{"id":8050,"date":"2025-10-25T12:11:34","date_gmt":"2025-10-25T16:11:34","guid":{"rendered":"https:\/\/datavizexplorer.org\/dir\/?page_id=8050"},"modified":"2025-12-15T00:46:29","modified_gmt":"2025-12-15T04:46:29","slug":"checklist-data-scientist","status":"publish","type":"page","link":"https:\/\/datavizexplorer.org\/dir\/checklist-data-scientist\/","title":{"rendered":"Free Exclusive Career Checklist: Beginner Data Scientist"},"content":{"rendered":"
\n

\ud83d\udd11 Free Exclusive Career Checklist: Beginner Data Scientist<\/h1>\n

This checklist outlines the fundamental technical skills and project work necessary to land your first role in the Data Science field.<\/p>\n

\n

1. Core Technical Foundation<\/h3>\n\n\n\n\n\n\n\n\n\n
Step<\/th>\nAction Item<\/th>\nStatus<\/th>\n<\/tr>\n<\/thead>\n
1.1<\/td>\nPython\/R Mastery:<\/strong> Master the basics of one (or both) and demonstrate proficiency with key libraries (Pandas, NumPy, Matplotlib\/Seaborn).<\/td>\n<\/td>\n<\/tr>\n
1.2<\/td>\nSQL for Data Science:<\/strong> Practice writing complex queries including window functions, stored procedures, and handling nested data structures (JSON).<\/td>\n<\/td>\n<\/tr>\n
1.3<\/td>\nCore Statistical Concepts:<\/strong> Understand Hypothesis Testing (t-tests, ANOVA), probability distributions, and basic regression modeling.<\/td>\n<\/td>\n<\/tr>\n
1.4<\/td>\nIntro to Machine Learning:<\/strong> Complete a project using Scikit-learn to apply and evaluate linear\/logistic regression and k-nearest neighbors.<\/td>\n<\/td>\n<\/tr>\n
1.5<\/td>\nGit & Version Control:<\/strong> Set up a GitHub repository and practice the workflow of committing, branching, and merging for a project.<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

2. Portfolio & Machine Learning Projects<\/h3>\n\n\n\n\n\n\n\n\n\n
Step<\/th>\nAction Item<\/th>\nStatus<\/th>\n<\/tr>\n<\/thead>\n
2.1<\/td>\nClassification Project:<\/strong> Complete a project using a complex dataset (e.g., Titanic or Iris) and evaluate performance using confusion matrices and ROC curves.<\/td>\n<\/td>\n<\/tr>\n
2.2<\/td>\nRegression Project:<\/strong> Complete a prediction project (e.g., housing prices) focusing on feature engineering and model tuning.<\/td>\n<\/td>\n<\/tr>\n
2.3<\/td>\nDocument with Business Focus:<\/strong> For all projects, write a structured report\/README focusing on the **business problem**, findings, and implementation potential.<\/td>\n<\/td>\n<\/tr>\n
2.4<\/td>\nKaggle\/Competition Participation:<\/strong> Submit a solution to a beginner-friendly Kaggle competition to practice working under competition constraints.<\/td>\n<\/td>\n<\/tr>\n
2.5<\/td>\nSimple Deployment:<\/strong> Create a basic, working deployment of one of your models using Flask\/Streamlit\/Gradio to show real-world application.<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

3. Interview & Communication Skills<\/h3>\n\n\n\n\n\n\n\n\n\n
Step<\/th>\nAction Item<\/th>\nStatus<\/th>\n<\/tr>\n<\/thead>\n
3.1<\/td>\nBehavioral Prep:<\/strong> Develop and practice 5 specific answers using the STAR method for questions like “Tell me about a time your model failed.”<\/td>\n<\/td>\n<\/tr>\n
3.2<\/td>\nA\/B Testing Knowledge:<\/strong> Be able to clearly explain the process of setting up, executing, and interpreting results from a basic A\/B test.<\/td>\n<\/td>\n<\/tr>\n
3.3<\/td>\nPython\/SQL Whiteboarding:<\/strong> Practice solving common LeetCode\/HackerRank easy\/medium problems on paper or a whiteboard.<\/td>\n<\/td>\n<\/tr>\n
3.4<\/td>\nExplaining ML:<\/strong> Practice explaining *why* you chose a specific model (e.g., Logistic Regression vs. Decision Tree) to a non-technical audience.<\/td>\n<\/td>\n<\/tr>\n
3.5<\/td>\nTargeting Titles:<\/strong> Research and list 5 target job titles (e.g., Junior Data Scientist, Machine Learning Analyst, Data Science Fellow).<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Data Career Checklist Series<\/strong>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 DataViz Explorer\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Page 1 of 1\u00a0<\/span>\n<\/div>\n<\/div>\n
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\nDataViz Explorer<\/span> C.A.I.P.O Barbados Business Registration \u211687900\u00ae
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\ud83d\udd11 Free Exclusive Career Checklist: Beginner Data Scientist This checklist outlines the fundamental technical skills and project work necessary to land your first role in the Data Science field. 1. Core Technical Foundation Step Action Item Status 1.1 Python\/R Mastery: Master the basics of one (or both) and demonstrate proficiency with key libraries (Pandas, NumPy, […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"yasr_overall_rating":0,"yasr_post_is_review":"","yasr_auto_insert_disabled":"","yasr_review_type":"","footnotes":""},"class_list":["post-8050","page","type-page","status-publish","hentry"],"yoast_head":"\nDataViz Explorer - Free Exclusive Career Checklist: Beginner Data Scientist<\/title>\n<meta name=\"description\" content=\"\ud83d\udd11 Free Exclusive Career Checklist: Beginner Data Scientist This checklist outlines the fundamental technical skills and project work necessary to land\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datavizexplorer.org\/dir\/checklist-data-scientist\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DataViz Explorer - 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