舉報

會員
Practical Data Analysis Using Jupyter Notebook
Dataliteracyistheabilitytoread,analyze,workwith,andargueusingdata.Dataanalysisistheprocessofcleaningandmodelingyourdatatodiscoverusefulinformation.Thisbookcombinesthesetwoconceptsbysharingproventechniquesandhands-onexamplessothatyoucanlearnhowtocommunicateeffectivelyusingdata.AfterintroducingyoutothebasicsofdataanalysisusingJupyterNotebookandPython,thebookwilltakeyouthroughthefundamentalsofdata.Packedwithpracticalexamples,thisguidewillteachyouhowtoclean,wrangle,analyze,andvisualizedatatogainusefulinsights,andyou'lldiscoverhowtoanswerquestionsusingdatawitheasy-to-followsteps.Laterchaptersteachyouaboutstorytellingwithdatausingcharts,suchashistogramsandscatterplots.Asyouadvance,you'llunderstandhowtoworkwithunstructureddatausingnaturallanguageprocessing(NLP)techniquestoperformsentimentanalysis.Alltheknowledgeyougainwillhelpyoudiscoverkeypatternsandtrendsindatausingreal-worldexamples.Inadditiontothis,youwilllearnhowtohandledataofvaryingcomplexitytoperformefficientdataanalysisusingmodernPythonlibraries.Bytheendofthisbook,you'llhavegainedthepracticalskillsyouneedtoanalyzedatawithconfidence.
目錄(109章)
倒序
- 封面
- 版權信息
- About Packt
- Why subscribe?
- Foreword
- About the author
- Preface
- Section 1: Data Analysis Essentials
- Fundamentals of Data Analysis
- The evolution of data analysis and why it is important
- What makes a good data analyst?
- Understanding data types and their significance
- Data classifications and data attributes explained
- Understanding data literacy
- Summary
- Further reading
- Overview of Python and Installing Jupyter Notebook
- Technical requirements
- Installing Python and using Jupyter Notebook
- Storing and retrieving data files
- Hello World! – running your first Python code
- Exploring Python packages
- Summary
- Future reading
- Getting Started with NumPy
- Technical requirements
- Understanding a Python NumPy array and its importance
- Making your first NumPy array
- Practical use cases of NumPy and arrays
- Summary
- Further reading
- Creating Your First pandas DataFrame
- Technical requirements
- Techniques for manipulating tabular data
- Understanding pandas and DataFrames
- Handling essential data formats
- Data dictionaries and data types
- Creating our first DataFrame
- Summary
- Further reading
- Gathering and Loading Data in Python
- Technical requirements
- Introduction to SQL and relational databases
- From SQL to pandas DataFrames
- Data about your data explained
- The importance of data lineage
- Summary
- Further reading
- Section 2: Solutions for Data Discovery
- Visualizing and Working with Time Series Data
- Technical requirements
- Data modeling for results
- Anatomy of a chart and data viz best practices
- Comparative analysis
- The shape of the curve
- Summary
- Further reading
- Exploring Cleaning Refining and Blending Datasets
- Technical requirements
- Retrieving viewing and storing tabular data
- Learning how to restrict sort and sift through data
- Cleaning refining and purifying data using Python
- Combining and binning data
- Summary
- Further reading
- Understanding Joins Relationships and Aggregates
- Technical requirements
- Foundations of join relationships
- Join types in action
- Explaining data aggregation
- Summary statistics and outliers
- Summary
- Further reading
- Plotting Visualization and Storytelling
- Technical requirements
- Explaining distribution analysis
- Understanding outliers and trends
- Geoanalytical techniques and tips
- Finding patterns in data
- Summary
- Further reading
- Section 3: Working with Unstructured Big Data
- Exploring Text Data and Unstructured Data
- Technical requirements
- Preparing to work with unstructured data
- Tokenization explained
- Counting words and exploring results
- Normalizing text techniques
- Excluding words from analysis
- Summary
- Further reading
- Practical Sentiment Analysis
- Technical requirements
- Why sentiment analysis is important
- Elements of an NLP model
- Sentiment analysis packages
- Sentiment analysis in action
- Summary
- Further reading
- Bringing It All Together
- Technical requirements
- Discovering real-world datasets
- Reporting results
- The Capstone project
- Summary
- Further reading
- Works Cited
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間:2021-06-18 18:59:18
推薦閱讀
- 數據庫基礎教程(SQL Server平臺)
- Hands-On Data Structures and Algorithms with Rust
- 數據產品經理高效學習手冊:產品設計、技術常識與機器學習
- Architects of Intelligence
- 數據驅動:從方法到實踐
- Mockito Cookbook
- 大數據時代下的智能轉型進程精選(套裝共10冊)
- 智能數據時代:企業大數據戰略與實戰
- Power BI商業數據分析完全自學教程
- 探索新型智庫發展之路:藍迪國際智庫報告·2015(上冊)
- Visual Studio 2013 and .NET 4.5 Expert Cookbook
- 爬蟲實戰:從數據到產品
- 機器學習:實用案例解析
- 數據中心經營之道
- 數據時代的品牌智造
- TypeScript Microservices
- 大數據:從海量到精準
- 數據質量管理:數據可靠性與數據質量問題解決之道
- 大數據架構師指南
- 大數據處理框架Apache Spark設計與實現
- Oracle數據庫性能優化方法論和最佳實踐
- 數據流上頻繁模式和高效用模式挖掘
- 數字化轉型 架構與方法
- 基于ggplot的政經數據可視化
- Git Essentials(Second Edition)
- 循序漸進學Spark
- 算法詳解(卷2):圖算法和數據結構
- 數據庫系統管理應用
- MySQL數據庫基礎與實踐
- 數據庫系統內幕