## Data Science using Python, R & SAS Course

## 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.

- Hands-On Capstone project
- For the industry by the industry
- Case studies
- Cutting edge curriculum

- 4 industry-based projects
- Lifetime access to self-paced learning

- Hands-On Capstone project
- Case studies

- For the industry by the industry
- Cutting edge curriculum

### 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

Support :9830247087

## 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

## Call Me Back or Download Syllabus

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- Counselling based on your skills and preferrence
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### +91 9830247087

## 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

- SAS Data Libraries

- 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

- One Way ANOVA
- Two Way ANOVA

- 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

## Why to choose Handson?

### Hands-On Training

Using a hands-on approach to training, participants will adopt the form of learning that typically benefit from the most. It allows people to learn by doing.

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