# Data Science Training

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PSN Trainings is the Best Data Science Online Training institute in Hyderabad,india

Data Science Course Description

### Data scientist Content Basic Concepts of Statistics:

• 1.Descriptive Statistics and Probability Distributions:

• Different Types of Variables
• Measures of Central Tendency with examples
•      Mean
Mode
Median

• Measures of Dispersion
•      Range
Variance
Standard Deviation

• Probability & Distributions
• Probability Basics
• Binomial Distribution and its properties
• Poisson distribution and its properties
• Normal distribution and its properties

• 2. Inferential Statistics and Testing of Hypothesis

• Sampling methods
•      Sampling and types of sampling
Definitions of Sample and Population
Importance of sampling in real time
Different methods of sampling
Simple Random Sampling with replacement and without replacement
Stratified Random Sampling

• Different methods of estimation
• Testing of Hypothesis & Tests
•      Null Hypothesis and Alternate Hypothesis
Level of Significance and P value
t-test and its properties
Chi-square test and its properties
Z test

• Analysis of Variance
•      F-test
One and Two way ANOVA
3. Covariance & Correlation
Importance and Properties of Correlation
Types of Correlation with examples
Predictive Modeling Steps and Methodology with Live example:
• Data Preparation
•      Variable Selection
Transformation of the variables
Normalization of the variables

• Exploratory Data analysis
•      Summary Statistics
Understanding the patterns of the data at single and multiple dimensions
Missing data treatment using different methods
Outliers identification and treating outliers
Visualization of the data using the One Dimensional, Two Dimensional and Multi Dimensional Graphs. Bar chart, Histogram, Box plot, Scatter plot, Bubble chart, Word cloud etc

• Model Development
•      Selection of the sample data
Selecting the appropriate model based on the requirement and data availability

• Model Validation
•      Model Implementation
Key Statistical parameters checking
Validating the model results with the actual result

• Model Implementation
•      Implementing the model for future prediction

• Real time telecom business use case with detail explanation
• Introducing couple of real time use cases and solutions of Banking and Retail domains using the different statistical methods.

• Supervised Techniques:

• Multiple linear Regression
•      Linear Regression - Introduction - Applications
Assumptions of Linear Regression
Building Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)
Validation of Linear Regression Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc)
Interpretation of Results - Business Validation - Implementation on new data
Real time case study of Manufacturing and Telecom Industry to estimate the future revenue using the models

• Logistic Regression
•      Logistic Regression - Introduction - Applications
Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
Building Logistic Regression Model
Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification etc)
Validation of Logistic Regression Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, ROC Curve)
Probability Cut-offs, Lift charts, Model equation, drivers etc)
Interpretation of Results - Business Validation - Implementation on new data
Real time case study to Predict the Churn customers in the Banking and Retail industry

• Partial Least Square Regression
•      Partial Least square Regression - Introduction - Applications
Difference between Linear Regression and Partial Least Square Regression
Building PLS Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)
Interpretation of Results - Business Validation - Implementation on new data
Sharing the real time example to identify the key factors which are driving the Revenue
Variable Reduction Techniques

• Factor Analysis
• Principle component analysis
•      Assumptions of PCA
Working Mechanism of PCA
Types of Rotations
Standardization
Positives and Negatives of PCA
Supervised Techniques Classification:

• CHAID
• CART
• Difference between CHAID and CART
• Random Forest
•      Decision tree vs. Random Forest
Data Preparation
Missing data imputation
Outlier detection
Handling imbalance data
Random Record selection
Random Forest R parameters
Random Variable selection
Optimal number of variables selection
Calculating Out Of Bag (OOB) error rate
Calculating Out of Bag Predictions

• Couple of Real time use cases which are related to Telecom and Retail Industry. Identification of the Churn.

• Unsupervised Techniques:

• Segmentation for Marketing Analysis
•      Need for segmentation
Criterion of segmentation
Types of distances
Clustering algorithms
Hierarchical clustering
K-means clustering
Deciding number of clusters
Case study

• Real time use case to identify the Most Valuable revenue generating Customers.
• Timeseries Analysis:

• Forecasting - Introduction - Applications
• Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
• Basic Techniques
•      Averages,      Smoothening etc
•      AR Models,
ARIMA
UCM
Hybrid Model

• Understanding Forecasting Accuracy - MAPE, MAD, MSE etc
• Couple of use cases, To forecast the future sales of products

• Text Analytics:

• Gathering text data from web and other sources
• Processing raw web data
• Naive Bayes Algorithm
•      Assumptions and of Nave Bayes
Processing of Text data
Handling Standard and Text data
Building Nave Bayes Model
Understanding standard model metrics
Validation of the Models (Re running Vs. Scoring)

• Sentiment analysis
•      Goal Setting
Text Preprocessing
Parsing the content
Text refinement
Analysis and Scoring

• Use case of Health care industry, To identify the sentiment of the patients on Specified hospital by extracting the data from the TWITTER.
Visualization Using Tableau:
• Live connectivity from R to Tableau
• Generating the Reports and Charts

Test

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