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Datascience With SAS Training

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The Data Science with SAS course has been created to bestow a top to bottom information of information science strategies and their application utilizing SAS to explain business issues. It manufactures a solid calculated establishment and gives hands-on preparing on SAS apparatuses (Base SAS and SAS Studio) and SAS programming to learn and apply prescient demonstrating, determining, arrangement and affiliation methods.

As a feature of the program you will take a shot at genuine industry-based undertakings over various spaces like web-based social networking, retail, amusement and internet business utilizing our Learning Management System. You will take a shot at various assignments, contextual investigations and practice activities to sharpen your aptitudes.

SAS is a costly business programming and is for the most part utilized by vast enterprises with gigantic spending plans. Python and R are free programming that can be downloaded by anybody.

You don’t require earlier information in programming to learn SAS, and its simple to-utilize GUI makes it the least demanding to learn of all the three. The capacity to parse SQL codes, joined with macros and other local bundles make learning SAS easy breezy for experts with fundamental SQL information.

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To investigate information in Python, you will utilize information mining libraries like Pandas, Numpy, and Scipy. In other words, you won’t code in local Python dialect while breaking down information. The code you write in these libraries looks fairly like the code you write in R. Subsequently, it is simpler to learn R when you are now comfortable with the Python information mining libraries. On the off chance that you definitely know R, at that point you ought to take in the nuts and bolts of Python programming dialect before you begin to take in the Python information mining biological system. In this way, don’t feel that R is troublesome, and Python is anything but difficult to learn!

SAS is to a great degree effective at consecutive information access, and database access through SQL is well coordinated. The intuitive interface makes it simple for you to make better measurable models rapidly. It has fair useful graphical capacities, however it’s hard to make complex graphical plots in SAS. R is known for In-memory examination and is mostly utilized when the information examination undertakings require an independent server. R is a brilliant instrument for investigating information. As of now, R has more than 5000 group contributed bundles in CRAN. The extensive variety of bundles and modules accessible for insights and information investigation makes it the most prevalent and intense dialect in information science. Measurable models can be composed in a couple of lines of code. You can draw confused diagrams delightfully in R utilizing bundles like Ggplot2, grid, rCharts, and so forth. Python libraries like Pandas, Numpy, Scipy and Scikit-learn makes it the second most well known programming dialect in information science after R. You can likewise make delightful outlines and charts utilizing libraries like Matlplotlib and Seaborn. Python is effectively utilized by the machine learning group to scrap and investigate unstructured information from the web. I Python scratch pad – an online intuitive condition – makes it simpler to share your code with anther.

Hye Infotech provides the best training on Datascience with SAS Training in chennai. We arrange classes based on student feasible timings, to take online or classroom trainings in chennai. We are the Best Datascience with SAS Training Institute in Chennai as far as Datascience with SAS syllabus is concerned.

Course Objectives

  • 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

Best Datascience with SAS Training:

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