English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 189 lectures (21h 4m) | 5.87 GB

An Ultimate Hands On Masterclass for Applying Machine Learning, Data Science techniques on SAP Data to derive insights

This course has been created to bridge the gap between SAP Professionals and Data Scientists. As you progress in this learning journey, You will realize that most of the Activities performed by Data Scientists are very much similar to the way we SAP Professionals implement the Business Requirements on an ERP Software – SAP. The key difference is: Data Scientists know how to ask better questions on the data

To bridge this gap, we have designed this curriculum of Data Science for SAP Professionals which encompasses a wide range of topics.

- Understanding of the data science field and the type of analysis carried out
- Statistics
- Python
- Applying advanced statistical techniques in Python
- Data Visualization
- Machine Learning
- Using Pretrained Models like Google Cloud Natural Language Processing API to have a Jumpstart for your SAP Application implementation.

Each of these topics builds on the previous ones. Due to this reason, we recommend you acquire these skills in the right order as mentioned in our curriculum so that, it won’t be an overwhelming experience for a learner.

So, in an effort to create the most effective, time-efficient, structured, and business case-driven data science training available online, we have created this course: Data Science with SAP – Machine Learning for Enterprise Data

We believe this is the first training program that solves the challenge of SAP professionals to entering the field of data science by enabling the learners to have all the necessary resources in one place.

The focus of our course is to teach topics that flow smoothly and complement each other and can be easily related to Enterprise Data SAP. The course teaches you everything you need to know to become a data scientist from SAP Consultant at a fraction of the cost of traditional programs (not to mention the amount of time you will save).

What you’ll learn

- Course Provides the Entire Toolbox you need to apply Data Science – Machine Learning Algorithms for your SAP Data
- Get ahead of crowd by knowing the hottest skill in the current Market
- Start coding in Python and learn how to use it for Statistical Analysis of SAP Data
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn –
- Essential tools for Performing Data Science with SAP Data
- Carry out cluster and factor analysis on SAP Data
- Learn to Extract the Required Data from SAP System to perform Statistical Analysis & Apply various
- Machine Learning Models
- Learn how to pre-process the extracted data from SAP
- Apply the Skills to real-life business cases
- Be Industry Ready to apply everything you have learnt to more and more real-life scenarios in the ocean of SAP
- Build Recommendation Engine Using SAP Data
- Create a Project Implementation to Perform the Predictive Analytics on SAP Data & Perform the
- Advanced Time Series Analysis using ARIMA Model
- Learn to use Advanced Techniques, and make use of Pre-trained Model from Google Cloud Natural
- Language Processing API for Text Data

## Table of Contents

**Getting System Ready**

1 What & Why Python – Getting System Ready

2 Resources for help in Installation

3 Start Jupyter Notebook

4 Start iPython Notebook

5 Default Folder Path of ipython notebook files

6 Other Helpful Resources

**Python Programming**

7 Section Attachments

8 Taste of Py

9 Variables in Python Programming Language

10 Rules for Creation of Variables in Python

11 Data Types in Python – Numerical

12 Working in Jupyter Notebook

13 Working in Jupyter Notebook – Hands On Exploration

14 Data Types in Python – Boolean & Sequence

15 Data Types in Python – Boolean & Sequence – Hands On

16 Data Types in Python – Dictionaries & Sets

17 Data Types in Python – Dictionaries & Sets – Hands On

18 Operators in Python

19 Operators in Python – Hands On

20 Adding the Comments in Python

21 Adding the comments in Python – Hands On

22 Working with Print Function

23 Exploring Print Function

24 Type Casting in Python_Data Type Conversion

25 Type Casting in Python

26 Getting Input from User

**Control Statements in Python**

27 Section Attachments

28 Control Statements in Python – If

29 Control Statements in Python – If – Hands On

30 Logical Operators

31 Logical Operators on Conditional Statements

32 Control Statement – if_else

33 Control Statements – if_elif_else

34 Control Statements – if_elif_else – Python

35 Control Statements – While loop

36 Control Statements – While loop – Python

37 Control Statements – For loop

38 Control Statements – For loop – Python

39 Control Statements Break , Continue & Pass

40 Control Statements Break , Continue & Pass – Python

**Data Structures in Python**

41 Section Attachments

42 Intro to Data Structures

43 Lists in Python

44 Python lists – Jupyter Notebook

45 Python Tuples

46 Python Tuples – Hands On

47 Python Dictionaries

48 Python Dictionaries – Hands On

49 Sets in Python

50 Sets in Python – Hands On

51 Sets – Operations

52 Strings

53 Strings – Hands On

54 Strings – Other Methods

55 Negative Indexing and Escape Characters

**Functions & Classes in Python**

56 Section Attachments

57 Functions in Python

58 Functions – Contd

59 Calling Functions inside a function

60 Object Oriented Python

61 Working with Classes and Objects in Python

**Capstone Project using Python Programming**

62 Section Attachment

63 Details of Capstone Project

64 Selecting the Random Word for the Game

65 Initializing the Game

66 Logic of word validation

67 Logic for Letter Validation

68 Final Testing

**Numpy for Data Science**

69 Section Attachment

70 Introduction to Numpy Library

71 Basics of Numpy Array Object

72 Import Numpy & Access help

73 Creation of Array Object – np.array()

74 Attributes of Numpy Array

75 Array Indexing and Slicing

76 Array Creation Functions

77 Copy Arrays

78 Mathematical Operation on Numpy Arrays

79 Linear Algebra Functions in Numpy

80 Shape Modification of Arrays

81 np.arange()

82 Relational Operators on Numpy Arrays

83 Boolean Masking

84 Broadcasting on Numpy Arrays

85 Summary of Numpy Library journey

**Pandas for Data Science**

86 Section Attachment

87 Introduction to Pandas

88 Working with Pandas Series

89 Mathematical Operation on Pandas Series

90 Dataframes in Pandas

91 Working with Data in Pandas DataFrame

92 Combining the DataFrames

93 Other Functions on Pandas DataFrame

94 Advanced Functions in Pandas DataFrame

**Exploratory Data Analysis on Real Life Dataset**

95 Section Attachment

96 Introduction to EDA

97 Accessing Google Colab

98 Loading the Large Dataset for Working

99 Preliminary Analysis on DataFrame

100 null values in the Dataframe

101 Data Cleaning

102 Assignment Solution

**Data Visualization – Matplotlib**

103 Section Attachment

104 Introduction to Data Visualization

105 Matplotlib Basics

106 Types of Plot – Line plot

107 Line Plots Hands On

108 Adjusting the Plots

109 Plot Adjustment Hands On

110 Scatter Plot

111 Scatter Plot hands on

112 Historgram Plot

113 Assignment Solution Notebook

**Visualization with Seaborn**

114 Section Attachment

115 Introduction to Seaborn

116 Exploring the data

117 Univariate & Bivariate Plots – Continuous Data

118 Plot – Categorical Data

119 Advanced Plots in Seaborn

120 Which Plot to use

121 Solution for Assignment

**Intro to Data Science – Machine Learning with SAP Data**

122 Intro to Data Science – Machine Learning with SAP Data

**Clustering on SAP Data – Mastering KNN Algorithm**

123 Section Attachments

124 Understanding Clustering

125 Mathematical Working of KMeans

126 Apply K Means on an Example Dataset

127 Explore KMeans() Classs

128 Measure Cluster Quality

129 Measure Cluster Quality Hands On

130 List Comprehension

131 Scaling the data with StandardScaler

132 Clustering on ML Example data

**Clustering & Segmentation -Implementation on SAP Data**

133 Section Attachments

134 Project Overview

135 Data Extration Steps SAP

136 Data Loading & Analysis

137 Data Transformation

138 Apply KMeans on SAP Data

139 Summary of KMeans

**Build Recommendation System Using SAP Data**

140 Section Attachments

141 Section Overview & Introduction to Recommendation Systems

142 Hands On Overview

143 Data Transformation & Data Manipulation

144 Generate Association Rules

145 Data Extraction from SAP

146 Custom Program in SAP ABAP for Data Extraction

147 Data Extraction using SQVI

148 Data Preparation of External File

149 Data Pre-Processing on Extracted SAP Data

150 Generate Association Rules on SAP Data

151 Summary – Association Rule Mining

**Predictive Analytics on SAP Data – Time Series Forecasting**

152 Section Attachments

153 Learning Objectives & Section Overview

154 Why do we require Forecasting

155 Understand Forecasting

156 Project Overview

157 Data Extraction from SAP System

158 Data Loading & Manipulation

159 Understand the Time Series Data

160 Knowledge Check

161 Quick Recap

162 Differencing Hands On

163 Data Loading & Preliminary Analysis – Hands On

164 Hypothesis Testing

165 Hypothesis Testing Hands On – ADF Test

166 AutoCorrelation in Time Series Plot

167 AutoCorrelation Hands On

168 Features of ACF plot

169 Relation Between ACF Plot & Time Series Plot

170 Introduction to ARIMA

171 Understanding p & q in ARIMA

172 Overview of Hands On Implementation of ARIMA

173 ARIMA Hands On

174 Project Completion – ARIMA Model on SAP Data

175 Time Series Analysis Flow Chart – Summary

**Natural Language Processing with Google Cloud API – Text Data**

176 Section Attachments

177 Learning Objective & Section Overview – NLP

178 Overview of NLP – Natural Language Processing

179 Text Pre-processing techniques

180 Setting up Google Cloud Account

181 Load the Dataset

182 Connecting with Google Cloud Natural Language API

183 Summary – NLP with Text Data – Classification

184 Bonus Content – Get More from learning journey

**temp**

**Machine Learning Model Deployment for Projects**

185 Machine learning Deployment Part 1 – Model Prep – End to End

186 Machine learning Deployment Part 2 – Deploy Flask App – End to End

187 Streamlit Tutorial

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