Description

Data Science Prodegree program is co-created with Genpact, which acts as the ‘knowledge partner’, and features an industry-aligned curriculum delivered in two modes: Classroom study and Online training, exploring the practical learning methodology. The program is best suited for graduate students or professionals with up to 3 years of working experience and who are interested in exploring the analytics industry and wish to enhance their technical skills and business understanding of the subject. The Data Science Prodegree is a 180-hour program focussing on Data Analysis and Statistics, along with business perspectives and practices using SAS, Python, R, and Tableau. A key differentiator of this course is the concentrated focus on project work on key analytical concepts and tools. The students ought to spend approximately 80 hours of this program getting hands-on with 6 industry projects and build a portfolio of demonstrable work.

This course can be studied in two modes: Classroom and Online (Live Virtual Classes) to streamline your learning preferences with maximum learning efficacy. The course facilitates hands-on learning with 14 industry projects. The Imarticus Careers Assistance Services (CAS) team provides an industry mentorship process, customized as per your needs. The institute makes sure the students are job-ready after the successful completion of the course with resume building workshops, proper interview preparation, and 1-1 mock interviews with the industry experts.

What will I Learn?

  • R programming and Data Visualization using R
  • Python programming and how to use python in Data Science
  • SAS programming
  • Data visualization tool like Tableau.

Specifications

  • Free Demo
  • 100% Placement Assistance
  • Interactive Learning
  • Missed Class Recovery
  • Certification by Institute
  • Certification by Accredited body
  • Instalment Facility
  • Interview Training

R For Data Science

  • R Installation
  • R Studio
  • Understanding Data Structures in R - Lists
  • Matrices
  • Vectors
  • R Studio the IDE
  • Basic Building Blocks in R
  • UnderstandingVectors in R
  • Basic Operations Operators and Types 
  • Handling Missing
  • Values in R
  • Subsetting Vectors in R
  • Matrices and Data Frames in R
  • Logical Statements in R
  • Lapply, Sapply, Vapply and Tapply Functions

 

Data Visualization Using R

  • Grammar of Graphics
  • Bar Charts
  • Histograms
  • Pie Charts |
  • Scatter Plots
  • Line Plots and Regression
  • Word Clouds
  • Box Plots
  • GGPLOT2

 

Statistical Learning

  • Measures of Central Tendency in Data
  • Measures of Dispersion 
  • Understanding Skewness in Data
  • Probability Theory
  • Bayes Theorem 
  • Probability Distributions
  • Hypothesis Testing

 

Analysis Of Variance And Covariance

  • One-Way Analysis of Variance
  • Assumption of ANOVA
  • Statistics Associated with One-Way Analysis of Variance
  • Interpreting the ANOVA Results
  • Two- Way Analysis of Variance
  • Interpreting the ANOVA Results
  • Analysis of Covariance

 

Exploratory Data Analysis With R

  • Merge, Rollup, Transpose and Append
  • Missing Analysis and Treatment 
  • Outlier Analysis and Treatment
  • Summarizing and Visualizing the Important Characteristics of Data
  • Univariate, Bivariate Analysis
  • Crosstabs, Correlation

 

Linear Regression

  • What is Regression Analysis
  • Limitations of Regression
  • Covariance and Correlation
  • Multivariate Analysis
  • Assumptions of Linearity Hypothesis Testing
  • Limitations of Regression
  • Implementing Simple & Multiple Linear Regression
  • Making Sense of Result Parameters
  • Model Validation 
  • Handling Other Issues/Assumptions in Linear Regression
  • Handling Outliers, Categorical Variables, Autocorrelation, Multicollinearity, Heteroskedasticity Prediction and Confidence Intervals

 

Logistic Regression

  • Implementing Logistic Regression
  • Making Sense of Result Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test Goodness of Fit Measures 
  • Model Validation: Cross-Validation, ROC Curve, Confusion Matrix

 

Decision Trees

  • Introduction to Predictive Modelling with Decision Trees
  • Entropy & Information Gain
  • Standard Deviation Reduction (SDR)
  • Overfitting Problem
  • Cross-Validation for Overfitting Problem
  • Running as a Solution for Overfitting

 

Basics Of Python For Data Science

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data

 

Data Frame Manipulation

  • Data Acquisition (Import & Export)
  • Indexing
  • Selection and Filtering Sorting & Summarizing
  • Descriptive Statistics
  • Combining and Merging Data Frames
  • Removing Duplicates
  • Discretization and Binning
  • String Manipulation

 

Exploration Of Data Analysis

  • Data Visualization & EDA

 

Time Series Forecasting

  • Understand Time Series Data
  • Visualizing Time Series Components  Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA 
  • ARCH & GARCH

 

Unsupervised Learning

  • K-Means Clustering

 

Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Scree Plot
  • One-Eigen Value Criterion Factor Analysis

 

Introduction To Machine Learning

  • Machine Learning Modelling Flow
  • How to Treat Data in ML
  • Parametric & Non-Parametric ML Algorithm
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting  Optimization Techniques
  • Scikit-Learn Library

 

Logistic Regression

  • Logistic Regression with Stochastic Gradient Descent, Batch GD 
  • Optimizing Learning Rate
  • Momentum

 

K Nearest Neighbor

  • Understanding KNN
  • Voronoi Tessellation
  • Choosing K
  • Distance Metrics - Euclidean, Manhattan, Chebyshev

 

Decision Tree & Random Forest

  • Fundamental Concepts of Ensemble
  • Hyper-Parameters

Trainer-Data Science-Imarticus

The trainer has 5 years of industry experience and more than 10 years of teaching experience and trained 1000+ students.

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Batch Start Date End Date Timings Batch Type
Batch 1 05-08-2020 08-01-2021 Mon-Fri 9:30 AM-11:30 AM Weekday
Batch 2 19-08-2020 20-01-2021 Mon-Fri 9:30 AM-11:30 AM Weekday
Batch 3 02-09-2020 03-02-2021 Mon-Fri 9:30 AM-11:30 AM Weekday
Batch 4 16-09-2020 17-02-2021 Mon-Fri 9:30 AM-11:30 AM Weekday

Description

Data Science Prodegree program is co-created with Genpact, which acts as the ‘knowledge partner’, and features an industry-aligned curriculum delivered in two modes: Classroom study and Online training, exploring the practical learning methodology. The program is best suited for graduate students or professionals with up to 3 years of working experience and who are interested in exploring the analytics industry and wish to enhance their technical skills and business understanding of the subject. The Data Science Prodegree is a 180-hour program focussing on Data Analysis and Statistics, along with business perspectives and practices using SAS, Python, R, and Tableau. A key differentiator of this course is the concentrated focus on project work on key analytical concepts and tools. The students ought to spend approximately 80 hours of this program getting hands-on with 6 industry projects and build a portfolio of demonstrable work.

This course can be studied in two modes: Classroom and Online (Live Virtual Classes) to streamline your learning preferences with maximum learning efficacy. The course facilitates hands-on learning with 14 industry projects. The Imarticus Careers Assistance Services (CAS) team provides an industry mentorship process, customized as per your needs. The institute makes sure the students are job-ready after the successful completion of the course with resume building workshops, proper interview preparation, and 1-1 mock interviews with the industry experts.

What will I Learn?

  • R programming and Data Visualization using R
  • Python programming and how to use python in Data Science
  • SAS programming
  • Data visualization tool like Tableau.

Specifications

  • Free Demo
  • 100% Placement Assistance
  • Interactive Learning
  • Missed Class Recovery
  • Certification by Institute
  • Certification by Accredited body
  • Instalment Facility
  • Interview Training
₹80,000 ₹ 80,000

Use Coupon Code "SKILLUP2020" To Get Flat INR 500/- Off

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