Data Analytics Career Path: 72 Days of Data Analyst Bootcamp

Discover how data analysis collects, cleans, explores, and interprets data to deliver actionable insights that guide decisions. See patterns and trends with real-world examples like delivery times and customer behavior.

Learn the four major data analysis types, descriptive, diagnostic, predictive, and prescriptive, and how each answers what happened, why it happened, what may happen next, and what to do.

Explore tools and technologies used in data analysis, from statistical software to machine learning libraries. Learn how Python, R, Julia, SQL, Tableau, and TensorFlow enable data manipulation, visualization, and modeling.

Learn the steps in data analysis as part of the data analytics career path bootcamp.

Discover how data starts as raw facts and becomes meaningful information through collection, organization, analysis, and interpretation, including primary and secondary data and tabular, image, audio, video, and text forms.

Define population as the complete group we study. Use a representative sample to estimate the population, saving time and resources while reflecting its characteristics.

Master the basics of data cleaning, identify and correct errors, inconsistencies, and missing values, standardize formats, and ensure completeness, reliability, and data integrity for trustworthy analytics.

Learn the essential steps of data cleaning, including missing values, incorrect values, data types, outliers, and duplicates, with practical imputation, transformation, and removal techniques.

Clean inconsistent data by identifying unrealistic values, mixed types, and inconsistent categories, then standardize columns for reliable analysis. Explore column types, patterns, and a step-by-step correction flow before analysis.

Learn how data joining connects separate tables using a common column to form a complete dataset, and distinguish horizontal versus vertical joins for integrated analysis.

Learn row-wise and column-wise data concatenation to merge datasets by appending rows or adding columns, creating a single data set for analysis.

Explore frequency and percentage analysis to summarize categorical data, revealing most and least common categories, distribution, and easy group comparisons using a simple Python workflow.

Explore exploratory data analysis by mastering the mean, median, mode, and variance, including standard deviation, and understand how outliers and unimodal, bimodal, multimodal distributions shape central tendency and spread.

Explore symmetric and asymmetric distributions, including the normal bell curve and mean, median, and mode. Learn about skewness and the 25th, 50th, and 75th percentiles (Q1, median, Q3).

Explore frequency analysis, group by techniques, cross tabulation, and correlation analysis to uncover patterns, compare groups, and visualize relationships using histograms, bar charts, and scatter plots.

Understand how probability guides data analysis and decision making, from die examples to classical, empirical, subjective, conditional, and joint probabilities.

Learn classical probability by counting equally likely outcomes, from coin flips to marbles, and apply it to decision making with examples showing 60% vs 40% demand insight.

Explore empirical probability, or experimental probability, based on past data and experiments to estimate likelihoods. Learn to compute it as successes over trials and apply to real-world inventory decisions.

Learn how sets keep unique elements and use union and intersection to compare groups, handle overlaps, and avoid double counting with real-world workshop examples.

Explore joint probability, the likelihood that two events occur together, and apply it to real-world data analysis, such as bundling sunscreen and sunglasses and interpreting card draws.

Explore degrees of freedom in statistics with an average-based three-number example, define the concept, and show how df = n−1 shapes t, chi-square, and f distributions and hypothesis testing.

Learn the basics of statistical data analysis, including examining, cleaning, transforming, and modeling data. Explore when to apply tests for groups, relationships, and trends using R, Python, or SPSS.

Analyze descriptive and inferential statistics to summarize data and infer population trends, using mean, median, mode, standard deviation, and hypothesis testing with a t test.

Explore one sample t test, independent sample t test, paired sample t test, and one way ANOVA to compare means and assess significance across groups.

Explore chi square tests for independence to assess relationships between categorical variables, and use Pearson correlation to measure relationships between continuous variables with a -1 to 1 coefficient.

Master linear regression by modeling a dependent variable with one or more predictors, mastering simple and multiple regression, r-square, beta values, and the regression equation.

Explore the key measures in hypothesis testing, confidence level, significance level (alpha), and p-value, and learn how to use them to make decisions and draw conclusions in data analytics.

Compare the p value to the 5% significance level to decide between the null and alternative hypotheses, and report the conclusion of the hypothesis test.

Compare class a's average scores with a new teaching method to class b's traditional method using hypothesis testing, H0/H1, alpha 0.05, Python-based independent samples t-test, and p-values.