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UI/Web Professional IT Training CenterBuilding World Class IT Professional Online TrainingOwn your future learning new skills online

UI/Web Professional IT Training Center

CorptechIT has fast become one of the most sought after disciplines by companies today

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Building World Class IT Professional Online Training

For the skilled manpower in high end technology courses like SAP, SAP-MM, SAP-WM, SAP-SRM, SAP-PP, SAP-SD, SAP-FICO, SAP-APO, SAP-BW/BI,SAP HANA and ERP domains.

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Own your future learning new skills online

CorptechIT is all the more effective as it is very flexible and convenient and can be undertaken at any time even by working professionals.

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DATA SCIENCE

CorptechIT provides Online Training by experienced IT professionals. Our faculties dedicated to complete your course as per the schedule given. We record the classes from your end to refer the classes once again whenever is required. Data Science Online Training being a really important module, we have taken precise steps in teaching a full-fledged Data Science Online Training course curriculum that covers all the concepts. Our customer support team and trainers will solve all your queries as and when required. Data Science Online Training has got the right training that can fulfill a candidate’s training expectations. We see to that the value and quality of our Data Science Online Training will not be compromised at all. We will market your resume in USA, UK, SINGAPORE, NEWZELAND, CANADA, AUSTRALIA, JAPAN, SWEDEN, SOUTH AFRICA. We clarify your questions during the training even after the course completion. After completion of training we will help you to assist you to get certified on Data Science . We will give you 100% Satisfaction.

Data Science Online Training Course Content

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 visualisation

  • Investigating characteristics of a data set through visualisation
  • Charting data distributions with boxplots, histograms and density plots
  • Identifying outliers in data

Working with Unstructured and Large Data Sets

Mining unstructured data for business applications

  • Preprocessing unstructured data in preparation for deeper analysis
  • Describing a corpus of documents with a term-document matrix

Coping with the additional complexities of Big Data

  • Examining the MapReduce and Hadoop architectures
  • Integrating R and Hadoop with RHadoop

Predicting Outcomes with Regression Techniques

Estimating future values with linear and logistic regression

  • Modelling the relationship between an output variable and several input variables
  • Correctly interpreting coefficients of continuous and categorical data

Regression techniques for dealing with Big Data

  • Overcoming issues of volume with RHadoop
  • Creating regression modules for RHadoop

Categorising Data with Classification Techniques

Automating the labelling of new data items

  • Predicting target values using Decision Trees
  • Building a model from existing data for future predictions
  • Combining tree predictors with random forests in RHadoop

Assessing model performance

  • Visualising model performance with a ROC curve
  • Evaluating classifiers with confusion matrices

Detecting Patterns in Complex Data with Clustering and Link 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
  • Meeting the challenge of large data sets when searching for rules with RHadoop

Constructing recommendation engines

  • Cross-selling, up-selling and substitution as motivations
  • Leveraging recommendations based on collaborative filtering

Implementing Analytics within Your Organisation

Expanding analytic capabilities

  • Breaking down Big Data Analytics into manageable steps
  • Integrating analytics into current business processes
  • Reviewing Spark, MLib and Mahout for machine learning

Dissemination and Big Data policies

  • Examining ethical questions of privacy in Big Data
  • Disseminating results to different types of stakeholders
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