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APIs & Data CollectionREST APIs, Webscraping, and Working with FilesREST APIs & HTTP Requests - Part 1HTTP ProtocolHTTP Request Overview of HTTP<p data-ke-size..

APIs & Data Collection Simple APIs Simple APIs (Part 1) What is an API? Using an API Library import pandas as pd dict_ = {'a':[11, 21, 31], 'b':[12, 22, 32]} df = pd.DataFrame(dict_) df.head() df.mean() REST APIs - REpresentational State Transfer APIs - REST APIs are used to interact with web services, i.e., Applications that you call through the internet - They have a set of rules regarding: Co..

Python for Data Science Numpy in Python One Dimensional Numpy The Basics & Array Creation List Numpy Basic Operations - Vector Addition and Subtraction # one-line code u = np.array([1, 0]) v = np.array([0, 1]) z = u+v z:array([1, 1]) # multi-line code u = [1, 0] v = [0, 1] z = [] for n, m in zip(u, v): z.append(n+m) # one-line subtraction u = np.array([1, 0]) v = np.array([0, 1]) z = u-v z : arr..

Python for Data Science Pandas Loading Data with Pandas Importing Pandas import pandas csv_path = 'file1.csv' df = pandas.read_csv(csv_path) Importing import pandas as pd csv_path = 'file1.csv' df = pd.read_csv(csv_path) Dataframes (CSV) csv_path = 'file.csv' df = pd.read_csv(csv_path) df.head() - df = dataframe의 준말 - head() 사용하면 위에서부터 다섯 행 가져오기 가능 Dataframes (XLSX) xlsx_path = 'file1.xlsx' df =..

Python for Data Science Reading & Writing Files with Open Reading Files with Open File Object File1 = open("/resources/data/Example2.txt", "r") - 위처럼 사용하면 close 진행해줘야 열려 있던 파일 닫힘처리 됨 - 아래처럼 with를 사용하면 자동으로 파일 닫음 with open("Example1.txt", "r") as File1: file_stuff = File1.read() print(file_stuff) print(File1.closed) print(file_stuff) - 아래는 open 뒤에 어떤 type로 내용을 읽는지에 따른 차이 - readlines : 한 줄씩 읽으면서 다..

Data Science Methodology Final Project Introduction to CRISP-DM What is CRISP-DM? - an acronym for Cross-industry Standard Process for Data Mining (acronym : 앞글자만 딴 단어) - a structured approach to guide data-driven decision-making CRSIP-DM as a data methodology the CRISP-DM model includes: - data mining stages - data mining stage descriptions - explanations of the relationships between tasks and ..

Data Science Methodology From Deployment to Feedback Deployment Case Study : Understand the results Assimilate knowledge for business: (assimilate : 완전히 이해하다, 동화되다, 흡수하다) - Practical understanding of the meaning of model results - Implications of model results for designing intervention actions (implication : 영향, 함축, 암시) Case Study : Gathering application requirements Application requirements: -..

Data Science Methodology From Modeling to Evaluation Modeling - Concepts From Modeling to Evaluation - Modeling : In what way can the data be visualized to get to the answer that is required? - Evaluation : Does the model used really answer the initial question or does it need to be adjusted? Data Modeling : Using Predictive or Descriptive? Data Modeling : Using training/test sets Understand the..