting & removing duplicates
- Basic Descriptive Statistics
- Population and Sample
- Measures of Central tendency
- Measures of dispersion
- Reporting and Data Validation
- Percentiles & Quartiles
- Box plots and outlier detection
- Creating Graphs and Reporting
- Project on Data handling
- Data exploration
- Data validation
- Missing values identification
- Outliers identification
- Data Cleaning
- Basic Descriptive statistics
- Regression Analysis
- Correlation
- Simple Regression models
- R-Square
- Multiple regression
- Multi collinearity
- Individual Variable Impact
- Logistic Regression
- Need of logistic Regression
- Logistic regression models
- Validation of logistic regression models
- Multi collinearity in logistic regression
- Individual Impact of variables
- Confusion Matrix
- Decision Trees
- Segmentation
- Entropy
- Building Decision Trees
- Validation of Trees
- Fine tuning and Prediction using Trees
- 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
- Objective
- Model building-1
- Model building-2
- Model validation
- Variable selection
- Model calibration
- Out of time validation
- 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
- SVM
- Introduction
- The decision boundary with largest margin
- SVM- The large margin classifier
- SVM algorithm
- The kernel trick
- Building SVM model
- Conclusion
- 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
- Objective
- ML Model-1
- ML Model-2
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
- Data Handling in Python
- Data importing
- Working with datasets
- Manipulating the datasets
- Creating new variables
- Exporting the datasets into external files
- Data Merging
- Python Basic Statistics
- Taking a random sample from data
- Descriptive statistics
- Central Tendency
- Variance e. Quartiles, Percentiles
- Box Plots
- Graphs
- Python Data Handling project
- Project on Data handling
- Data exploration
- Data validation
- Missing values identification
- Outliers identification
- Data Cleaning
- Basic Descriptive statistics
Regression Analysis
- Correlation
- Simple Regression models
- R-Square
- Multiple regressions
- Multi collinearity
- Individual Variable Impact
- Logistic Regression
- Need of logistic Regression
- Logistic regression models
- Validation of logistic regression models
- Multi collinearity in logistic regression
- Individual Impact of variables
- Confusion Matrix
- 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
- Types of errors
- The problem of over fitting
- The problem of under fitting
- Bias Variance Tradeoff
- Cross validation
- Boot strapping
-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
- SVM
- Introduction
- The decision boundary with largest margin
- SVM- The large margin classifier
- SVM algorithm
- The kernel trick
- Building SVM model
- Conclusion
- 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
- Objective
- ML Model-1
- ML Model-2
Final Project: Enroll to data online science completion
- Data exploratio
- Model building
- Testing the score and rank
- Variable selection
- Future reengineering
- Checking the score and rank
- Final Submission