Wednesday 27 May 2015
Thursday 12 March 2015
Tuesday 19 August 2014
DV Analytics Trainin - Best Analytics Training Institute in Bangalore
Course Pre-Requisite
Bangalore-560093
Land Mark: Hp Main Gate Road,
- Minimum bachelors degree
- Basic idea about computers and M.S office
- Basic idea on analytics industry
- Candidate need not know statistics
Course Covers
- SAS Base
- SAS Advanced:
- Basic Data handling & Analytics
- Advanced Analytics and Predictive Modeling
- Excel & VBA
- Access & SQL
- Tableau
- Qlikview
- Data Analysis projects
- Resume preparation and Interview support
- You will have complete understanding of analytics industry
- You can confidently add below tools & techniques to your CV
- SAS Base, SAS Advanced
- Excel, Excel VBA
- Access, Sql
- Analytics
- Predictive modeling
- Tableau, Qlikview
- 4 real time projects in your CV on SAS, Automation and analytics
- A perfect CV for analytics job
- Skills Required for Analytics Industry
- 4000+ hours of training
- 3 years in the industry
- Trained over 100 students
- Completed more than 15 analytics batches
- More than 70% success rate
- Students across all MNCs in all industries
- Well qualified and knowledgeable trainers
- Different SME trainers for different tools
- Corporate training experience
- Best rated analytics training institute in Karnataka
Contact us
9591793303
dvanalytics.training@gmail.com
DV Analytics
Krishnappa Garden,
Bhagmane techpark
CV Raman Nagar,Bangalore-560093
Friday 18 April 2014
Dv Analytics Course Contents - Training in Data Analytics, SAS Base Advanced, SAS BI, Excel, Access, VBA, SQL Quilk view
Training Contents:
SAS Programming Base and
Advanced
SAS Base Programming:
SAS Programming 1:
ü Introduction SAS system and Getting Familiar to SAS
environment
ü Creating Libraries and Datasets using Data Step
ü Producing List Reports using Proc Step
ü Data Manipulation Techniques using
§ Data Step Vs Proc Step
§ Format Vs Informat
§ Reading raw data files using Infile and Proc Import
statement
§ PDV
§ Examining Errors in SAS programing
§ Conditional processing using If, Where, Keep, Drop
statement
§ Remove Duplicate records using Proc Sort
§ Combining SAS dataset using SAS Merge and Set
statement
ü Summary
Reports
§ Proc Means, Proc Freq, Proc Summary, Proc Univariate,
Proc Report, Proc Tabulate
SAS Programming 2:
ü Introduction to Base SAS programming with Statements,
Options and Functions
ü Controlling Input and Output observation
ü Data
Manipulation Techniques using
§ Writing Multiple Dataset
§ Data Transformation
§ Transposing and Expanding Dataset
§ SAS Functions (Numeric and Character)
§ Writing to External File
§ Creating An Accumulating Total Variable
§ Combining Duplicate Records Using First. And Last.
§ Reading Delimited Raw Data File in .txt (text
File),.csv (CSV File),.xlsx (Excel File) and .accdb (Access Database)
§ DSD, DLM, MISSOVER,TRUNCOVER, STOPOVER and FLOWOVER
options used in reading raw data file
§ Connecting SAS to Other Database Server
§ Debugging Techniques
§ Put Statement
§ Debug Options
ü Processing Data Interactively
§ DO Loop
§ SAS Arrays
SAS Advanced Programming:
SAS SQL Processing
§ Accessing Data Using SQL
§ Generate detail reports by working with a single
table or joining tables using PROC SQL and the appropriate options
§ Generate summary reports by working with a single
table or joining tables using PROC SQL and the appropriate options
§ Construct sub queries within a PROC SQL step
§ Compare solving a problem using the SQL procedure
versus using traditional SAS programming techniques
§ Access Dictionary Tables using the SQL procedure
§ Demonstrate advanced PROC SQL skills by creating and
updating tables, updating data values, working with indexes using the macro
interface/creating macro variables with SQL, defining integrity constraints,
SQL views and SET operators
Macro Processing
§ Creating and using user-defined and automatic macro
variables within the SAS Macro Language
§ Automate programs by defining and calling macros
using the SAS Macro Language
§ Understand the use of macro functions
§ Recognize various system options that are available
for macro debugging and displaying values of user-defined and automatic macro
variables in the SAS log
Advanced Programming Techniques
§ Demonstrate advanced data set processing techniques
such as updating master data sets, transposing data, combining/merging data,
sampling data, using generation data sets, integrity constraints and audit
trails
§ Reduce the space required to store SAS data sets and
numeric variables within SAS data sets by using compression techniques, length
statements or DATA step views
§ Develop efficient programs by using advanced
programming techniques such as permanent formats and array processing
§ Use SAS System options and SAS data set options for
controlling memory usage
§ Control the processing of variables and observations
in the DATA step
§ Create sorted or indexed data in order to avoid
unnecessary sorts, eliminate duplicate data and to provide more efficient data
access and retrieval
§ Use PROC DATASETS to demonstrate advanced programming
skills (e.g. renaming columns, displaying metadata, creating indexes, creating
integrity constraints, creating audit trails)
SAS Project-Practical
EXCEL Base and Advanced
Excel Base:
§ Introduction MS Excel
§ Navigation technique in Excel
§ Cells Reference, Range, Rows and Columns
§ Format Paint, Border Style and Designing, Cell
Merging, Conditional Formatting, Sorting and filtering, Data Validation, Data
consolidation
§ Data Import and Export
§ Basic Pivot Table, Chart
§ Excel Formulas and Functions like IF and Nested IF,
Vlook-up, HLook-up, Sum,Sum IF,Match, Offset and Index etc.
§ Running Manual Excel Macro and Recording
Excel
Advanced:
§ Advanced Data Manipulation Techniques
§ Advanced Pivot Design
§ Advanced Pivot Options for reporting
§ Power Pivot technique
§ Excel Dashboard using Excel functions and VBA Macros
§ Excel VBA Programming
Excel Project-Practical
ACCESS Base and Advanced
ACCESS
Base and Advanced:
§ Introduction MS ACCESS
§ Navigation technique in ACCESS and Access Objects
§ Creating Database, Tables, Field Properties
§ Access Queries (Select, Make Table, Append, Update,
Delete, Crosstab, Union and Union All)
§ Data Import and Export in Access
§ Access Pivot Table, Chart
§ Access Join
§ Forms and Reports
§ Access Formulas and Functions
§ Access Modules using Access VBA
§ Access Data Manipulation technique using SQL queries
Access Project-Practical
Qlikview and Tableau BI
Dashboard Making
§ Introduction to Qlikview
§ Various data & dash board related options
§ Creating dashboards using Qlikview
§ Introduction to tableau
§ Various data & dash board related options
§ Creating dashboards using Tableau
Basic and Advanced Data
analytics
Introduction
to Analytics Tool:
·
SAS
ü What is SAS
ü Main features of SAS
·
R
ü What is R
ü Main features of R
·
Other Tools
ü SPSS
ü STATA
ü MATLAB
Business
Applications for SAS Advanced analytics
·
Analytics Life Cycle
i)
Modules of data analytics problem
ii)
Steps in Data analytics Life
cycle
(1)
The business objective
(2)
Data collection
(3)
Data Exploration
(4)
Model Building
(5)
Validation
(6)
Implementation
·
Applications of Advanced Analytics in Various
Industries
(1)
Banking:
(a)
Credit Risk Model building
(b)
Other applications
(2)
Healthcare
(a)
Heath insurance Fraud detection
(b)
Other applications
(3)
Retail
(a)
Customer Behavior analysis
(b)
Other applications
(4)
Pharmacy
(a)
(5)
Social Media
(a)
Sentiment and brand value
analysis
(b)
Other applications
·
Other Industry Applications
·
Introduction to basic descriptive statistics
o
Basic Forms of Data Analysis
o
Basics of Reporting
o
Measures of Central Tendency
o
Measures of Dispersion
o
Univariate Analysis
o
Bivariate Analysis
o
Measures of Association
·
Introduction to basic statistical analysis
o
Hands-on
exercises
·
Data exploration & Data preparation
o
Understanding the Data
o
Validating the Data
o
Problems in Data
o
Data Sanitization and Preparing
Data for Analysis
o
Hands-on exercises
§
Regression Analysis
o
Simple Linear Regression
o
Fitting Regression Line
o
Goodness of Fit
o
Interpretation of Beta
o
Prediction Using the Line
o
Multiple Liner Regression
o
Multi-Co-linearity
o
Goodness of Fit
o
Nonlinear Regression
o
Logistic Regression
o
Introduction
o
Fitting Logistic Regression Line
o
Goodness of Fit
o
Interpretation
o
Prediction Using the Line
o
Hands-on exercises on simple
linear model
o
Hands-on exercises on multiple
linear models
o
Logistic Regression model
building
o
Hands-on exercises on Logistic
Regression
§
Customer segmentation
using cluster analysis
o
Cluster Analysis
o
Introduction
o
Distance Measures
o
Types of Cluster Analysis
o
K-Means Cluster Analysis
o
Interpretation and Inference
o
Decision Trees
o
Introduction
o
Entropy
o
Information Gain
o
Decision Tree Algorithms
o
Building a Decision Tree
o
Interpretation and Inference
o
Hands-on exercises on sample data
§
Decision tree models
o
Hands on exercises on sample data
§
Hypothesis testing with
examples
o
Null Hypothesis
o
Alternate Hypothesis
o
Test Statistic
o
Sampling Distribution
o
P-Value
o
Inference
o
Tests
o
Z-test for Mean
o
T-test for Mean
o
Paired T-test
o
Chi-square test
o
F-Test
o
Hands on exercises on sample data
§
Time series forecasting
o
TSI Method
o
Trend Determination
o
Seasonality Indices
o
Model Fitting Using TSI
Techniques
o
ARIMA Method
o
AR Process
o
MA process
o
ACF and PACF Plots
o
Model Fitting Using ARIMA
Technique
o
Hands on excesses on prediction
§
Text Mining and Sentiment
Analysis
o
Basics of Text Mining
o
Applications of Text Mining
o
Sentiment Analysis
§ Bag of Words Algorithm
§ Bayes Algorithm
Data
analysis practical project
§ Practical Data importing
§ Data cleaning
§ Analysis design
§ Creating the BI report
§ Designing the analysis solution
§ Performing the analysis and building a predictive
model
§ Presentation of result
§ Final documentation
§ Step by step process of credit risk model building
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