Easy Python for A-Z Data Analysis Bootcamp for Beginners

Open and name a Jupyter notebook environment, then write and run your first Python code using the print function to display hello world as a beginner.

learn how variables store and label data in Python using the assignment operator, follow naming rules with letters, numbers, and underscores, cannot start with a number, and use descriptive names.

Learn how integers, floats, strings, and booleans behave in Python and how to identify them with type for accurate calculations and data analysis.

Learn how to convert data types in Python using int, float, and str to prepare data for analysis and data frames, with practical casting examples and arithmetic.

Explore Python arithmetic operators, including plus, minus, multiplication, division, modulus, and exponentiation, with practical examples that show how to perform numeric calculations and compute remainders.

Understand python comparison operators like greater than, less than, greater than or equal to, less than or equal to, equal to, and not equal to, and they yield boolean values.

Explore how and or not combine conditional and comparison operators to yield boolean results in Python, with practical examples showing true and false outcomes.

Learn to create and manipulate Python lists with strings, numbers, and booleans; perform indexing, slicing, and modifying lists using append, insert, remove, and replace techniques.

Learn Python sets to store unique elements, perform union, intersection, and difference, and modify sets by adding, removing, and multiplying values through iteration.

Explore Python dictionaries by learning key-value pairs, items, and common methods to access, modify, and extend dictionaries, including lists as values and adding new keys like email.

Explore conditional statements in Python using if, elif, and else to control outputs based on comparisons like 25 and 10, with practical print examples for data analysis.

Explore integrating logical expressions with Python conditional statements to classify ages as young, adult, or senior citizen; use or and not to assess weather and eligibility.

Explore looping structures with for loops and while loops to iterate over lists, print values, build new lists with conditions, and control flow using break and modulus checks.

Define, create, and call custom Python functions using def to compute percentages from total respondents and a category, using male and female examples to demonstrate.

Explore how to inspect, clean, transform, and model data to uncover insights and support decision making. Trace descriptive, diagnostic, predictive, and prescriptive analyses through the data collection flow.

Explore the complete data analysis workflow from cleaning and manipulation to exploratory data analysis, distribution handling, statistical analysis, and hypothesis testing to turn data into insights.

Learn how to load an Excel data set into a Jupyter notebook, install and import pandas, and view the first rows with df.head to begin cleaning data.

Identify and impute missing values in a data frame using pandas and SimpleImputer from scikit-learn, applying median for numeric data and most frequent for categorical data.

Identify inconsistent data types in your data using python and pandas. Find non-numeric values in numeric variables, convert cost to float, and ensure correct data types for accurate analysis.

Learn to remove a row with an inconsistent value from a numeric column in pandas, using mask and tilde, and convert data types to float and date time 64.

Delete duplicate values from your data set using Python and the drop_duplicates function to produce a clean, ready-to-analyze data frame.

Learn to sort a dataset by numeric values in ascending or descending order using pandas sort_values, unlocking basic insights from the data.

Discover how to filter a dataset with Python using conditions, such as France or games, and combine or exclude criteria with logical operators in a Jupyter notebook.

Learn to merge extra data into an existing cleaned dataset using Python, matching on the order_id key to add the refund variable with a simple merge.

Learn how to append data by rows to a merged dataset using vertical concatenation with pandas pd.concat, loading extra_data from an xls file to create final_df.

Explore exploratory data analysis to understand sales data, uncover insights, and guide decisions before modeling, such as identifying profitable customers, sales representatives and sales managers, and target markets by country.

Use frequency analysis to identify the majority country of residence, top product categories, and device usage by computing value counts and visualizing results with bar and pie charts.

Explore descriptive analysis for numeric variables using mean, median, percentiles, and standard deviation. Apply to order value, cost, and refund with Python and visualize distributions and box plots.

Combine categorical and numeric data using groupby to compute median order value and median cost. Identify top product categories and customers by profitability, and analyze refunds by sales reps.

Identify the most loyal customers using pivot tables in Python, combining repeat purchases, median order value, and median cost, and visualize insights through exploratory data analysis.

Explore cross tabulation of two categorical variables using pandas crosstab, analyze comparative and relative patterns, and visualize results with a heatmap to reveal category relationships.

Explore correlation analysis to reveal relationships between numeric variables like order value, cost, and refund amount using Python to compute a correlation matrix and visualize it with a heatmap.

Explore hypothesis testing in inferential data analysis, defining population vs sample, null and alternative hypotheses, and how sample data reveals evidence to support or refute claims.

Explore confidence level, significance level, and p-value to master hypothesis testing, decide on null versus alternative hypotheses, and understand how these measures guide decisions and conclusions in data analysis.

Learn the step-by-step hypothesis testing process, including formulating null and alternative hypotheses, setting 5% significance, checking normality, and choosing one-way ANOVA, correlation, or regression.

Assess normality of numeric data before statistical tests, examine order value, costs of goods sold, and refunds, and apply transformations to achieve normal distributions for reliable analysis.

Apply the square root transformation to numeric variables with numpy in Python to reduce right skewness and approach near-normal distributions, transforming order value, cost, and refund, using cdf plots.

Demonstrate logarithmic transformation in python with numpy on variables, compare log and square root, show order value euro and cost improve with square root, while refund does not, next box-cox.

Apply box-cox transformation to order, value, cost, and refund in Python using numpy and scipy.stats, compare with sqrt and log transforms, visualize via KDE, and preview Johansson method next.

Apply the Yeo-johnson transformation to three numeric variables in Python. Compare with Box-Cox using KDE plots and decide transformations for order value euro, cost, and refund.

Perform an independent sample t-test to compare the average refund between France and Sweden and assess significance. Implement the test in Python using scipy.stats ttest_ind.

Apply one-way ANOVA to test cost of goods sold across PC, mobile, and tablet devices at 0.05 significance, and interpret the F-statistic and p-value to conclude no significant difference.

Explore Pearson correlation analysis among order value, cost, and refund, test statistical significance at 0.05 using Box-Cox transformed variables, and interpret correlation coefficients and p-values.

Perform regression analysis to measure impact of order value and cost on refund at a 0.05 level, showing order value significantly influences refund; cost is not significant, with r-squared 0.921.