Complete Python Data Analytics Bootcamp in 2025

Analyze data by inspecting, cleaning, transforming, and modeling to uncover insights and support decision making. Learn descriptive, diagnostic, predictive, prescriptive, and exploratory analytics to forecast trends and optimize outcomes.

Explore real-world data analysis across sectors including business intelligence, healthcare, finance, marketing, research, and supply chain, driving insights and optimizing decisions.

Master data cleaning by handling missing values through deletion or imputation and correcting incorrect values. Normalize data types, transform outliers, and remove duplicates to ensure accurate analysis.

Explore how joining data links rows from multiple tables using a key column to create a unified dataset, covering inner, left, right, and full joins and their effects on results.

Explore methods of exploratory data analysis, Part 1, focusing on central tendency measures (mean, median, and mode) and dispersion measures (variance and standard deviation) with practical calculation insights.

Explore normal and skewed distributions, explain the mean, median, and mode alignment, and analyze skewness, plus key percentiles (Q1, Q2, Q3) and their roles in exploratory data analysis.

Explore exploratory data analysis techniques, including frequency and percentage analysis, group by analysis, cross tabulation, and correlation using scatter plots and Pearson's r.

Explore population versus sample in data analytics. Learn seven sampling methods—simple random, stratified, systematic, cluster, convenience, and snowball—and their advantages and challenges.

Describe descriptive and inferential statistics, using metrics like mean, median, mode, and standard deviation. Inferential analysis uses samples to infer population trends via tests such as a t test.

Review the key descriptive statistics measures—mean, median, mode, range, variance, standard deviation, IQR, skewness, kurtosis, and quartiles—and examine their role in summarizing data alongside the basics of inferential statistics.

Explore inferential statistics by applying one sample t test, independent and paired t tests, and one-way ANOVA to compare means across single and multiple groups.

Explore chi-square tests for independence, comparing observed vs expected frequencies in tables to assess relationships between categorical variables, and apply Pearson correlation test to measure linear relationships between continuous variables.

Explore how linear regression links independent variables to a dependent variable, covering simple and multiple regression, R-squared, beta coefficients, and the regression equation for prediction.

Explore how hypothesis testing uses sample data to infer population parameters, compare groups, assess evidence with p-values, and decide on null versus alternative hypotheses.

Select the right statistical test by aligning the scenario and hypotheses, then apply the appropriate tests (t tests, anova, chi-square, Pearson correlation, or regression) and check core assumptions.

Explore confidence level, significance level, and p-value to guide decisions in hypothesis testing and determine the probability of error.

Evaluate statistical findings by comparing the p value to the 5% significance level, deciding to accept or reject the null and alternative hypotheses, then state the conclusion.

Apply hypothesis testing to compare classes' math scores—new teaching method vs traditional—using a two-sample t-test at alpha 0.05, then use p-values to reject the null in favor of the alternative.

Explore data transformation techniques to preprocess data for better analysis and model performance, including logarithmic and Box-Cox transformations, binding (binning), and one-hot/dummy encoding for categorical variables.

Master data transformation techniques such as feature engineering with profit from revenue and cost, date extraction, scaling via z-score standardization and mean-max scaling, and PCA for dimensionality reduction.

Explore data visualization methods like bar charts for comparing category means, stacked bar charts for totals and subcategory composition, and line graphs for trends over time.

Explore pie charts for proportions, bar charts and histograms for distribution, scatter plots for relationships, and heatmaps for magnitude and correlation, showing normal distribution cues.

Explore area charts and line charts with colored areas to differentiate trends, learn bubble plots indicating a dimension by bubble size, and review box plots for quartiles, medians, and outliers.

Explore how machine learning enhances data analysis, enabling predictive analytics, real-time decisions, and automated insights—from optimizing inventory in retail to personalized customer experiences.

Explore widely used machine learning models, including supervised classification and regression, with examples like logistic regression, decision trees, and random forest, plus unsupervised clustering such as k-means.

Define the core problem, collect and clean data, engineer features, choose a suitable model, train with a split, evaluate with accuracy, MSE, and MAPE, then deploy and monitor.

Get started with Python in Jupyter Notebook by writing and running your first Hello World program using the print function, and set up a beginner-friendly analysis environment for data analytics.

Learn how to define Python variables with the assignment operator, store strings and numbers, and follow naming conventions using letters, digits, and underscores, while respecting case sensitivity and reserved keywords.

Explore Python data types, including integers, floats, strings or objects, and booleans, with examples and type checks that show how each type supports operations and calculations.

Learn how to convert Python data types—int, float, and string—using int(), float(), and str(), preparing data for analysis and arithmetic in data frames.

Learn how Python's arithmetic operators—plus, minus, multiply, divide, modulus, and exponent—perform numeric calculations, from simple additions to remainders and powers.

Explore Python comparison operators such as greater than, less than, greater than or equal to, less than or equal to, equal to, and not equal to, and their boolean outputs.

Explore how and, or, and not combine comparisons to yield boolean results in Python, with practical examples showing when conditions are true or false.