Roadmap To Become Data Scientist

Data Science = Statistics + Programming + Business + Machine Learning + Tools
You extract insights and build predictive models from data to help businesses make better decisions.

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Data Science Learning Roadmap (2025)

Phase 1: Programming & Foundations (1–1.5 months)

Topics:

• • Variables, loops, functions

  • Lists, tuples, dictionaries, sets

  • File handling

  • Modules and packages

• Libraries:

  • NumPy (numerical computing)

  • Pandas (data manipulation)

  • Matplotlib, Seaborn (data visualization)

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Phase 2: Statistics & Mathematics (1–1.5 months)

Statistics

• Descriptive stats: Mean, Median, Mode, Variance, Std. Dev

• Probability & distributions (Normal, Binomial, Poisson)

• Hypothesis testing (Z-test, T-test, Chi-square)

• Correlation & regression basics

Mathematics

• Linear algebra (vectors, matrices)

• Calculus (derivatives for optimization)

• Probability theory basics

Tools: Excel, Python (SciPy, statsmodels)

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Phase 3: Data Handling & Preprocessing (1 month)

Data Wrangling

• Handling missing values

• Outlier detection

• Encoding categorical data

• Feature scaling (Normalization, Standardization)

• Merging, joining, and grouping datasets

Tools

• Pandas, NumPy, OpenPyXL, SQL

  


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Phase 4: SQL for Data Science (1 month)

• Basic queries: SELECT, WHERE, GROUP BY, ORDER BY

• Joins, subqueries

• Window functions, CTEs

• Writing analytical queries

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Phase 5: Exploratory Data Analysis (EDA) (1 month)

• Univariate & bivariate analysis

• Correlation heatmaps

• Outlier & pattern detection

• Data storytelling with visuals

Libraries: Matplotlib, Seaborn, Plotly




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Phase 6: Machine Learning (2–3 months)

Supervised Learning

• Linear & Logistic Regression

• Decision Trees, Random Forest, XGBoost

• Support Vector Machines (SVM)

• k-Nearest Neighbors (kNN)

  
  

  Unsupervised Learning

• Clustering (KMeans, DBSCAN)

• Dimensionality Reduction (PCA, t-SNE)

  
  

Model Evaluation

• Confusion Matrix, Precision, Recall, F1-score

• ROC-AUC curve, Cross-validation

Libraries: scikit-learn, xgboost, catboost




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Phase 7: Deep Learning (2 months)

• Basics of Neural Networks

• Gradient Descent, Backpropagation

• Activation functions, Loss functions

• CNNs (for images)

• RNNs, LSTMs (for sequences)

• Transformers (for NLP)

Libraries: TensorFlow, Keras, PyTorch




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Phase 8: Real-World Tools & Projects (2–3 months)

Tools:

• Version control: Git + GitHub

• Data visualization: Power BI / Tableau

• Big Data: Spark / PySpark

• Cloud: AWS / GCP / Azure

• MLOps: MLflow, Docker, Airflow

  
  

Projects (Portfolio builders):

• Customer Churn Prediction (ML)

• Stock Price Prediction (DL)

• Sentiment Analysis (NLP)

• Recommendation System

• Fraud Detection

• EDA on Real Datasets (Kaggle)

  
  

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Phase 9: Business & Communication Skills (Ongoing)

• Storytelling with data

• Domain understanding (finance, healthcare, marketing)

• Making dashboards & reports

• Explaining ML models to non-technical people

  
  

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Phase 10: Portfolio & Job Readiness (Final Phase)

Build a Strong Portfolio

• 3–5 solid projects on GitHub

• Write blogs explaining your insights (Medium/Kaggle)

• Create a LinkedIn data portfolio

• Mock interviews & resume prep

  
  

  Roles to Target:

• Data Analyst

• Data Scientist

• ML Engineer

• Data Engineer

• Business Intelligence Analyst

  
  

  Suggested Learning Timeline (Total ~8–10 months)

S.No Duration Focus 1 1.5 months Python & Libraries 2 1.5 months Statistics & Math 3 1 month Data Cleaning 4 1 month SQL 5 1 month EDA 6 2–3 months Machine Learning 7 2 months Deep Learning 8 2–3 months Projects & Tools

• 
  

  
  

  
  

  Data Source:

• • Kaggle