Data Science online training USA - Best Data Science Online Coaching in USA

Courses

Quick Contact

Your Name

Your Email

Phone Number

Type your requirement/message

Popular Courses

Data Science

Data Science training course

MODULE 1: DESCRIPTIVE AND INFERENTIAL STATISTICS

Turning Data into Information
1. Data Visualization
2. Measures of Central Tendency
3. Measures of Variables
4. Covariance, Correlation
5. Real time Problems
Probability Distributions
1. Discrete Random Variables
2. Binomial, Poisson Distributions
3. Mean, Expected Values
4. Continuous Random Variables
5. Normal Distributions
6. Real time Problems
Sampling Distributions
1. Sampling Distributions for Sample Mean and Proportions
2. Central Limit Theorem
3. Real time Problems
Confidence Intervals
1. Statistical Inference
2. Construction Confidence Intervals to Estimate a Population Mean, Variance and Proportions
3. Real time Problems
Hypothesis Testing
1. Hypothesis Testing
2. Type 1 and Type 2 Errors
3. Decision Making in Hypothesis Testing
4. Hypothesis Testing for a Mean, Variance and Proportion
5. Real Time Problems
Analysis of Variance (ANOVA)
1. ANOVA Assumptions
2. One-way and Two-way ANOVA
3. Multiple Comparisons (Tukey, Dunnett)
4. Real time Problems

MODULE 2: PREDICTIVE ANALYTICS/REGRESSION ANALYSIS

Simple Linear Regression
1. Simple Linear Regression Model
2. Least Square Estimation of Parameters
3. Hypothesis Testing on Slope and Intercepts
4. Coefficient Determination
5. Real time Problems
Multiple Linear Regression
1. Multiple Regression Models
2. Estimation of Model Parameters
3. Hypothesis Testing in MLR
4. Multi-collinearity
5. Real time Problems
Model Adequacy Checking
1. Residual Analysis
2. The PRESS Statistic
3. Detection and Treatment of Outliers
4. Real time Problems
Transformations
1. Variance Stabilizing Transformations
2. Transformations to Linearize the model
3. Box-cox, Tidwell Transformations
4. Generalized and weighted Least Squares
5. Real time Problems
Diagnosis for leverage and influential Points
1. Leverage/Cook’s D/DFFITS/ DFBETAS
2. Treatment of Influential Observations
3. Real time Problems
Polynomial Regression
1. Polynomial Model in one, two or More Variables
2. Real time Problems
Variables selection and Model Building
1. Forward Selection
2. Backward Elimination
3. Stepwise Regression
4. Real time Problems

MODULE 3: Applied Multivariate Analysis

Measures of Central Tendency, Dispersion and Association.
1. Measure of Central Tendency
2. Measure of Dispersion
3. Real time Problems
Multivariate Normal Distribution
1. Multivariate Normality and Outliers
2. Exponent of Multivariate Normal Distribution
3. Eigen Values and Eigen Vectors
4. Spectral Decomposition
5. Single Value Decomposition
Sample Mean Vector and Sample Correlation
1. Distribution of Sample Mean Vector
2. Interval Estimate of Population Mean
3. Inferences for Correlations
4. Real time Problems
Principal Component Analysis (PCA)
1. Principal Component Analysis Procedure
2. Real time Problems
Discriminant Analysis
1. Discriminant Analysis (Linear/Quadratic)
2. Estimating Misclassification Problems
3. Real time Problems

MODULE 4: Machine Learning

Introduction
1. Application Examples
2. Supervised Learning
3. Unsupervised Learning
Cluster Analysis
1. Agglomerative Hierarchical Clustering
2. K-Means Procedure
3. Medoid Cluster Analysis
Dimensionality Reduction
1. Principal Component Analysis
2. Real time Problems
Association Rules
1. Market Basket Analysis
2. Apriori/Support/Confidence/Lift
3. Real time Problems
Classification
1. Bayes Law
2. Naive Bayes
3. Variance-Bias Tradeoff
4. Gradient Descent/Ascent Procedure
5. Maximum Likelihood Method
6. Logistic Regression
7. Nearest-Neighbor Methods (K-NN Classifier)
8. Using Software-Real Time Problems
Treebased Methods
1. Basics of Decision Trees
2. Regression Trees
3. Classification Trees
4. Ensemble Methods
5. Bagging, Bootstrap, Random Forests Boosting
6. Using software-Real time Problems
Support Vector Machines
1. Maximum Marginal Classifier
2. Support Vector Classifier
3. Kernel Trick
4. Support Vector Machine
5. SVMs with more than two Classes
6. Using Software Real time Problems
Regression Shrikage Methods
1. Ridge Regression
2. Lasso Regression
3. Using Software Real time Problems

Module 5

R Programming
1. R Basics
2. Numbers, Attributes
3. Creating Vector
4. Mixing Objects
5. Explicit Coercion
6. Formatting Data Values
7. Matrices, List, Factors, Data Frames, Missing Values
8. Names , Reading and Writing Data
9. Using Dput/DDump
10. Interface to the Outside world
11. Sub setting R objects
12. Vectorized Operations
13. Dates and Times
14. Managing Data Frames with the DPLYR package
15. Control Structures
16. Functions
17. Lexical /Dynamic Scoping
18. Loop Functions
Data Analytics Using R
1. Module 1-4 demonstrated

Avail free career counselling

Ignorance is not bliss! It is necessary to evolve with time. It is essential that we realize that times are not the same. There are so many new types to thriving career options that have emerged. One should choose a career that they can stick to and offers ample opportunities for them to explore in the future. It is equally important to stay at the top of the game in the chosen field.

So where does it all start? How can one make the best career decision for themselves? Given the maze of opportunities that lie at hand, is it just the performance in a subject or stream that counts? These are some of the most important questions to ask oneself before choosing a career path. And, career counselling is one of the most effective processes to undergo in order to find the right direction for a successful professional life.

Career counselling helps to assess the development of career paths from time to time. It helps you to understand your own goals and share it confidently. This shows the employer that you are passionate about. Career counselling helps you maintain that passion and become the person you intended out to be.