Data Analytics 360: Become Data Analyst in 40 Days

Learn how to distinguish population from sample and explore sampling methods: simple random, stratified, systematic, cluster, convenience, and snowball, understanding their advantages, challenges, and applications for accurate data analysis.

Learn data cleaning, also called data cleansing or data scrubbing, to fix errors and inconsistencies, standardize formats, and ensure missing values and outliers are addressed for reliable, actionable insights.

Master data cleaning by handling missing values via deletion or imputation (mean, median, mode), correcting incorrect data, converting data types, and addressing outliers and duplicates with transformations and winsorization.

Master joining data by merging rows from two or more tables with a key column to uncover unified insights in SQL. Explore inner, left, right, and full joins with examples.

Learn data concatenation to merge data sets into a single larger data set by stitching along the axis, row-wise or column-wise, for easier analysis and visualization.

Explore exploratory data analysis to uncover patterns, detect anomalies, and generate hypotheses that guide data-driven decisions and improve campaign insights.

Explore the methods of exploratory data analysis by examining mean, median, mode, variance, and standard deviation, including their uses with outliers and skewed data.

Explore symmetric and asymmetric distributions and understand normal bell-shaped data. Interpret skewness, kurtosis, and percentiles (Q1, Q2, Q3) along with min and max in exploratory data analysis.

Explore the differences between exploratory data analysis and statistical tests, learning how EDA visually summarizes data and reveals patterns, while statistical tests infer hypotheses from samples.

Explore inferential statistics by applying one sample t test, independent sample t test, paired sample t test, and one way anova to compare means and test hypotheses about population means.

Assess relationships between data variables by applying chi square tests for independence to two categorical variables and using Pearson correlation to quantify linear ties between two continuous variables.

Explore linear regression in inferential statistics: model relationships between dependent and independent variables, and predict outcomes with simple and multiple regression, r-squared, and beta values.

Discover how probability guides decision making in data analysis, measures likelihoods in statistics, and apply classical, empirical, and conditional probabilities to real-world risk and forecasts.

Explore classical probability by calculating chances when outcomes are equally likely, illustrated with coin flips, red marbles, and birthday vs wedding decoration inquiries.

Explore empirical probability, or experimental probability, which estimates likelihood from actual experiments or historical data by dividing outcomes by trials, with real-world examples like sales data and marble draws.

Explore joint probability as the likelihood two or more events occur together, illustrated by bundling sunscreen and sunglasses with 15% joint probability, and a red queen deck sample.

Explore hypothesis testing as a structured inferential method to decide about population parameters from sample data, defining null and alternative hypotheses and evaluating evidence with a test statistic and p-value.

Explore confidence level, significance level, and p-value in hypothesis testing, with alpha thresholds guiding evidence strength, and learn to decide between null and alternative hypotheses using a statistical test.

Perform a hypothesis testing workflow by comparing class a and class b scores with an independent t-test, formulating H0 and H1, setting alpha, and interpreting p-values.

Explore logarithmic and box-cox transformations to reduce skewness and improve normality, then apply binding and one-hot encoding to convert ordinal and nominal categories into usable numerical features.

Learn how to create new features and apply feature engineering, such as profit, extract day, month, and year for temporal analysis, and use standardization, normalization, and PCA for data preparation.

Explore how machine learning learns from data to support data analysis and informed decisions across industries, and enables real-time analysis, demand forecasting, and personalized marketing.

Explore supervised and unsupervised machine learning in data analytics, including classification, regression, logistic regression, decision trees, random forests, and k-means clustering for customer segmentation and sales prediction.