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
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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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