Week 1: Linear Algebra (9:00 am - 12:00 pm)
- Day 1: Review basic concepts, such as vectors, matrices, and matrix operations.
- Day 2: Dive deeper into matrix operations, including matrix multiplication, inverse, and transpose.
- Day 3: Focus on solving systems of linear equations and understanding the concepts of rank and determinant.
- Day 4: Learn about vector spaces, basis, and dimension.
- Day 5: Review eigenvalues, eigenvectors, and diagonalization.
Python (2:00 pm - 5:00 pm)
- Day 1: Refresh your knowledge of Python fundamentals, including variables, data types, and control flow statements.
- Day 2: Dive into functions and modules in Python.
- Day 3: Explore object-oriented programming (OOP) concepts in Python.
- Day 4: Learn about file input/output and handling exceptions in Python.
- Day 5: Practice with libraries commonly used in data science, such as NumPy and Pandas.
Week 2: Statistics (9:00 am - 12:00 pm)
- Day 1: Review probability concepts, including basic probability rules and probability distributions.
- Day 2: Study descriptive statistics, including measures of central tendency and variability.
- Day 3: Understand hypothesis testing and statistical significance.
- Day 4: Learn about correlation and regression analysis.
- Day 5: Focus on probability distributions relevant to statistical inference, such as the normal distribution and t-distribution.
Python (2:00 pm - 5:00 pm)
- Day 1: Practice data manipulation and analysis using NumPy and Pandas.
- Day 2: Explore data visualization with libraries like Matplotlib and Seaborn.
- Day 3: Dive into statistical analysis using libraries like SciPy and StatsModels.
- Day 4: Work on implementing statistical models, such as linear regression, logistic regression, and decision trees.
- Day 5: Combine Python and statistics to analyze real-world datasets, focusing on hypothesis testing and interpreting results.
Week 3: Linear Algebra (9:00 am - 12:00 pm)
- Day 1: Review linear transformations and their properties.
- Day 2: Study orthogonality and orthogonal projections.
- Day 3: Understand eigendecomposition, singular value decomposition (SVD), and applications.
- Day 4: Explore additional topics like matrix factorizations and determinants.
- Day 5: Practice solving advanced problems that combine multiple linear algebra concepts.
Python (2:00 pm - 5:00 pm)
- Day 1: Deepen your knowledge of data visualization using advanced libraries like Plotly and Bokeh.
- Day 2: Explore machine learning algorithms and libraries like scikit-learn.
- Day 3: Dive into deep learning concepts using libraries like TensorFlow or PyTorch.
- Day 4: Practice implementing machine learning models and evaluating their performance.
- Day 5: Combine Python, statistics, and machine learning to solve real-world data science problems.
Week 4: Statistics (9:00 am - 12:00 pm)
- Day 1: Review experimental design and sampling methods.
- Day 2: Study inference techniques, such as confidence intervals and hypothesis testing.
- Day 3: Deepen your understanding of analysis of variance (ANOVA) and experimental design.
- Day 4: Learn about non-parametric tests and their applications.
- Day 5: Review advanced topics like time