DataScience3 Stages1 enrolled

Data Science Roadmap: From Zero to Hired

A complete data science roadmap following the real industry lifecycle: data collection and cleaning, EDA, statistics, feature engineering, modeling, evaluation, and communication. Built for aspiring data scientists who want job-ready, deliverable-driven skills.

RCreated by Route RadiantSep 9, 2026
6 Modules26 Milestones3 Phases

Curriculum Blueprint (3 Phases)

1

Beginner

2 modules

Foundations & Tooling

  • Python Programming FundamentalsPython is the primary working language of data science teams.Can you write functions, loops, and comprehensions fluently?Can you manage environments with virtualenv or conda?Can you read and write CSV, JSON, and text files?
  • NumPy & PandasCore libraries for all data manipulation and analysis work.Can you filter, group, join, and reshape DataFrames?Can you handle vectorized operations instead of loops?Can you chain methods to transform raw data in one pipeline?
  • SQL for Data AccessMost real data lives in relational databases, not files.Can you write SELECT queries with WHERE, JOIN, and GROUP BY?Can you use window functions and CTEs?Can you extract a business dataset from a live database?
  • Data Visualization BasicsCharts are the first tool for exploring data and communicating findings.Can you produce histograms, scatter plots, and box plots in Matplotlib/Seaborn?Can you choose the right chart type for a given question?Can you label and format plots for a non-technical audience?
  • Git & Reproducible WorkflowsVersion control is mandatory for collaboration and hiring screens.Can you commit, branch, and merge on GitHub?Can you structure a project repo with a clear README?Can you reproduce an analysis end-to-end from a clean clone?

Data Collection & Cleaning

  • Data Sourcing & APIsReal projects start by acquiring messy, external data.Can you pull data from a REST API with authentication?Can you scrape a simple web page responsibly?Can you document the provenance of every dataset you use?
  • Missing Data & Outlier HandlingCleaning is the 80% of the job nobody warns about.Can you diagnose missingness patterns (MCAR/MAR/MNAR)?Can you apply imputation strategies and justify the choice?Can you detect and treat outliers without deleting signal?
  • Data Types, Duplicates & ConsistencyDirty schemas silently corrupt every downstream result.Can you normalize dates, currencies, and categorical encodings?Can you deduplicate records with fuzzy matching?Can you deliver a fully cleaned, documented dataset?
  • Exploratory Data Analysis (EDA)EDA reveals structure, anomalies, and hypotheses before modeling.Can you profile distributions and correlations systematically?Can you produce a complete EDA notebook answering business questions?Can you summarize findings in five bullet points for a stakeholder?
2

Intermediate

2 modules

Statistics & Experimentation

  • Descriptive & Inferential StatisticsStatistics turns observations into defensible conclusions.Can you compute and interpret confidence intervals?Can you distinguish correlation from causation in analysis?Can you select the right summary statistics for skewed data?
  • Hypothesis TestingTesting is how data scientists validate claims rigorously.Can you run t-tests, chi-square tests, and ANOVA correctly?Can you interpret p-values and effect sizes without misstatement?Can you check test assumptions before applying them?
  • Probability & DistributionsDistributions underpin modeling, sampling, and uncertainty.Can you identify normal, binomial, and Poisson processes in data?Can you apply Bayes' theorem to a real classification problem?Can you simulate scenarios with Monte Carlo methods?
  • A/B Testing & Experiment DesignExperimentation is the core decision engine of product data science.Can you size a test with power analysis?Can you design an experiment avoiding peeking and bias?Can you write a results readout with a clear ship/no-ship call?

Feature Engineering & Modeling

  • Feature EngineeringBetter features beat fancier models in most real projects.Can you create interaction, ratio, and time-based features?Can you encode high-cardinality categoricals appropriately?Can you build a reusable feature pipeline with scikit-learn?
  • Supervised LearningRegression and classification cover most industry prediction tasks.Can you train and tune linear, tree, and ensemble models?Can you implement proper cross-validation without leakage?Can you deliver a trained model beating a sensible baseline?
  • Model Evaluation & SelectionChoosing the right metric prevents shipping the wrong model.Can you select metrics aligned to business cost (precision vs recall)?Can you diagnose overfitting with learning curves?Can you compare models statistically, not just by one score?
  • Unsupervised LearningClustering and dimensionality reduction unlock unlabeled data.Can you apply k-means and hierarchical clustering with validation?Can you use PCA/UMAP for visualization and compression?Can you segment customers and present actionable segments?
  • Time Series AnalysisTemporal data requires specialized validation and forecasting methods.Can you decompose trend, seasonality, and residuals?Can you forecast with ARIMA/Prophet and backtest properly?Can you avoid leakage with time-based train/test splits?
3

Advanced

2 modules

Advanced Modeling & Deep Learning

  • Gradient Boosting & Model StackingBoosted ensembles dominate structured-data competitions and industry.Can you tune XGBoost/LightGBM with early stopping?Can you build stacked ensembles with out-of-fold predictions?Can you explain model output with SHAP values?
  • Neural Networks & Deep LearningDeep learning handles unstructured data: text, images, audio.Can you train and regularize a network in PyTorch or TensorFlow?Can you fine-tune a pretrained model for a custom task?Can you diagnose vanishing gradients and overfitting?
  • Natural Language ProcessingText is the most common unstructured data in business.Can you build a text classification pipeline end-to-end?Can you apply embeddings and transformer models?Can you evaluate NLP models beyond accuracy?
  • Causal InferenceAnswering 'what if' questions requires more than prediction.Can you apply propensity scores and matching methods?Can you use difference-in-differences on observational data?Can you articulate limits of causal claims to stakeholders?

Deployment, Communication & Career

  • Model Deployment & MLOpsA model only creates value when it runs in production.Can you serve a model via a REST API (FastAPI/Flask)?Can you containerize and deploy with Docker?Can you monitor drift and set up retraining triggers?
  • Big Data & Cloud PlatformsEnterprise data exceeds single-machine capacity.Can you process data with Spark on distributed clusters?Can you build pipelines on AWS/GCP/Azure services?Can you optimize queries and jobs for cost and speed?
  • Data Storytelling & Stakeholder CommunicationImpact depends on persuading decision-makers, not just analysis.Can you build an executive dashboard answering one question?Can you present findings with a clear recommendation narrative?Can you defend methodological choices under questioning?
  • Portfolio & Interview ReadinessHired data scientists prove skills through visible deliverables.Do you have 3 end-to-end projects with cleaned data, EDA, and deployed models?Can you whiteboard a modeling approach in 30 minutes?Can you pass SQL and statistics live-coding screens?

Milestones Along the Way

  • Foundations & Tooling
  • Data Collection & Cleaning
  • Statistics & Experimentation
  • Feature Engineering & Modeling
  • Advanced Modeling & Deep Learning
  • Deployment, Communication & Career

Frequently Asked Questions

What is the Data Science Roadmap: From Zero to Hired roadmap?

The Data Science Roadmap: From Zero to Hired roadmap is a free, step-by-step learning path for DataScience. It is organized into 3 phases spanning 6 modules and 26 milestones, guiding you from foundations to advanced mastery.

How do I learn with a roadmap?

Enroll for free to clone the roadmap into your interactive workspace. Work through each phase, module, and milestone, check off topics as you go, and open the linked courses to go deeper on each subject.

Who is Data Science Roadmap: From Zero to Hired for?

The roadmap is built for self-learners who want a guided path. Because it is broken into beginner, intermediate, and advanced stages, you can follow it start-to-finish or jump to the stage that matches your current level.

Is this roadmap really free?

Yes. Data Science Roadmap: From Zero to Hired lives in the Route Radiant community library and is completely free to clone and follow. Your workspace, progress, and linked lessons stay in your account.

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