Data Science Training in Hebbal

Overview of SAS

• Introduction and History of SAS
• Significance of SAS software solutions in various industries
• Demonstrate SAS Capabilities
• Job Profile / career opportunities with SAS worldwide?

Base SAS Fundamentals

• Explore SAS Windowing Environment
• SAS Tasks
• Working with SAS Syntax
• Create and submit a SAS sample program

Data Access & Data Transformation

• Accessing SAS Data libraries
• Getting familiar with SAS Data set

Reading SAS data set

• Introduction to reading data
• Examine structure of SAS data set
• Understanding of SAS works

Reading Excel worksheets

• Using Excel data as input
• Create as sample program to import and export excel sheets

Reading Raw data from External File

• Introduction to raw data
• Reading delimited raw data file (List Input)
• Using standard delimited data as input
• Using nonstandard delimited data as input
• Reading raw data aligned to columns (Fixed or column input)
• Reading raw data with special instructions (Formatted input)

Writing to an External file

• Write data values from SAS data set to an external file

Data transformations (Data step processing)

• Create multiple output datasets from single SAS dataset
• Writing observations to one or more SAS datasets
• Controlling which observations and variables to be written to output data

Creating subset of observations using

• Where condition
• Conditional processing using: IF statements

Processing Data Iteratively

• Iterative DO loop processing with END statement
• DO WHILE & DO UNTIL Statement
• SAS Array statement

Summarizing data

• Creating and Accumulating total variable (Retain)
• Using Assignment statement
• Accumulating totals for a group of data (BY group)

Manipulating Data

• Sorting SAS data sets
• Manipulating SAS data values
• Presentation of user defined values /data/currency values using FORMAT procedure
• SAS functions to manipulate char and num data
• Convert data type form char-to num and num-to-char
• SAS variables lists/ SAS variables lists range
• Debugging SAS program
• Accessing observations by creating index

Restructuring a SAS data set

• Rotating with the data step
• Using the transpose procedure

Combining SAS data sets

• Concatenation
• Interleaving
• One to one reading
• One to one merging (with non-matching)
• Match merging (Merging types with IN=option)

SAS Access & SAS Connect

• Validating and cleaning data
• Detect and correct syntax errors
• Examining data errors

Analysis & Presentation

• SAS/REPORTS SAS/GRAPH
• SAS/STATS SAS/ODS

Producing detailed /Summary Reports

• Freq Report
• Means Report
• Tabulate Report
• Proc report
• Summary report
• Univariate report
• Contents report
• Print report
• Compare proc
• Copy proc
• Datasets proc
• Proc append
• Proc delete

Generating Statistical Reports using

• Regression proc
• Uni/Multivariate proc
• Anova proc

Generating Graphical reports using

• Producing Bar and Pie charts (GCHART Proc)
• Producing plots (GPLOT Proc)
• Presenting Output Report result in:
• PDF
• Text files
• Excel
• HTML Files

SAS/SQL Programming

• Introduction and overview to SQL procedure
• Proc SQL and Data step comparisons

Basics Queries

• Proc SQL syntax overview
• Specifying columns/creating new columns
• Specifying rows/subsetting on rows
• Ordering or sorting data
• Formatting output results
• Presenting detailed data
• Presenting summarized data

Sub Queries

• Non correlated sub queries
• Correlated sub queries

SQL Joins (Combining SAS data sets using SQL Joins)

• Introduction to SQL joins
• Types of joins with examples
• Simple to complex joins
• Choosing between data step merges and SQL joins

SET Operators

• Introduction to set operations
• Except/Intersect/Union/Outer union operator

Additional SQL Procedures features

• Creating views with SQL procedure
• Dictionary tables and views
• Interfacing Proc SQL with the macro programming language
• Creating and maintaining indexes
• SQL Pass-Through facility

SAS Macro Language

• Introduction to macro facility
• Generate SAS code using macros
• Macro compilation
• Creating macro variables
• Scope or macro variables
• Global/Local Macro variables
• User defined /Automatic Macro variables
• Macro variables references
• Combing macro variables references with text
• Macro functions
• Quoting (Masking)
• Creating macro variables in Data step (Call SYMPUT Routine)
• Obtaining variable value during macro execution (SYMGET function)
• Creating macro variables during PROC SQL execution (INTO Clause)
• Creating a delimited list of values
• Macro parameters
• Strong Macro using Autocall Features
• Permanently storing and using stored compiled macro program
• SAS Macro debugging options to track problems

Basics Statistics

• Standard deviation
• Correlation Coefficients
• Outliers
• Linear regressions
• Clustering
• Chi Square

For More Details About Data Science with Python Course Click Here! →

Module 1: Introduction to Data Science (Duration-1hr)

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types

Module 2: Introduction to R (Duration-1hr)

• What is R?
• Why R?
• Installing R
• R environment
• How to get help in R
• R Studio Overview

Module 3: R Basics (Duration-5hrs)

•  Environment setup
• Data Types
• Variables Vectors
• Lists
• Matrix
• Array
• Factors
• Data Frames
• Loops
• Packages
• Functions
• In-Built Data sets

Module 4: R Packages (Duration-2hrs)

• DMwR
• Dplyr/plyr
• Caret
• Lubridate
• E1071
• Cluster/fpc
• Data.table
• Stats/utils
• Ggplot/ggplot2
• Glmnet

Module 5: Importing Data (Duration-1hr)

• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to csv file

Module 6: Manipulating Data (Duration-1hr)

• Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques

Module 7: Statistics Basics (Duration-11hrs)

• Central Tendency
  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
•  Probability Basics
  • What does mean by probability?
  • Types of Probability
  • ODDS Ratio?
• Standard Deviation
  • Data deviation & distribution
  • Variance
• Bias variance Trade off
  • Underfitting
  • Overfitting
• Distance metrics
  • Euclidean Distance
  • Manhattan Distance
• Outlier analysis
  • What is an Outlier?
  • Inter Quartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • Scatter plot
  • Cook’s Distance
• Missing Value treatments
  • What is a NA?
  • Central Imputation
  • KNN imputation
  • Dummification
• Correlation
  • Pearson correlation
  • Positive & Negative correlation

Module 8: Error Metrics (Duration-3hrs)

• Classification
  • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score
• Regression
  • MSE
  • RMSE
  • MAPE

Module 9: Machine Learning

Module 10: Supervised Learning (Duration-6hrs)

• Linear Regression
  • Linear Equation
  • Slope
  • Intercept
  • R square value
•  Logistic regression
  • ODDS ratio
  • Probability of success
  • Probability of failure
  • ROC curve
  • Bias Variance Tradeoff

Module 11: Unsupervised Learning (Duration-4hrs)

• K-Means
• K-Means ++
• Hierarchical Clustering

Module 12: Machine Learning using R (Duration-10hrs)

• Linear Regression
• Logistic Regression
• K-Means
• K-Means++
• Hierarchical Clustering – Agglomerative
• CART
• 5.0
• Random forest
• Naïve Bayes