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Big Data Analytics Training

Live Online & Classroom Enterprise Training

Learn key technologies and techniques, including R and Apache Spark, to analyse large-scale data sets to uncover valuable business information.

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What is Big Data Analytics Training about?

In this course, part of the Big Data MicroMasters program, you will develop your knowledge of big data analytics and enhance your programming and mathematical skills. You will learn to use essential analytic tools such as Apache Spark and R. 

What are the objectives of Big Data Analytics Training ?

  • You will be able to approach large-scale data science problems creatively and creatively. 
  • How to develop algorithms for the statistical analysis of big data 
  • Knowledge of big data applications 
  • How to use fundamental principles used in predictive analytics 
  • Evaluate and apply appropriate principles, techniques, and theories to large-scale data science problems. 

Who is Big Data Analytics Training for?

Leveraging Data for an Optimal Audience Targeting Campaign, Acknowledge the fact that you have a specific target audience, Identify the needs of your customers and many more  

What are the prerequisites for Big Data Analytics Training?

  • Big Data Fundamentals 
  • Programming for Data Science  
  • Computational Thinking 

Available Training Modes

Live Online Training

60 Hours

Self-Paced Training

60 Hours

Course Outline Expand All

Expand All

  • Fit a simple linear regression between two variables in R; Interpret output from R; Use models to predict a response variable; validate the model’s assumptions.
  • Adapt the simple linear regression model in R to deal with multiple variables; Incorporate continuous and categorical variables in their models; Select the best-fitting model by inspecting the R output.
  • Manipulate nested data frames in R; Use R to apply simultaneous linear models to large data frames by stratifying the data; Interpret the output of learner models.
  • Adapt linear models to consider when the response is a categorical variable; Implement Logistic regression (LR) in R; Implement Generalised linear models (GLMs) in R; Implement Linear discriminant analysis (LDA) in R.
  • Implement the principles of building a model to do prediction using classification; Split data into training and test sets, perform cross-validation and model evaluation metrics; Use model selection for explaining data with models; Analyse the overfitting and bias-variance trade-off in prediction problems.
  • Set up and apply sparkly; Use logical verbs in R by applying native sparkly versions of the verbs.
  • Apply sparkly to machine learning regression and classification models; Use machine learning models for prediction; Illustrate how distributed computing techniques can be used for “bigger” problems.
  • Use massive amounts of data to train multi-layer networks for classification; Understand some of the guiding principles behind training deep networks, including autoencoders, dropout, regularisation, and early termination; Use sparkly and H2O to train deep networks.
  • Understand some of how massive amounts of unlabelled data, and partially labelled data, are used to train neural network models; Leverage existing trained networks for targeting new applications; Implement architectures for object classification and object detection and assess their effectiveness.
  • Consolidate your understanding of relationships between the methodologies presented in this course, their relative strengths, weaknesses and the range of applicability of these methods.

Who is the instructor for this training?

The trainer for this Big Data Analytics Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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