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Instructor-led Classroom Adult Training in Singapore - Modular Fast Track Skill-Based Trainings

Data Mining Training with RapidMiner

RapidMiner allows organizations to use predictive analytics in order to gain competitive advantage through optimizing their businesses. RapidMiner provides the advanced analytics needed to increase marketing response rates, reduce customer churn, detect machine failures, plan preventive maintenance, and detect fraud, among others

RapidMiner’s unique visual development paradigm lets you derive benefits from analytics more quickly than with any other tool. Results are displayed in easy-to-understand charts that provide the “predictive intelligence” needed for better decision making. Prediction based actions in the form of millions of integrated micro-predictions can even automate everyday decision making and deliver direct value with every single triggered action.

Course Highlights

  • RapidMiner Studio
  • Data Preparation
  • Predictive Models 
  • Model Evalaution
  • Feature Engineering

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant Applications

Click the links below to apply. Note that you need to register the course first.

For Singaporeans: SkillsFuture Credit

For Company: SSG Training Grant

Course Code: CRS-N-0042585

Course Booking

$498.00 (GST-exclusive)
$532.86(GST-inclusive)

Course Date

Course Time

* Required Fields

Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.


Course Details

Day 1

Module 1: Getting Started with RapidMiner Studio

  • User Interface
  • Creating and Managing RapidMiner Repositories
  • Operators and Processes
  • Storing Data, Processes, and Result Sets
  • Loading Data
  • Visualizing Data & Basic Charting

Module 2: Data Preparation

  • Basic Data ETL (Extract, Transform, and Load)
  • Data Types & Transformations of Value Types
  • Handling Missing Values
  • Handling Attribute Roles
  • Filtering Examples and Attributes
  • Normalization and Standardization

Module 3: Building Better Processes

  • Organizing, Renaming, & Relative Paths
  • Sub-Processes
  • Building Blocks
  • Breakpoints

Module 4: Predictive Modeling Algorithms

  • k-Nearest Neighbor
  • Naïve Bayes
  • Linear Regression
  • Decision Trees & Rules
  • Support Vector Machines
  • Logistic Regression

Day 2

Module 5: Model Construction and Evaluation

  • Machine Learning Theory: Bias, Variance, Overfitting & Underfitting
  • Splitting Data
  • Split and Cross Validation
  • Evaluation Methods & Performance Criteria
  • Optimization and Parameter Tuning
  • Applying Models
  • ROC Plots
  • Comparison between Models
  • Sampling
  • Weighting
  • Feature Selection: Forward Selection
  • Feature Selection: Backward Elimination
  • Dimensionality Reduction: Principal Components Analysis (PCA)
  • Validation of Preprocessing and Preprocessing Models
  • Optimization & Logging Results

Module 7: Advanced Data Preparation

  • Multiple Sources
  • Joins & Set Theory
  • Understanding New Attributes
  • Advanced Data ETL (Extract, Transform, and Load)
  • Aggregation & Multi-Level Aggregation
  • Pivot & De-Pivot
  • Calculated Values
  • Regular Expressions
  • Changing Value Types
  • Feature Generation and Feature Engineering
  • Loops
  • Macros

Module 8: Advanced Predictive Modeling Algorithms

    • Outlier Detection
    • Random Forests
    • Ensemble Modeling
    • Neural Networks

    Course Admin

    Nil

    Who Should Attend

    • Data Scientists
    • Data Analytsts
    • Finance Analysts
    • Marketers

    Trainers

    RapidMiner TrainerDwight Nuwan Fonseka have a degree in Biotechnology (from NUS) ,Advanced diploma in Pharamceutical management (from MDIS) and Masters in Education (from NTU). He have 8 years experience of teaching biology at O and A levels/ IB level in international schools in Singapore and overseas.

    RapidMiner TrainerTint is currently a PhD student studying at Wee Kim Wee School of Communication and Information (WKWSCI) in Nanyang Technological University (NTU). She has a mixed academic background with undergraduate and postgraduate degrees in information science and computer science from Nanyang Technological University (NTU) and University of Computer Studies Yangon (UCSY). She also has Specialist Diploma in Data Mining and Statistical Analysis from Singapore Polytechnic.

    As an ACTA certified trainer, Tint has been conducting WSQ courses offered by Library Association of Singapore (LAS) since 2010. As an academic librarian, she has also more than ten years of experience in conducting workshops for undergraduate and postgraduate students in NTU and SMU in the areas of information literacy and research skills.

    RapidMiner TrainerKannan Gopal has a Masters in Computer Applications with over 20 years of experience in creating, delivering and supporting, products, projects and program in the area of enterprise data ops and analytics. Lately he has been involved in the creation of the digital banking app and working on IoT to create smart organizations. His expertise is in using data tools for analysis and visualization to solve challenges in organizations that rely on data for strategic and tactical decisions. He has experience in Design Thinking (HCD- human centered design) techniques and in leading organizations in the quest of digital transformation with data as its core for decisions and information.

    Through his career, he has also developed teams and trained / coached many consultants and developers on SAP BI and related products and worked extensively in Europe and Asia. Through his career, he has developed solutions in the area of data modeling, data analysis and analytics – application of tools and process for solving customer issues. While at SAP, his team has won numerous patents for developing and applying data mining methods to enterprise management systems.

    His keen interest lies in sharing and collaborating with people on new technologies and to create best practices in the area of analytics.

    Customer Reviews (1)

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