[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-573209-105":53,"doc-detail-573209-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","data-science-cheat-sheet-working-solution-workflow","Data Science Cheat Sheet - Working Solution Workflow","","Data Science Cheat Sheet outlines a practical workflow for solving data science problems end-to-end, emphasizing iterative movement between data preprocessing and machine learning. It covers defining the problem and data point structure, preprocessing steps such as loading, inspection, feature extraction, cleaning, handling missing values, outlier detection, and time handling. It also details exploratory analysis, unsupervised learning (scaling, PCA, t-SNE, clustering), feature engineering, and supervised learning through train/test splitting and model evaluation.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/data-science-cheat-sheet-working-solution-workflow/573209/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/data-science-cheat-sheet-working-solution-workflow/573209.png","ImageObject",442,249,{"name":88,"@type":89},"Emma Wilson","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-18","2026-10-13",true,{"@type":98,"interactionType":99,"userInteractionCount":73},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is the typical workflow for solving a data science problem?","Question",{"text":108,"@type":109},"Define the problem and what each data point represents, then follow an iterative process between data preprocessing and machine learning. Preprocessing focuses on turning raw inputs into informative features, while model selection and evaluation come afterward and may trigger further preprocessing changes.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"Why is preprocessing as important as machine learning itself?",{"text":113,"@type":109},"A good solution depends on informative feature representations to avoid “garbage in/garbage out,” and on choosing a model that fits the task without overfitting or underfitting. Poor preprocessing can prevent even strong models from performing well.",{"name":115,"@type":106,"acceptedAnswer":116},"Which steps are included in preprocessing and exploratory analysis?",{"text":117,"@type":109},"Load and inspect raw data, extract meaningful numerical features, clean missing or invalid values, remove zero-variance and nonsensical features, handle outliers and timestamp/date conversion, then visualize distributions, check correlations, and use interactive plots like parallel coordinates.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},573209,1792316504,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":73,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":76,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":9},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Data Science Cheat Sheet: Devising a working solution  \nTo solve a data science problem, you will typically follow this workflow:  \nquestion/problem  \ndomain knowledge!!  \ncommunicate results  \ndeploy  \nMost steps can be summarized under either “data preprocessing”(red) or “machine learning”(green) .  \nPreprocessing is at least as important as the “real” machine learning stuff, i.e., you will only arrive at a decent solution ifa) the data points are represented by informative features (to avoid a “garbage in/garbage out” situation) and b) you have chosen a model appropriate for the task (i.e. that is not over-or underfitting) .  \nArriving at a good solution is an iterative process: Typically, you will start with a simple pipeline (loading the data + some basic cleaning; making predictions with a simple model to get a baseline evaluation score) and then improve the results with feature engineering and a careful model selection. This means in the steps listed below you will o􀀜en find yourself jumping from the preprocessing part to the machine learning steps and back again.  \nThe code examples given in the following rely on these Python libraries:  \nimport numpy as np  \nimport pandas as pd  \nimport matplotlib . pyplot as plt from [sklearn . xxx](sklearn . xxx) import Model  \n\\# math  \n\\# data manipulation  \n\\# plotting  \n\\# machine learning algorithms  \n1. Understand the problem/goal  \n􀀁 What constitutes one data point? What will be the raw inputs for your ML solution?  \n􀀁 What kind of output should be produced for each data point?  \n→ What category of ML algorithms can solve this task (e. g. regression, classification, clustering, ...)?  \n2. Preprocessing: Make the most of your data Data Loading & Basic Cleaning  \n1. Get some raw data (e. g. as a .csv file) & load it: [df = pd . read_csv](df = pd . read_csv) ( \"path / to / data . csv \" )  \n2. Inspect the data, e. g., using df.head(), df.describe(), or [df.info](df.info) ()  \n3. Feature extraction: Possibly transform raw data into meaningful numerical features (e. g. categorical variables with sklearn.preprocessing.OneHotEncoder or text with sklearn.feature_extraction.text.TfidfVectorizer)  \n4. Basic data cleaning:  \n􀀁 handle missing values (e. g. remove with df.dropna() or impute)  \n􀀁 exclude zero variance features and nonsensical variables (e. g. IDs)  \n􀀁 identify wrongly entered data, e. g., with an outlier detection algorithm like the 􀀍-index  \n􀀁 make sure anytimestamp/date columns are represented accordingly(important for plotting) → [pd.to_datetime](pd.to_datetime) ()  \nDr. Franziska Horn 1 [https://www.franziskahorn.de](https://www.franziskahorn.de)  \nExploratory Analysis  \n5. Visualize individual variables ([e.g. as](e.g. as) histograms or by plotting their time course) df . hist () \\# distribution of values for each variable  \ndf . plot () \\# plot values over index ( possibly set a timestamp column as df . index )→ Are the variables in a reasonable range and normally/uniformly distributed?  \n6. Check if pairs of variables are correlated and remove completely redundant features (with a correlation of 1) df . corr () \\# correlation matrix  \nplt . scatter ( df [ \"var1 \" ], df [ \"var2 \" ]) \\# plot one variable vs . another  \n7. Examine multiple variables at once with an interactive parallel coordinates plot (using plotly) px . parallel_coordinates ( df ) \\# px : [plotly . express](plotly . express)  \nUnsupervised Learning  \n8. If the variables are on very different scales, consider normalizing them (StandardScaler or MinMaxScaler)  \n\\# transform pd . DataFrame into np . array where columns have mean : 0 , std : 1  \nscaler = sklearn . preprocessing . Standard Scaler () . fit ( df ) X = scaler . transform ( df )  \n9. Examine the intrinsic dimensionality of the data & remove noise with PCA pca = sklearn . decomposition . PCA ( n_components )  \n\\# transformed data has n_components columns  \n[X_reduced = pca . fit_transform](X_reduced = pca . fit_transform) ( X )  \n\\# information c","cbCain4JKdBatODk","https://ap.wps.com/l/cbCain4JKdBatODk","pdf",81308,"English","# Data science problem workflow\n## Problem definition and approach\n## Preprocessing and exploratory analysis\n## Unsupervised learning\n## Feature engineering\n## Supervised learning: training, prediction, evaluation","[{\"question\":\"What is the typical workflow for solving a data science problem?\",\"answer\":\"Define the problem and what each data point represents, then follow an iterative process between data preprocessing and machine learning. Preprocessing focuses on turning raw inputs into informative features, while model selection and evaluation come afterward and may trigger further preprocessing changes.\"},{\"question\":\"Why is preprocessing as important as machine learning itself?\",\"answer\":\"A good solution depends on informative feature representations to avoid “garbage in/garbage out,” and on choosing a model that fits the task without overfitting or underfitting. Poor preprocessing can prevent even strong models from performing well.\"},{\"question\":\"Which steps are included in preprocessing and exploratory analysis?\",\"answer\":\"Load and inspect raw data, extract meaningful numerical features, clean missing or invalid values, remove zero-variance and nonsensical features, handle outliers and timestamp/date conversion, then visualize distributions, check correlations, and use interactive plots like parallel coordinates.\"}]","Data Science Cheat Sheet - Working Solution Workflow | PDF",1791934619]