Data Scientist Course

Course Overview:

Module: 1 – Descriptive & Inferential Statistics (30Hrs)

Module: 2 – Prediction Analytics (25Hrs)

Module: 3 – Applied Multivariate Analysis (25hrs)

Module: 4 – Machine Learning (30hrs)

Module: 5 – R-Programming (30hrs)

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 of 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 drove approach
Live Project
Placement assistance
Qualification

Any Graduate. No programming and statistics knowledge or skills required
Duration of the course:

Three months (Every day 2 hours of teaching).
Classes on weekdays.

Module: 1 – Descriptive & Inferential Statistics (30Hrs)
Turning Data into Information
· Data Visualization
· Measures of Central Tendency
· Measures of Variability
· Measures of Shape
· Covariance, Correlation
· Using Software-Real Time Problems
· Probability Distributions
· Probability Distributions: Discrete Random Variables
· Mean, Expected Value
· Binomial Random Variable
· Poisson Random Variable
· Continuous Random Variable
· Normal distribution
· Using Software-Real Time Problems
· Sampling Distributions
· Central Limit Theorem
· Sampling Distributions for Sample Proportion, p-hat
· Sampling Distribution of the Sample Mean, x-bar
· Using Software-Real Time Problems
· Confidence Intervals
· Statistical Inference
· Constructing confidence intervals to estimate a population Mean, Variance, Proportion
· Using Software-Real Time Problems
· Hypothesis Testing
· Hypothesis Testing
· Type I and Type II Errors
· Decision Making in Hypothesis Testing
· Hypothesis Testing for a Mean, Variance, Proportion
· Power in Hypothesis Testing
· Using Software-Real Time Problems
· Comparing Two Groups
· Comparing Two Groups
· Comparing Two Independent Means, Proportions
· Pairs wise Testing for Means
· Two Variances Test(F-Test)
· Using Software-Real Time Problems
· Analysis of Variance (ANOVA)
· One-Way and Two-wayANOVA
· ANOVA Assumptions
· Multiple Comparisons (Tukey, Dunnett)
· Using Software-Real Time Problems
· Association Between Categorical Variables
· Two Categorical Variables Relation
· Statistical Significance of Observed Relationship / Chi-Square Test
· Calculating the Chi-Square Test Statistic
· Contingency Table
· Using Software-Real Time Problems
Module: 2 – Prediction Analytics (25Hrs)
Simple Linear Regression
· Simple Linear Regression Model
· Least-Square Estimation of the Parameters
· Hypothesis Testing on the Slope and Intercept
· Coefficient of Determination
· Estimation by Maximum Likelihood
· Using Software-Real Time
· Multiple Regression
· Multiple Regression Models
· Estimation of Model Parameters
· Hypothesis Testing in Multiple Linear Regression
· Multicollinearity
· Using Software-Real Time Problems
· Model Adequacy Checking
· Residual Analysis
· The PRESS Statistic
· Detection and Treatment of Outliers
· Lack of Fit of the Regression Model
· Using Software-Real Time Problems
· Transformations
· Variance-Stabilizing Transformations
· Transformations to Linearize the Model
· Analytical Methods for selecting a Transformation
· Generalized and Weighted Least Squares
· Using Software-Real Time Problems
· Multiple Linear Regression
· The Multiple Linear Regression Model
· Using Software-Real Time Problems
Diagnostics for Leverage and Influence
· Leverage/Cook’s D/DFFITS/DFBETAS
· Treatment of Influential Observations
· Using Software-Real Time Problems
· Polynomial Regression
· Polynomial Model in One/Two /More Variable
· Orthogonal Polynomials
· Using Software-Real Time Problems
· Dummy Variables
· The General Concept of Indicator Variables
· Using Software-Real Time Problems
· Variables Selection and Model Building
· Forward Selection/Backward Elimination
· Stepwise Regression
· Using Software-Real Time Problems
· Generalized Linear Models
· Concept of GLM
· Logistic Regression
· Poisson Regression
· Negative Binomial Regression
· Exponential Regression
· Autocorrelation
· Regression Models with Autocorrelation Errors
Module: 3 – Applied Multivariate Analysis (25hrs)
· Measures of Central Tendency, Dispersion, and Association
· Measures of Central Tendency/ Measures of Dispersion
· Using Software-Real Time Problems
· 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
· Sample Mean Vector and Sample Correlation
· Distribution of Sample Mean Vector
· Interval Estimate of Population Mean
· Inferences from Correlations
· Using Software-Real Time Problems
· Principal Components Analysis (PCA)
· Principal Component Analysis (PCA) Procedure
· Using Software-Real Time Problems
· Factor Analysis
· Principal Component Method
· Communalities
· Factor Rotations
· Varimax Rotation
Using Software-Real Time Problem
· Discriminant Analysis
· Discriminant Analysis (Linear/Quadratic)
· Estimating Misclassification Probabilities
· Using Software-Real Time Problems
· MANOVA
· MANOVA
· Test Statistics for MANOVA
· Hypothesis Tests
· MANOVA table
· Using Software-Real Time Problems
Module: 4 – Machine Learning (30hrs)
· Introduction
· Application Examples
· Supervised Learning
· Unsupervised Learning
· Regression Shrinkage Methods
· Ridge Regression
· Lasso Regression
· Using Software-Real Time Problems
· Classification
· Logistic Regression
· Bayes Rule and Classification Problem
· Discriminant Analysis(LDA/QDA)
· Nearest-Neighbor Methods (K-NN Classifier)
· Using Software-Real Time Problems
· Tree-based Methods
· The Basics of Decision Trees
· Regression Trees
· Classification Trees
· Ensemble Methods
· Bagging, Bootstrap, Random Forests
· Using Software-Real Time Problems
· Neural Networks
· Introduction
· Single Layer Perceptron
· Multilayer Perceptron
· Forward Feed and Backward Propagation
· Using Software-Real Time Problems
· Support Vector Machine
· Maximum Marginal Classifier
· Support Vector Classifier
· Support Vector Machine
· SVMs with More than Two Classes
· Using Software-Real Time Problems
· Cluster Analysis
· Agglomerative Hierarchical Clustering
· K-Means Procedure
· Meloid Cluster Analysis
· Using Software-Real Time Problems
· Dimensionality Reduction
· Principal Component Analysis
· Using Software-Real Time Problems
· Association rules
· Market Basket Analysis
· Using Software-Real Time Problems
Module: 5 – R-Programming (30hrs)
· R Programming
· R Basics
· Numbers, Attributes
· Creating Vector
· Mixing Objects
· Explicit Coercion
· Formatting Data Values
· Matrices, List, Factors, Data Frames, MissingValues, Names
· Reading and Writing Data
· Using Dput/DDump
· Interface to the Outside world
· Subsetting R objects
· Vectorized Operations
· Dates and Times
· Managing Data Frames with the DPLYR package
· Control Structures
· Functions
· Lexical /Dynamic Scoping
· Loop Functions
· Debugging
Data Analytics Using/ R
· Module 1-4 demonstrated using R programming