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Big Data Predictive Analytics

By: eXampleCG

Lagos State, Nigeria

28 - 30 Apr, 2016  3 days

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NGN 150,000

"If you do not ask the right question, you discover nothing" - W. Edward Deming participants with a keen interest to learn and implement big data predictive business data analytic models, data mining techniques and participate or manage data analytics projects and drive accelerated organizational and personal growth can enrol and benefit from this training program.

 What is the market demand for analytics skilled professionals? View research reports by: Gartner says big data creates big jobs: 4.4 million it jobs globally to support big data by 2015 Gartner predicts that by 2018, 70 percent of mobile phone workers will use a tablet or a hybrid device that has tablet-like characteristics.

Accenture mckinsey program objectives:

Impart an understanding of big data predictive analytics concepts, terms and basic techniques.

Equip with skills to analyze and interpret data, recognize patterns, derive insights and make conclusions course outline:

  • Cluster introduction
  • Predictive analytics overview

Types of Methods

  • Business value of analytics
  • Application areas measurement and data
  • Project objectives, methodology and project management phases
  • Data - scale and types
  • Sampling methods
  • Descriptive statistics
  • Data visualization
  • Data distributions (Binomial, Poisson, Normal)
  • Probability estimates statistical inference
  • Hypothesis formulation
  • Point and interval estimates
  • Confidence intervals
  • Sample size calculations (Proportion)
  • Sample size calculations (Variable)
  • Testing hypothesis (Means - One And Two Samples)
  • Testing hypothesis (Variance - One And Two Samples)
  • Non-parametric hypothesis (Proportions - One And Two Samples)
  • Non-parametric hypothesis (Median - One And Two Samples)
  • Testing hypothesis (Means - Multiple Samples)
  • Testing hypothesis (Variance - Multiple Samples)
  • Non-parametric hypothesis (Proportion - Multiple Samples)
  • Non-parametric hypothesis (Median - Multiple Samples) statistical modeling
  • Classification - decision trees
  • Classification, naive bayes
  • Classification - k-nearest neighbours (k-NN)
  • Classification - neural networks (ANN)
  • Cluster analysis (k-means)
  • Association rule mining
  • Time series forecasting - naive, moving average, exponential smoothing, ARIMA, seasonality and box-Jenkins models
  • Regression - linear
  • Regression - non-linear (Polynomial, Logarithmic, Exponential, Power)
  • Regression - logistic
  • Regression hypothesis testing
  • Model identification, estimation and assessment case study exercise feedback and assessment

Who Should Attend?

Candidates desirous of acquiring Predictive Analytics competencies, Graduates or Diploma holders with or without work experience, Certification and PDUs Certified Predictive Analytics Professional (Basic or Advanced) Level - Participation and Course Completion Certificate Earn 25 PDUs / 2.5 CEUs (Advanced) Assessment Multiple Choice Objective Type Assessment (60 min)

12, Jibowu Street, Yaba, Lagos, Nigeria (LONADEK Building) Apr 28 - 30 Apr, 2016
NGN 150,000.00 + 7,500.00 (VAT)
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Group discount available at 7% to a minimum of 5 persons to a company. Registration at venue attracts NGR 7,500 extra.
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