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Introduction to Data Science
• What is Data Science?
• Role of Data Science 
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1. Descriptive and Inferential Statistics
 Samples and Populations
• Sample Statistics
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v  Overview

v  History

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Ø Data Warehouse Fundamentals (3 days)

ü An introduction to Data Warehousing Purpose of Data Warehouse Data Warehouse Architecture Operational Data Store OLTP Vs Warehouse Applications Data Marts Data marts Vs Data WarehousesData Warehouse Life cycle.

Ø Data Modeling ( 2days) Introduction to Data Modeling  Data Modeling for Data Warehouse, Normalization process  Dimensions and fact tables  Star Schema and Snowflake Schemas.

Ø ETL Design Process (2days) Introduction to Extraction, Transformation & Loading  Types of ETL Tools Key tools in the market.

Ø Introduction to Data stage Version 7.5x2 & 8.1& 8.5 (1day)

ü Data stage project Administration  Editing projects and Adding Projects Deleting projects Cleansing up project files  Environmental Variables Environment management   Auto purging  Runtime Column Propagation(RCP) Add checkpoints for sequencer Generated OSH (Orchestra Engine) System formats like data, timestamp Projects protect – Version details

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DATA SCIENCE COURSE 

Introduction to Data Science

•

What is Data Science?

•

Role of Data Science

•

Scope of Data Science

1. Descriptive and Inferential Statistics

•

Samples and Populations,Sample Statistics,Estimations of Population,Parameters,Random and Non,random Sampling,Sampli

ng Distributions,The Central limit Theorem,Degree of Freedom,Percentiles and Quartiles,Measures of Central Tendency,Mean,Median,Mode,Measures of Variability/Dispersions,Range,IQR,Variance,Standard Deviation,Distributions,Normal Distributions,Binomial Di,stribution

Probability Distribution,Events, Sample Space and Probabilities

Conditional Probabilities,Independence of Events,Bayes’ Theorem,Random Variable ,Confidence Intervals,Hypothesis,Testing,Null Hypothesis

more details- clickhere:

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DATAWAREHOUSE BASICS                                                      

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·         Decision support systems

·         What is an OLTP

·         Characteristics of OLTP

·         OLTP vs. Data warehouse (OLAP)

·         Data mart

·         Data mart vs. Data warehouse

·         Different kinds of source systems

a)      Data ware housing approaches, Top-down, Bottom-up.

·         Staging area & its operations,Extraction,Transformation, Loading, Data cleansing, Data merging, Data sampling, Data Aggregation, Data Scrubbing.

DATA MODELLING                                                                                             

·         What is fact &dimension, Surrogate keys, Different kinds of facts, Additive,Semi-additive, Non additive, Fact less fact.

·        Different kinds of dimensions, Junk dimension, Confirmed dimension, Role playing dimension, Slowly changing dimension, Inferred Dimension, Star schema, Snow-flake-schema, Dimensional modelling process.

·         INFORMATICA 9.X                                                                     

  Introduction to Informatica &Power enter Components More details clickhere :

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DATA SCIENCE COURSE CONTENT

Introduction to Data Science

•

What is Data Science?

•

Role of Data Science

•

Scope of Data Science

1. Descriptive and Inferential Statistics

•

Samples and Populations,Sample Statistics,Estimations of Population,Parameters,Random and Non,random Sampling,Sampli

ng Distributions,The Central limit Theorem,Degree of Freedom,Percentiles and Quartiles,Measures of Central Tendency,Mean,Median,Mode,Measures of Variability/Dispersions,Range,IQR,Variance,Standard Deviation,Distributions,Normal Distributions,Binomial Di,stribution

Probability Distribution,Events, Sample Space and Probabilities

Conditional Probabilities,Independence of Events,Bayes’ Theorem,Random Variable ,Confidence Intervals,Hypothesis,Testing,Null Hypothesis

more details- clickhere:

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Datastage training center in ameerpet, hyderabad.

Ø Data Warehouse Fundamentals (3 days)

ü An introduction to Data Warehousing Purpose of Data Warehouse Data Warehouse Architecture Operational Data Store OLTP Vs Warehouse Applications Data Marts Data marts Vs Data WarehousesData Warehouse Life cycle.

Ø Data Modeling ( 2days) Introduction to Data Modeling  Data Modeling for Data Warehouse, Normalization process  Dimensions and fact tables  Star Schema and Snowflake Schemas.

Ø ETL Design Process (2days) Introduction to Extraction, Transformation & Loading  Types of ETL Tools Key tools in the market.

Ø Introduction to Data stage Version 7.5x2 & 8.1& 8.5 (1day)

ü Data stage introduction  IBM information Server architecture Data stage components Data Stage main functions Client components- Adding different Servers to our workspace.

Ø Data stage Administrator (1day)

ü Data stage project Administration  Editing projects and Adding Projects Deleting projects Cleansing up project files  Environmental Variables Environment management   Auto purging  Runtime Column Propagation(RCP) Add checkpoints for sequencer Generated OSH (Orchestra Engine) System formats like data, timestamp Projects protect – Version details

more details clickhere : 

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Informatica training center in ameerpet, hyderabad

DATAWAREHOUSE BASICS                                                      

What is BI & Data warehouse& Data warehousing

Decision support systems,What is an OLTP,Characteristics of OLTP,OLTP vs. Data warehouse (OLAP),Data mart,Data mart vs. Data warehouse,Different kinds of source systems,Data ware housing approaches

a)      Top-down

b)      Bottom-up,Staging area & its operations

a)      Extraction

b)      Transformation

c)      Loading

d)     Data cleansing

e)      Data merging

f)       Data sampling

g)      Data Aggregation

h)      Data Scrubbing

DATA MODELLING What is fact &dimension,Surrogate keys,Different kinds of facts

a)      Additive

b)      Semi-additive

c)      Non additive

d)     Fact less fact

·         Different kinds of dimensions

a)      Junk dimension

b)      Confirmed dimension

c)      Role playing dimension

d)     Slowly changing dimension

e)      Inferred Dimension

·         Star schema,Snow-flake-schema Dimensional modelling process
more details click here :