1 week ago
Career path
Data Scientist
A complete, in-depth path from CS fundamentals to a data scientist at top companies.
19 learning stages · project ideas
Computer Science Fundamentals
The foundation every engineering discipline is built on.
- Computer Fundamentals
- Operating Systems
- Networking Basics
- Data Structures
- Algorithms
- Object-Oriented Programming
- Problem Solving
- Time Complexity
- Space Complexity
- Git Basics
- Linux Basics
- Command Line
- Software Engineering
- SDLC
- Debugging
- Clean Code
- Version Control
- Agile Basics
- Programming Logic
- Best Practices
Python Programming
The working language behind almost every data science workflow.
- Variables
- Data Types
- Loops
- Functions
- OOP
- Modules
- File Handling
- Exception Handling
- NumPy
- Pandas
- Virtual Environment
- Pip
- Logging
- Lambda Functions
- Decorators
- List Comprehension
- Testing
- APIs
- Python Projects
- Best Practices
Mathematics & Statistics
The analytical toolkit behind every finding you'll report.
- Linear Algebra
- Probability
- Statistics
- Mean
- Median
- Mode
- Variance
- Standard Deviation
- Correlation
- Covariance
- Hypothesis Testing
- Bayes Theorem
- Normal Distribution
- Sampling
- Confidence Interval
- Regression
- Optimization
- Gradient Descent
- Loss Functions
- Best Practices
SQL & Databases
Where the data you'll analyze actually lives.
- SQL Basics
- MySQL
- PostgreSQL
- SQLite
- MongoDB
- CRUD
- Joins
- Aggregation
- Window Functions
- Views
- Indexing
- Stored Procedures
- Transactions
- Database Design
- Optimization
- Data Warehousing
- ETL Basics
- Backup
- Security
- Best Practices
Data Analysis
Cleaning messy, real-world data into something you can actually analyze.
- NumPy
- Pandas
- Data Cleaning
- Missing Values
- Data Transformation
- Feature Engineering
- Data Wrangling
- Exploratory Data Analysis
- Outlier Detection
- Duplicate Handling
- Time Series Basics
- Data Validation
- Data Pipelines
- Data Profiling
- Business Metrics
- Reporting
- Data Storytelling
- Automation
- Case Studies
- Best Practices
Data Visualization
Making findings clear to non-technical stakeholders.
- Matplotlib
- Seaborn
- Plotly
- Power BI
- Tableau
- Dashboards
- Charts
- KPI Reporting
- Storytelling
- Interactive Reports
- Business Reports
- Heatmaps
- Scatter Plots
- Histograms
- Pie Charts
- Time Series Charts
- Geo Maps
- Visualization Best Practices
- Executive Reports
- Case Studies
Machine Learning Fundamentals
Building predictive models on top of your analysis.
- Supervised Learning
- Unsupervised Learning
- Regression
- Classification
- Clustering
- Decision Trees
- Random Forest
- SVM
- KNN
- Naive Bayes
- Ensemble Learning
- Model Evaluation
- Cross Validation
- Hyperparameter Tuning
- Scikit-learn
- Feature Selection
- Model Pipeline
- ML Workflow
- Model Comparison
- Best Practices
Feature Engineering
Turning raw data into what a model can actually learn from.
- Missing Data
- Encoding
- Scaling
- Normalization
- Standardization
- Feature Selection
- Feature Extraction
- PCA
- Dimensionality Reduction
- Polynomial Features
- SMOTE
- Data Balancing
- Time Features
- Date Features
- Text Features
- Image Features
- Feature Importance
- Pipelines
- Automation
- Best Practices
Deep Learning Basics
Neural networks for the problems classic ML can't solve well.
- Neural Networks
- CNN
- RNN
- LSTM
- Transformers
- TensorFlow
- PyTorch
- Keras
- Transfer Learning
- Fine-Tuning
- GPU Training
- Optimizers
- Loss Functions
- Batch Normalization
- Dropout
- Image Models
- NLP Models
- Model Evaluation
- Projects
- Best Practices
Business Intelligence (BI)
Turning data into decisions the business actually acts on.
- KPI Design
- Business Metrics
- Data Warehousing
- ETL
- Dashboard Design
- Reporting
- Power BI
- Tableau
- Excel Advanced
- SQL Analytics
- Decision Making
- Forecasting
- Trend Analysis
- Customer Analytics
- Sales Analytics
- Marketing Analytics
- Financial Analytics
- HR Analytics
- Executive Reporting
- Best Practices
Big Data
Working with data that no longer fits on one machine.
- Hadoop
- Spark
- PySpark
- Hive
- Kafka
- Data Lakes
- Data Warehouse
- Distributed Computing
- Batch Processing
- Stream Processing
- HDFS
- Data Pipelines
- Airflow
- Data Engineering Basics
- ETL
- Scalability
- Monitoring
- Performance
- Cloud Storage
- Best Practices
Cloud for Data Science
Running analytics and models on real infrastructure.
- AWS
- Azure
- Google Cloud
- BigQuery
- Redshift
- Snowflake
- S3
- Cloud Storage
- SageMaker
- Vertex AI
- Databricks
- Docker
- APIs
- Monitoring
- Security
- IAM
- Serverless
- Networking
- Billing
- Best Practices
Model Deployment
Getting your model into a real, usable service.
- Flask
- FastAPI
- Streamlit
- Gradio
- REST APIs
- Docker
- Cloud Deployment
- Authentication
- Monitoring
- Logging
- Versioning
- CI/CD
- Scaling
- Performance
- Security
- Rollback
- Model Serving
- Automation
- Production ML
- Best Practices
MLOps & Data Pipelines
Keeping models and pipelines running reliably over time.
- MLflow
- DVC
- Airflow
- Kubeflow
- Docker
- Kubernetes
- CI/CD
- Model Monitoring
- Data Pipelines
- Drift Detection
- Retraining
- Experiment Tracking
- Feature Store
- Logging
- Automation
- Scaling
- Security
- Deployment
- Optimization
- Best Practices
Interview Preparation
Everything between applying and getting the offer.
- Python Questions
- SQL Questions
- Statistics Questions
- Probability Questions
- Machine Learning
- Data Analysis
- Tableau Questions
- Power BI Questions
- Business Case Studies
- Coding Questions
- HR Questions
- Behavioral Questions
- Resume Review
- Live Coding
- Analytics Questions
- ML Questions
- Salary Negotiation
- Mock Interviews
- Final Checklist
- FAANG Preparation
Career Growth
Building a career, not just landing one job.
- ATS Resume
- LinkedIn Profile
- GitHub Portfolio
- Kaggle Profile
- Tableau Public
- Power BI Portfolio
- Open Source
- Certifications
- Networking
- Conferences
- Personal Branding
- Freelancing
- Remote Jobs
- Consulting
- Mentorship
- Productivity
- Time Management
- Career Planning
- Continuous Learning
- Best Practices
AI Tools for Data Scientists
Working faster by working alongside AI itself.
- ChatGPT
- Claude
- Gemini
- GitHub Copilot
- Cursor AI
- NotebookLM
- Perplexity
- Kaggle
- Jupyter Notebook
- Google Colab
- MLflow
- TensorBoard
- Power BI Copilot
- Tableau AI
- Prompt Engineering
- AI Data Analysis
- AI Visualization
- AI Report Generation
- AI Automation
- Best Practices
Data Ethics & Governance
Handling data and models responsibly.
- Data Privacy
- GDPR Basics
- Data Governance
- Data Quality
- Bias Detection
- Fairness
- Responsible AI
- Explainable AI
- Security
- Compliance
- Copyright
- Data Ownership
- Encryption
- Risk Assessment
- Ethical Analytics
- AI Governance
- Data Auditing
- Secure Pipelines
- Case Studies
- Best Practices
Becoming a Data Scientist at Top Companies
The final layer — system design, communication, and interview readiness for top companies.
- Data Science Portfolio
- Kaggle Competitions
- GitHub Projects
- Tableau Portfolio
- Power BI Portfolio
- SQL Mastery
- Business Case Studies
- AI Resume
- Technical Interviews
- Communication Skills
- Problem Solving
- Storytelling with Data
- Mock Interviews
- Salary Negotiation
- FAANG Preparation
- Startup vs MNC
- Personal Branding
- Networking
- 30-60-90 Day Learning Plan
- Final Career Checklist
Project ideas by level
Build one or two from each level and write up what you decided and why — that's what interviewers ask about.
Beginner
- Sales Forecasting
- Customer Churn Prediction
- Recommendation System
- Fraud Detection
- Credit Risk Analysis
- House Price Prediction
- Sentiment Analysis
Intermediate
- Demand Forecasting
- HR Analytics
- Marketing Analytics
- Healthcare Analytics
- Financial Dashboard
- AI Chatbot
- Resume Analyzer
Advanced
- NLP Project
- Time Series Forecasting
- Stock Prediction
- AI SaaS Dashboard
- End-to-End Analytics Platform
- Final Capstone Project
Open roles on this path
Data Scientist: common questions
Is Data Science a good career?
Yes — it's in demand across nearly every industry that collects data, and it's a good hybrid role for people who like both analysis and communicating findings to non-technical stakeholders.
How to prepare for a Data Scientist interview?
Be strong in statistics/probability and SQL, have a portfolio of real analysis projects (not just Kaggle notebooks), and expect case-study-style business questions.
What are the job titles of a Data Scientist?
Common titles include Data Scientist, Data Analyst (typically junior/entry), and Applied Scientist at larger companies.
How to become a Data Scientist?
Learn statistics and SQL first, then Python (pandas/numpy), followed by ML fundamentals — and build real analysis projects using public datasets you can talk through in interviews.
How long does it take to become a Data Scientist?
Typically 6-10 months of consistent practice, depending on your math/stats background going in.
What are Data Scientist salaries?
Salaries vary a lot by seniority, domain (finance and product companies often pay more), and location.
What skills does a Data Scientist need?
SQL, Python or R, statistics, data visualization, ML fundamentals, and the ability to explain findings clearly to people who aren't technical.