Data Science with Python

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INTRODUCTION AND DATA PROCESSING OF SAS

  1. SAS Introduction
  2. Fundaments of creating Datasets
  3. Create Temporary Datasets
  4. Handling Missing Values
  5. Define Variables Length
  6. Understanding of library
  7. Create Permanent Datasets

SAS Environment

  1. Creating a SAS Programs
  2. Submitting SAS Program
  3. Reading SAS Log
  4. Browsing the Data Portion
  5. Components of SAS Programs

Method of Getting Data from Different Sources

  1.  Data from Excel/CSV
  2. Data from Text file

INPUT METHODS

  1. List input
  2. Column input
  3. Formatted input
  4. Column Input
  5. Format Input
  6. Date and Time Input

FORMAT METHOD

  1. System Defined Formats
  2. User Defined Formats
  3. Numeric Formats
  4. Character Formats
  5. Date Formats
  6. Temporary Formats
  7. Permanents Formats
  8. Labeling of Variable Names

FUNCTIONS AND SUBSETTING

  1. Arithmetic Functions
  2. Character Functions
  3. Date Functions
  4. Subsetting a SAS Data Set
  5. Creating More Than One Subset Data Set in One DATA Step
  6. Adding Observations to a SAS Data Set

SAS Procedures

  1. Proc Append
  2. Proc Print
  3. Proc Contents
  4. Proc Means
  5. Proc Sort
  6. Proc Datasets
  7. Proc plot<>
  8. Proc Gplot
  9. Proc Chart
  10. Proc Gchart
  11. Proc Summary
  12. Proc Freq
  13. Proc Tabulate
  14. Proc Report
  15. Proc Univariate

Loop

    1. Do Loop Statement
    2. Nested Loop Statement
    3. Conditional Loop
    4. While and Until
    5. Leave &amp; Continue

Array

  1. Simple Array
  2. Array with Dim Statement
  3. Lower and upper bound Arrary
  4. One Dimensional Array
  5. Two Dimensional Array

Array

  1. Table Append using Data Steps
  2. Interleave
  3. Concatenation of Tables
  4. Inner Join using Data Steps
  5. Outer Join using Data Steps
  6. Full Join using Data Steps
  7. Anti Join using Data Steps

Proc SQL

  1. Basic SQL Commands
  2. Case Statement
  3. Inner Join using SQL
  4. Full Joins using SQL
  5. Outer Join using SQL
  6. Anti Join using SQL
  7. SQL to create Macro Variable

SAS Macro

  1. Rules to create Macro
  2. Compilation of program
  3. Creating Macro Variables
  4. Call Symput and call Symget Method
  5. Macro Functions
  6. Debugging Options
  7. Saving Macro Permanently

Introduction

  1. Application of Machine Learning

  2. Installation of Python

  3. Introduction of Python

Python Programming

  1. Control Flow
    1. If-THEN-ELSE
    2. Loop with For and While
    3. Break and Continue Statement
    4. Range () Function
  2. Function
    1. User Defined
    2. System Defined
  3. Data Structures
    1. Linear Structure
    2. Qeue, Stack and Deque Interfaces
    3. List, Uset and SSet Interfaces
    4. Tuples & Ranges
    5. Strings
  4. Classes
    1. Names and Objects
    2. Scopes and Namespaces
    3. Classes and Variables
    4. Odds and Ends
  5. Exceptional Handling
    1. File Handling
    2. File Operations
    3. Dealing with Errors
    4. Importing Modules

Useful Data Science Libraries

  1. Numpy
  2. Sckit-learn
  3. Panda
  4. NLTK
  5. PILLOW
  6. Scrapy
  7. Matplotlib

Import Datasets and Modules

  1. Import Datasets and Modules
  2. Create and Import Python Modules

Data Preparation and Visulization

  1. Data Cleansing
  2. Variable Transformation
  3. Expoloratory Data Analysis
  4. Plotting the Graphs

Basic Statistics

  1. Descriptive Statistics
  2. Hypothesis
  3. Ttest
    1. One Sample T-Test
    2. Two Sample T-test
    3. Paired T-Test
  4. Anova
    1. One Way Anova
    2. Two Way Anova
    3. Multivariate Anova
  5. Chi-Square
  6. Correlation
  7. Interpreation of Statistical Results

Linear Regression Model for Prediction

  1. Introduction
  2. Calculations, Equation and Assumptions
  3. Area of Application and Technique
  4. Validation Techniques
  5. Interpretation
  6. Training and Testing the Model

Logistic Regression Model for Prediction

  1. Introduction
  2. Assumptions
  3. Area of Application and Methods
  4. Diagnostics
  5. Validation Methods
  6. Interpretation
  7. Training and Testing the Model

Cluster Analysis for Segmentation

  1. Introduction
  2. Area of Application and Methods
  3. Hierarchal Clustering
  4. K-Means Clustering
  5. Segmentation of Clusters
  6. Interpretation of Outputs
  7. Validation

Time Series for Forecasting

  1. Introduction
  2. Components
  3. Area of Applications
  4. Moving Average Methods
  5. Exponential Smoothing Method
  6. Holt Winter Method
  7. Box-Jenkins Method
  8. ARIMA
  9. Interpretation of Outputs
  10. Validation

Machine Learning Algorithms:

  1. Introduction of Machine Learning and Components
  2. Supervised and Unsupervised Methods
  3. Sampling Techniques in Machine Learning
  4. Multi fold Validation Technique
  5. Bagging and Boosting Methods
  6. Ensemble Techniques
  7. Random Forest with Interpretation of Outputs
  8. Gradient Boosting Machines with Interpretation of Outputs
  9. XGBoost with Interpretation of Outputs

Artificial Neural Networking

  1. Introduction
  2. Different Layers of Neural Network
  3. Multi Layers Neural Network
  4. Regression Problem
  5. Classification Problem
  6. Fine Tuning of Hyper Parameters
  7. Interpretation of Outputs
  8. Validation

Support Vector Machine

  1. Introduction
  2. Regression Problem
  3. Setting of Hyper Parameters
  4. Interpretation of Outputs
  5. Validation
Course Detail
Fees:
Rs 29,000

 

Class Mode:
Classroom/Online

 

Duration:
4 Months

 

Course Contents:
Data Science with SAS, Python

 

Course Benefits:
Student Will able to Learn SAS, Python and apply Data Science Techniques and Methods.

 

RS 29,000

4 Months

Data Science with using Python

Data Science with SAS, R, Python

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RS 35,000

5 Months

Data Science Pro

S-A-S Script, R, Python, Machine Learning, Artificial Intelligence, Power BI, Data Visualization

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KEY FEATURES

4 Months of Classroom Training.

 

Session have assignments and daily task.

 

Recorded Videos and presentation for reference.

Books and related data sets in form of excel and csv.

 

Interview Questions for related topic.

 

Used Case Study and Projects to understand real scenarios.

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