資料科學課程

給軟體工程師的不廢話 R 語言精要班已額滿

給軟體工程師的不廢話 R 語言精要班

在這個資料科學蔚為風潮的年代,身為一個對新技術充滿好奇的攻城獅,自然會想要擴充自己的武器庫,學習嶄新的資料分析工具;而 R 語言,一個由統計學家專門為了資料探索與分析所開發的腳本語言,具有龐大的開源社群支持以及琳瑯滿目、數以萬計的各式套件,正是當今學習資料科學相關工具的首選。然而,R 語言的設計邏輯與一般的程式語言不同,工程師們過去學習程式語言的經驗,往往造成學習 R 語言的障礙,本課程將從 R 語言的基礎開始,讓同學們從課堂講解以及互動式上機課程中,得以徹底理解 R 語言的核心概念與精要,學習如何利用 R 語言問資料問題,並且從資料分析的角度撰寫效率良好同時具有高度可讀性的 R 語言代碼。 

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實戰 D3.js — 最熱門的互動資料視覺化技術已額滿

實戰 D3.js — 最熱門的互動資料視覺化技術

D3.js 立基於網頁技術、並從設計的核心納入互動、即時等概念,可說是當代最先進的一組視覺化工具。這個課程將從 D3.js 4.4 的基本資料模型開始到各種視覺化元件與布局模組,以穿插實作的方式帶領學員實際做出各種圖表,包含從基本的長條圖、圓餅圖、到泡泡圖、樹狀圖等複雜圖表,同時也將說明使用 D3.js 時常見的陷阱與不易理解的部份。
課程介紹
文字探勘者的入門心法已額滿

文字探勘者的入門心法

我們都知道資料探勘是資料科學中的基礎科目,這個科目總讓大部分的人有個錯覺:以為學了這門課以後,就可以從雜亂、巨大的資料中抽取出有意義的知識。然而實際上,即使上完了資料探勘課卻仍然發現資料往往混亂、難以著手,無法真正從透過資料探勘技術得到有價值的知識。 為甚麼?大部份的資料探勘或機器學習方法其實都是利用數值資料,文字資料要分析、整理往往要經過許多前處理,才有可能挖掘文字中的深層知識,即使是同一批文字資料,透過不同處理方式能得到的資訊常常差異甚大。本課程的目的在引領資料工作者:分析、整理原始文字資料,針對文字、與待解問題的特性,找到適合的轉換方式,進而可以利用資料探勘技術,找出有價值的知識。
課程介紹
使用 R 語言建立自己的演算法交易事業已額滿

使用 R 語言建立自己的演算法交易事業

我們希望藉由近幾年資料科學的興起,推廣正確的金融交易知識,包括金融資料分析與建立自己的演算法交易事業。自己的策略自己做,自己的風險自己控,自己的部位自己顧。在本課程裡,1 就是 1,2 就是 2,40 就是 40 (事實),沒有怪力亂神,沒有定義不清,一切的一切,統計說話,數據說話!本課程與上一課程 (用 R 輕鬆做交易策略分析及自動下單) 部分重覆,本次除針對理論做更深入講解,亦增加股票、期貨、選擇權開發實務分享。
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資料視覺化之理論、賞析與實作已額滿

資料視覺化之理論、賞析與實作

這個課程目標是教授『人怎麼看資料』,『怎麼把資料給人看』兩大主題。這個課程濃縮了康教授在臺大所開設的『資料視覺化』課程的精華,總共包含賞析、理論、設計與實作四節課程。在賞析課程中,他將解析經典的案例,從案例中培養出資料視覺設計師的眼光,懂得看到設計下面的精妙。第二節課是理論課,他將介紹心理學中相關的研究發現,解釋人類的視覺系統是接收資與理解資訊得流程,並內化這些心理學理論,成為資料視覺化設計的基礎。第三節課是設計方法,他會具體說明如何從資料維度與資料特性,發展好資料視覺化方法,達到解決問題的目的。課程最後,康教授將介紹他使用各類資料視覺化的工具的經驗,包含 Tableau, Processing, D3.js, PowerBI 等,也將展示他和他的學生所開發的實務案例。本課程是高階的課程,適合有處理資料經驗,需要做重要決策的專業人員。本課程將提升資料呈現的品質,幫助決策者快速地從複雜資料中,接收、判讀、發現關鍵問題。本課程也同時幫助跨領域的溝通,能以圖像式的方式,說明、探索與分析資料背後的意義,提昇溝通的效率與品質。 

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機器學習速遊 (Quick Tour of Machine Learning)已額滿

機器學習速遊 (Quick Tour of Machine Learning)

機器學習旨在讓電腦能由資料中累積的經驗來自我進步,近年來已廣泛應用於資料探勘、計算機視覺、自然語言處理、生物特徵識別、搜尋引擎、醫學診斷、檢測信用卡欺詐、證券市場分析、DNA 序列測序、語音和手寫識別、戰略遊戲和機器人等領域。它已成為資料科學的基礎學科之一,為任何資料科學家必備的工具。這門課程將由 Appier 首席資料科學家林軒田利用短短的六個小時,快速地帶大家探索機器學習的基石、介紹核心的模型及一些熱門的技法,希望幫助大家有效率而紮實地了解這個領域,以妥善地使用各式機器學習的工具。此課程適合所有希望開始運用資料的資料分析者,推薦給所有有志於資料分析領域的資料科學愛好者。  

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手把手的深度學習實務已額滿

手把手的深度學習實務

深度學習 (Deep Learning) 是近年來備受重視的機器學習方法,現今許多語音與影像辨識的研究與應用,都基於深度學習的技術來完成。深度學習的工具不斷推陳出新,若你已經聽過這項技術,卻不知道該從何開始下手,這堂手把手的實務課程將會很適合你。本課程利用六個小時的時間,介紹 Keras 這個熱門的深度學習工具,從最簡單的前饋類神經網路 (Feedforward Neural Network) 開始,用 Keras 加入各種訓練技巧 (Regularization, Early Stopping, Dropout) 以得到好的預測模型。亦介紹深度學習模型的另一個變形:捲積式類神經網路 (Convolutional Neural Network, CNN),以完整的實務操作,讓你邁出成為深度學習訓練大師的第一步。

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製造資料科學:從預測性思維到處方性決策已額滿

製造資料科學:從預測性思維到處方性決策

資料科學 (Data Science) 與工業 4.0 (Industry 4.0) 是近幾年來廣為討論的主題,本課程以製造現場為實證對象,從資料的視角來尋求改善的契機。在複雜的製造現場環境裡,存在著各式各樣的議題,諸如品質、成本、交期、創新、彈性等,皆需持續改善以提升公司核心競爭力。事實上,製造業在台灣經濟發展上也扮演著舉足輕重的角色,在製造業的轉型過程中,如何以資料科學的角度,整合自動化實務和管理經驗,導入方法論以累積製造智慧 (Manufacturing Intelligence),相信是這個世代關注的焦點之一。 這門課程由國立成功大學資訊系暨製造所李家岩副教授主講,希望以深入淺出的方式,對製造資料科學作一整體性的介紹。課程專注於「問題本質的探索與觀念的釐清」,並輔以案例介紹工程資料分析時會遭遇的困難與挑戰。此外,對於機器學習或資料探勘強調的預測性分析 (Predictive Analytics),課程更進一步地延伸到處方性分析 (Prescriptive Analytics),以連結到管理者視角下,風險評估與決策制定的過程。希望課程內容能引起大家的興趣,並帶給大家在未來繼續學習進階知識的基礎。

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智慧城市中的時空大數據應用已額滿

智慧城市中的時空大數據應用

伴隨著城市化的腳步越來越快與城市人口日漸增加,提供城市居民必要的服務如交通、醫療、能源、環境、教育與公共安全等設施與都市規劃,受到龐大的壓力挑戰,錯誤的政策將導致大量的資源浪費與民怨,城市居民需要生活品質、便利設施、健康環境、便民政策、合理房價與更多社會福利等各種面向的公共需求。 由於資訊技術與行動裝置的快速發展,雲端運算、物聯網、感測網路、行動通訊、社交媒體、地理資訊等技術與服務日漸成熟,城市中由不同平台產生的異質性資料 (如感測網路、社群媒體、交通資訊、人群移動、地理特性與環境監測等資料) 已成為解決城市發展課題的重要手段,分析城市中的時空大數據將有利於開發者提供各種個人化或客製化的智慧型服務,也可輔助政府或企業管理高層做出聰明的決策,在本課程中將介紹如何處理城市的異質性大數據以及善用其開發社群媒體、商業策略、交通、能源、環保、旅遊與都市規劃等相關的智慧應用。
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統計學家教你穩紮穩打 R 語言資料分析已額滿

統計學家教你穩紮穩打 R 語言資料分析

統計學家教你穩紮穩打 R 語言資料分析資料科學是綜合統計學、資訊科學及領域知識的一門新興學科。因此,要成為一名資料科學家,對統計當然要有基本的認識。舉凡日常的機率統計問題,例如:算機率值,統計參數估計,假設檢定及模型配適等等,除了需要理論証明推導之外,也需以程式做數值的模擬與驗証。而 R 語言正是研究者分析資料及執行統計模擬不可或缺的工具。也因為 R 軟體具有豐富的統計套件,也成為資料科學領域裡最受歡迎的程式語言之一。本課程由統計學家教您用 R 進行資料處理、呈現及統計分析。從第一天 R 的基礎 + 進階 (資料輸出輸入及處理、統計圖形、機率分佈、假設檢定、變異數分析、模擬統計模型與迴歸分析) 到第二天的統計分析方法應用(探索式資料分析、資料探勘案例研究),除了著重在方法理論的講述、假設及觀念的闡釋,同時也強調實際 R 程式撰寫操作,希望藉由本課程之訓練,奠定學員 R 程式撰寫及資料統計分析的基礎,並具有判斷不同狀況該用什麼不同的統計方法的數據能力。 

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Machine Learning 機器學習課程已額滿

Machine Learning 機器學習課程

近年來,從機器學習、深度學習到人工智慧,都是透過分析這些巨量的資料,從中萃取出有價值的訊息,進而讓「電腦」對各種狀況作出正確判斷,甚至對一些未來的狀況做出預測。這些技術的高度發展,也帶動了各式各樣的創新應用。從 IBM 的 Watson 在 Jeopardy 搶答賽贏過參賽者、Google Deep Mind 的 AlphaGo 以四勝一負戰勝南韓九段天才棋王李世乭、Amazon 的 Echo 到工業 4.0、智慧城市等等。這些改變,已徹底改變了人類思考、學習與生活方式。 本課程將介紹機器學習的基礎與原理,也將涵蓋各式機器學習演算法的核心技術:數值最佳化方法,同時也提供各種評估指標,以利在演算法中的參數挑選與不同演算法之間的比較。Washington University Prof. Pedro Domingos (The Master Algorithm, 大演算一書的作者),曾將機器學習分成三個主要部分:表現 (Representation)、最佳化 (Optimization)、評估 (Evaluation)。與本課程的安排相符。
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使用 R 語言建立自己的演算法交易事業已額滿

使用 R 語言建立自己的演算法交易事業

我們希望藉由近幾年資料科學的興起,推廣正確的金融交易知識,包括金融資料分析與建立自己的演算法交易事業。自己的策略自己做,自己的風險自己控,自己的部位自己顧。在本課程裡,1 就是 1,2 就是 2,40 就是 40 (事實),沒有怪力亂神,沒有定義不清,一切的一切,統計說話,數據說話!本課程與上一課程 (用 R 輕鬆做交易策略分析及自動下單) 部分重覆,本次除針對理論做更深入講解,亦增加股票、期貨、選擇權開發實務分享。
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