Thinking Data Science: A Data Science Practitioner's Guidebook

This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single "cheat sheet".

The challenge for data science is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to process such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the correct algorithms and appropriate architectures, whether the data be small or big.

這本關於機器學習專案的權威指南回答了一位有抱負或有經驗的資料科學家經常遇到的問題：對於機器學習開發，不知道該使用哪種技術？應該使用GOFAI、ANN/DNN還是遷移學習？我能依賴AutoML進行模型開發嗎？如果客戶提供了大量的數據來開發分析模型，我該怎麼辦？如何處理高頻動態數據集？這本書提供了一份整合整個數據科學流程的「秘笈」給實踐者。

對於資料科學來說，挑戰在於從龐大的數據集中提取有意義的信息，以幫助企業制定更好的策略。許多機器學習算法和神經網絡都是為處理這樣的數據集而設計的。對於資料科學家來說，選擇在給定數據集上使用哪種算法是一個艱難的決定。雖然這個問題沒有單一的答案，但需要一種系統性的解決問題的方法。這本書在概念上描述了各種機器學習算法，並定義/討論了選擇機器學習/深度學習模型的過程。整合現有算法和設計高效機器學習模型的技術是這本書的關鍵。《Thinking Data Science》將幫助實踐中的資料科學家、學者、研究人員和學生，無論數據是小還是大，都能使用正確的算法和適當的架構來建立機器學習模型。

Poornachandra Sarang, in his IT career spanning four decades, has been consulting large IT organizations on the design and architecture of systems using state-of-the-art technologies. He has authored several books covering a wide range of emerging technologies. Dr. Sarang is a Ph.D. advisor for Computer Science and Engineering and is on the thesis advisory committee for aspiring doctoral candidates. He has designed and delivered courses/curricula for universities at the postgraduate level, including courses and workshops on emerging technologies for industry. He is a known face at technical and research conferences delivering both keynote and technical talks.

Poornachandra Sarang在他的IT職業生涯中，涵蓋了四十年的時間，一直在大型IT組織中提供關於使用最先進技術的系統設計和架構的諮詢。他撰寫了幾本涵蓋各種新興技術的書籍。Sarang博士是計算機科學和工程的博士生導師，並且是有志於攻讀博士學位的候選人的論文指導委員會成員。他為大學的研究生課程設計和提供了課程/課程大綱，包括針對工業界的新興技術的課程和研討會。他在技術和研究會議上是一個熟悉的面孔，發表過主題演講和技術演講。