Best Data Science with R Training

Introduction to R

Exploratory Data Analysis with R

Loading, querying and manipulating data in R

Cleaning raw data for modelling

Reducing dimensions with Principal Component Analysis

Extending R with user-defined packages

Facilitating good analytical thinking with data visualization

Investigating characteristics of a data set through visualization

Charting data distributions with boxplots, histograms and density plots

Identifying outliers in data.

Working with Unstructured Data

Mining unstructured data for business applications

Preprocessing unstructured data in preparation for deeper analysis

Describing a corpus of documents with a term-document matrix

Make predictions from textual data

Predicting Outcomes with Regression Techniques

Estimating future values with a linear regression

Modelling the numeric relationship between an output variable and several input variables

Correctly interpreting coefficients of continuous data

Assess your regression models for ‘goodness of fit

Categorizing Data with Classification Techniques

Automating the labelling of new data items

Predicting target values using Decision Trees

Constructing training and test data sets for predictive model building

Dealing with issues of overfitting

Assessing model performance

Evaluating classifiers with confusion matrices

Calculating a model’s error rate

Detecting Patterns in Complex Data with Clustering and Social Network Analysis

Identifying previously unknown groupings within a data set

Segmenting the customer market with the K–Means algorithm

Defining similarity with appropriate distance measures

Constructing tree–like clusters with hierarchical clustering

Clustering text documents and tweets to aid understanding

Discovering connections with Link Analysis

Capturing important connections with Social Network Analysis

Exploring how social networks results are used in marketing

Leveraging Transaction Data to Yield Recommendations and Association Rules

Building and evaluating association rules

Capturing true customer preferences in transaction data to enhance the customer experience

Calculating support, confidence and lift to distinguish “good” rules from “bad” rules

Differentiating actionable, trivial and inexplicable rules

Constructing recommendation engines

Cross-selling, up-selling and substitution as motivations

Leveraging recommendations based on collaborative filtering

Learning from Data Examples with Neural Networks

Machine learning with neural networks

Learning the weight of a neuron

Learning about how neural networks are being applied to object recognition, image segmentation, human motion and language modelling

Analyzing labelled data examples to find patterns in those examples that consistently correlate with particular labels for object recognition

Implementing Analytics within Your Organization

Expanding analytic capabilities

Breaking down Data Analytics into manageable steps

Integrating analytics into current business processes

Reviewing Hadoop, Spark, and Azure services for machine learning

Dissemination and Data Science policies

Examining ethical questions of privacy in Data Science

Disseminating results to different types of stakeholders