High school mathematics, basic programming concepts, analytical thinking
Python/R programming
Statistical analysis
Machine learning
Data visualization
Big data tools
Follow this carefully crafted path to master your skills step by step. Each milestone builds upon the previous one to ensure comprehensive learning.
Master Python fundamentals and data science libraries: NumPy, Pandas, and Matplotlib.
Learn descriptive and inferential statistics, probability distributions, and hypothesis testing.
Master data preprocessing, cleaning techniques, and exploratory data analysis (EDA).
Create compelling visualizations using matplotlib, seaborn, and interactive tools like Plotly.
Learn supervised and unsupervised learning algorithms using scikit-learn and TensorFlow.
Query databases efficiently, understand database design, and work with big data tools.
Deploy machine learning models to production and understand MLOps best practices.