Row 38909

Row ID: 38909 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 38909 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Retail DS Project as Transition Vehicle:

I am attempting to transition from mechanical engineering (currently doing nuclear power projects) to data science. I have a friend who is willing to refer me to an entry level role with his company, which provides a wide range of DS services to retail clients. I taught myself Python back in college for fun and went to a few hackathons, but as far as coding in industry I have only done some VBA automation (ew).

In an effort to build up my resume, I would like to execute a DS project on retail data I found on Kaggle. The target company mostly uses Python so, with some occasional guidance from ChatGPT, I have done some initial exploration of the data with pandas, matplotlib, etc. Now I would like to move onto feature engineering, and then predictive modeling and visualization. I am looking for recommendations for where to focus my efforts in these phases. What types of features should I be most concerned with? What are some common analyses or model that are employed for predictive purposes in retail? To be clear, my aim is not to do anything novel or profound, but simply to show that I have made a good faith effort to prepare for the role.

Any advice on how I should proceed with this project would be greatly appreciated!

FieldValue
text Retail DS Project as Transition Vehicle: I am attempting to transition from mechanical engineering (currently doing nuclear power projects) to data science. I have a friend who is willing to refer me to an entry level role with his company, which provides a wide range of DS services to retail clients. I taught myself Python back in college for fun and went to a few hackathons, but as far as coding in industry I have only done some VBA automation (ew). In an effort to build up my resume, I wou…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-22
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Raw Record

{
  "text": "Retail DS Project as Transition Vehicle:\n\nI am attempting to transition from mechanical engineering (currently doing nuclear power projects) to data science. I have a friend who is willing to refer me to an entry level role with his company, which provides a wide range of DS services to retail clients. I taught myself Python back in college for fun and went to a few hackathons, but as far as coding in industry I have only done some VBA automation (ew). \n\nIn an effort to build up my resume, I would like to execute a DS project on retail data I found on Kaggle. The target company mostly uses Python so, with some occasional guidance from ChatGPT, I have done some initial exploration of the data with pandas, matplotlib, etc. Now I would like to move onto feature engineering, and then predictive modeling and visualization. I am looking for recommendations for where to focus my efforts in these phases. What types of features should I be most concerned with? What are some common analyses or model that are employed for predictive purposes in retail? To be clear, my aim is not to do anything novel or profound, but simply to show that I have made a good faith effort to prepare for the role.\n\nAny advice on how I should proceed with this project would be greatly appreciated!",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1MTGxDWWFnQUlEb0l5eDNkVTJQcHk1Y2NrWUNvVnI1YkdnOUN1b1kybk84TElFaWVISkZ0MEFFY1NQZ2JycDZTXy1DQ1ZpLUY2bEUyUDlNZlhLWGwzekE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9hWUJJZ2w3Tm9DRnEzOVFGLUNBUHlEdXQ0bFM2N1dnejNPNVNfc1RJWWo1dFV6UUltOVkwUG8zTThNX29qc3BUQnh4RVNNZ0ZWVjN0dXR1TVN2UjhpRS16SHpodFFaR2EzbmtHYTRxWS1UalJ3a18xOFE1ckg0UXlPNjNVMGRETm9KSThId095dnV2aDVXclc1UjFfUzhlR3hCUjc0QUtqekU1alZHaExJRjRNOW50M2VPR202TmllTDJ2WDdGbWZXazZfa3hQY0RQN3p3MDQ4c1l6VkJnQT09"
}
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