Data Science Roadmap

Data Science Roadmap – Beginner to Entry-Level Job-Ready (Estimated total: 8–16 months with 15–30 hours/week consistent effort. Focus on building a strong software stack: Python-based ecosystem for core work, SQL for data querying, BI tools for visualization/reporting, and cloud platforms for scalability. Key tools include Python, Pandas, NumPy, scikit-learn, Matplotlib & Seaborn, SQL or SQLite for databases, BI tools like Power BI or Tableau Public, and cloud stacks like Google Cloud, AWS, or Azure free tiers.)

Fast-Track Certifications for Entry-Level Jobs To land entry-level roles (e.g., Data Analyst, Junior Data Scientist) quickly (1–3 months of focused study), prioritize these accessible, employer-recognized certs. They emphasize practical skills in Python, SQL, BI, ML basics, and can be completed part-time. Many are audit-free on Coursera/edX or low-cost with financial aid. Build a portfolio alongside to stand out.

• Google Data Analytics Professional Certificate: 6 courses, ~6 months (faster if accelerated), covers SQL, spreadsheets, R/Python basics, visualization. Free to audit; cert ~$49/month. Highly valued for analyst roles.
• IBM Data Science Professional Certificate: 10 courses, ~3–6 months, Python, SQL, ML, data viz. Free to audit; cert via subscription. Great for DS entry.
• IBM Data Analyst Professional Certificate: Shorter (~4 months), focuses on Excel, SQL, Python, BI (Cognos). Free audit.
• Microsoft Certified: Power BI Data Analyst Associate: Exam-based (PL-300), study 1–2 months via free Microsoft Learn videos/modules. Covers BI dashboards, data modeling. Exam fee ~$165, but free practice. Entry to BI/analyst jobs.
• Google IT Support Professional Certificate: Not DS-specific but quick (~3 months), builds tech basics (SQL intro). Leads to support roles that pivot to data. Free audit.
• SQL-Specific Certs:
  • DataCamp SQL Associate Certification: 1–2 months, interactive tracks + exam. Free basic access; cert ~$25/month.
  • HackerRank SQL Certification: Free, quick tests (hours to days) for basic/intermediate/advanced SQL badges. Recognized for interviews.
• Other Quick Ones:
  • AWS Certified Cloud Practitioner: 1 month study, free training, exam ~$100. Cloud basics for data roles.
  • Great Learning Free Certificates: SQL, Python, DS intros – complete in days/weeks with free certs. These certs help with minimal time investment; aim for 2–3 (e.g., Google + IBM + SQL) to boost resumes. Employers like Google, IBM, Microsoft value them for entry-level (salaries ~$60K–$80K US starting).

Phase 1 – Fundamentals (1–2 months) Goal: Master math/stats and Python.

1. Math & Statistics
   • Probability, descriptive/inferential stats, linear algebra, calculus. Free Video Resources:
   • Khan Academy: Statistics & Probability (videos + exercises).
   • 3Blue1Brown: Linear Algebra & Calculus (visual series).
   • StatQuest (stats/ML intuition).
   • MIT OpenCourseWare: Probability (free videos).
2. Programming – Python
   • Syntax, data structures, file I/O. Free Resources & Videos:
   • Automate the Boring Stuff (book + videos).
   • Corey Schafer Python Tutorials.
   • freeCodeCamp: Python for Data Science.
   • Krish Naik: Python for DS.
   Tools: Google Colab, Jupyter, VS Code with Python extension.

Phase 2 – Data Manipulation & Visualization (1–2 months) Goal: Clean/explore data.

1. Data Handling & VizFree Video Courses:
   • Data School Pandas.
   • Kaggle Pandas & Viz Micro-Courses.
   • freeCodeCamp Data Analysis.
   • Great Learning: Data Viz.
2. Practice: EDA on Kaggle Datasets. Tools:Plotly for interactive viz.

Phase 3 – Machine Learning Basics (2–3 months) Goal: Core ML.

1. Concepts & AlgorithmsFree Video Courses:
   • Coursera: Machine Learning by Andrew Ng (audit free).
   • Kaggle ML Micro-Courses.
   • StatQuest Algorithms.
   • Codebasics ML Playlists.
2. Practice: Titanic .

Phase 4 – Intermediate & Advanced Topics (3–6 months) Goal: Advanced skills, including data annotation (labeling data for ML models – key for entry-level roles in AI companies).

1. Advanced ML
   • Tuning, ensembles . Resources:Krish Naik Advanced ML, Fast.ai Deep Learning (free videos).
2. SQLFree Video Courses:
   • Khan Academy SQL.
   • Codecademy SQL.
   • Great Learning SQL.
   • Kaggle SQL. Tools:DB-Fiddle or SQLite Browser.
3. Big Data/Cloud & Deep Learning
   • PySpark, free tiers.
   • TensorFlow or PyTorch.
4. Data Annotation Info Annotation is labeling data (e.g., images/text for ML training) – a fast entry skill for jobs at companies like Scale AI or Appen. Learn via free tools: Label Studio (open-source, install via pip), CVAT (computer vision). Practice on public datasets ; certs aren’t common, but add projects to portfolio. Entry jobs: ~$15–$25/hour remote, pivot to DS in 6–12 months.

Phase 5 – Portfolio & Real Projects (1–3 months) Ideas: Churn models, dashboards . Host on GitHub.

Phase 6 – Job Preparation (Ongoing)

• Resume: Projects + certs.
• Practice: LeetCode (SQL/Python).
• Communities: Reddit r/datascience, LinkedIn.

Timeline Overview