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Data Analysis

Data Visualization

Data Predication

Data Analytics With IBM SPSS

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

Modules Included

 The Research Process

  Initial Observation

  Generate Theory

  Generate Hypotheses

  Types of statistical models

  Populations and samples

 Simple statistical models

 The mean as a model

 The variance and standard deviation

 Central Limit Theorem

 The standard error

 Confidence Intervals

 Test statistics

  Non-significant results and Significant results:

 One- and two-tailed tests

 Type I and Type II errors

 Effect Sizes

 Statistical power

 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


 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

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.


 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


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


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


 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"

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Upcoming Batches


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