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Big Data Scientist Training

Big Data Scientist Training
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USD $1,290.75 eLearning Kit (exam vouchers)




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We are currently planning the workshops for this course. You may purchase an eLearning kit for immediate access or contact us for further details.

Overview
There are quintillion bytes of data being generated every day. Big Data Science techniques provide the necessary approaches to sift through this avalanche of data and find the information that is relevant to the needs of your organisation. In this course, you will learn the algorithms, mathematical and statistical techniques and application of analytics to process Big Data.


Duration
5-days


Intended for
Data Scientists, BI Specialists, Business Analysts, Data Architects and other IT Professionals that are responsible for the creating the algorithms and approaches that will be used to analyse Big Data.


Pre-requisites
An understanding of Data Analytics or analytical mathematics and IT systems is recommended.


 Learning Outcomes
A solid understanding of Big Data and the techniques and approaches required to analyse it will be gained throughout this course. Some of the topics include:

  • Fundamental Terminology and Concepts
  • Big Data and Traditional Business Intelligence and Data Warehouses
  • Common Analysis and Analytics Techniques
  • Automated Recommendation, Classification, Clustering
  • Machine Language, Natural Language, Semantics
  • Data Visualization and Visual Analysis
  • Assessing Hierarchies, Part-to-Whole Relationships
  • Plotting Connections and Relationships, Mapping Geo-Spatial Data
  • Data Science, Data Mining & Data Modeling
  • Exploratory Data Analysis (EDA)
  • Statistics Analysis (including descriptive, inferential, correlation, covariance & hypothesis testing)
  • Data Munging & Machine Learning
  • Statistical Measures & Statistical Inference
  • Distributions & Data Processing Techniques
  • Data Discretization, Binning, Clustering
  • Visualization Techniques & Numerical Summaries
  • Machine Learning Algorithms, Pattern Identification
  • Advanced Statistical Techniques
  • Linear Regression & Logistic Regression for Big Data
  • Decision Trees for Big Data
  • Classification Rules for Big Data
  • K Nearest Neighbor (kNN) for Big Data
  • Naïve Bayes for Big Data
  • Association Rules for Big Data
  • K-means for Big Data
  • Text Analytics for Big Data
  • Outlier Detection for Big Data

And more…


 

In the Details tab you can find more information about this workshop:

  • Workshop Materials - A list of the materials and text books that are included in the registration fee.
  • Agenda - The individual courses are briefly described and links to full course outlines are provided.
  • Schedule
  • Registration information regarding the cancellation policy.
  • Location Details regarding the planned location of the workshop.
  • Exams and Certification - An explanation of how to take exams and get certified upon completion of the workshop.

 

Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.

Agenda

Day 1 / Module 1 (9:00 AM - 4:00 PM / Monday)

Fundamental Big Data

A foundational course that establishes a basic understanding of Big Data from business and technology perspectives, including common benefits, challenges and adoption issues.

The following primary topics are covered: 

  • Fundamental Terminology and Concepts
  • A Brief History of Big Data
  • Business Drivers that Have Led to Big Data Innovations
  • Characteristics of Big Data•Benefits of Adopting Big Data
  • Challenges and Limitations of Big Data
  • Basic Big Data Analytics
  • Big Data and Traditional Business Intelligence and Data Warehouses
  • Big Data Visualization
  • Common Adoption Issues
  • Planning for Big Data Initiatives
  • New Roles Introduced by Big Data Projects
  • Emerging Trends

Day 2 / Module 2 (9:00 AM - 4:00 PM / Tuesday)
Big Data Analysis & Technology Concepts

Explores contemporary analysis practices, technologies, and tools for Big Data environments at a conceptual level, focusing on common analysis functions and features of Big Data solutions.

The following primary topics are covered:

  • The Big Data Analysis Lifecycle (from dataset identification to integration, analysis, and visualization)
  • Common Analysis and Analytics Techniques•A/B testing, Regression, Correlation, Text Analytics
  • Sentiment Analysis, Time Series Analysis
  • Network Analysis, Spatial Analysis
  • Automated Recommendation, Classification, Clustering
  • Machine Language, Natural Language, Semantics
  • Data Visualization and Visual Analysis
  • Assessing Hierarchies, Part-to-Whole Relationships
  • Plotting Connections and Relationships, Mapping Geo-Spatial Data
  • Foundational Big Data Technology Mechanisms
  • Big Data Storage (Query Workload, Sharding, Replication, CAP, ACID, BASE)
  • Big Data Processing (Parallel Data Processing, Distributed Data Processing, SharedEverything/Nothing Architecture, SCV)

Day 3 / Module 4 (9:00 AM - 4:00 PM / Wednesday)
Fundamental Big Data Analysis & Science

Essential coverage of Big Data analysis algorithms, as well as the application of analytics, data mining and basic mathematical and statistical techniques.

The following primary topics are covered: 

  • Data Science, Data Mining & Data Modeling
  • Big Data Dataset Categories•Exploratory Data Analysis (EDA) (including numerical summaries, rules & data reduction)
  • EDA analysis types (including univariate, bivariate & multivariate)
  • Essential Statistics (including variable categories & relevant mathematics)
  • Statistics Analysis (including descriptive, inferential, correlation, covariance & hypothesis testing)
  • Data Munging & Machine Learning
  • Variables & Basic Mathematical Notations
  • Statistical Measures & Statistical Inference
  • Distributions & Data Processing Techniques
  • Data Discretization, Binning, Clustering
  • Visualization Techniques & Numerical Summaries
  • Correlation for Big Data
  • Time Series Analysis for Big Data

Day 4  / Module 5 (9:00 AM - 4:00 PM / Thursday)
Advanced Big Data Analysis & Science

An in-depth course that covers the application of a range of advanced analysis techniques, including machine learning algorithms, data visualization and various forms of data preparation and querying.

The following primary topics are covered:

  • Statistical Models, Model Evaluation Measures (including cross-validation, biasvariance, confusion matrix & f-score)
  • Machine Learning Algorithms, Pattern Identification (including association rules & apriori algorithm)
  • Advanced Statistical Techniques (including parametric vs. non-parametric, clustering vs. non-clustering distance-based,
  • supervised vs. semi-supervised)
  • Linear Regression & Logistic Regression for Big Data
  • Decision Trees for Big Data
  • Classification Rules for Big Data
  • K Nearest Neighbor (kNN) for Big Data
  • Naïve Bayes for Big Data
  • Association Rules for Big Data
  • K-means for Big Data
  • Text Analytics for Big Data
  • Outlier Detection for Big Data

Day 5 / Module 6 (9:00 AM - 4:00 PM / Friday)
Big Data Analysis & Science Lab

A case study-based lab providing a series of real-world exercises that require participants to apply Big Data analysis and analytics techniques to fulfill requirements and solve problems.

This course module covers a series of exercises and problems designed to test the participant’s ability to apply knowledge of topics covered previously in course modules 4 and 5. Completing this lab will help highlight areas that require further attention, and will further prove hands-on proficiency in Big Data analysis and science practices as they are applied and combined to solve real-world problems.
As a hands-on lab, this course incorporates a set of detailed exercises that require participants to solve various inter-related problems, with the goal of fostering a comprehensive understanding of how different data analysis techniques can be applied to solve problems in Big Data environments and used to make significant, relevant predictions that offer increased business value.
The Certified Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion. 


Workshop Materials
The following materials will be provided to attendees of the workshop:

  • Big Data Fundamentals (eBook)
  • Full-colour course booklets
  • Mind-map posters 

Schedule

  • Training starts at 9:00AM and we aim to finish around 4:30PM each day.
  • Breaks are scheduled at 10:30AM, 12:00 noon and 2:30PM but the exact times will be determined by the trainer.
  • The course is fully catered for; Morning Tea, Lunch and Afternoon Tea are provided.

 


Registration

  • Please select your preferred location from the options and select whether you'd like Exam Vouchers then follow the registration process.
  • Alternatively, you can e-mail info@silverplatypus.com and request a quote or an invoice
  • We do offer private workshops for companies that want to hold workshops specifically for their employees, please contact us directly for a discussion or quote.
  • Accepted payment methods include Invoice, Wire Transfer, Credit Card (Amex, Visa, Mastercard) and Paypal.

Cancellation

Please see our cancellation policy.


Location

Please select the relevant city from the choices provided. The exact address of the workshop will be provided closer to the workshop date.


Exams & Certification

  • You are not required to complete exams to attend this workshop. Exams only need to be completed by those interested in attaining certification credit.
  • All workshop attendees are issued an official "Certificate of Completion" for this workshop.
  • Those that pass the exams required for the Certified Big Data Architect designation will receive official certificate for this designation and will have access to the benefits associated with this certification.
  • The exams required for certification can be taken at any Pearson VUE testing center in the world or online via Pearson VUE Online Proctoring. See the Exams page for more information.

 

Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.

Trainers are yet to be detailed.


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