Skill Shiksha

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Masters In Data Science

Building on centuries of statistics and mathematics, Data Science uses computational techniques to apply and scale these approaches, and get some truly ground breaking results.

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1000+

Student Empowered

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Online

Format

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6 Months

Recommended 7-9 hrs/week

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Assignments

After every class



Program Highlights

Major Highlights


  • Certificate from DIDM and SkillShiksha
  • Case Studies and Projects
  • Dedicated Student Success Trainer
  • Personalised Resume Feedback
  • 1-to-1 With Industry Trainer
  • Promote Your Brand Online
  • Practical Knowloedge Of Data Science
  • Think Big and Earn Big/Make Money Online
  • Assignment after every class
  • Internship In Leading Companies

We are partner with the most promising Delhi Institute Of Digital Marketing to create the set of leaders in the field of Digial Marketing.This course will definately give you the career boost with highly paid job like no other digital marketing course




Data Science Course Curriculum


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  • video-iconRecap of Demo
  • video-icon Introduction to Types of Analytics
  • video-icon Project Life Cycle

Description: Learn about High-level overview of Data Science project management methodology, Statistical Analysis using examples, understand Statistics and Statistics 101. Also, learn about exploratory data analysis, data cleansing, data preparation, feature engineering.

  • video-iconHigh-Level overview of Data Science / Machine Learning project management methodology
  • video-iconVideos for Data Collection - Surveys and Design of Experiments will be provided
  • video-iconThe various Data Types namely continuous, discrete, categorical, count,qualitative, quantitative and its identification and application. Further classification of data in terms of Nominal, Ordinal, Interval and Ratio types
  • video-iconRandom Variable and its definition
  • video-iconProbability and Probability Distribution – Continuous probability distribution / Probability density function and Discrete probability distribution/ Probability mass function

Description: Description: Continue with the discussion on understanding Statistics, the various Moments of business decision and other Basic Statistics Concepts.Also, learn about some graphical techniques in Analytics.

  • video-iconBalanced vs Imbalanced datasets
  • video-iconVarious sampling techniques for handling balanced vs imbalanced datasets
  • video-iconVideos for handling imbalanced data will be provided
  • video-iconWhat is Sampling Funnel, its application and its components?
  • video-iconPopulation
  • video-iconSampling frame
  • video-iconSimple random sampling
  • video-iconSample
  • video-iconMeasure of central tendency
  • video-iconMean / Average
  • video-iconMedian
  • video-iconMode
  • video-iconMeasure of Dispersion
  • video-iconVariance
  • video-iconStandard Deviation
  • video-iconRange
  • video-iconExpected value of probability distribution

Description: Learn about the other moments of business decision as part of Statistical Analysis. Learn more about Visual data representation and graphical techniques. Learn about Python, R programming with respect to Data Science and Machine Learning. Understand how to work with different Python IDE and Python programming examples.

  • video-iconMeasure of Skewness
  • video-iconMeasure of Kurtosis
  • video-iconVarious graphical techniques to understand data
  • video-iconBar plot
  • video-iconHistogram
  • video-iconBox plot
  • video-iconScatter plot
  • video-iconIntroduction to R and RStudio
  • video-iconInstallation of Python IDE
  • video-iconAnaconda and Spyder
  • video-iconWorking with Python and R with some basic commands

Description: Learn about Normal Distribution and Standard Normal Distribution. Rules and Principles of Normal distribution. And how to check for normality by QQ normal distribution Plot.

  • video-iconNormal Distribution
  • video-iconStandard Normal Distribution / Z distribution
  • video-iconZ scores and Z table
  • video-iconRandom Variable and its definition

Description: Under this last topic on Basics of statistics, learn some higher statistical concepts and gain understanding on interval estimates.

  • video-iconSampling Variation
  • video-iconCentral Limit Theorem
  • video-iconSample size calculator
  • video-iconT-distribution / Student's-t distribution
  • video-iconConfidence interval
  • video-iconPopulation parameter - Standard deviation known
  • video-iconPopulation parameter - Standard deviation unknown
  • course-icon3 Weeks
  • course-icon2 Assignment

Description: Get introduced to Hypothesis testing, various Hypothesis testing Statistics, understand what is Null Hypothesis, Alternative hypothesis and types of hypothesis testing.

  • video-iconParametric vs Non-parametric tests
  • video-iconFormulating a Hypothesis
  • video-iconChoosing Null and Alternative hypothesis
  • video-iconType I and Type II errors
  • video-iconComparative study of sample proportions using Hypothesis testing
  • video-icon2 sample test

Description: Learn about the various types of tests in Hypothesis testing. Get introduced to the prerequisites and conditions needed to proceed with a Hypothesis test. Understand the interpretation of the results of a Hypothesis testing and probabilities of Alpha error.

  • video-icon1 sample t test
  • video-icon1 sample z test
  • video-iconANOVA
  • video-icon2 Proportion test
  • video-iconChi-Square test
  • video-iconNon-Parametric test

Description: Continuing the discussion on Hypothesis testing, learn more about non-parametric tests. Perform different tests and interpret the results.

  • video-iconNon-Parametric test continued
  • video-iconHypothesis testing using Python and R
  • course-icon3 Weeks
  • course-icon2 Assignment
  • video-iconScatter Diagram
  • video-iconCorrelation Analysis
  • video-iconPrinciples of Regression
  • video-iconIntroduction to Simple Linear Regression
  • video-iconR shiny and Python Flask
  • video-iconIntroduction to R shiny and Python Flask (deployment)
  • video-iconMultiple Linear Regression

Description: Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation.Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.

  • video-iconScatter diagram
  • video-iconCorrelation Analysis
  • video-iconCorrelation coefficient
  • video-iconOrdinary least squares
  • video-iconPrinciples of regression
  • video-iconSplitting the data into training, validation and testing datasets
  • video-iconUnderstanding Overfitting (Variance) vs Underfitting (Bias)
  • video-iconGeneralization error and Regularization techniques
  • video-iconIntroduction to Simple Linear Regression
  • video-iconHeteroscedasticity / Equal Variance
  • video-iconHeteroscedasticity / Equal Variance

Description: In the second part of the tutorial, you will learn about the Models and Assumptions for building Linear Regression Models, build Multiple Linear Regression Models and evaluate the results of the Linear Regression Analysis.

  • video-iconLINE assumption
  • video-iconCollinearity (Variance Inflation Factor)
  • video-iconLinearity
  • video-iconNormality
  • video-iconMultiple Linear Regression
  • video-iconModel Quality metrics
  • course-icon3 Weeks
  • course-icon2 Assignment

Description: Learn to analyse Attribute Data, understand the principles of Logistic Regression, Logit Model. Learn about Regression Statistics and Logistic Regression Analysis.

  • video-iconPrinciples of Logistic Regression
  • video-iconTypes of Logistic Regression
  • video-iconAssumption and Steps in Logistic Regression
  • video-iconAnalysis of Simple Logistic Regression result

Description: Learn about the Multiple Logistic Regression and understand the Regression Analysis, Probability measures and its interpretation. Know what is a confusion matrix and its elements. Get introduced to “Cut off value” estimation using ROC curve. Work with gain chart and lift chart.

  • video-iconMultiple Logistic Regression
  • video-iconConfusion matrix
  • video-iconFalse Positive, False Negative
  • video-iconTrue Positive, True Negative
  • video-iconSensitivity, Recall, Specificity, F1
  • video-iconReceiver operating characteristics curve (ROC curve)
  • video-iconLift charts and Gain charts
  • video-iconLasso and Ridge Regressions
  • course-icon3 Weeks
  • course-icon2 Assignment

Description: Description: Get introduced to Multinomial regression, or otherwise known as multinomial logistic regression, learn about multinomial logit models and multinomial logistic regression examples.

  • video-iconLogit and Log Likelihood
  • video-iconCategory Baselining
  • video-iconModeling Nominal categorical data
  • video-iconAdditional videos are provided on Lasso / Ridge regression for identifying the most significant variables
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Interactive & Practical

Assignment after every class to ensure complete mastery of topic


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