Data Analysis with Python

Objective: Understand the data analysis workflow and set up Python environment.

Topics Covered:

• What is data analysis? The data analysis lifecycle
• Types of data: structured, unstructured, time series
• Installing Python and Anaconda distribution
• Introduction to Jupyter Notebook and JupyterLab
• Python basics for data analysis (variables, data types, loops, functions)
• Understanding data formats (CSV, Excel, JSON, SQL)

Tools: Anaconda, Jupyter Notebook, Python 3.x

Objective: Master NumPy for efficient numerical operations on arrays.

Topics Covered:

• Introduction to NumPy and its advantages
• Creating NumPy arrays from lists and other sources
• Array attributes and data types
• Array indexing, slicing, and reshaping
• Universal functions (ufuncs) for element-wise operations
• Broadcasting and vectorization
• Statistical operations on arrays (mean, median, std, etc.)

Tools: NumPy, Jupyter Notebook

Objective: Learn to use Pandas Series and DataFrames for data manipulation.

Topics Covered:

• Introduction to Pandas and its data structures
• Creating and working with Pandas Series
• Creating DataFrames from dictionaries, lists, and external files
• Inspecting DataFrames (head, tail, info, describe)
• Selecting and filtering data (loc, iloc, boolean indexing)
• Adding, modifying, and deleting columns
• Handling missing data (isnull, dropna, fillna)

Tools: Pandas, Jupyter Notebook

Objective: Master techniques for cleaning and preparing messy data.

Topics Covered:

• Identifying and handling duplicate data
• Dealing with outliers and inconsistent data
• Data type conversion and parsing dates
• String operations and text cleaning
• Renaming and reordering columns
• Applying functions to DataFrames (apply, map, applymap)
• Data transformation and normalization

Tools: Pandas, Python datetime module

Objective: Learn advanced data manipulation and aggregation techniques.

Topics Covered:

• Grouping data with groupby() and aggregate functions
• Pivot tables and cross-tabulations
• Merging, joining, and concatenating DataFrames
• Reshaping data with melt and pivot
• Working with time series data (date ranges, resampling)
• Window functions and rolling calculations

Tools: Pandas, NumPy

Objective: Create professional visualizations using Matplotlib.

Topics Covered:

• Introduction to Matplotlib architecture
• Creating line plots, scatter plots, and bar charts
• Histograms, box plots, and pie charts
• Customizing plots (colors, labels, titles, legends, grids)
• Working with subplots and multiple axes
• Saving and exporting visualizations
• Styling plots with matplotlib styles

Tools: Matplotlib, Jupyter Notebook

Objective: Create beautiful statistical visualizations using Seaborn.

Topics Covered:

• Introduction to Seaborn and its advantages
• Distribution plots (distplot, kdeplot, rugplot)
• Relational plots (scatterplot, lineplot)
• Categorical plots (boxplot, violinplot, barplot, countplot)
• Matrix plots (heatmaps, cluster maps)
• Pair plots and joint plots for multivariate analysis
• Customizing Seaborn themes and color palettes

Tools: Seaborn, Matplotlib, Pandas

Objective: Master EDA techniques to uncover patterns and insights.

Topics Covered:

• The EDA framework and methodology
• Summary statistics and descriptive analysis
• Identifying data distributions and outliers
• Correlation analysis and heatmaps
• Handling missing values during EDA
• Feature engineering and creating new variables
• Communicating findings from EDA

Tools: Pandas, NumPy, Matplotlib, Seaborn

Objective: Apply statistical methods to analyze and interpret data.

Topics Covered:

• Descriptive statistics (mean, median, mode, variance, standard deviation)
• Inferential statistics concepts (population vs. sample)
• Hypothesis testing fundamentals (t-tests, chi-square)
• ANOVA for comparing multiple groups
• Regression analysis basics (linear regression)
• Interpreting p-values and confidence intervals
• Using SciPy for statistical tests

Tools: SciPy, StatsModels, Pandas

Objective: Apply all skills to a complete data analysis project.

Topics Covered:

• Define a business problem and data requirements
• Load and explore real-world datasets
• Clean and prepare data for analysis
• Perform EDA and create visualizations
• Apply statistical methods to answer questions
• Draw conclusions and make recommendations
• Create a final report or presentation of findings

Tools: Complete Python data analysis stack