You Will Learn How To

• Translate business questions into Machine Learning problems to understand what your data is telling you
• Explore and analyze data from the Web, Word Documents, Email, Twitter feeds, NoSQL stores, Relational Databases and more, for patterns and trends relevant to your business
• Build Decision Tree, Logistic Regression and Naïve Bayes classifiers to make predictions about your customers’ future behaviors as well as other business critical events
• Use K-Means and Hierarchical Clustering algorithms to more effectively segment your customer market or to discover outliers in your data
• Discover hidden customer behaviors from Association Rules and Build Recommendation Engines based on behavioral patterns
• Use biologically-inspired Neural Networks to learn from observational data as humans do
• Investigate relationships and flows between people, computers and other connected entities using Social Network Analysis

• Professionals interested in entering the fields of data analytics, technology, informatics, business intelligence, web analytics or data collection
• Professionals with limited academic and work experience in data analytics and related technology fields
• Anyone interested in data analysis and data collection
• Students interested in prerequisite knowledge prior to pursuing our intermediate or advanced data programs such as Data Science and Big Data Programming & Architecture

This interactive Training will be highly interactive, with opportunities to advance your opinions and ideas and will include;

• Lectures
• Workshop & Work Presentation
• Case Studies and Practical Exercise
• Videos and General Discussions

Introduction to R

Exploratory Data Analysis with R

• Loading, querying and manipulating data in R
• Cleaning raw data for modeling
• 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 linear regression

• Modeling 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 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 modeling
• 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
• Visualizing data to tell a story

BTS attendance certificate will be issued to all attendees completing a minimum of 80% of the total course duration.

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