Master data analysis, machine learning, and statistical modeling
Data Science is one of the most sought-after careers in tech, combining statistics, programming, and domain expertise to extract insights from data. As a data scientist, you will collect, clean, analyze, and visualize data to help organizations make data-driven decisions. This comprehensive roadmap guides you through mastering Python programming, statistical analysis, machine learning algorithms, deep learning, and big data technologies. You will learn to work with popular libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch. Data scientists are crucial in industries like finance, healthcare, e-commerce, and technology, helping companies optimize operations, predict trends, and build intelligent systems. The role requires strong analytical thinking, programming skills, statistical knowledge, and the ability to communicate complex findings to non-technical stakeholders.
6-8 weeks
Variables, data types, control flow, functions, OOP
Arrays, mathematical operations, broadcasting
DataFrames, data manipulation, cleaning, merging
Interactive development environment
8-10 weeks
Mean, median, mode, variance, standard deviation
Probability distributions, Bayes theorem, hypothesis testing
Confidence intervals, p-values, A/B testing
Vectors, matrices, eigenvalues for ML
4-6 weeks
Basic plotting, customization, subplots
Statistical visualizations, heatmaps
Interactive visualizations, dashboards
Business intelligence tools
10-12 weeks
Linear/logistic regression, decision trees, random forests, SVM
K-means, hierarchical clustering, PCA
Cross-validation, confusion matrix, ROC curves
Feature selection, scaling, encoding
8-10 weeks
Perceptrons, backpropagation, activation functions
Deep learning frameworks
Computer vision, image classification
Text processing, sentiment analysis, transformers
6-8 weeks
Database querying, joins, aggregations
Distributed computing, PySpark
Flask/FastAPI, Docker, cloud deployment
Model versioning, monitoring, CI/CD