2025年大數(shù)據(jù)分析師職業(yè)技能測(cè)試卷：大數(shù)據(jù)分析與數(shù)據(jù)可視化實(shí)戰(zhàn)案例試題考試時(shí)間：______分鐘總分：______分姓名：______一、數(shù)據(jù)采集與處理要求：請(qǐng)根據(jù)以下要求，從給定的數(shù)據(jù)中提取有用信息，并處理數(shù)據(jù)以滿足后續(xù)分析需求。1.以下是一段關(guān)于某電商網(wǎng)站用戶購(gòu)買行為的文本數(shù)據(jù)，請(qǐng)使用Python進(jìn)行數(shù)據(jù)清洗，去除重復(fù)記錄，并計(jì)算不同商品類別的訂單數(shù)量。```{"order_id":1,"user_id":1001,"product_category":"Electronics","product_name":"Laptop","quantity":1,"price":1000.00},{"order_id":2,"user_id":1002,"product_category":"Clothing","product_name":"T-Shirt","quantity":2,"price":50.00},{"order_id":3,"user_id":1001,"product_category":"Electronics","product_name":"Laptop","quantity":1,"price":1000.00},{"order_id":4,"user_id":1003,"product_category":"Books","product_name":"ProgrammingBook","quantity":1,"price":30.00},{"order_id":5,"user_id":1004,"product_category":"Clothing","product_name":"Jeans","quantity":1,"price":70.00},{"order_id":6,"user_id":1001,"product_category":"Electronics","product_name":"Laptop","quantity":1,"price":1000.00},{"order_id":7,"user_id":1005,"product_category":"Books","product_name":"DataScienceBook","quantity":1,"price":40.00}```2.以下是一段關(guān)于某電商網(wǎng)站用戶購(gòu)買行為的CSV文件數(shù)據(jù)，請(qǐng)使用Python進(jìn)行數(shù)據(jù)讀取，篩選出價(jià)格在50元以上的訂單記錄，并按用戶ID進(jìn)行分組統(tǒng)計(jì)，輸出分組后的訂單數(shù)量。```order_id,user_id,product_category,product_name,quantity,price1,1001,Electronics,Laptop,1,1000.002,1002,Clothing,T-Shirt,2,50.003,1001,Electronics,Laptop,1,1000.004,1003,Books,ProgrammingBook,1,30.005,1004,Clothing,Jeans,1,70.006,1001,Electronics,Laptop,1,1000.007,1005,Books,DataScienceBook,1,40.00```二、數(shù)據(jù)分析與挖掘要求：請(qǐng)根據(jù)以下要求，對(duì)數(shù)據(jù)進(jìn)行統(tǒng)計(jì)分析，并提取有價(jià)值的信息。1.以下是一段關(guān)于某電商平臺(tái)用戶購(gòu)買行為的CSV文件數(shù)據(jù)，請(qǐng)使用Python進(jìn)行數(shù)據(jù)讀取，計(jì)算不同商品類別的訂單數(shù)量，并輸出各類商品類別的訂單占比。```order_id,user_id,product_category,product_name,quantity,price1,1001,Electronics,Laptop,1,1000.002,1002,Clothing,T-Shirt,2,50.003,1001,Electronics,Laptop,1,1000.004,1003,Books,ProgrammingBook,1,30.005,1004,Clothing,Jeans,1,70.006,1001,Electronics,Laptop,1,1000.007,1005,Books,DataScienceBook,1,40.00```2.以下是一段關(guān)于某電商平臺(tái)用戶購(gòu)買行為的CSV文件數(shù)據(jù)，請(qǐng)使用Python進(jìn)行數(shù)據(jù)讀取，計(jì)算不同用戶年齡段（20歲以下、20-30歲、30-40歲、40歲以上）的訂單數(shù)量，并輸出每個(gè)年齡段的訂單占比。```order_id,user_id,age,product_category,product_name,quantity,price1,1001,22,Electronics,Laptop,1,1000.002,1002,25,Clothing,T-Shirt,2,50.003,1003,28,Electronics,Laptop,1,1000.004,1004,35,Books,ProgrammingBook,1,30.005,1005,45,Clothing,Jeans,1,70.006,1006,55,Books,DataScienceBook,1,40.007,1007,65,Electronics,Smartphone,1,1500.008,1008,18,Electronics,Laptop,1,1000.009,1009,30,Electronics,Smartphone,1,1500.0010,1010,20,Electronics,Laptop,1,1000.00```四、數(shù)據(jù)可視化要求：請(qǐng)根據(jù)以下要求，使用Python中的可視化庫(kù)（如Matplotlib、Seaborn等）對(duì)數(shù)據(jù)進(jìn)行可視化，并解釋可視化結(jié)果。1.使用Python中的可視化庫(kù)，對(duì)以下數(shù)據(jù)繪制散點(diǎn)圖，展示用戶年齡與購(gòu)買金額之間的關(guān)系。```age=[22,25,28,35,45,55,65,18,30,20]purchase_amount=[1000.00,50.00,1000.00,30.00,70.00,40.00,1500.00,1000.00,1500.00,1000.00]```2.使用Python中的可視化庫(kù)，對(duì)以下數(shù)據(jù)繪制柱狀圖，展示不同商品類別的訂單數(shù)量。```product_category=['Electronics','Clothing','Books']order_count=[3,2,2]```五、預(yù)測(cè)分析要求：請(qǐng)根據(jù)以下要求，使用Python中的機(jī)器學(xué)習(xí)庫(kù)（如scikit-learn等）對(duì)數(shù)據(jù)進(jìn)行預(yù)測(cè)分析，并解釋預(yù)測(cè)結(jié)果。1.使用Python中的機(jī)器學(xué)習(xí)庫(kù)，對(duì)以下數(shù)據(jù)進(jìn)行線性回歸分析，預(yù)測(cè)用戶購(gòu)買金額。```age=[22,25,28,35,45,55,65,18,30,20]purchase_amount=[1000.00,50.00,1000.00,30.00,70.00,40.00,1500.00,1000.00,1500.00,1000.00]```2.使用Python中的機(jī)器學(xué)習(xí)庫(kù)，對(duì)以下數(shù)據(jù)進(jìn)行決策樹分類分析，預(yù)測(cè)用戶購(gòu)買的商品類別。```age=[22,25,28,35,45,55,65,18,30,20]product_category=['Electronics','Clothing','Books']```六、數(shù)據(jù)報(bào)告要求：請(qǐng)根據(jù)以下要求，撰寫一份關(guān)于某電商平臺(tái)用戶購(gòu)買行為的數(shù)據(jù)報(bào)告。1.在報(bào)告中簡(jiǎn)要介紹數(shù)據(jù)來源和樣本量。2.分析不同商品類別的訂單數(shù)量和占比，找出最受歡迎的商品類別。3.分析不同用戶年齡段的訂單數(shù)量和占比，找出主要消費(fèi)群體。4.分析用戶購(gòu)買金額與年齡之間的關(guān)系，探討是否存在相關(guān)性。5.根據(jù)分析結(jié)果，提出針對(duì)電商平臺(tái)的優(yōu)化建議。本次試卷答案如下：一、數(shù)據(jù)采集與處理1.以下是對(duì)第一題的解析思路：-導(dǎo)入Python中的`json`模塊來解析JSON數(shù)據(jù)。-將JSON數(shù)據(jù)轉(zhuǎn)換為Python字典列表。-使用列表推導(dǎo)式來創(chuàng)建一個(gè)沒有重復(fù)`order_id`的列表。-使用`collections.Counter`來統(tǒng)計(jì)每個(gè)`product_category`的訂單數(shù)量。-輸出每個(gè)商品類別的訂單數(shù)量。```pythonimportjsonfromcollectionsimportCounterdata='''[{"order_id":1,"user_id":1001,"product_category":"Electronics","product_name":"Laptop","quantity":1,"price":1000.00},{"order_id":2,"user_id":1002,"product_category":"Clothing","product_name":"T-Shirt","quantity":2,"price":50.00},{"order_id":3,"user_id":1001,"product_category":"Electronics","product_name":"Laptop","quantity":1,"price":1000.00},{"order_id":4,"user_id":1003,"product_category":"Books","product_name":"ProgrammingBook","quantity":1,"price":30.00},{"order_id":5,"user_id":1004,"product_category":"Clothing","product_name":"Jeans","quantity":1,"price":70.00},{"order_id":6,"user_id":1001,"product_category":"Electronics","product_name":"Laptop","quantity":1,"price":1000.00},{"order_id":7,"user_id":1005,"product_category":"Books","product_name":"DataScienceBook","quantity":1,"price":40.00}]'''#ParseJSONdataorders=json.loads(data)#Removeduplicatesbasedonorder_idunique_orders=[orderfororderinordersiforder['order_id']notin[o['order_id']foroinorders]]#Countordersperproductcategorycategory_counts=Counter(order['product_category']fororderinunique_orders)#Outputthecountsforcategory,countincategory_counts.items():print(f"{category}:{count}")```2.以下是對(duì)第二題的解析思路：-導(dǎo)入Python中的`pandas`模塊來處理CSV數(shù)據(jù)。-使用`pandas.read_csv`函數(shù)讀取CSV文件。-使用布爾索引來篩選價(jià)格在50元以上的訂單。-使用`groupby`方法按`user_id`分組，并使用`size`方法計(jì)算每個(gè)用戶的訂單數(shù)量。-輸出分組后的訂單數(shù)量。```pythonimportpandasaspd#ReadCSVdatadf=pd.read_csv('path_to_csv_file.csv')#Filterorderswithpriceabove50filtered_orders=df[df['price']>50]#Groupbyuser_idandcountordersuser_order_counts=filtered_orders.groupby('user_id').size()#Outputthecountsprint(user_order_counts)```二、數(shù)據(jù)分析與挖掘1.以下是對(duì)第一題的解析思路：-使用`pandas`模塊讀取CSV數(shù)據(jù)。-使用`value_counts`方法計(jì)算每個(gè)商品類別的訂單數(shù)量。-使用`plot`方法繪制餅圖，展示不同商品類別的訂單占比。```pythonimportpandasaspd#ReadCSVdatadf=pd.read_csv('path_to_csv_file.csv')#Countordersperproductcategorycategory_counts=df['prod