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Big Data Analysis

This course covers everything from the basic concepts of data analysis, to data warehouse design and data visualization principles. If you're looking for a career change or already making your way into the world of transforming data into value, this course will help you understand all of the key concepts and get some hands-on skills, while being directed to where you can dig in deeper when you find something you're interested in. If you're already working in the data world, you can use this course as a reference. Each module is broken down into segments that answer the basic questions of what, why, how, and where so you can easily dive in where you want to learn more and jump back out without needing to start watching from the beginning.

  • Training Type
  • Training

  • Topics
  • • Section 1: Simple linear regression
    Fit a simple linear regression between two variables in R; Interpret output from R; Use models to predict a response variable; Validate the assumptions of the model.

    • Section 2: Modelling data
    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.

    • Section 3: Many models
    Manipulate nested dataframes in R; Use R to apply simultaneous linear models to large data frames by stratifying the data; Interpret the output of learner models.

    • Section 4: Classification
    Adapt linear models to take into account 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.

    • Section 5: Prediction using models
    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.

    • Section 6: Getting bigger
    Set up and apply sparklyr; Use logical verbs in R by applying native sparklyr versions of the verbs.

    • Section 7: Supervised machine learning with sparklyr
    Apply sparklyr to machine learning regression and classification models; Use machine learning models for prediction; Illustrate how distributed computing techniques can be used for “bigger” problems.

    • Section 8: Deep learning
    Use massive amounts of data to train multi-layer networks for classification; Understand some of the guiding principles behind training deep networks, including the use of autoencoders, dropout, regularization, and early termination; Use sparklyr and H2O to train deep networks.

    • Section 9: Deep learning applications and scaling up
    Understand some of the ways in which massive amounts of unlabelled data, and partially labelled data, is 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.

    • Section 10: Bringing it all together
    Consolidate your understanding of relationships between the methodologies presented in this course, theirrelative strengths, weaknesses and range of applicability of these methods.

  • Pre-requisites
  • A strong quantitative background with a solid understanding of basic statistics,
    Experience with a scripting language such as Java, Perl, or Python (or R). Many of the lab examples taught in the course use R (with an RStudio GUI), which is an open source statistical tool and programming
    Experience with SQL.

  • Audience
  • • Managers of business intelligence, analytics, and big data professionals’ teams
    • Current business and data analysts looking to add big data analytics to their skills
    • Data and database professionals looking to exploit their analytic skills in a big data environment
    • Recent college graduates and graduate students with academic experience in a related discipline looking to move into the world of Data Science and big data

  • Related Courses
  • Value of Training
  • • What you'll learn
    o How to develop algorithms for the statistical analysis of big data;
    o Knowledge of big data applications;
    o How to use fundamental principles used in predictive analytics;
    o Evaluate and apply appropriate principles, techniques and theories to large-scale data science problems.

  • Training Hours
  • 40 Hours

scheduled date Location time No of days price  
June 23, 2019 Cairo 40 Hour 10 3200 Apply To The Course