Statistics.com offers 110+ courses for
novices, experts, and those in between. Most courses are 4-weeks long and put
you in direct contact with leading experts and authors. Whether you want to
learn the fundamentals, polish your skills, master new methods or tackle new
cutting edge topics, we have a course for you.

**Learn topics in Data Science**

Data analytics (data mining, predictive
modeling, forecasting, social network anaysis, text analytics)

Statistical programming (how to use R, Python,
SQL and Hadoop for analytics and statistical analysis)

**Research statistics**

Biostatistics (controlled clinical trials,
epidemiology and environmental science)

Social science (statistical methods needed for
designing and analyzing studies - including Bayesian analysis, conducting
surveys and analyzing the data they yield, and a unique concentration strand
devoted to Rasch methods)

**Introductory Statistics**

We, the Center for eLearning and Training
(C-eLT), Pune, partner with Statistics.com and offer these courses to Indian
participants at special prices payable in INR.

Let me tell you in very brief why prescribing
these courses for your personnel is such a great idea:

- The courses enable mastery over statistical skill sets and give your team a competitive edge
- They enhance your personnel’s ability to make smarter business decisions that are data driven
- They put professional growth on the fast track
- The participants have the opportunity to learn from top statisticians who are authors of popular books on Statistics and often professors at leading universities.
- The courses are online and participants can follow a flexible schedule in terms of time and space

Need advice on what which course to take?
Contact us with your goals and background and we will provide some suggestions.

Here is the list of courses –

**Data Mining and Prediction**

Applied Predictive Analytics,
in partnership with CrowdANALYTIX

Data Mining in R - Learning
with Case Studies

Data Mining: Unsupervised
Techniques

Decision Trees and Rule-Based
Segmentation

Political Analytics

Predictive Analytics 1 -
Machine Learning Tools

Predictive Analytics 2-
Neural Nets and Regression

**Data Analytics**

Cluster Analysis

Discrete Choice Modeling and
Conjoint Analysis

Forecasting Analytics

Interactive Data
Visualization

Introduction to Social
Network Analysis

Logistic Regression

Statistical Analysis of
Microarray Data with R

**Using R**

Data Mining in R - Learning
with Case Studies

Graphics in R

Mapping in R

Modeling in R

R for Statistical Analysis

R Programming - Advanced

R Programming - Intermediate

R Programming - Introduction
1

R Programming - Introduction
2

Visualization in R with
ggplot2

**Text Analytics**

Natural Language Processing

Sentiment Analysis

Text Mining

**Operations Research and Risk**

Advanced Optimization

Financial Risk Modeling

Introduction to Optimization

Introduction to Quantitative
Risk Analysis

Risk Simulation and Queuing

**IT/Programming**

Advanced Analytics and
Machine Learning with Hadoop

Introduction to Analytics
using Hadoop

Introduction to Python for
Analytics

Social Data Mining With
Python

SQL and R - Introduction to
Database Queries

**Biostatistics**

Advanced Survival Analysis

Biostatistics 1

Biostatistics 2

Epidemiologic Statistics

Meta Analysis

Meta Analysis 2

Sample Size and Power
Determination

Statistical Analysis of
Microarray Data with R

Survival Analysis

**Clinical Trials**

Adaptive Designs for Clinical
Trials

Biostatistics in R: Clinical
Trial Applications

Clinical Trials -
Pharmacokinetics and Bioequivalence

Introduction to Statistical
Issues in Clinical Trials

Safety Monitoring Committees
in Clinical Trials

Sample Size and
Power-Analysis for Cluster-Randomized and Multi-Site Studies

**Social Science**

Advanced Structural Equation
Modeling

Analysis of Survey Data from
Complex Sample Designs

Introduction to Assessment
and Measurement

Introduction to Structural
Equation Modeling

Many-Facet Rasch Measurement

Modeling Longitudinal and
Panel Data: GEE

Practical Rasch Measurement -
Core Topics

Practical Rasch Measurement -
Further Topics

Rasch Applications, Part 1:
How to Construct a Rasch Scale

Rasch Applications, Part 2:
Clinical Assessment, Survey Research, and Educational Measurement

Survey Analysis

Survey Analysis in R

Survey Design and Sampling
Procedures

**Spatial Analytics**

Mapping in R

Spatial Analysis Techniques
in R

Spatial Statistics with
Geographic Information Systems

**Statistical Modeling**

Advanced Logistic Regression

Advanced Structural Equation
Modeling

Categorical Data - Applied
Modeling

Generalized Linear Models

Introduction to Smoothing and
P-spline Techniques using R

Introduction to Statistical
Modeling

Introduction to Structural
Equation Modeling

Logistic Regression

Mixed and Hierarchical Linear
Models

Modeling Count Data

Modeling in R

Modeling Longitudinal and
Panel Data: GEE

Multivariate Statistics

Regression Analysis

**Survey Statistics**

Analysis of Survey Data from
Complex Sample Designs

Survey Analysis

Survey Analysis in R

Survey Design and Sampling
Procedures

**Bayesian**

Bayesian Regression Modeling
via MCMC Techniques

Bayesian Statistics in R

Introduction to Bayesian
Computing and Techniques

Introduction to Bayesian
Hierarchical and Multi-level Models

Introduction to Bayesian
Statistics

**Engineering**

Advanced Survival Analysis

Introduction to Design of
Experiments

Prediction & Tolerance
Intervals; Measurement and Reliability

Probability Distributions

Statistical Process Control

Survival Analysis

**Environmental**

Ecological and Environmental
Sampling

Spatial Analysis Techniques
in R

Spatial Statistics with
Geographic Information Systems

**Introductory**

Calculus Review

Introduction to Statistics 1
AP: Inference for a Single Variable

Introduction to Statistics 2
AP: Working with Bivariate Data

Statistics 1 – Probability and Study Design

Statistics 2 – Inference and
Association

Statistics 3 – ANOVA and
Regression

**Methods**

Analysis and Sensitivity
Analysis for Missing Data

Bootstrap Methods

Categorical Data Analysis

Cluster Analysis

Introduction to Resampling
Methods

Maximum Likelihood Estimation

Missing Data

Principal Components and
Factor Analysis

**Review/Prep**

Calculus Review

Designing Valid Statistical
Studies

Introduction to Resampling
Methods

Introduction to Statistical
Modeling

Matrix Algebra Review

Survey of Statistics for
Beginners

We, the Center for eLearning and Training
(C-eLT), Pune, partner with Statistics.com and offer these courses to Indian
participants at special prices payable in INR.

Call: 020 6680 0300 / 322

Websites:

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