Python Programming Journey

A comprehensive overview of Python fundamentals and advanced concepts from the TripleTen bootcamp.

Sprint 1: Python Fundamentals

Dictionaries

  • Dictionary creation and manipulation
  • Key-value pair operations
  • Dictionary methods and iteration

Functions

  • Function syntax and definition
  • Arguments and parameters
  • Positional and keyword arguments
  • Return values and scope

Pandas Basics

  • DataFrame indexing and selection
  • Package importing and management
  • Logical indexing and filtering
  • Series object manipulation
  • Column renaming and reorganization
  • Handling missing values
  • Duplicate value management
  • Grouping and sorting operations
  • Descriptive statistics

Project: Data Analysis Project

Data manipulationStatistical analysisPython programming

Sprint 2: Data Loading and Processing

File Operations

  • Reading CSV files with read_csv()
  • Loading Excel files with read_excel()
  • Handling different separators with sep parameter
  • Managing headers and column names
  • Working with multiple Excel sheets
  • Understanding decimal formats
  • File encoding and error handling

Data Exploration

  • Using describe() for statistical summary
  • Checking data info with info()
  • Sampling data with sample()
  • Viewing data with head() and tail()
  • Including specific data types
  • Renaming columns efficiently
  • Detecting missing values with isna().sum()
  • Analyzing value distributions with value_counts()
  • Finding duplicates with duplicated() method
  • Understanding variable types (Quantitative vs Categorical)
  • Data type conversion with astype()
  • Error handling in type conversion (raise, coerce, ignore)

Missing Data Handling

  • Basic imputation with fillna()
  • Statistical imputation (mean, median)
  • Understanding median's robustness to outliers
  • Advanced imputation techniques:
  • - Regression imputation
  • - K-nearest neighbor (KNN)
  • - Iterative imputation (MICE)

Data Visualization

  • Working with Matplotlib, Seaborn, and Plotly
  • Creating scatter plots and line plots
  • Customizing plot elements:
  • - Titles and labels (title, xlabel, ylabel)
  • - Axis limits (xlim, ylim)
  • - Figure size and style
  • - Grid and legend
  • Creating histograms with hist()
  • Correlation analysis with corr()
  • Using scatter_matrix for multivariate analysis
  • Saving plots with savefig()
  • Plot customization with style parameters
  • Rotation options with rot parameter

Advanced Data Operations

  • DateTime operations with .dt accessor
  • Timezone handling (tz_localize, tz_convert)
  • Feature engineering techniques
  • Boolean column generation
  • Category creation with apply()
  • Aggregating grouped data with agg()
  • Split-apply-combine methodology
  • Creating pivot tables with pivot_table()
  • Combining data with concat() and merge()
  • Row and column removal with drop()
  • Advanced filtering with isin() and query()
  • Data transformation with where() and replace()

Project: Data Processing Project

Data loadingFile handlingData explorationMissing data imputationData visualization

Sprint 3: Statistical Analysis and Probability

Variable Types and Distributions

  • Continuous vs. Discrete Variables
  • Frequency Histograms
  • Density Histograms
  • Measures of Location (Mean, Median)
  • Data Distribution Shapes
  • Positive and Negative Skew
  • Normal Distribution (Bell Curve)
  • Three-Sigma Rule

Measures of Dispersion

  • Distance from Mean Calculations
  • Variance Calculation
  • Standard Deviation using NumPy std()
  • Understanding Sigma Squared
  • Covariance Concepts
  • Mathematical Formulas:
  • - Mean Distance Formula
  • - Variance Formula
  • - Covariance Formula

Probability Theory

  • Sample Space and Elementary Outcomes
  • Event Probability Calculations
  • Law of Large Numbers
  • Mutually Exclusive Events
  • Independent vs. Dependent Events
  • Venn Diagrams
  • Random Variables (Discrete and Continuous)
  • Expected Value and Variance
  • Binomial Experiments (Bernoulli)
  • Probability Density Plots
  • Normal Distribution Functions:
  • - scipy.stats.norm.cdf()
  • - scipy.stats.norm.ppf()
  • Normal Approximation to Binomial

Statistical Testing

  • Random Sampling Methods
  • Statistical Population Analysis
  • Stratified Sampling Techniques
  • Sampling Distribution Concepts
  • Standard Error Calculations
  • Hypothesis Testing:
  • - Two-Tailed Hypotheses
  • - One-Tailed Hypotheses
  • - Null Hypothesis (H₀)
  • Statistical Tests:
  • - scipy.stats.ttest_1samp
  • - scipy.stats.ttest_rel
  • Paired Sample Analysis
  • Interpreting Test Results

Project: Statistical Analysis Project

Probability CalculationsDistribution AnalysisStatistical TestingData Visualization