WHY ASPIRE ?

We are the Information Technology training division of Aspire Techsoft Pvt Ltd, an IT company founded in 2011.We are ranked among the software training institutes in the india. We are specialized in ERP, SAP, SAS, JAVA, Data Warehousing,Hadoop etc.

Kothrud Branch (Head Office)
  • Address: Malhar Building, Plot No 2, H. A. Colony,
    Paud Phata, Eardawana, Pune 38.
  • Phone:7058-198-728 / 7058-733-423
  • Email:[email protected]
Dange Chowk Branch
  • Address: Office No 108, 1st Floor, ABC Nirman Complex,
    Opp Macdonald, 16 No Stop, Near Dange Chowk, Pimpri Chinchwad, Pune - 33.
  • Phone:8856-033-664
  • Email:[email protected]

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Training Structure

Live Web

Features:

    • Get trained by experts.
    • Attend classes from anywhere right from your computer.
    • Get all the benefits of classroom training.
    • Get access to course recordings up to 20 business days.
    • Access to latest software for hands-on.
    • Live interaction with trainers & trainees of different locations.

    Live Web

    Audio Requirements:

    • You should have working speakers, so that you can listen properly.
    • You should have working mics, so that you can interact with trainers.
    • We recommend to have Headphones with mics.

    Live Web

    System Requirements:

    • Internet Explorer 9, Mozilla Firefox 34, Google Chrome 39 (JavaScript enabled) or the latest version of each web browser.
    • Windows 7, Windows 8 or later.
    • Cable modem, DSL, or better Internet connection.
    • Internet bandwidth 2 mbps(recommended).
    • Dual-core 2.4GHz CPU or faster with 2GB of RAM (recommended).

    Live Web

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      SAS Digital Badge

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        SAS Statistical Bussiness Analyst

        This course is designed for SAS professionals who use SAS/STAT software to conduct and interpret complex statistical data analysis. It covers analysis of variance, linear and logistic regression, preparing inputs for predictive models, and measuring model performance. Suitable for Statisticians, researchers, and business analysts who use SAS programming to generate analyses using either continuous or categorical response (dependent) variables; as well as modelers and analysts who need to build predictive models, particularly models from the banking, financial services, direct marketing, insurance and telecommunications industries.

        Learn How To:

        Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

        • generate descriptive statistics and explore data with graphs
        • perform analysis of variance and apply multiple comparison techniques
        • perform linear regression and assess the assumptions
        • use regression model selection techniques to aid in the choice of predictor variables in multiple regression
        • use diagnostic statistics to assess statistical assumptions and identify potential outliers in multiple regression
        • use chi-square statistics to detect associations among categorical variables
        • fit a multiple logistic regression model.

        Predictive Modeling Using Logistic Regression

        • use logistic regression to model an individual’s behavior as a function of known inputs
        • create effect plots and odds ratio plots using ODS Statistical Graphics
        • handle missing data values
        • tackle multicollinearity in your predictors
        • assess model performance and compare models.

        Prerequisites:

        Before attending this course, you should have completed the equivalent of an undergraduate course in statistics covering p-values, hypothesis testing, analysis of variance, and regression.


        Course Outline:

        Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

        • Prerequisite Basic Concepts
        • Introduction to Statistics
        • Tests and Analysis of Variance
        • Linear Regression
        • Linear Regression Diagnostics
        • Categorical Data Analysis
        Predictive Modeling Using Logistic Regression

        • Predictive Modeling
        • Fitting the Model
        • Preparing the Input Variables
        • Classifier Performance

        The fees is inclusive of:

        • Fee: ₹72,000/-  ₹60,000+GST | Duration: 40 Hours.
        • Training & Digital Badge.
        • Course material.
        • 2 attempts of Global certification.

        Download Course Curriculum

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