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Big Data Scientist

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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.



USD $655 $327.50 eLearning Kit only

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.

Get a sample of the contents of this course with this short video lesson that provides a non-technical introduction to Big Data Analysis. This second video lesson is an introduction to Big Data Analytics. Both of these topics are part of Module 1 of the course.

The eLearning kit bundle provides access to a 5 Module course bundle that will be available for 1 year from the date of purchase. The course outline is provided in the Details tab, you can also download a pdf version here. You can also take the on-line exam to get a Digital Badge to show your expertise in this field.


Course contents: 5 Modules

  • Module 1: Fundamental Big Data
  • Module 2: Big Data Analysis & Technology Concepts
  • Module 4: Fundamental Big Data Analysis & Science
  • Module 5: Advanced Big Data Analysis & Science
  • Module 6: Big Data Analysis & Science Lab

 


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…


 

Exams & 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.
  • Please ensure you purchase the correct exam voucher as they are not exchangable.
  • Those that pass the exams required for the Certified Big Data Science Professional designation will receive official certificate for this designation and will have access to the benefits associated with this certification.

 

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

Contents

Module 1 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

Module 2 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)

Module 4 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

Module 5 Advanced Big Data Analysis & Science

An in-depth module 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

Module 6 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.

 


 

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



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