Stage 1: Foundations (Weeks 1–6)
Start with Python fundamentals and basic statistics (mean, median, standard deviation, probability). Get comfortable with Pandas and NumPy for data manipulation before touching any machine learning.
Stage 2: Data Handling & Visualization (Weeks 7–10)
- SQL for querying and joining datasets
- Data cleaning: handling missing values, outliers, duplicates
- Visualization with Matplotlib/Seaborn and a BI tool like Power BI
Stage 3: Machine Learning (Weeks 11–18)
- Supervised learning: regression, classification (scikit-learn)
- Unsupervised learning: clustering basics
- Model evaluation: accuracy, precision/recall, cross-validation
Stage 4: Projects & Portfolio (Weeks 19–22)
Build 3–4 end-to-end projects using real datasets (Kaggle is a good source), documenting your process from raw data to final insight or model. This portfolio matters more than any single certificate when applying for jobs.
Frequently Asked Questions
How long does it realistically take to become a Data Scientist?
With consistent, focused effort, most learners reach an entry-level job-ready standard in about 5–6 months through structured training, though deep expertise continues to develop over years.
Do I need to learn Deep Learning to get a Data Science job?
Not for most entry-level Data Analyst or Data Scientist roles — solid statistics, SQL and classical machine learning cover the majority of real-world business use cases.
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