90-DAY TRADITIONAL DATA SCIENTIST ROADMAP
Your Complete Guide to Mastering Classical Data Science & Machine Learning
Breaking into traditional data science requires a systematic approach focused on statistical foundations, classical machine learning algorithms, and real-world business analytics. This roadmap gives you a structured, day-by-day plan to transform into a job-ready data scientist specializing in traditional ML techniques within 90 days.
WHY THIS 90-DAY ROADMAP WORKS
Traditional data science roles emphasize statistical rigor, business analytics, predictive modeling, and classical ML algorithms that power most production systems today. While GenAI gets media attention, 80% of industry ML applications still use traditional techniques—regression, classification, clustering, time series, and ensemble methods.
This roadmap prepares you for roles at companies like Infosys, TCS, Accenture, Wipro, Cognizant, Deloitte, and analytics firms in Bangalore, Hyderabad, Pune, Mumbai, Chennai, Gurgaon, and Noida who need data scientists for business intelligence, predictive analytics, customer analytics, risk modeling, and operations optimization.
🔍 Explore structured learning paths designed to build strong data science foundations →
MONTH 1: BUILDING YOUR FOUNDATION (Days 1-30)
WEEK 1: Python Programming Fundamentals (Days 1-7)
Day 1: Understanding the Data Science Ecosystem
- Why Python dominates data science and analytics
- Install Python, PyCharm, Anaconda, Jupyter Notebook
- Understand Python interpreter and IDLE interface
- Write your first programs—calculator, unit converter
Day 2: Numeric Data Types and Variables
- Master integers, floats, complex numbers
- Learn variables, objects, references, shared references
- Understand garbage collection and memory management
- Practice: Build a compound interest calculator
Day 3: Strings, Lists, and Data Structures
- Master strings, lists, dictionaries, tuples, sets
- File handling for reading and writing data
- Practice creating, accessing, modifying collections
- Project: Contact management system
Day 4: Control Flow and Conditional Logic
- if-else, if-elif-else, ternary expressions
- Write programs with decision-making logic
- Practice: Grade calculator, eligibility checker
- Understand logical operators and boolean logic
Day 5: Loops and Iterations
- while and for loops for data processing
- List/dict/set comprehensions for clean code
- map, zip, range, len, enumerate functions
- Project: Text analyzer (word count, frequency)
Day 6: Functions and Code Organization
- Define reusable functions with def
- Variable scoping (LEGB rules), global, nonlocal
- Function arguments, *args, **kwargs
- Practice: Build utility library with 10+ functions
Day 7: Advanced Functions and Week Review
- Recursive functions, lambda expressions
- Generators with yield for memory efficiency
- Week 1 mini-project: Data validation and cleaning tool
- Review: Solve 20 Python problems
💼 Career Tip: Draft LinkedIn headline—”Aspiring Data Scientist | Python | Statistics | Machine Learning | Business Analytics”
📘 Discover more preparation-focused insights to strengthen analytics and problem-solving depth →
WEEK 2: Modules, Packages & Object-Oriented Programming (Days 8-14)
Day 8: Python Modules and Import Systems
- Module definitions and program architecture
- Import, from, from * statements
- How imports work: find, compile, run
- Standard library exploration
Day 9: Standard Library and Module Management
- datetime, os, sys, collections, itertools modules
- pycache folder and byte code
- Module search paths and PYTHONPATH
- Practice: Build reusable analytics modules
Day 10: Module Namespaces and Packages
- Namespace dictionaries
- Package import basics, relative imports
- Creating professional package structures
- Practice: Organize code into analytics package
Day 11: Advanced Module Techniques
- Data hiding, name and main
- as extension for imports
- Creating distributable packages
- Project: Build installable Python library
Day 12: Introduction to Classes and OOP
- Why classes for organizing complex code
- Constructors (init), method calls
- Attribute inheritance, code reuse
- Practice: Bank account management system
Day 13: Class Attributes and Object Persistence
- Class vs instance attributes
- Pickles and shelves for data persistence
- Object serialization and deserialization
- Project: Customer database with persistence
Day 14: Advanced Classes and Week Review
- Abstract superclasses, nested classes
- Namespace dictionaries, LEGB in classes
- Week 2 project: Library management system
- Review: OOP design patterns
WEEK 3: Operator Overloading & Exception Handling (Days 15-21)
Day 15-17: Operator Overloading
- init, getitem, setitem
- getattr, setattr
- repr, str for string representation
- radd, iadd for arithmetic
- lt, gt for comparisons
Day 18-19: Special Class Features
- Inheritance (IS-A) vs Composition (HAS-A)
- Method Resolution Order (MRO)
- Static methods, class methods, super()
- Mix-in classes for flexible design
Day 20: Exception Handling Fundamentals
- try/except/else/finally statements
- Raising exceptions, custom exceptions
- with/as context managers
- Best practices for error handling
Day 21: Advanced Exception Handling & Week Review
- Exception hierarchies
- Logging and debugging strategies
- Week 3 project: Robust data processing pipeline
- Review: Build error-resistant applications
💼 Career Tip: Document learning on LinkedIn with #DataScience #Python #MachineLearning #Analytics
WEEK 4: Data Manipulation with NumPy & Pandas (Days 22-30)
Day 22-23: NumPy for Numerical Computing
- Array creation, indexing, slicing, transposition
- Universal functions (ufuncs)
- Broadcasting and vectorization
- Linear algebra operations
- Practice: Statistical computations, matrix operations
Day 24: Introduction to Pandas
- Series and DataFrames fundamentals
- Reading CSV, Excel, JSON, SQL data
- DataFrame creation from various sources
- Practice: Load and explore 5 real datasets
Day 25: Data Cleaning
- Handling missing values (dropna, fillna, interpolate)
- Removing duplicates
- Data type conversions
- Outlier detection and treatment
- Project: Clean messy real-world dataset
Day 26: Data Wrangling and Transformation
- Merge, join, concatenate operations
- Reshaping (pivot, melt, stack, unstack)
- GroupBy operations and aggregations
- Apply custom functions
- Practice: Combine 3 datasets into analysis-ready form
Day 27: Data Selection and Extraction
- loc, iloc, boolean indexing
- Filtering rows, selecting columns
- Sorting, ranking, sampling
- Query method for SQL-like filtering
- Practice: Extract insights from sales data
Day 28: Regular Expressions for Pattern Matching
- regex module fundamentals
- match, search, findall, sub functions
- Meta characters and advanced patterns
- Practice: Extract emails, phone numbers, validate inputs
Day 29: Month 1 Capstone Project – Data Analysis Pipeline
- Load data from multiple sources
- Clean and transform data
- Perform exploratory analysis
- Generate insights and visualizations
- Document in Jupyter notebook
Day 30: Month 1 Review & LinkedIn Setup
- Review all Python, NumPy, Pandas concepts
- Complete LinkedIn profile optimization
- Upload Month 1 project to GitHub
- Connect with 50+ data professionals
💼 Career Action: Optimize LinkedIn—professional photo, compelling summary, skills section, featured projects
MONTH 2: MATHEMATICS, STATISTICS & MACHINE LEARNING (Days 31-60)
📂 Access complete learning resources to advance model building, evaluation, and deployment readiness
WEEK 5: Mathematics & Statistics Foundations (Days 31-37)
Day 31: Linear Algebra Fundamentals
- Vectors, matrices, matrix operations
- Transpose, inverse, determinant
- Eigenvalues and eigenvectors
- Applications in PCA and dimensionality reduction
Day 32: Calculus for Machine Learning
- Derivatives and gradients
- Chain rule for backpropagation
- Optimization concepts
- Gradient descent intuition
Day 33: Probability Theory
- Probability distributions (uniform, binomial, Poisson, normal)
- Conditional probability
- Bayes’ theorem applications
- Practice: Calculate probabilities for business scenarios
Day 34: Descriptive Statistics
- Mean, median, mode, variance, standard deviation
- Quartiles, percentiles, IQR
- Skewness and kurtosis
- Practice: Summarize 5 real datasets
Day 35: Inferential Statistics
- Sampling methods and central limit theorem
- Confidence intervals
- Hypothesis testing (t-tests, chi-square)
- p-values and statistical significance
Day 36: Regression Analysis Fundamentals
- Correlation vs causation
- Simple linear regression
- Multiple linear regression
- Interpreting coefficients and R-squared
Day 37: A/B Testing and Experimental Design
- Hypothesis formulation
- Power analysis and sample size
- A/B testing methodology
- Practice: Design and analyze experiments
WEEK 6: Machine Learning Foundations (Days 38-44)
Day 38: Introduction to Machine Learning
- What is ML, why it matters, real-world applications
- Supervised vs unsupervised learning
- Batch vs online learning
- Instance-based vs model-based learning
Day 39: ML Challenges and Best Practices
- Overfitting vs underfitting
- Train-validation-test split
- Cross-validation techniques
- End-to-end ML project phases
Day 40: Binary Classification
- Classification use cases (spam, fraud, churn)
- Logistic regression fundamentals
- Decision boundaries
- Practice: Build spam classifier
Day 41: Performance Measures for Classification
- Accuracy, precision, recall, F1-score
- Confusion matrix interpretation
- ROC curves and AUC
- When to use which metric
Day 42: Multi-Class and Multi-Label Classification
- One-vs-rest (OvR) strategies
- One-vs-one (OvO) approaches
- Softmax regression
- Practice: Multi-class image/text classification
Day 43: Linear Regression Deep Dive
- Ordinary least squares (OLS)
- Gradient descent algorithms
- Batch, stochastic, mini-batch GD
- Practice: Implement from scratch
Day 44: Polynomial and Regularized Regression
- Polynomial features for non-linearity
- Ridge regression (L2 regularization)
- Lasso regression (L1 regularization)
- Elastic Net, early stopping
💼 Career Tip: Create GitHub portfolio with clear README files for each project
WEEK 7: Advanced ML Algorithms (Days 45-51)
Day 45: Logistic Regression for Classification
- Sigmoid function and log-loss
- Multi-class logistic regression
- Regularization in classification
- Practice: Customer churn prediction
Day 46: Support Vector Machines (SVM)
- Linear SVM classification
- Soft margin vs hard margin
- Kernel trick for non-linear data
- SVM for regression (SVR)
Day 47: Decision Trees
- Recursive splitting algorithm (CART)
- Gini impurity vs entropy
- Tree visualization
- Regularization via hyperparameters
- Feature importance
Day 48: Ensemble Learning – Voting Classifiers
- Why ensemble methods work
- Hard voting vs soft voting
- Combining diverse models
- Practice: Ensemble for loan approval
Day 49: Bagging, Pasting & Random Forests
- Bootstrap aggregating (bagging)
- Out-of-bag (OOB) evaluation
- Random forests algorithm
- Feature importance and interpretation
Day 50: Boosting Algorithms
- AdaBoost (Adaptive Boosting)
- Gradient Boosting Machines (GBM)
- XGBoost, LightGBM, CatBoost
- Practice: Kaggle competition with boosting
Day 51: Stacking and Week Review
- Stacking meta-learners
- Comparing all algorithms
- Week 7 project: Multi-algorithm comparison
- Model selection strategies
WEEK 8: Unsupervised Learning & Advanced Topics (Days 52-60)
Day 52: K-Means Clustering
- K-Means algorithm steps
- Choosing K (elbow method, silhouette)
- Applications: customer segmentation
- Practice: Cluster analysis on real data
Day 53: Hierarchical & Density-Based Clustering
- Agglomerative and divisive clustering
- Dendrograms
- DBSCAN for arbitrary shapes
- Outlier detection
Day 54: Dimensionality Reduction
- Principal Component Analysis (PCA)
- Feature selection techniques
- t-SNE for visualization
- Applications in preprocessing
Day 55: Time Series Analysis Fundamentals
- Time series components (trend, seasonality, residual)
- Moving averages and smoothing
- Autocorrelation and partial autocorrelation
- Stationarity and differencing
Day 56: Time Series Forecasting
- ARIMA models
- Exponential smoothing (Holt-Winters)
- Prophet for business forecasting
- Practice: Sales forecasting project
Day 57: Recommender Systems
- Collaborative filtering (user-based, item-based)
- Content-based filtering
- Matrix factorization (SVD)
- Hybrid approaches
Day 58: Month 2 Capstone – Predictive Model
- End-to-end ML project on real business problem
- Feature engineering, model selection, tuning
- Performance evaluation, interpretation
- Deploy as API or web app
Day 59: Resume Building Workshop
- Create data science resume
- Quantify achievements with metrics
- Action verbs and impact statements
- Get peer/mentor feedback
Day 60: Month 2 Review & Portfolio Website
- Review all ML concepts and implementations
- Create portfolio website or GitHub Pages
- Write project case studies
- Prepare for interviews
💼 Career Action: Update LinkedIn with Month 2 capstone, write detailed project post
MONTH 3: ADVANCED ANALYTICS & CAREER PREPARATION (Days 61-90)
WEEK 9: Advanced Machine Learning (Days 61-67)
Day 61: Feature Engineering Mastery
- Creating polynomial features
- Binning and discretization
- Encoding categorical variables (one-hot, target, frequency)
- Feature scaling (standardization, normalization)
- Feature selection (filter, wrapper, embedded methods)
Day 62: Hyperparameter Tuning
- Grid search vs random search
- Bayesian optimization
- Cross-validation strategies
- Practice: Optimize XGBoost model
Day 63: Model Interpretability
- Feature importance techniques
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-agnostic Explanations)
- Partial dependence plots
- Business stakeholder communication
Day 64: Imbalanced Data Handling
- Resampling techniques (oversampling, undersampling)
- SMOTE and variants
- Class weighting
- Evaluation metrics for imbalanced data
Day 65: Advanced Cross-Validation
- Stratified K-fold
- Time series split
- Group K-fold
- Nested cross-validation
Day 66: Model Deployment Basics
- Pickle for model serialization
- Flask API creation
- Streamlit dashboards
- Practice: Deploy model as web service
Day 67: MLOps Introduction
- Model versioning with MLflow
- Experiment tracking
- Model monitoring in production
- Data drift detection
WEEK 10: Business Analytics & Visualization (Days 68-74)
Day 68: Data Visualization with Matplotlib
- Line plots, scatter plots, bar charts
- Histograms, box plots, violin plots
- Subplots and figure customization
- Annotations and styling
Day 69: Advanced Visualization with Seaborn
- Distribution plots (distplot, kde, rug)
- Categorical plots (bar, box, violin, swarm)
- Heatmaps and correlation matrices
- Pair plots for quick EDA
Day 70: Interactive Dashboards with Plotly
- Plotly Express for quick visualizations
- Interactive plots (zoom, hover, filter)
- Dash for web dashboards
- Practice: Build sales analytics dashboard
Day 71: Business Intelligence & Reporting
- KPI definition and tracking
- Executive dashboards
- Storytelling with data
- Practice: Create monthly business report
Day 72: SQL for Data Scientists
- SELECT, WHERE, JOIN operations
- Aggregations (GROUP BY, HAVING)
- Window functions
- Subqueries and CTEs
- Practice: Extract insights from database
Day 73: Advanced SQL & Database Design
- Database normalization
- Indexes and query optimization
- Working with dates and strings
- Practice: Complex multi-table queries
Day 74: Excel for Data Analysis
- Pivot tables and charts
- VLOOKUP, INDEX-MATCH
- Power Query for ETL
- Practice: Financial analysis in Excel
WEEK 11: Domain Applications & Case Studies (Days 75-81)
Day 75: Customer Analytics
- Customer segmentation (RFM analysis)
- Churn prediction
- Customer lifetime value (CLV)
- Next-best-action modeling
Day 76: Marketing Analytics
- Campaign effectiveness measurement
- Attribution modeling
- Market basket analysis
- Pricing optimization
Day 77: Financial Analytics
- Credit risk modeling
- Fraud detection
- Algorithmic trading basics
- Portfolio optimization
Day 78: Healthcare Analytics
- Patient readmission prediction
- Disease diagnosis support
- Resource optimization
- Survival analysis
Day 79: Supply Chain & Operations Analytics
- Demand forecasting
- Inventory optimization
- Route optimization
- Predictive maintenance
Day 80: HR Analytics
- Employee attrition prediction
- Recruitment optimization
- Performance prediction
- Workforce planning
Day 81: Week Review & Capstone Project Refinement
- Choose domain for capstone
- Define business problem
- Plan data collection and analysis
- Start implementation
💼 Career Tip: Create demo videos of projects, post on LinkedIn for engagement
WEEK 12: Career Launch Preparation (Days 82-90)
Day 82: Portfolio Refinement Workshop
- Select top 5 projects for showcase
- Write compelling project descriptions
- Quantify business impact
- Ensure code documentation
Day 83: Resume Optimization for ATS
- Keyword optimization for data science roles
- Quantify all achievements
- Format for ATS parsing
- Create multiple versions for different roles
Day 84: LinkedIn Profile Deep Optimization
- Headline: “Data Scientist | Python | ML | Statistics | Business Analytics”
- Summary with story, skills, accomplishments
- Featured section with projects
- Skills endorsements strategy
Day 85: Job Platform Mastery
- Naukri, LinkedIn Jobs, Indeed profiles
- Job alerts for “Data Scientist,” “Data Analyst,” “ML Engineer”
- Target companies research
- Application tracking system
Day 86: Technical Interview Prep – Python & Statistics
- 50+ Python coding problems
- Statistics fundamentals Q&A
- Probability distributions, hypothesis testing
- Practice explaining solutions
Day 87: Technical Interview Prep – Machine Learning
- Supervised/unsupervised algorithms explanation
- Overfitting, bias-variance, regularization
- Model evaluation metrics
- Feature engineering techniques
Day 88: Technical Interview Prep – SQL & Business
- SQL query writing practice
- Database design questions
- Business case analysis
- Domain-specific questions
Day 89: Behavioral Interview & Communication
- STAR method for behavioral questions
- Explaining technical concepts simply
- Mock interview practice
- Salary negotiation strategies
Day 90: Mock Interviews & Final Preparation
- Full mock technical interview
- Full mock behavioral interview
- Feedback incorporation
- Job search action plan (10 applications/day target)
LINKEDIN PROFILE OPTIMIZATION FOR TRADITIONAL DATA SCIENTISTS
Headline That Converts
“Data Scientist | Python, SQL, Statistics | ML Specialist | Predictive Analytics | Business Intelligence”
Summary Template
“Passionate data scientist with expertise in statistical modeling, machine learning, and business analytics. Proven track record building predictive models that drive business decisions and improve KPIs.
Technical Skills: Python (Pandas, NumPy, Scikit-learn), SQL, Statistics, Machine Learning (Regression, Classification, Clustering, Time Series), Data Visualization (Matplotlib, Seaborn, Plotly), Excel
Notable Projects:
- Customer Churn Prediction Model (87% accuracy, reduced churn by 15%)
- Sales Forecasting System (ARIMA + XGBoost, improved forecast accuracy by 22%)
- Marketing Campaign Optimization (Uplift modeling, increased ROI by 30%)
Seeking opportunities to leverage data science for business impact in [target industries].”
Skills Section (30-50 skills)
Python, Machine Learning, Statistics, SQL, Predictive Modeling, Data Analysis, Pandas, NumPy, Scikit-learn, XGBoost, Data Visualization, Matplotlib, Seaborn, Regression Analysis, Classification, Clustering, Time Series Analysis, Feature Engineering, A/B Testing, Hypothesis Testing, Business Analytics, Excel, Tableau, Power BI, Jupyter Notebook
TOP JOB PLATFORMS FOR TRADITIONAL DATA SCIENCE ROLES
LinkedIn Jobs
- Filters: “Data Scientist,” “Data Analyst,” “ML Engineer,” “Business Analyst”
- Locations: Bangalore, Hyderabad, Pune, Mumbai, Chennai, Gurgaon, Noida
- Companies: Infosys, TCS, Accenture, Wipro, Cognizant, Deloitte, EY, KPMG
Naukri.com
- Optimize profile with keywords
- Update weekly for visibility
- Apply to 5-10 positions daily
AngelList (Wellfound)
- Target startups and mid-sized companies
- Highlight full-stack analytics capabilities
Company Career Pages
- Direct applications to target companies
- Tailor resume to job descriptions
- Follow up on LinkedIn
TRADITIONAL DATA SCIENCE INTERVIEW PREPARATION
Python Coding (30% of interview)
- Data structures, algorithms
- Pandas operations
- NumPy calculations
- Code optimization
Statistics & Probability (25% of interview)
- Distributions, hypothesis testing
- A/B testing, experimental design
- Regression analysis
- Statistical inference
Machine Learning (30% of interview)
- Algorithm selection and trade-offs
- Overfitting prevention
- Feature engineering
- Model evaluation
SQL & Databases (10% of interview)
- Complex queries with joins
- Aggregations and window functions
- Query optimization
Behavioral & Communication (5% of interview)
- STAR method responses
- Technical explanation to non-technical audience
- Project walkthrough
YOUR NEXT STEPS
Week 1 Actions:
- Set up Python environment
- Create LinkedIn and GitHub
- Start daily coding practice
Monthly Milestones:
- Month 1: Data manipulation portfolio project
- Month 2: ML predictive model
- Month 3: Domain-specific capstone
Post-90 Days:
- Apply to 10+ positions weekly
- Network on LinkedIn
- Continue building projects
- Attend data science meetups
🧭 Continue learning with expert-written guides, roadmaps, and improvement-focused frameworks →
SUCCESS METRICS
Traditional data science roles offer stable, high-paying careers solving real business problems with proven analytical techniques. Master this roadmap in 90 days and land your data science role! 🎯