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Data Analysis

Data Visualization

Data Predication

Antrix Academy

Business Analytics Program – Tools Based

  • 60 + hrs. Live Mentoring
  • 40 + hrs. Coding Assignments
  • 4 + Real-Life Projects
  • 3 + Industry cases

Modules Included

 Introduction to SAS, GUI

 Concepts of Libraries, PDV, data execution etc

  Building blocks of SAS (Data & Proc Steps - Statements & options)

  Debugging SAS Codes

  Importing different types of data & connecting to data bases

 Data Understanding(Meta data, variable attributes(format, informat, length, label etc)

 SAS Procedures for data import /export / understanding(Proc import/Proc contents/Proc print/Proc means/Proc feq)

  Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type converstions, renaming, formatting, etc)

  Data manipulation tools (Operators, Functions, Procedures, control structures, Loops, arrays etc)

 SAS Functions (Text, numeric, date, utility functions)

 SAS Procedures for data manipulation (Proc sort, proc format etc)

 SAS Options (System Level, procedure level)

 Introduction exploratory data analysis

 Descriptive statistics, Frequency Tables and summarization

 Univariate Analysis (Distribution of data & Graphical Analysis)

 Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

 SAS Procedures for Data Analysis(proc freq/Proc means/proc summary/proc tabulate/Proc univariate etc)

 SAS Procedures for Graphical Analysis (Proc Sgplot, proc gplot etc)

  Introduction to Reporting

 SAS Reporting Procedures (Proc print, Proc Report, Proc Tabulate etc)

 Exporting data sets into different formats (Using proc export)

 Concept of ODS (output delivery system)

  ODS System - Exporting output into different formats

 Introduction to Advanced SAS - Proc SQL & Macros

  Understanding select statement (From, where, group by, having, order by etc)

 Proc SQL - Data creation/extraction

 Proc SQL - Data Manipulation steps

  Proc SQL - Summarizing Data

  Proc SQL - Concept of sub queries, indexes etc

 SAS Macros - Creating/defining macro variables

 SAS Macros - Defining/calling macros

 SAS Macros- Concept of local/global variables

  Introduction of Statistics

 Descriptive and inferential statistics

 Explanatory Versus Predictive Modeling

 Population and samples

  Uses of variable independent and dependent

 Types of variables quantitative and categorical

  Descriptive Statistics Introduction

 Descriptive Statistics Introduction

 Histogram

  Measures of shape skewness

  Box Plots

 Univariante Procedure

  Statistical graphics procedures

 The SGPLOT Procedure

 ODS Graphics Output

 Using SAS to picture your data

 Confidence Intervals for the Mean Introduction

  Distribution of sample means

 Normality and the central limit theorem

  Calculation of 95% confidence interval

 Hypothesis Testing introduction

  Decision Making Process

  Steps in Hypothesis Testing

  Types of error and power

 The p value effect size and sample size

  Statistical Hypothesis Test

 the t statistic t distribution and two sided t test

 Using proc univariate to generate a t statistic

 Accessing SPSS

 To explore the key windows in SPSS

 Data editor

 The viewer

 The syntax editor

 How to create variables

 Enter Data and adjust the properties of your variables

 How to Load Files and Save

 Opening Excel Files

 Recoding Variables

 Deleting/Inserting a Case or a Column

 Selecting Cases

 Using SPSS Help

  The art of presenting data

 The SPSS Chart Builder

 Histograms: a good way to spot obvious problems

  Boxplots (box–whisker diagrams)

 Graphing means: bar charts and error bars

  Simple bar charts for independent means

 Line charts

  Graphing relationships: the scatterplot

 Simple scatterplot

  Grouped scatterplot

  Simple and grouped -D scatterplots

  Matrix scatterplot

  Simple dot plot or density plot

  Drop-line graph

  Editing graphs

  Type I and Type II errors

  Effect Sizes

 Statistical power

 What are assumptions?

 Assumptions of parametric data

 The assumption of normality

 Quantifying normality with numbers

  Exploring groups of data

 Testing whether a distribution is normal

  Kolmogorov–Smirnov test on SPSS

 Testing for homogeneity of variance

  Correcting problems in the data

 Looking at relationships

  Standardization and the correlation coefficient

 The significance of the correlation coefficient

  Confidence intervals for r

  1. Bivariate correlation
  2. Pearson’s correlation coefficient
  3. Spearman’s correlation coefficient
  4. Kendall’s tau (non-parametric)
  5. Biserial and point–biserial correlations
  6. Partial correlation
  7. The theory behind part and partial correlation
  8. Partial correlation using SPSS
  9. Semi-partial (or part) correlations

  Comparing correlations

  Comparing independent rs

  dependent rs

  Calculating the effect size

  How to report correlation coefficients

  An introduction to regression

 Some important information about straight lines

 The method of least squares

 Assessing the goodness of fit: sums of squares, R and R2

 Doing simple regression on SPSS

 Multiple regression: the basics

 How to do multiple regression using SPSS

 Descriptive

 Checking assumptions

 Background to logistic regression

 What are the principles behind logistic regression?

 Assessing the model: the log-likelihood statistic

 Assessing the model: R and R2

 Methods of logistic regression

 Interpreting logistic regression

 How to report logistic regression

 Testing assumptions

 Predicting several categories: multinomial logistic regression

 Running multinomial logistic regression in SPSS

 Looking at differences

 The t-test

 Rationale for the t-test

 Reporting the dependent t-test

 Reporting the independent t-test

 Between groups or repeated measures?

 The t-test as a general linear model

 Comparing several means : ANOVA (GLM)

  The theory behind ANOVA

  The theory behind ANOVA

  Inflated error rates

  Interpreting f-test

  ANOVA as regression

 Assumptions of ANOVA

 Planned contrasts

 Post hoc procedure

 Introduction to Tableau Desktop

  Use and benefits of Tableau Desktop

  Tableau's Offerings

  Data Source Page

  Worksheet Interface

 Creating a Basic View

 Data Types

 Data Roles

 Visual Cues for Fields

 Data Preparation

 Joins

 Cross Database Joins

  Data Blending

 Joining vs. Blending

 Union

  Creating Data Extracts

 Writing Custom SQL

  Filtering Data

 Sorting Data

  Creating Combined Fields

  Creating Groups and Defining Aliases

 Working with Sets and Combined Sets

  Drilling and Hierarchy

 Adding Grand Totals and Subtotals

  Changing Aggregation Functions

 Creating Bins

  Cross Data Source Filter

 Effectively use Titles, Captions, and Tooltips

  Format Results with the Edit Axes

 Formatting your View

 Formatting results with Labels and Annotations

 Enabling Legends per Measure

 Calculations

 Use Strings, Date, Logical, and Arithmetic Calculations

 Create Table Calculations

 Discover Ad-hoc Analytics

  Perform LOD Calculations

  Creating Basic Charts such as Heat Map, Tree Map, Bullet Chart, and so on

 Creating Advanced Chart as Waterfall, Pareto, Gantt, Market Basket

  Dashboard Interface

  Build Interactive Dashboards

  Explore Dashboard Actions

  Best Practices for Creating Effective Dashboards

  Story Interface

  Creating Stories

  Share Your Work

What You Get?

  Learn from our comprehensive collection of project case-studies, hand-picked by industry experts, to give you an in-depth understanding of how data science moves industries like telecom, transportation, e-commerce & more.

  1. Global Sales Store Data Analytics - WallMart
  2. Service Calls & Engineers Utilization Data Analytics – HCL Services
  3. Clinical Data Analytics of Cancer Patients Diagnosis & Medication – Global Health Care
  4. Data Analytics of Training & Development Program of Defense Forces – Indian Navy ...many more...

  You will be having the opportunity of 10-15 Hrs e-learning exercises along with instructor-led-training which enable candidates to get the maximum out of the subjects and empowering them to build logics to hand any new requirement.

 This program has been designed in collaboration with some of the most influential analytics leader and top academician in data science.


RAJINDER

 Professional with over 14 years of experience.
 Specialization : Big Data & Hadoop, SAS, R, Python, MS Excel
 Companies worked with : HCL, NIIT, IBM, Tata AIG, CSC, BHEL, AIR FORCE, INDIAN NAVY

MONICA

 Professional with over 17 years of experience.
 Specialization :MS Excel & VBA
 Companies worked with : HCL, NIIT, I.E.&T, Guwahati , Mahindra Comviva , Orange , AON

ANSHUMAN

 Professional with over 10 years of experience.
 Specialization :Python, PHP
 Companies worked with : IBM, Gurgaon, HCL - Noida, CDAC - Delhi

BINIT KUMAR

 Professional with over 10 years of experience.
 Specialization :IoT, Embedded Systems

Final Outcome

 Thanks to the digital revolution that is sweeping the world and India in particular, data scientists are now the most sought-after professionals by big corporations as well as startups. And companies across industries are rewarding good data analysts and scientists with desirable career growth and salaries.

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Mentorship by Leading Experts


Mr. Manoj Yadav Learning Head

"Appreciate the way trainer handle our induction batch, and level of knowledge of that faculty is really good and appreciable."




Mr. R. Senthil Kumar Assistant Director T (Trg)

"Our Participants are highly satisfied with the quality of training (on R and Python), course material (if applicable), and level of knowledge that the faculty has. We appreciate the way trainer handled our training batches."




Ms. Anuradha Singhal Sr. Faculty

"Thanks for such a wonderful training on Data Analytics with Python... thanks to trainer also.. look forward to future collaborations as well ..."




Krishan Kumar Participant,Corporate Training Program, VisionSping

"Excellent Training Delivery on Advanced Excel by Antrix Academy, "




Ankur Tiwari Candidate

"I am very much satisfied with the quality of education which i got from Antrix Academy, The trainer is very much helpful and is also very much educated enough to solve your queries..."




Neelam Rajput HRM College,

"Antrix Academy of Data Science in Best Training Company Sector_15 Noida "




Shubham Saraswat IIT Delhi

"Best place in terms of content delivery. Faculty is experienced and teases your mind to every angle in terms of a data scientist and makes you think like a data scientist. Fee is nominal too.Batch strength is upto their commitment..."




Blessy Varghese IIMT College

"Happy to be here in this academy. Able to understand the contents pretty well."




Anju Kushwaha Student

"its a good ...as am a beginner they help me from my basic ....thanks for every things."




A N Singh Student

"Antrix academy is very good institute for Data Science and Machine learning in noida"




Abhishek Chaudhary Student

"best platform to learn languages and Data-Analytics"




Anuj Vashistha Student

"Best academy to learn about data science"




Monika Narang Ramjas College,Delhi University

"Excellent for practical learning"








Upcoming Batches

Classroom

Invest Now in a Data Science Career

The field of data science is thriving as it is proving to be effective not just across industries but also across departments within organizations.

In-Demand Skills

6 out of 10 developers are gaining or looking to gain skills in machine learning and deep learning.


Antrix Academy

High Salaries

Data scientists make around 75 Lakhs on average.



Antrix Academy

Shortage of Data Scientists

India alone will need around 2,00,000 data scientists by 2020



Speak to Our Course Advisor If You Have Queries

 

Our Participants are highly satisfied with the quality of training (on R and Python), course material (if applicable), and level of knowledge that the faculty has. We appreciate the way