If you are looking to move into the world of Data Science, this course is the first step for you to consider. Our comprehenive introduction to DS, ML and AI uses open source tools to demonstrate and cover the concepts within the entire data science pipeline, and whether you intend to continue working with open source tools, or later opt for proprietary services, this course will give you the foundation you need to assess which options best suit your needs.
This course is meant to act as an introduction to the various concepts, rather than a deep-dive, with instructor demos and simple delegate tasks to show outcomes
The course is aimed at delegates at all levels looking for an introduction to the topics making up Data Science, Machine Learning and Artificial Intelligence, together with the R language.
Using R for simple Data Analysis
Accessing, querying and manipulating data in R
Data cleansing for accurate modelling
Reducing dimensions with Principal Component Analysis
Extending R with user–defined packages
Facilitating good analytical thinking with data visualisation
Investigating characteristics of a data set through visualisation
Charting data distributions with boxplots, histogrammes and density plots
Identifying outliers in 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
Estimating future values with 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’
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
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
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
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
Analysing labelled data examples to find patterns in those examples that consistently correlate with particular labels for object recognition
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
Visualising data to tell a story
See why people choose JBI
24/01/2018: Python and R are the two leading contenders for data scientists, with many statisticians running out to complete an R or Pythondata science course....
16/01/2018: As Big Data becomes an integral part of the data-driven enterprise, businesses are encountering problems securing the skills they need to make...
15/01/2018: There is currently a general agreement that Python is one the most versatile languages out there. People use Python for data analysis, and the...
19/10/2017: Nowadays, there is a significant business advantage in being able analyse, process and visualize "big data". While there is no agreed definition...
13/10/2017: This organisation needed their Supply Chain department to get fully involved with Microsoft’s Power BI reporting product as soon as possible....
07/10/2017: This client was expanding its capability to deliver technical training across EMEA. It had recently acquired a large technology company which...
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