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
Extracting
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
Clustering
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
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