Data Science using Python, R & SAS Course

Choose from these three incredible options to start your journey with Handson!

Welcome to Handson!

Data Science using Python, R & SAS Course Overview

This is an integrated course with SAS programming essentials, data step manipulation, Predictive modelling, SQL, R and Python. This program is most suitable for job seekers who want to become a Data Scientist on SAS, R and Python to manipulate data, perform queries and analyses, and generate reports, with predictive modeling techniques. This program will cover the curriculum for SAS Global certifications. 

Demo Certificate

Course Fees : Rs. 44000

  • Duration : 200 hours/8.5  months
  • No Cost EMI : INR 5500 x 8 month
  • NEFT Payment :

Bank account details: Account Name – KLMS Hands-On Systems Private Limited, Account No – 50200042627525, IFSC – HDFC000027

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Course Description!

Data Scienc using Python, R & SAS Course Curriculum

This course covers Base SAS, SAS Macro, SQL, SAS Predictive modelling, complete data science with Python & R, Statistics, Capstone project in python & R, Assignment Series for practice, Hands-on project on python & Case studies

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Course content

  • What is Data Science?
  • A comparative study between Data Science and Big Data Analytics.
  • Types of Data.
  • The Data Science Lifecycle.
  • Necessary Programming Languages and Software Used in Data Science
  • 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
  • History of R language.
  • Why to learn R-language
  • Importance of R-language
  • Installation and setup Environment
  • Packages interfaces and library
  • Expressions and Operations
  • Data Types and Data Structures- Vectors, Factors,Matrix, Dataframes,Lists
  • Vector Basics
  • Vector Operations 
  • Vector Indexing and Slicing
  • Matrix Operations
  • Data Frames Indexing and Selection
  • Different operations on Dataframes.
  • CSV files with R.
  • Operators
  • Conditional Statements
  • Loops and Functions
  • Built-in R Features & Apply
  • Dates and Timestamps
  • Understanding and Working with Graph libraries
  • Overview of ggplot2
  • Histograms
  • ScatterPlots
  • Bar Plot
  • Boxplots
  • 2 Variable Plotting
  • Sorting,Concatenation of Datasets
  • 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.
  • Principal Component Analysis
  • Concept of 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
  • Introduction to Machine Learning
  • Data Munging in R
  • Cyclical vs Seasonal Analysis
  • One Way Anova
  • Two Way Anova
  • Concept of Linear Regression
  • Important features of a straight line.
  • Method of Least Squares
  • Assumptions of Classical Linear Regression Model
  • Understanding the Goodness of Fit
  • Test of Significance of The Estimated Parameters
  • Concept of Multocollinearity
  • The Concept Of Autocorrelation
  • Practical Application of Linear Regression using R

 

  • 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
  • Confusion Matrix and its Measures
  • Graphical Representation Related to logistic Regression.
  • Concept of Time Series and its Applications
  • Assumptions of Time Series Analysis
  • Components of Time Series
  • Smoothening techniques
  • Stationarity
  • Random Walk Hypothesis
  • ARIMA Modeling
  • Box Jenkins Technology
  • Merits and Demerits of BJ Technology
  • Concept of Decision Tree
  • Decision Tree Application using R
  • Concept of K-Means Clustering
  • Types of Cluster Analysis
  • Concept of Linkage
  • Ward’s Minimum Variance Criteria
  • Clustering related Statistics-Semi-Partial R-Square,R Square
  • Graphical Representation of Cluster Analysis
  • Practical Application of K-Means Clustering using R.
  • Concept of Text Mining and Sentiment Analysis
  • Concept of Stopwords
  • Practical Application of Text Mining and Sentiment Analysis
  • Concept of Market Basket Analysis
  • Measures of Market Basket Analysis -Support,lift,Confidence
  • Advantages of Market Basket Analysis
  • Practical Application of Market Basket Analysis
  • Introduction to python
  • History o 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
  • Nested while loop, Nested for loop Break, Continue and Pass
  • 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
  • Method Overriding, List of objects Inheritance
  • Defining a file, Types of file and it’s 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 etc.
  • 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
  • Python Implementation
  • Concept and Theory
  • Sigmoid function
  • Mathematical Concepts of Logistic Regression
  • Binary and Multivariate Classification Problems
  • Implementation of Logistic Regression
  • 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
  • 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
  • Backpropogation
  • 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
  • Components of the SAS System
  • Data-Driven Tasks
  • Turning data into Information
  • Design of the SAS System
  • 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

 

  • 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 of 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.
  • One Way Anova
  • Two Way Anova
  • 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
  • Types of Clusters
  • Metric and linkage
  • Ward’s Minimum Variance Criteria
  • Semi-Partial R-Square and R-Square
  • Diagrammatic Representation of clusters
  • Problems of Cluster Analysis
  • 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 Multocollinearity
  • Signs of Multicollinearity
  • The Idea Of Autocorrelation
  • 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.
  • 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
  • Mutable and Immutable data types
  • Numeric types and it’s operation
  • String and it’s operation
  • Tuple and it’s operation
  • List and it’s operation
  • Dictionary and it’s operation
  • Sets and It’s operation
  • Conditional statement
  • Loop
  • Dictionary& List
  • Comprehension techniqus
  • Module and package
  • Function
  • Inheritance
  • Basics Defining function
  • Function call Return statement
  • Function with parameter and without parameter
  • local and global variable
  • Recursion, Anonymous (lambda) function
  • User define functions
  • OOPS concepts Defining
  • Class Creating object, Constructor
  • Method vs function Calling methods
  • Method Overriding, List of objects Inheritance
  • Defining module, Importing module
  • Dir(), Module search path, Sys module, Os module
  • Namespace
  • Defining and create package
  • Installing third party packages
  • Defining a file, Types of file and it’s operations
  • Opening a File, Closing file, File modes, File attributes
  • Writing to file, Reading from file, Appending to file
  • File positions, Binary file
  • Pickle module
  • Introduction to Numpy, Pandas, Matplotlib
  • Process of Data science projects
  • Identify the most appropriate solution design for the given problem statement
  • Array, Array indexing, Array operation
  • Data frame, series, Groupby
  • Missing values
  • Data input and output
  • Analysis exercise
  • Chart prepration
  • Box plot, Scatter plot, Chart styling
  • Histogram, Bar chart etc.
  • Groupby ploting
  • Data visualization exercise
  • Introduction of Naïve Bayes
  • Theory of classification
  • Concept of probability: prior and posterior
  • Bayes Theorem
  • Mathematical concepts
  • Limitation of Naïve Bayes
  • Dataset with problem description
  • Practical application on Python
  • Data Frame Indexing and Selection
  • Operations on Data Frame
  • List Basics operations
  • CSV Files with R
  • Excel Files with R
  • SQL with R
  • Operators
  • Conditional Statements
  • Loops & Functions
  • Built-in R Features & Apply
  • Dates and Timestamps
  • Package Building
  • Example of Decision Tree with R- programming
  • Explanation and introduction Random Forests in R-language
  • What is clusters
  • Type of clusters
  • K Means Clustering with R
  • Overview of Shiny
  • Creating a populated dashboard: UI, Server
  • Shiny input: Text input, Numeric input, Checkbox input, Slider input, Radio button
  • Shiny output: text output, table output, plot output, data table output
  • Single page layout
  • Data download, Data upload
  • Progress indicator
  • Build your own app: Shiny dashboard
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