Data Warehousing & Big Data

PROGRAM OVERVIEW

This course will focus on job-oriented technical skills. Learning modelling techniques and how to populate these data models, using Extract, Transform and Load (ELT) Technologies. Learning software integration by using Java language and other technologies. Giving a quick introduction to Big Data, MapReduce algorithm, and Hadoop Distributed File System.

PROGRAM DESCRIPTION

Qualification: Certificate
Duration: 12 Weeks
Delivery: Blended
Video: 240 Hours
Practicum: No

CAREER PATHS

MACHINE LEARNING ENGINEER

Machine Learning Engineers are required to build machine learning systems as well as implement and maintain machine learning market applications in technology and business products. Using Keras library in TensorFlow, the key focus of the system is scale-ability. This career path requires expert-level programming skills in ‘R’ or Python and comprehensive knowledge in machine learning models and their applications according to the business sector needs.

DATA ENGINEER

Data Engineers design, build and maintain data structures for large-scale technology applications, as well as supervise and manage an entire data life cycle. This career path requires strong software engineering and learning skills.

DATA SCIENTIST

Data Scientists perform advanced technical analysis to understand complex systems and make related forecasts about them. This is done by using scientific and mathematical methods such as statistics, mathematics and computer science with the main mission of extracting useful patterns and insights from data. The results are demonstrated using statistical models, visualizations and product data.

DATA ANALYST

Data Analysts are expected to use and implement tools such as Excel, SQL, ‘R’ or Python and conclude data to answer specific questions. Data Analysts must have a deep understanding of the organizations’ data. This career path requires some visualization of data to guide the organization to make valuable judgments by taking and implementing key business decisions.

ADMISSIONS REQUIREMENTS

Education

Alberta High School Diploma, General Education Diploma G.E.D., or Equivalent:

  • Verified By Transcript
  • Prioritize Aptitude for Technology and Programming 

English Proficiency

English as a Second Language ESL must pass one of the following

  • International English Language Testing System IELTS score of 5
  • Canadian Language Benchmark C.L.B. level 5
  • Listening 5
  • Speaking 5
  • Reading 5
  • Writing 5

If you do not meet the admissions requirements and are interested in our program, please do not hesitate to get in touch with us anyway as we understand every situation is different and we will find ways to accommodate you.

 

PROGRAM OUTLINE

This Program will cover the following

  • Fundamental Python programming
  • Statistical concepts such as probability, inference, and modeling and how to apply them in practice
  • Gain experience with the Numpy, including data visualization with Matplotlib and data wrangling with pandas
  • Become familiar with essential tools such as Hadoop, Hive, R Implement machine learning algorithms
  • In-depth knowledge of fundamental data science concepts through motivating real-world case studies

PROGRAM CURRICULUM

PROFESSIONAL SKILLS

● Interview techniques
● Communication skills
● Conflict resolution
● Assertiveness at work
● Time Management
● Presentation Skills
● Effective report writing
● Improving personal success

HOURS: 20

SQL / RDBMS

● Table joins, queries
● Data types
● SQL best practice
● Functions and procedures
● DDL, DML, DCL
● Statements, schema, syntax
● Normalization

HOURS: 20

UNIX

● Functions, parameters, and variable scope.
● Grep and regular expressions
● Use of the vi editor
● Piping and redirection
● Writing shell scripts
● File permissions
● I/O streams
● Conditionals
● Loops 20

HOURS: 20

ETL (EXTRACT, TRANSFORM, LOAD)

● Dimensional Modeling Conceptions
● Rational Dimensional Modeling
● From Rational Model to Star Schema
● ETL process with SAP data services

HOURS: 40

IN-MEMORY DATABASE

● Implementation of dimensional modeling
● SAP HANA

HOURS: 20

BIG DATA

● Introduction to Big Data
● Hadoop
● Querying big data with Hive
● Big data & Machine learning

HOURS: 80

SOFTWARE INTEGRATION

● Java
● Object-oriented design pillars & SOLID principles
● ERD TDD
● Waterfall
● Agile
● R
● Scala
● Cloudera
● Use of unified modeling
● Language (UML) for analysis and design

HOURS: 40

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