| Introduction to Python | - Identify Python data types (int, float, str, bool) and use them in calculations and variables.
- Recognize how to create, subset, and modify lists, including nested lists.
- Differentiate between functions, methods, and packages, and apply them to solve tasks.
- Identify NumPy arrays, distinguish them from lists, and assess their role in data analysis.
- Evaluate NumPy statistical tools (mean, median, std, correlation) for data insights.
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| Intermediate Python | - Identify and apply Matplotlib functions to create line, scatter, and histogram plots.
- Recognize how to create, update, and manipulate dictionaries and pandas DataFrames.
- Differentiate between comparison, boolean, and logical operators, and assess their use in filtering data.
- Identify the use of loops (for, while) and apply them to iterate over lists, dictionaries, NumPy arrays, and pandas DataFrames.
- Evaluate random number generation and simulations (random walks, distributions) to analyze probabilities and outcomes.
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| Data Manipulation with pandas | - Identify methods to import, inspect, and subset pandas DataFrames using functions like .head(), .info(), and .loc[].
- Differentiate between aggregation techniques using .groupby() and pivot tables for grouped statistics.
- Recognize how to modify DataFrames by adding new columns, setting indexes, and handling missing values.
- Define common visualization types in pandas, including bar, line, scatter, and histogram plots.
- Assess when to use Boolean masking, sorting, and slicing techniques for efficient data selection.
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| Joining Data with pandas | - Concatenate and manage DataFrame integrity.
- Filter, query, and reshape data for analysis.
- Merge using index, hierarchical, and time-series structures.
- Perform and customize DataFrame joins.
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| Introduction to Statistics in Python | - Apply probability concepts and sampling principles to real-world problems.
- Examine relationships between variables using correlation and experimental design.
- Interpret and apply the normal distribution and the central limit theorem.
- Summarize data using appropriate measures of center and spread.
- Use discrete and continuous probability distributions to model real situations.
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| Introduction to Data Visualization with Seaborn | - Assess appropriate techniques for adding and positioning titles, axis labels, and rotated tick marks.
- Differentiate tidy from untidy pandas DataFrames and state how this distinction affects Seaborn plotting functionality.
- Evaluate plot customization choices to improve interpretability.
- Identify the Seaborn plot category that best visualizes specified quantitative and/or categorical data relationships.
- Recognize the correct Python syntax and key parameters in relplot() and catplot() to build scatter, line, count, bar, box, and point plots.
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| Exploratory Data Analysis in Python | - Engineer and transform features from categorical and text data.
- Evaluate and manage outliers to maintain representative data distributions.
- Explore and validate datasets to assess structure and data quality.
- Extend EDA by generating features and evaluating representativeness.
- Identify, assess, and address missing or inconsistent data.
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| Working with Categorial Data in Python | - Summarize non-numerical (categorical) data types
- Manipulate categorical columns in pandas
- Manipulate categorical data using pandas' categorical data type
- Visualize categorical data using seaborn
- Apply categorical data techniques to real-world datasets
- Interpret categorical variables
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| Sampling in Python | - Discover when to use sampling and how to perform common types of sampling.
- Learn to estimate population statistics and quantify uncertainty in your estimates by generating sampling distributions and bootstrap distributions.
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| Hypothesis Testing in Python | - Differentiate between Type I and Type II errors and their consequences for statistical conclusions.
- Distinguish between parametric and non-parametric approaches.
- Evaluate p-values, confidence intervals, and standardized test statistics produced by Python libraries.
- Identify the suitable hypothesis test in Python for a given research question, data type, and sample conditions.
- Recognize the correct null and alternative hypotheses, significance level, and tail direction for typical analytical scenarios.
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