Thursday 20 March 2014

Online courses on Applied Statistics - Data Science, Research Science and more

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.

For India Registration and pricing, please visit us at www.india.statistics.com.

Call: 020 6680 0300 / 322

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