Data scienceTraining Institute In Hyderabad|SathyaTechnologies

1. Course in Data Science Contact: +917095167689 About the Course: In this course you will get an introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like R Programming, SAS, MINITAB and EXCEL. Course features:  140+ hours of teaching  Exam on every weekend  Exclusive doubt clarification session on every weekend  Real Time Case Study driven approach  Live Project  Placement Assistance Qualification  Any Graduate. No programming and statistics knowledge or skills required Duration of the course:  3 months Mode of course delivery  Online Training Faculty Details: A team of faculty having an average 20 + years’ experience in the data analysis across various industries and training.

2. Module:1 - Descriptive & Inferential Statistics:(30 Hrs) 1. Turning Data into Information 5. Hypothesis Testing Data Visualization Hypothesis Testing  Measures of Central Tendency  Type I and Type II Errors  Measures of Variability  Decision Making in Hypothesis Testing  Measures of Shape  Hypothesis Testing for a Mean,  Covariance, Correlation Variance, Proportion  Using Software-Real Time Problems 2. Probability Distributions  Power in Hypothesis Testing  Using Software-Real Time Problems 6. Comparing Two Groups  Probability Distributions: Discrete Random Variables  Comparing Two Groups  Mean, Expected Value  Comparing Two Independent Means,  Binomial Random Variable Proportions  Poisson Random Variable  Pairs wise testing for Means  Continuous Random Variable  Two Variances Test(F-Test)  Normal distribution  Using Software-Real Time Problems 7. Analysis of Variance (ANOVA)  Using Software-Real Time Problems 3. Sampling Distributions  One-Way and Two-way ANOVA  Central Limit Theorem  ANOVA Assumptions  Sampling Distributions for Sample  Multiple Comparisons (Tukey, Dunnett) Proportion, p-hat  Using Software-Real Time Problems 8. Association Between Categorical Variables  Sampling Distribution of the Sample Mean, x-bar  Using Software-Real Time Problems 4. Confidence Intervals  Two Categorical Variables Relation  Statistical Significance of Observed  Statistical Inference Relationship / Chi-Square Test  Constructing confidence intervals to  Calculating the Chi-Square Test estimate a population Mean, Variance, Statistic Proportion Using Software-Real Time Problems  Contingency Table  Using Software-Real Time Problems

3. Module:2 – Prediction Analytics (25Hrs) 1. Simple Linear Regression 5. Diagnostics for Leverage and Influence  Simple Linear Regression Model  Least-Square Estimation of the  Leverage/ Cook’s D /DFFITS/DFBETAS Parameters  Treatment of Influential Observations  Hypothesis Testing on the Slope and  Using Software-Real Time Problems 6.Polynomial Regression Intercept  Coefficient of Determination  Polynomial Model in One/ Two /More  Estimation by Maximum Likelihood Variable  Using Software-Real Time 2. Multiple Regression  Using Software-Real Time Problems 7.Dummy Variables  Multiple Regression Models  The General Concept of Indicator  Estimation of Model Parameters Variables  Hypothesis Testing in Multiple Linear  Using Software-Real Time Problems 8. Variables Selection and Model Building Regression  Multicollinearity  Using Software-Real Time Problems 3.Model Adequacy Checking  Forward Selection/Backward Elimination  Stepwise Regression  Residual Analysis  Using Software-Real Time Problems 9. Generalized Linear Models  The PRESS Statistic  Detection and Treatment of Outliers  Concept of GLM  Lack of Fit of the Regression Model  Logistic Regression  Using Software-Real Time Problems 4.Transformations  Poisson Regression  Negative Binomial Regression  Variance-Stabilizing Transformations  Exponential Regression 10. Autocorrelation  Transformations to Linearize the Model  Analytical Methods for selecting a  Regression Models with Autocorrelation Transformation Errors  Generalized and Weighted Least Squares  Using Software-Real Time Problems

4. Module:3 – Applied Multivariate Analysis (25hrs) 1. Measures of Central Tendency, Dispersion and Association 5.Discriminant Analysis  Discriminant Analysis (Linear/Quadratic) Measures of Central Tendency/  Estimating Misclassification Probabilities Measures of Dispersion  Using Software-Real Time Problems  Using Software-Real Time Problems 2. Multivariate Normal Distribution  Exponent of Multivariate Normal Distribution  Multivariate Normality and Outliers  Eigenvalues and Eigenvectors  Spectral Value Decomposition  Single Value Decomposition  Using Software-Real Time Problems 3. Principal Components Analysis (PCA)  Principal Component Analysis (PCA) Procedure  Using Software-Real Time Problems 4. Factor Analysis  Principal Component method  Communalities  Factor Rotations  Using Software-Real Time Problem

5. Module:4 - Machine Learning(30hrs) 1. Introduction 6. Support Vector Machine  Application Examples  Maximum Marginal Classifier  Supervised Learning Support Vector Classifier  Unsupervised Learning 2. Regression Shrinkage Methods  Support Vector Machine  SVMs with More than Two Classes  Ridge Regression  Using Software-Real Time Problems 7. Cluster Analysis  Lasso Regression  Using Software-Real Time Problems 3. Classification Agglomerative Hierarchical Clustering  K-Means Procedure  Logistic Regression  Meloid Cluster Analysis  Bayes Rule and Classification Problem  Using Software-Real Time Problems 8. Dimensionality Reduction  Discriminant Analysis(LDA/QDA)  Nearest-Neighbor Methods (K-NN  Principal Component Analysis Classifier)  Using Software-Real Time Problems 9. Association rules  Using Software-Real Time Problems 4. Tree-based Methods  Market Basket Analysis  The Basics of Decision Trees  Using Software-Real Time Problems  Regression Trees Classification Trees  Ensemble Methods  Bagging, Bootstrap, Random Forests  Using Software-Real Time Problems 5. Neural Networks  Introduction  Single Layer Perceptron  Multi-layer Perceptron  Forward Feed and Backward Propagation  Using Software-Real Time Problems

6. Module:5 - R Programming (30hrs) 1. R Programming 2. Data Analytics Using R Module 1-4 demonstrated using R  R Basics  Numbers, Attributes programming  Creating Vector  Mixing Objects  Explicit Coercion  Formatting Data Values  Matrices, List, Factors, Data Frames, Missing Values, Names  Reading and Writing Data  Interface to the Outside world  Sub setting R objects  Vectorized Operations  Dates and Times  Managing Data Frames with the DPLYR package  Control Structures  Functions  Lexical /Dynamic Scoping  Loop Functions  Debugging