Course Duration in Hours
60
60
Fundamentals of Statistics
Introduction to Statistics
Types of data
Measures of central tendency and dispersion
Statistical Graphics
Probability and Probability Distributions
Binomial Distribution
Poisson Distribution
Normal Distribution
R Programming Basics
Introduction to R
Data Types
Reading data, Subsetting Data
Visualizing the Data
Input Output Sub setting
Control structure
Functions
Data Exploration
Data Harmonization
Descriptive & Inferential Statistics
Estimation Theory
Sampling Distribution
Point Estimation
Interval Estimation
Sampling Distribution
Test of Hypothesis
Inference about one population means
Inference about two populations means
Analysis of Variance Concept
Inference about one & two population (Means & Proportion)
Analysis of Variance ( 1 Way & 2 Way)
Machine Learning: Supervised Algorithms
Introduction to Machine Learning
Na?ve Bays Algorithm
K-Nearest Neighbor Algorithm
Decision Tress (SingleTree)
Regression
Correlation coefficient
Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Time Series Analysis
Moving Average
Simple Exponential Smoothening
Holt-Winters Method
ARIMA Models
Support Vector Machines
Random Forest
Support Vector Machines
Model Ensembling
Bagging
Boosting
Stacking
Unsupervised Learning Algorithms
Cluster Analysis
Hierarchical Clustering
K-means Clustering
Association Rules Mining
Principal Components Analysis
Natural Language Processing
Term Document Matrix
TF-IDF
Word Cloud
Recommendations Systems
Neural Network
• Basic knowledge of any programming Language.
• Basic knowledge of Database (SQL) and files (MS Excel, CSV etc.…)
• Basic high school Algebra and Geometry
3RI Technologies Pvt Ltd, Pimple Saudagar (Pune),Pune,IN