Python Data Science: the ultimate and complete beginners guide to master data science with Python step by step
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
[Erscheinungsort nicht ermittelbar]
[Andrew Park]
[2020]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | 132 Seiten |
| ISBN: | 9798664667851 |
Staff View
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| adam_text | Table of Contents INTRODUCTION......................................................................................... 5 Data discovery............................................................................................... 7 Getting the data prepared..........................................................................8 Mathematical models.................................................................................. 9 Putting it all into action.......................................................................... ю The importance of communication........................................................... u CHAPTER і: HISTORY OF DATA SCIENCE...................................... 14 CHAPTER 2: WORiaNG WITH PYTHON FOR DATA SCIENCE...................................................................................................18 Why Is Python Important?........................................................................ 18 The Role of Python in Data Science....................................................... 20 PythonEngineering Structure................................................................27 CHAPTER 3: MACHINE LEARNING AND HOW IT FITS WITH DATA SCIENCE......................................................................................... ЗО What is Machine Learning?............................................................... 31 CHAPTER 4: DATA STRUCTURES OBJECT-ORIENTED PYTHON........................................................................ ............................ 35 Immutable structured data
(static)....................................................... 36 Tuples............................................................................................................. 45 Indexing, trimming and other tuple operations...................................47 Tuple Methods..................................................................... 52 Zip function...................................................... 53 Frozen sets (Frozen set)............................................................................ 53 Indexing, trimming and other list operations................ 56 Object-oriented programming with Python................................. 60 CHAPTER 5։ DATA SCIENCE ALGORITHMS AND MODELS..... 66 Neural Networks............................................ 66 Naïve Bayes..................................................................... 69 Clustering algorithms.............................................................................. 70 Support Vector Machines...........................................................................71 Decision Trees.............................................................................................. 73 К-Nearest Neighbors..................................................................................74 The Markov Algorithm...............................................................................76 3
CHAPTER 6: REGRESSION ANALYSIS (LINEAR AND LOGISTIC REGRESSION)............................... 78 Linear Regression.................................................. 79 Logistic Regression.................................................................................... 83 Logistic Regression vs. Linear Regression........................................... 88 How Does Machine Learning Compare to AI......................................... 89 CHAPTER 7: DATA AGGREGATION AND GROUP OPERATIONS.......................................................................................... 94 What is Data Aggregation......................................................................... 95 CHAPTER 8:PRACTICAL CODES AND EXERCISES TO USE PYTHON................................................................................................... 98 CHAPTER 9 : FUNCTIONS AND MODULES IN PYTHON.......... 108 The Syntax of Functions.......................................................................... 108 How Functions are Called in Python................................................... 109 CHAPTER ««INTERACTION WITH DATABASES........................ ill The Pickle Module...................................................................................... 111 The SQL1TE3 Module.................................................................................. 113 Building a Digital Spider.......................................................................... 116 CHAPTER lliDATA MINING TECHNIQUES IN DATA
SCIENCE.................................................................................................... 119 Mapreduce Technique............................................................................... 119 Distance Measures.....................................................................................120 Link Analysis............................................................................................... 120 Pagerank........................................................... 121 The Content................................................................................................. 121 Data Streaming............................................................................................121 Sampling data in a Stream........................................................................ 122 Filtering Streams...................................................................................... 122 Count Specific Elements in a Stream....................... 122 Frequent Item - Set Analysis.................................................................. 123 CHAPTER 12SDATA IN THE CLOUD................................................. 124 What is the Cloud?............................................ 124 Cloud and Big data Governance.............................................................128 CONCLUSION............................................................................................131 4
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| adam_txt |
Table of Contents INTRODUCTION. 5 Data discovery. 7 Getting the data prepared.8 Mathematical models. 9 Putting it all into action. ю The importance of communication. u CHAPTER і: HISTORY OF DATA SCIENCE. 14 CHAPTER 2: WORiaNG WITH PYTHON FOR DATA SCIENCE.18 Why Is Python Important?. 18 The Role of Python in Data Science. 20 PythonEngineering Structure.27 CHAPTER 3: MACHINE LEARNING AND HOW IT FITS WITH DATA SCIENCE. ЗО What is Machine Learning?. 31 CHAPTER 4: DATA STRUCTURES OBJECT-ORIENTED PYTHON. . 35 Immutable structured data
(static). 36 Tuples. 45 Indexing, trimming and other tuple operations.47 Tuple Methods. 52 Zip function. 53 Frozen sets (Frozen set). 53 Indexing, trimming and other list operations. 56 Object-oriented programming with Python. 60 CHAPTER 5։ DATA SCIENCE ALGORITHMS AND MODELS. 66 Neural Networks. 66 Naïve Bayes. 69 Clustering algorithms. 70 Support Vector Machines.71 Decision Trees. 73 К-Nearest Neighbors.74 The Markov Algorithm.76 3
CHAPTER 6: REGRESSION ANALYSIS (LINEAR AND LOGISTIC REGRESSION). 78 Linear Regression. 79 Logistic Regression. 83 Logistic Regression vs. Linear Regression. 88 How Does Machine Learning Compare to AI. 89 CHAPTER 7: DATA AGGREGATION AND GROUP OPERATIONS. 94 What is Data Aggregation. 95 CHAPTER 8:PRACTICAL CODES AND EXERCISES TO USE PYTHON. 98 CHAPTER 9 : FUNCTIONS AND MODULES IN PYTHON. 108 The Syntax of Functions. 108 How Functions are Called in Python. 109 CHAPTER ««INTERACTION WITH DATABASES. ill The Pickle Module. 111 The SQL1TE3 Module. 113 Building a Digital Spider. 116 CHAPTER lliDATA MINING TECHNIQUES IN DATA
SCIENCE. 119 Mapreduce Technique. 119 Distance Measures.120 Link Analysis. 120 Pagerank. 121 The Content. 121 Data Streaming.121 Sampling data in a Stream. 122 Filtering Streams. 122 Count Specific Elements in a Stream. 122 Frequent Item - Set Analysis. 123 CHAPTER 12SDATA IN THE CLOUD. 124 What is the Cloud?. 124 Cloud and Big data Governance.128 CONCLUSION.131 4 |
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| spellingShingle | Park, Andrew Python Data Science the ultimate and complete beginners guide to master data science with Python step by step Data Science (DE-588)1140936166 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
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| title | Python Data Science the ultimate and complete beginners guide to master data science with Python step by step |
| title_auth | Python Data Science the ultimate and complete beginners guide to master data science with Python step by step |
| title_exact_search | Python Data Science the ultimate and complete beginners guide to master data science with Python step by step |
| title_exact_search_txtP | Python Data Science the ultimate and complete beginners guide to master data science with Python step by step |
| title_full | Python Data Science the ultimate and complete beginners guide to master data science with Python step by step Andrew Park |
| title_fullStr | Python Data Science the ultimate and complete beginners guide to master data science with Python step by step Andrew Park |
| title_full_unstemmed | Python Data Science the ultimate and complete beginners guide to master data science with Python step by step Andrew Park |
| title_short | Python Data Science |
| title_sort | python data science the ultimate and complete beginners guide to master data science with python step by step |
| title_sub | the ultimate and complete beginners guide to master data science with Python step by step |
| topic | Data Science (DE-588)1140936166 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Data Science Python Programmiersprache |
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