1. Data Acquisition and Preprocessing:

•    - File Handling: Reading and writing data from/to various file formats (CSV, Excel, JSON, SQL).
•    - Web Scraping: Extracting data from websites using libraries like BeautifulSoup or Scrapy.
•    - API Integration: Fetching data from APIs (e.g., RESTful APIs) using libraries like requests.
•    - Data Cleaning: Handling missing values, outliers, and inconsistencies in the data.
•    - Data Transformation: Reshaping data, encoding categorical variables, scaling numeric features.

 2. Exploratory Data Analysis (EDA):

•    - Descriptive Statistics: Calculating summary statistics (mean, median, variance, etc.).
•    - Data Visualization: Creating visualizations using libraries like Matplotlib, Seaborn, and Plotly.
•    - Correlation Analysis: Examining relationships between variables.
•    - Distribution Analysis: Understanding the distribution of data features.
•    - Dimensionality Reduction: Applying techniques like PCA (Principal Component Analysis) or t-SNE for visualization and feature selection.

 3. Data Manipulation and Analysis:

•    - Pandas Fundamentals: Working with Series and DataFrame objects, indexing, slicing, and filtering data.
•    - Grouping and Aggregation: Performing group-wise operations on data.
•    - Merging and Joining: Combining multiple datasets based on common keys.
•    - Time Series Analysis: Handling time-stamped data, resampling, and time series decomposition.

 4. Statistical Analysis:

•    - Probability Distributions: Understanding common probability distributions (normal, binomial, Poisson, etc.).
•    - Hypothesis Testing: Conducting hypothesis tests (t-tests, chi-square tests, etc.) for statistical inference.
•    - ANOVA and Regression Analysis: Performing analysis of variance (ANOVA) and regression analysis to model relationships between variables.
•    - Non-parametric Tests: Utilizing non-parametric tests for analyzing data that do not meet parametric assumptions.

 5. Machine Learning Basics:

•    - Introduction to Scikit-Learn: Understanding the Scikit-Learn library for machine learning in Python.
•    - Supervised Learning: Building and evaluating models for classification and regression tasks.
•    - Unsupervised Learning: Exploring clustering algorithms like K-means and hierarchical clustering.
•    - Model Evaluation: Assessing model performance using cross-validation, metrics like accuracy, precision, recall, F1-score, and ROC curves.

 6. Advanced Topics:

•    - Feature Engineering: Creating new features from existing data to improve model performance.
•    - Model Selection and Tuning: Selecting the appropriate model and optimizing hyperparameters using techniques like grid search or randomized search.
•    - Ensemble Methods: Understanding ensemble techniques such as random forests, gradient boosting, and stacking.
•    - Pipeline Construction: Building end-to-end machine learning pipelines for data preprocessing, feature engineering, and model training.