🔑 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.

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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, Matplotlib/Seaborn).
1.2	SQL for Data Science: Practice writing complex queries including window functions, stored procedures, and handling nested data structures (JSON).
1.3	Core Statistical Concepts: Understand Hypothesis Testing (t-tests, ANOVA), probability distributions, and basic regression modeling.
1.4	Intro to Machine Learning: Complete a project using Scikit-learn to apply and evaluate linear/logistic regression and k-nearest neighbors.
1.5	Git & Version Control: Set up a GitHub repository and practice the workflow of committing, branching, and merging for a project.

2. Portfolio & Machine Learning Projects

Step	Action Item	Status
2.1	Classification Project: Complete a project using a complex dataset (e.g., Titanic or Iris) and evaluate performance using confusion matrices and ROC curves.
2.2	Regression Project: Complete a prediction project (e.g., housing prices) focusing on feature engineering and model tuning.
2.3	Document with Business Focus: For all projects, write a structured report/README focusing on the **business problem**, findings, and implementation potential.
2.4	Kaggle/Competition Participation: Submit a solution to a beginner-friendly Kaggle competition to practice working under competition constraints.
2.5	Simple Deployment: Create a basic, working deployment of one of your models using Flask/Streamlit/Gradio to show real-world application.

3. Interview & Communication Skills

Step	Action Item	Status
3.1	Behavioral Prep: Develop and practice 5 specific answers using the STAR method for questions like “Tell me about a time your model failed.”
3.2	A/B Testing Knowledge: Be able to clearly explain the process of setting up, executing, and interpreting results from a basic A/B test.
3.3	Python/SQL Whiteboarding: Practice solving common LeetCode/HackerRank easy/medium problems on paper or a whiteboard.
3.4	Explaining ML: Practice explaining *why* you chose a specific model (e.g., Logistic Regression vs. Decision Tree) to a non-technical audience.
3.5	Targeting Titles: Research and list 5 target job titles (e.g., Junior Data Scientist, Machine Learning Analyst, Data Science Fellow).