Back to RoadmapsData Scientist & Analytics
Transform raw data into actionable business insights using statistics, machine learning, and compelling data storytelling
10 milestones in this roadmap
Step 1beginner5-7 weeks
Statistics & Probability
Master descriptive statistics, probability distributions, hypothesis testing, and confidence intervals
Curriculum
- 1Descriptive Statistics: Central Tendency & Dispersion
- 2Probability Theory: Bayes Theorem & Conditional Probability
- 3Distributions: Normal, Binomial, Poisson & Exponential
- 4Hypothesis Testing: t-tests, Chi-Squared & ANOVA
- 5Confidence Intervals, Effect Size & Statistical Power
Tools & Platforms
PythonSciPystatsmodelsRJupyter NotebookWolfram Alpha
Step 1beginner5-7 weeks
Statistics & Probability
Master descriptive statistics, probability distributions, hypothesis testing, and confidence intervals
Curriculum
- 1Descriptive Statistics: Central Tendency & Dispersion
- 2Probability Theory: Bayes Theorem & Conditional Probability
- 3Distributions: Normal, Binomial, Poisson & Exponential
- 4Hypothesis Testing: t-tests, Chi-Squared & ANOVA
- 5Confidence Intervals, Effect Size & Statistical Power
Step 2beginner5-6 weeks
Python for Data Science
Master Pandas, NumPy, Matplotlib, and Seaborn for data manipulation and visualization
Curriculum
- 1Pandas: DataFrames, GroupBy, Merge & Pivot Tables
- 2NumPy: Array Operations, Broadcasting & Linear Algebra
- 3Matplotlib: Publication-Quality Plots & Customization
- 4Seaborn: Statistical Graphics & Distribution Plots
- 5
Step 3intermediate4-6 weeks
Exploratory Data Analysis
Perform systematic EDA with data profiling, missing data handling, and feature engineering
Curriculum
- 1Data Profiling: Shape, Types & Distributions
- 2Missing Data: MCAR/MAR/MNAR & Imputation Strategies
- 3Outlier Detection: IQR, Z-Score & Isolation Forest
- 4Feature Engineering: Encoding, Binning & Transformations
- 5
Step 4intermediate6-8 weeks
Machine Learning for Data Science
Apply regression, classification, and clustering with proper model evaluation and validation
Curriculum
- 1Regression: Linear, Ridge, Lasso & Polynomial
- 2Classification: Logistic Regression, Random Forest & XGBoost
- 3Clustering: K-Means, DBSCAN & Hierarchical
- 4Model Evaluation: ROC-AUC, F1 & Cross-Validation
- 5
Step 5intermediate6-8 weeks
Advanced Statistical Methods
Apply Bayesian inference, time series analysis, A/B testing, and causal inference methods
Curriculum
- 1Bayesian Inference: Priors, MCMC & PyMC
- 2Time Series: ARIMA, Seasonal Decomposition & Prophet
- 3A/B Testing: Sample Size, Sequential Testing & MAB
- 4Causal Inference: DiD, RDD & Instrumental Variables
- 5
Step 6intermediate5-7 weeks
Deep Learning Applications
Apply deep learning to tabular data, text analytics, and image tasks with transfer learning
Curriculum
- 1Neural Networks for Tabular Data: Embeddings & TabNet
- 2NLP: Sentiment Analysis & Text Classification
- 3Image Classification & Transfer Learning
- 4When to Use Deep Learning vs Classical ML
- 5
Step 7advanced5-7 weeks
Data Visualization & Storytelling
Create compelling visualizations and dashboards with Tableau, Power BI, and D3.js
Curriculum
- 1Tableau: LOD Expressions, Dashboard Actions & Performance
- 2Power BI: DAX, Data Modeling & Row-Level Security
- 3D3.js: Custom Interactive Visualizations
- 4Visualization Design: Tufte Principles & Chart Selection
- 5
Step 8advanced5-7 weeks
Big Data Analytics
Scale analytics with Spark SQL, Databricks, and data lake patterns for large datasets
Curriculum
- 1Spark SQL: Distributed Analytical Queries
- 2Databricks: Collaborative Notebooks & Workflows
- 3Data Lake Analytics: Partitioning & Predicate Pushdown
- 4Delta Lake: ACID Transactions & Time Travel
- 5
Step 9advanced5-6 weeks
ML Engineering for Data Scientists
Deploy and monitor ML models with APIs, Docker, and experiment tracking
Curriculum
- 1Model Serialization: Pickle, Joblib & ONNX
- 2REST API Deployment: FastAPI & Flask
- 3Docker: Containerizing ML Models
- 4Experiment Tracking: MLflow & Reproducibility
- 5
Step 10advanced5-7 weeks
Domain Expertise & Business Impact
Apply data science to product analytics, marketing, finance, and recommendation systems
Curriculum
- 1Product Analytics: Funnels, Retention & LTV Prediction
- 2Marketing Analytics: Attribution & Customer Segmentation
- 3Financial Modeling: Risk Scoring & Fraud Detection
- 4Recommendation Systems: Collaborative & Content-Based
- 5
Ready to start this journey?
Browse our courses and books to begin your learning path.
Tools & Platforms
PythonSciPystatsmodelsRJupyter NotebookWolfram Alpha
Jupyter Notebooks: Best Practices & Extensions
Tools & Platforms
PandasNumPyMatplotlibSeabornJupyter LabPlotly
Step 2beginner5-6 weeks
Python for Data Science
Master Pandas, NumPy, Matplotlib, and Seaborn for data manipulation and visualization
Curriculum
- 1Pandas: DataFrames, GroupBy, Merge & Pivot Tables
- 2NumPy: Array Operations, Broadcasting & Linear Algebra
- 3Matplotlib: Publication-Quality Plots & Customization
- 4Seaborn: Statistical Graphics & Distribution Plots
- 5Jupyter Notebooks: Best Practices & Extensions
Tools & Platforms
PandasNumPyMatplotlibSeabornJupyter LabPlotly
Correlation Analysis: Pearson, Spearman & VIF
Tools & Platforms
Pandas ProfilingSweetvizMissingnoscikit-learnFeature-engineJupyter Lab
Step 3intermediate4-6 weeks
Exploratory Data Analysis
Perform systematic EDA with data profiling, missing data handling, and feature engineering
Curriculum
- 1Data Profiling: Shape, Types & Distributions
- 2Missing Data: MCAR/MAR/MNAR & Imputation Strategies
- 3Outlier Detection: IQR, Z-Score & Isolation Forest
- 4Feature Engineering: Encoding, Binning & Transformations
- 5Correlation Analysis: Pearson, Spearman & VIF
Tools & Platforms
Pandas ProfilingSweetvizMissingnoscikit-learnFeature-engineJupyter Lab
Common Pitfalls: Data Leakage & Class Imbalance
Tools & Platforms
scikit-learnXGBoostLightGBMimbalanced-learnOptunaSHAP
Step 4intermediate6-8 weeks
Machine Learning for Data Science
Apply regression, classification, and clustering with proper model evaluation and validation
Curriculum
- 1Regression: Linear, Ridge, Lasso & Polynomial
- 2Classification: Logistic Regression, Random Forest & XGBoost
- 3Clustering: K-Means, DBSCAN & Hierarchical
- 4Model Evaluation: ROC-AUC, F1 & Cross-Validation
- 5Common Pitfalls: Data Leakage & Class Imbalance
Tools & Platforms
scikit-learnXGBoostLightGBMimbalanced-learnOptunaSHAP
Experimental Design: Factorial, Randomization & Blocking
Tools & Platforms
PyMCProphetstatsmodelsCausalImpactscipy.statsDoWhy
Step 5intermediate6-8 weeks
Advanced Statistical Methods
Apply Bayesian inference, time series analysis, A/B testing, and causal inference methods
Curriculum
- 1Bayesian Inference: Priors, MCMC & PyMC
- 2Time Series: ARIMA, Seasonal Decomposition & Prophet
- 3A/B Testing: Sample Size, Sequential Testing & MAB
- 4Causal Inference: DiD, RDD & Instrumental Variables
- 5Experimental Design: Factorial, Randomization & Blocking
Tools & Platforms
PyMCProphetstatsmodelsCausalImpactscipy.statsDoWhy
Hugging Face Transformers for Applied NLP
Tools & Platforms
PyTorchTensorFlowHugging Face TransformersFastAIKerasscikit-learn
Step 6intermediate5-7 weeks
Deep Learning Applications
Apply deep learning to tabular data, text analytics, and image tasks with transfer learning
Curriculum
- 1Neural Networks for Tabular Data: Embeddings & TabNet
- 2NLP: Sentiment Analysis & Text Classification
- 3Image Classification & Transfer Learning
- 4When to Use Deep Learning vs Classical ML
- 5Hugging Face Transformers for Applied NLP
Tools & Platforms
PyTorchTensorFlowHugging Face TransformersFastAIKerasscikit-learn
Data Storytelling: Narrative Structure & Presentation
Tools & Platforms
TableauPower BID3.jsObservableGoogle Data StudioStreamlit
Step 7advanced5-7 weeks
Data Visualization & Storytelling
Create compelling visualizations and dashboards with Tableau, Power BI, and D3.js
Curriculum
- 1Tableau: LOD Expressions, Dashboard Actions & Performance
- 2Power BI: DAX, Data Modeling & Row-Level Security
- 3D3.js: Custom Interactive Visualizations
- 4Visualization Design: Tufte Principles & Chart Selection
- 5Data Storytelling: Narrative Structure & Presentation
Tools & Platforms
TableauPower BID3.jsObservableGoogle Data StudioStreamlit
When to Scale: Local vs Distributed Computing Decisions
Tools & Platforms
Apache SparkDatabricksDelta LakePySparkGoogle BigQuerydbt
Step 8advanced5-7 weeks
Big Data Analytics
Scale analytics with Spark SQL, Databricks, and data lake patterns for large datasets
Curriculum
- 1Spark SQL: Distributed Analytical Queries
- 2Databricks: Collaborative Notebooks & Workflows
- 3Data Lake Analytics: Partitioning & Predicate Pushdown
- 4Delta Lake: ACID Transactions & Time Travel
- 5When to Scale: Local vs Distributed Computing Decisions
Tools & Platforms
Apache SparkDatabricksDelta LakePySparkGoogle BigQuerydbt
Model Monitoring: Drift Detection & Alerting
Tools & Platforms
FastAPIDockerMLflowStreamlitEvidentlyGitHub Actions
Step 9advanced5-6 weeks
ML Engineering for Data Scientists
Deploy and monitor ML models with APIs, Docker, and experiment tracking
Curriculum
- 1Model Serialization: Pickle, Joblib & ONNX
- 2REST API Deployment: FastAPI & Flask
- 3Docker: Containerizing ML Models
- 4Experiment Tracking: MLflow & Reproducibility
- 5Model Monitoring: Drift Detection & Alerting
Tools & Platforms
FastAPIDockerMLflowStreamlitEvidentlyGitHub Actions
Experimentation Platforms & ROI Communication
Tools & Platforms
AmplitudeMixpanelLookerdbtMetabaseJupyter Lab
Step 10advanced5-7 weeks
Domain Expertise & Business Impact
Apply data science to product analytics, marketing, finance, and recommendation systems
Curriculum
- 1Product Analytics: Funnels, Retention & LTV Prediction
- 2Marketing Analytics: Attribution & Customer Segmentation
- 3Financial Modeling: Risk Scoring & Fraud Detection
- 4Recommendation Systems: Collaborative & Content-Based
- 5Experimentation Platforms & ROI Communication
Tools & Platforms
AmplitudeMixpanelLookerdbtMetabaseJupyter Lab