Data Analysis

Module 1: Python Fundamentals for Data Analysis

• Introduction to Python:
• Python installation and environment setup
• Basic data types (numbers, strings, lists, tuples, dictionaries)
• Control flow (if-else statements, loops)
• Functions

• NumPy and Pandas
• NumPy arrays and operations
• Pandas Series and DataFrames
• Data manipulation and cleaning
• Data aggregation and grouping

Module 2: Data Visualization with Matplotlib and Seaborn

• Matplotlib Basics:
• Line plots, scatter plots, histograms
• Bar charts, pie charts
• Customization and styling

• Seaborn:
• Statistical visualizations
• Heatmaps, pair plots
• Facet grids

Module 3: Descriptive Statistics with NumPy and Pandas

• Measures of Central Tendency:
• Mean, median, mode
• Percentiles and quartiles

• Measures of Dispersion:
• Range, variance, standard deviation
• Coefficient of variation

• Data Distribution:
• Skewness and kurtosis
• Normal distribution

Module 4: Probability and Hypothesis Testing with SciPy

• Basic Probability Concepts:
• Probability axioms
• Conditional probability
• Bayes' theorem

• Probability Distributions:
• Binomial, Poisson, normal distributions
• Conditional probability

• Hypothesis Testing:
• T-tests, ANOVA, chi-square tests

Module 5: Regression Analysis with Statsmodels

• Simple Linear Regression:
• Model building and interpretation
• Residual analysis

• Multiple Linear Regression:
• Model building and interpretation
• Multicollinearity

• Non-Linear Regression:
• Polynomial regression
• Logistic regression

Module 6: Time Series Analysis with Statsmodels

• Time Series Components:
• Trend, seasonality, cycle, noise

• Smoothing Techniques:
• Moving average, exponential smoothing

• Forecasting Methods:
• ARIMA models

Module 7: Data Mining and Machine Learning with Scikit-learn

• Introduction to Data Mining:
• Data mining tasks
• Data mining techniques

• Machine Learning Algorithms:
• Supervised learning (classification, regression)
• Unsupervised learning (clustering, dimensionality reduction)

• Model Evaluation:
• Performance metrics
• Cross-validation

Module 8: Case Studies and Projects

• Real-world data analysis projects:
• Business analytics
• Market research
• Scientific research
• Social science research

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