Data Analysis A-Z: Become Data Analyst in 30 Days

Explore inferential statistical data analysis through hypothesis testing, distinguishing population from sample, and interpreting null and alternative hypotheses to reveal significant patterns in data.

Explore confidence level, significance level, and p-value to guide hypothesis testing decisions, distinguish null and alternative hypotheses, and interpret study results with common thresholds.

Explore the hypothesis testing process, from null and alternative hypotheses to 0.05 significance, normal distribution checks, and using ANOVA, correlation, or regression to assess teaching method impact on exam scores.

Learn how to identify and replace missing values in Excel by categorizing non-numeric and numeric data, using pivot tables, find and replace, and the median value.

Identify and fix inconsistent values in Excel using pivot tables for categorical data, replacing anomalies with the most frequent value, and use the median to correct numeric data.

Identify outliers in Excel by calculating g scores from the mean and standard deviation of income, then replace the outlier with the median and apply the same method to age.

Learn to locate and remove duplicate values in Excel using conditional formatting to highlight duplicates and the data tab's remove duplicates feature, yielding unique values.

Learn to perform frequency and percentage analysis in Excel using pivot tables, counting professions, converting to percentages, and visualizing results with bar and pie charts.

Learn to perform descriptive analysis in Excel with the data analysis toolpak, generating summary statistics for numeric data and creating histograms and box plots.

Use pivot tables in Excel to compute average price, average quantity, and average rating by product category, then visualize total sales with a pivot chart, such as clustered bar chart.

Explore cross-tabulation analysis with pivot tables in Excel to reveal rating distributions across product categories and colors. Visualize results with a clustered bar chart to extract meaningful insights.

Learn to perform an independent sample t-test in Excel using the data analysis Toolpak to compare two groups and interpret the p-value for significance.

Learn to perform a paired sample t test in Excel to compare before and after scores, assess training effectiveness, and interpret p values at a 0.05 significance level.

Learn to perform one-way anova in Excel using the data analysis toolpak to compare prices across product categories and interpret the 0.05 significance result.

Perform Pearson correlation analysis in Excel with the Data Analysis Toolpak to measure the strong positive relationship between age and income, and its p value.

Learn to perform multiple linear regression in Excel using the data analysis toolpak to predict savings from income and expense, and interpret coefficients, r-squared, and p-values.

Create a polished data dashboard by arranging graphs, setting titles and backgrounds, and enabling interactivity with slicers for category and color filters.

Install essential Python packages in Jupyter Notebook, including pandas, numpy, matplotlib, seaborn, scipy, and scikit-learn, to load datasets and begin data analysis with Python.

Open a Jupyter notebook, set up a Python for beginners workspace, and write and run your first code using the print function to display hello world.

Learn how Python variables store data, use the assignment operator, and name them with descriptive names and underscores, while respecting case sensitivity and avoiding starting with a number or keywords.

Learn the core Python data types, including integer, float, string or object, and boolean, and see how the type function identifies each with practical examples.