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Datascience Course Content :

MAA Trainings is a brand and providing quality online and offline trainings for students in world wide.MAA Trainings providing Best DataScience training center in Hyderabad . and Datascience classroom training in Hyderabad

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  • Course Name

    Datascience Training
  • Course Duration

    50-60 days
  • Faculty

    Real time Expert
  • Category

    Selfpaced Learning
  • Support

    24/7 Technical Support

Description:

MAA Trainings is a One of the best quality training center for online and Corporate trainings In Hyderabad . We are providing training through world wide. MAA Trainings is excellent datascience training center in Hyderabad.After course we will give support for certification, Resume preparation and how to prepare for interviews

Who Can Learn

  • Professionals in Testing field
  • Software Developers
  • Professionals from Analytics background
  • Datawarehousing Professionals
  • Professionals from SAP BI background.

With the growing era of technology and need to constantly update oneself to outstand in the competitive market, MAA Trainings has come to existence to provide people the knowledge about the latest trends in technology . We provide a team of trainers who will put across a thorough and detailed idea about the respective technical courses that you wish to explore .

More Course Information

InMAA Trainings all trainers are well experts and providing training with practically..Here we are teaching from basic to advance. Our real time trainers fulfill your dreams and create professionally driven environment. In datascience training we are providing sample live projects, materials, explaining real time scenarios, Interview skills…We are providing Best datascience Training in Hyderabad, India

Course content

->> Introduction about Statistics
->> Different Types of Variables
->> Measures of Central Tendency with examples
->> Measures of Dispersion
->> Probability & Distributions
->> Probability Basics
->> Binomial Distribution and its properties
->> Poisson distribution and its properties
->> Normal distribution and its properties
->> Sampling methods
->> Different methods of estimation
->> Testing of Hypothesis & Tests
->> Analysis of Variance
->> Data Preparation
->> Exploratory Data analysis
->> Model Development
->> Model Validation
->> Model Implementation

->> 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

2.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

3.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

1.FACTOR ANALYSIS
2.PRINCIPLE COMPONENT ANALYSIS

->> Assumptions of PCA
->> Working Mechanism of PCA
->> Types of Rotations
->> Standardization
->> Positives and Negatives of PCA

1.CHAID
2.CART
3.DIFFERENCE BETWEEN CHAID AND CART
4.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

5.COUPLE OF REAL TIME USE CASES WHICH ARE RELATED TO TELECOM AND RETAIL INDUSTRY
1.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

2.BUSINESS RULES CRITERIA
3.REAL TIME USE CASE TO IDENTIFY THE MOST VALUABLE REVENUE GENERATING CUSTOMERS
1.TIME SERIES COMPONENTS AND DECOMPOSITION
2.BASIC TECHNIQUES

->> Averages,
->> Smoothening etc

3.ADVANCED TECHNIQUES

->> AR Models,
->> ARIMA
->> UCM
->> Hybrid Model

4.UNDERSTANDING FORECASTING ACCURACY - MAPE, MAD, MSE ETC
5.COUPLE OF USE CASES, TO FORECAST THE FUTURE SALES OF PRODUCTS
1.GATHERING TEXT DATA FROM WEB AND OTHER SOURCES
2.PROCESSING RAW WEB DATA
3.COLLECTING TWITTER DATA WITH TWITTER API
4.NAIVE BAYES ALGORITHM

->> Assumptions and of Naïve Bayes
->> Processing of Text data
->> Handling Standard and Text data
->> Building Naïve Bayes Model
->> Understanding standard model metrics
->> Validation of the Models (Re running Vs. Scoring)

5.SENTIMENT ANALYSIS

->> Goal Setting
->> Text Preprocessing
->> Parsing the content
->> Text refinement
->> Analysis and Scoring

6.USE CASE OF HEALTH CARE INDUSTRY, TO IDENTIFY THE SENTIMENT OF THE PATIENTS ON SPECIFIED HOSPITAL BY EXTRACTING THE DATA FROM THE TWITTER.
1.LIVE CONNECTIVITY FROM R TO TABLEAU
2.GENERATING THE REPORTS AND CHARTS
3. 5+-REAL TIME PROJECTS BY USING DIFFERENT USE CASES

Note:- Various Hands on exercises and Assignments on each and every Eco-component.

World class courses from our institute. Don’t hesitate to contact us for details