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Data Analyst Database Administrator Diploma
Data analysts and data administrators design, develop and administer data management solutions, as well as develop and implement data administration policy, standards and models. They are employed in information technology consulting firms and in information technology units throughout the private and public sectors.

1: SQL Server Administration

This course module provides students with the knowledge and skills required to install, configure, administer, and troubleshoot the client-server database management system of Microsoft SQL Server. Topics include:

SQL Server Overview
Planning to Install SQL Server
Managing Database Files
Managing Security
Performing Administrative Tasks
Backing Up Databases
Restoring Databases
Monitoring SQL Server for Performance
Transferring Data
Maintaining High Availability
Introducing Replication

2: SQL Server Design

This course module provides students with the knowledge and technical skills required to program a database by using Microsoft SQL Server. This course is designed for those who are responsible for implementing database objects and programming SQL Server databases by using Transact-SQL. Topics include

Overview of Programming SQL Server
Creating and Managing Databases
Creating Data Types and Tables
Implementing Data Integrity
Planning Indexes
Creating and Maintaining Indexes
Implementing Views
Implementing Stored Procedures
Implementing User-Defined Functions
Implementing Triggers
Programming Across Multiple Servers
Optimizing Query Performance
Performing Advance Query Analysis
Managing Transactions and Locks

3: Probability and Statistics

This course module provides an introduction to probability and statistics with applications. Topics include: basic probability models; combinatorics; random variables; discrete and continuous probability distributions; statistical estimation and testing; confidence intervals; and an introduction to linear regression.

Data and Statistics
Descriptive Statistics
Introduction to Probability
Discrete Probability Distributions
Continuous Probability Distributions
Sampling and Sampling Distributions
Interval Estimation
Hypothesis Testing
Comparisons about two populations
Simple Linear Regression Analysis
Multiple Regression Analysis

4: SAS for Data Analysis

This course module covers how to plan and write simple SAS programs to solve common data analysis problems, and provides practice running and debugging those programs in an interactive SAS session. . It also provides comparisons of manipulation techniques and resource cost benefits are designed to help programmers choose the most appropriate technique for their data situation. Topics include:

Accessing Data
Creating Data Structures
Managing Data
Generating Reports
Handling Errors
Accessing Data Using SQL
Macro Processing
Advanced Programming Techniques

5: Data Warehousing

This course module covers the issues involved in planning, designing, building, populating, and maintaining a successful data warehouse. Students learn the reasons why data warehousing is a compelling decision-support solution in today's business climate.
Knowledge. Topics include

Data Warehousing Overview
Definition, Architecture And Concepts
Data Modeling Options
Dimensional Modeling Development Life Cycle
Dimensional Modeling Design
Implementation Options
Extract, Transform, Load (Etl) Terms And Concepts
Data Cleaning And Conforming
Dimension Table Delivery
Slowly Changing Dimensions And Multivalued
Fact Table Delivery
fact table load considerations
Data Warehouse Performance Design
Introduction To Statistics, Analytic And Olap Sql Queries
Physical Design Considerations

6: Data Mining

This course module presents systems and methods for mining varied data and discovering knowledge from data. After detailing a data mining system architecture and tasks, the course examines and compares specific methods in data mining, such as concept learning, decision trees, Bayesian and belief networks, neural networks, case-based reasoning, statistical methods such as cluster analysis and multidimensional analysis, and text and multimedia mining. Several applications are detailed, and tools to build new applications are provided. Topics include

Data Mining Overview
Decision Tree Construction
Association Analysis
Rule Induction
Bayesian Methods
Dealing with Noise and Real-Valued Attributes
Data Mining from Very Large Databases

7: Introduction to Business Intelligence

This course module covers the design principles and best practices when planning, implementing, and deploying a Business Intelligence architecture and solution. Topics include

Introduction to Business Intelligence Architecture
Overview of the Business Intelligence Project Lifecycle
Introduction to Business Intelligence Development
Designing Business Intelligence Infrastructure
Managing Business Intelligence Operations

The Trainers
Ms. Jun Guan
Senior Data Analyst
Senior Biostatistian
Master of Science Degree in Statistics, U of T

The Achievement

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The Resources
Data Mining

Data Warehousing

SAS Solutions for Banking

SAS and Health Industry

OCOT Advantages

100 %Instructor-Led Class
State-of-the-Art Facilities
Unlimited Lab Time
Labs Open 7-days a Week
Free Repeat
Free Job Placement
Financial Aid Possible
Resume Writing
Interview Skills

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