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School of Data Science Management and Technology

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Applied Machine Learning & Data Science using SAS, R, Python & Tableau

100% guidance through question bank by Industry Experts from Topmost Companies

Duration
11 Months
1:1 Mentoring

Get ready for Interview

Training

Online Classes

EMI

Available

Discount

Available

Why Join Handson for this Course?

Learn about Machine Learning and Data Science related topics in details.

Through interactive exercises, you can learn and crack the most popular job-relevant interview questions.

Master the various activities and methodology used by Tableau

Learn to work with the versatile SAS and get eligible for several types of roles with great packages offered by multiple industries and much more.

Develop hands-on skills using some of the most popular Python libraries used by the ML Engineers.

Work with real-world datasets to draw predictions, reveal patterns and enable stakeholders to draw informed conclusions.

Program Overview

This Machine Learning and Data Science program is all about SAS, Python, R and Tableau which includes both case studies as well as grooming sessions. We cover critical topics on the programming languages, Statistics, Machine Learning algorithms, along with practical projects which helps in better understanding. This course deals with preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.

Key Features

Program Advantage

This Professional Certificate from Handson has a comprehensive course curriculum covering Statistics, Machine Learning algorithms, key Programming Languages like SAS, R, Python and more – with a great detail via our interactive learning model so that you are comfortable with any kinds of questions asked in job-interview. Upon successfully completing this course, you will be able to fast track your career in the field of interest and it will help you to kick start into an exciting profession in AI and Machine Learning.

LEARNING PATH

SAS Programming
  • Components of the SAS System
  •  Data-Driven Tasks
  •  Turning data into Information
  • Introducing to SAS Programs
  • Running SAS Programs
  • Mastering Fundamental Concepts
  • Diagnosing and Correcting Syntax Errors
  • Getting Started with the PRINT Procedure
  • Sequencing and Grouping Observations
  • Identifying Observations
  • Special WHERE Statement Operators
  • Customizing Report Appearance
  • Formatting Data Values
  • Creating HTML Reports
  • Reading Raw Data Files: Column Input
  • Reading Raw Data Files: Formatted Input
  • Examining Data Errors
  • Assigning Variable Attributes
  • Changing Variable Attributes
  • Reading Excel Spreadsheets
  • Reading SAS Data Sets and Creating Variables
  • Conditional Processing
  • Dropping and Keeping Variables
  • Reading Excel Spreadsheets Containing Date Fields
  • Concatenating SAS Data Sets
  • Merging SAS Data Sets
  • Combining SAS Data Sets : Additional Features
  • Introduction to Summary Reports
  • Basic Summary Reports
  • The Report Procedure
  • The Tabulate Procedure
  • Producing Bar and pie Chart
  • Enhancing output
  • Producing Plots
  • Overview
  • Review of SAS basics
  • Review of DATA Step Processing
  • Review of Displaying SAS Data Sets
  • Working with Existing SAS Data Sets
  • Outputting Multiple Observations
  • Writing to Multiple SAS Data Sets
  • Selecting Variables and Observations
  • Writing to an External File
  • Creating an Accumulating Total variable
  • Accumulating Totals for a Group of Data
  • Reading Delimited Raw Data Files
  • Controlling When a Record Loads
  • Reading Hierarchical Raw data Files
  • Introduction
  • Manipulating Character values
  • Manipulating Numeric values
  • Manipulating Numeric values based on Dates
  • Converting variable Type
  • Using the PUT Statement
  • Using the DEBUG Option
  • Do Loop Processing
  • SAS Array Processing
  • Using SAS Arrays
  • Match-merging Two or more SAS Data Sets
  • Simple Joins Using the SQL Procedure
  • What is SQL?
  • What is the SQL Procedure?
  • Terminology
  • Comparing PROC SQL with the SAS DATA step
  • Note about the Example Table
  • Overview of the select Statement
  • Selecting Columns in a Table
  • Creating New Columns
  • Sorting Data
  • Retrieving rows that satisfy a Condition
  • Summarizing Data
  • Grouping Data
  • Filtering Grouped Data
  • Introduction
  • Selecting Data from More Than One Table by Using joins
  • Using Subqueries to Select Data
  • When to Use Joins and Subqueries
  • Combining Queries with Set Operators
  • Introduction
  • Creating Tables
  • Inserting Rows into Tables
  • Updating Data Values in a Table
  • Deleting Rows
  • Altering Columns
  • Creating an Index
  • Deleting a Table
  • Using SQL Procedure Tables in SAS Software
  • Creating and Using Integrity Constraints in a Table
  • Introduction
  • Using Proc SQL Options to Create and Debug Quires
  • Improving Query Performance
  • Accessing SAS System Information Using DICTIONRY Tables
  • Using Proc SQL with the SAS Macro Facility
  • Formatting PROC SQL output Using the Report Procedure
  • Accessing a DBMS with SAS/ACCESS Software
  • Overview
  • Computing a Weighted Average
  • Comparing Tables
  • Overlaying Missing Data Values
  • Computing Percentages within Subtotals
  • Counting Duplicate Rows in a Table
  • Expanding Hierarchical Data in a Table
  • Summarizing Data in Multiple Columns
  • Creating a Summary Report
  • Creating a Customized Sort Order
  • Conditionally Updating a Table
  • Updating a Table with Values from Another Table
  • Creating and Using Macro Variables
  • SAS Macro Overview
  • SAS Macro Variables
  • Scope of Macro variables
  • Defining SAS Macros
  • Inserting Comments in Macros
  • Macros with Arguments
  • Conditional Macros
  • Macros Repeating PROC Execution
  • Macro Language
  • SAS Macro Processor
  • Types of Analytics
  • Properties of Measurements
  • Scales of Measurement
  • Types of Data
  • Measures of Central Tendency
  • Measures of Dispersion
  • Measures of Location
  • Presentation of Data
  • Skewness and Kurtosis
  • Three Approaches towards Probability
  • Concept of a Random Variable
  • Probability Mass Function
  • Probability Density Function
  • Expectation of A Random Variable
  • Probability Distributions
  • Concept of population and sample
  • Techniques of Sampling
  • Sampling Distributions
  • Concept of estimation
  • Different types of Estimation
  • Concept of hypothesis
  • Null hypothesis
  • Alternative hypothesis
  • Type-I error
  • Type-II error
  • Level of Significance
  • Confidence Interval
  • Parametric Tests and Non Parametric Tests
  • One Sample T test
  • Two independent sample T test
  • Paired Sample T test
  • Chi square Test for Independence of Attributes.
  • Practical Applications
  • One Way Anova
  • Two Way Anova
  • Practical Applications
  • Principal Component Analysis
  • Estimating the Initial Communalities
  • Eigen Values and Eigen Vectors
  • Correlation Matrix check and KMO-MSA check
  • Factor loading Matrix
  • Diagrammatic Representation of Factors
  • Problems of Factor Loadings and Solutions
  • Practical Applications
  • Principal Component Analysis
  • Estimating the Initial Communalities
  • Eigen Values and Eigen Vectors
  • Correlation Matrix check and KMO-MSA check
  • Factor loading Matrix
  • Diagrammatic Representation of Factors
  • Problems of Factor Loadings and Solutions
  • Practical Applications
  • Concept of Regression and features of linear line.
  • Assumptions of Classical Linear Model
  • Method of Least Squares
  • Understanding the Goodness of Fit
  • Test of Significance of The Estimated Parameters
  • Multiple linear Regression with their Assumptions
  • Concept of Multi co-linearity
  • Signs of Multi co-linearity
  • The Idea Of Autocorrelation
  • Practical Applications
  • Concept and Applications of Logistic Regression
  • Principles Behind Logistic Regression
  • Comparison between Linear probability Model and Logistic Regression
  • Mathematical Concepts related to Logistic Regression
  • Concordant Pairs, Discordant Pairs and Tied Pairs
  • Classification Table
  • Graphical Representation Related to logistic Regression.
  • Practical Applications
  • Concept of Time Series and its Applications
  • Assumptions of Time Series Analysis
  • Components of Time Series
  • Smoothening techniques
  • Stationarity
  • Random Walk
  • ARIMA Forecasting
  • Box Jenkins Technology
  • Merits and Demerits of BJ Technology
Data Science using Python
  • What is Data Science?
  • A comparative study between Data Science and Big Data
  • Types of
  • The Data Science Lifecycle
  • Data Acquisition and Preparation
  • Data Modeling and Visualization
  • Data Science Roles
  • Benefits of Data Science
  • Challenges of Data Science
  • Business Use Cases for Data Science
  • Concept of Analytics and Statistics
  • Categories of Analytics
  • Properties of Measurement
  • Scales of Measurement
  • Concept of Data visualization
  • Measures of Central Tendency
  • Measures of Dispersion
  • Moments, Skewness and Kurtosis
  • Concept of Correlation and Covariance
  • Introduction to Probability Theory
  • Probability Distributions
  • Sampling and Estimation
  • Testing of Hypothesis
  • Introduction to python
  • History of Python
  • Internal & External IDLE
  • Installation of Python &Anaconda
  • Compiler & Interpreter
  • Write your first program
  • Data types, Input and output function
  • Types of Operators
  • Conditional Statement: if-else, if-elif-else, Nested if else
  • Loop: While loop, For loop
  • Basic Data Types- Numeric & String
  • Tuple and it’s operation
  • List and it’s operation
  • Dictionary and it’s operation
  • Sets and It’s operation
  • Basics Defining function
  • Function call Return statement
  • Function with parameter and without parameter
  • Local and global variable
  • Recursion, Anonymous (lambda) function
  • User defined functions
  • OOPS concepts Defining
  • Class Creating object, Constructor
  • Method vs function Calling methods
  • Defining a file, Types of file and its operations
  • Python read Files
  • Python Write/Create Files
  • Python Delete Files
  • Pickle Module
  • Introduction to Numpy, Pandas, Matplotlib
  • Array, Array indexing, Array operation
  • Data frame, series, Groupby
  • Missing values
  • Box plot, Scatter plot, Chart styling
  • Histogram, Bar chart
  • Group by plotting
  • Concept of Supervised learning
  • Concept of Unsupervised learning
  • Concept of Reinforcement learning
    • Simple Linear Regression
    • Multiple Linear Regression
    • Implementation of Linear Regression
    • Advanced Topics: Normal Equation, Polynomial Regression, R-sq. Score
  • Concept and Theory
  • Sigmoid function
  • Mathematical Concepts of Logistic Regression
  • Binary and Multivariate Classification Problems
  • K-Nearest Neighbors-Concept and Theory
  • Implementation of K-Nearest Neighbors
  • Support Vector Machine(SVM)-Concept and Theory
  • Implementation of Support Vector Machine
  • Naïve Bayes Classifier- Concept
  • Implementation of Naïve Bayes Classifier
  • Decision Tree Classifier-Concept
  • Implementation of Decision Tree Classifier
  • Random Forest Classifier-Concept
  • Implementation of Random Forest Classifier
  • Dimensionality Reduction Problem- Curse of Dimensionality
  • Principal Component Analysis(PCA)
  • Implementation of PCA
  • K-Means Clustering- Concept
  • Implementation of K-Means Clustering
  • Hierarchical Clustering- Concept
  • Implementation of Hierarchical Clustering
  • DBSCAN Clustering-Concept
  • Implementation of DBSCAN Clustering
  • Introduction of Deep Learning and Neural Network
  • Types and Applications of Neural Network
  • Skills required for Neural network
  • Why Python is best for Neural Network
  • Anaconda Installation: Spyder & Jupyter Notebook
  • Introduction to Keras & Tensor Flow
  • Installation of Keras & Tensor Flow
  • ANN and Neuron Structure
  • How does Neural Network Works?
  • Practical Implementation of ANN
  • Train-Test Splitting
  • ANN model Training
  • Activation Function
  • Fit all the Layers
  • Backpropagation
  • Fitting to the training Dataset and finding Accuracy
  • Image Reading and CNN Process
  • Steps of CNN
  • Conclusion of CNN Process
  • Importing Required libraries
  • Reading Cat & Dog Dataset
  • Applying CNN layers
  • Fitting the Dataset in Model
  • Visualization of Accuracy and Loss
  • Prediction with single image
  • Introduction and Application
  • Process of RNN, Types of RNN, Gradient Problem
  • LSTM & GRU Explanation
  • Steps of LSTM
  • Creation of Data Structure with Time Steps
  • LSTM layers
  • Google Stock market prediction
Data Science using R
  1. History of R-language
  2. Why to learn R-language
  3. Importance of R-language
  4. Installation and setup Environment
  5. Packages interfaces and library
  1. Expressions and Operations
  2. Data Types and Data Structures- Vectors, Factors,Matrix, Dataframes,Lists
  3. Vector Basics
  4. Vector Operations 
  5. Vector Indexing and Slicing
  6. Matrix Operations
  1. Data Frame Indexing and Selection
  2. Operations on Data Frame
  3. CSV Files with R 
  4. Operators
  5. Conditional Statements
  6. Loops & Functions
  7. Built-in R Features & Apply
  8. Dates and Timestamps
  1. Understanding & Working with Graph Libraries.
  2. Overview of ggplot2
  3. Histograms
  4. Scatterplots
  5. Bar Plot
  6. Boxplots
  7. 2 Variable Plotting
  8. Sorting, Concatenation of Datasets
  1. Concept of Hypothesis.
  2. Null Hypothesis
  3. Alternative Hypothesis
  4. Type-I error
  5. Type-II error
  6. Level of Significance
  7. Confidence Intervals
  8. Parametric Tests and Non Parametric Tests
  9. One Sample T test
  10. Two Independent Sample T test
  11. Paired Sample T test
  12. Chi-square Test for Independence of Attributes
  1. Principal Component Analysis
  2. Concept of Communalities
  3. Eigen Values and Eigen Vectors
  4. Correlation Matrix check and KMO-MSA check
  5. Factor loading Matrix
  6. Diagrammatic Representation of Factors
  7. Problems of Factor Loadings and Solutions
  1. Introduction to Machine Learning
  2. Data Munging in R
  3. Cyclical vs Seasonal Analysis
  1. One Way Anova
  2. Two Way Anova
  1. Concept of Linear Regression.
  2. Important features of a Straight line.
  3. Method of least Square.
  4. Assumptions of Classical Linear Regression Model
  5. Understandig the Goodness of Fit
  6. Test of Significance of the Estimated parameters.
  7. Concept of multicollinearity
  8. Concept of VIF
  9. Concept of AutoCorrelation
  10. Practical Application of Linear Regression using R.
  1. Concept of Logistic Regression .
  2. Differences between Linear Regression and Logistic Regression.
  3. Logistic Regression Model.
  4. ODDS AND ODDS RATIO-Mathematical Concepts
  5. Concept of Concordant Pairs, Discordant Pairs, Tied Pairs.
  6. Confusion Matrix and its Measures
  7. Determining the Cut-Point Probability Level.
  8. Receiver Operating Characteristic Curves
  9. Practical Application of Logistic Regression using R.
  1. Concept of Time Series and its Applications
  2. Assumptions of Time Series Analysis
  3. Components of Time Series
  4. Smoothening techniques
  5. Stationarity
  6. Random Walk
  7. ARIMA Forecasting
  8. Box Jenkins Technology
  9. Merits and Demerits of BJ Technology
  1. Concept of Decision Tree
  2. Decision Tree Application using R.
  3. Concept of K-Means Clustering.
  4. Types of Cluster Analysis.
  5. Concept of Linkage.
  6. Ward’s Minimum Variance Criteria.
  7. Clustering related Statistics-Semi-Partial R-Square,R Square
  8. Graphical Representation of Cluster Analysis
  9. Practical Application of Clustering using R.
  1. Concept of Text Mining and Sentiment Analysis
  2. Concept of Stopwords
  3. Practical Application of Text Mining and Sentiment Analysis
  1. Concept of Market Basket Analysis
  2. Measures of Market Basket Analysis-Support,lift,Confidence
  3. Advantages of Market Basket Analysis
  4. Practical Application of Market Basket Analysis.
Tableau

SKILLS COVERED

ADMISSION DETAILS

APPLICATION PROCESS

The application process consists of few simple steps. The  admission process will be completed after the payment is done by the selected candidates.

Submit Application

Submit the required documents for admission 

Application Review

Candidates will be selected based on their application

Admission

Selected candidates will get login credentials to begin the program

ELIGIBLE CANDIDATES

For admission to this program, candidates should have:

Any Graduate Candidate from any stream

Bachelor's degree with a minimum of 50% marks

Any aspiring Candidates from any functional area

ADMISSION GUIDENCE

We have a team, dedicated to solve your admissions related issues, who are available to guide you to apply for the program. They are available to:

Contact Us

+91 9830247087

ADMISSION FEE

We are dedicated to making our programs accessible and make it more economical.

Total Program Fee

₹ 62,500

(Incl. taxes)

For any Instalment related queries, contact with our Admission Counselor in the above given number.

Apply Now

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