The Data Science Course: Complete Data Science Bootcamp 2026

Demonstrate how data underpins modern business by aligning data team, analytics, and data science roles. Explain how buzzwords like data mining and predictive analytics evolved.

Clarify the differences between analysis and analytics: analysis explains past events, while analytics explores future patterns using qualitative analytics and quantitative analytics.

Explore how business analytics, data analytics, and data science intersect through dashboards, visuals, and AB testing. Understand how data availability and past versus future analysis drive data-driven business decisions.

Explore how business intelligence analyzes past data within data science, and how machine learning and artificial intelligence enhance real-time dashboards, predictive analytics, fraud detection, and sales forecasting.

Explore how traditional AI improves structured data tasks with automation, while generative AI creates new data and content, powered by machine learning in data science.

Explore how generative AI enhances data science through text comprehension, content generation across text, code, audio and images, and AGI concepts with tools like DALL-E, ChatGPT, and Gemini.

Explore two scenarios for business and data science tasks, starting with a proper dataset, and use the 365 data science infographic outlining terms across five columns.

Explore differences between traditional data and big data, including 3Vs of big data: volume, variety, and velocity. See how business intelligence, traditional data science, and machine learning drive analytics.

Explore why data drives business decisions in data science, contrasting traditional methods with machine learning for predictive analytics, and using a timeline to map past and future data work.

Explore techniques for traditional data, including data collection, pre-processing, labeling, cleansing, balancing, and shuffling, with ER diagrams and relational schemas for databases used in data science.

Explore traditional data through customer and stock data to distinguish numerical from categorical variables, noting IDs as categorical, complaint counts as numerical, and dates as categorical in stock data.

Explore techniques for collecting, pre-processing, and cleansing big data across text, image, audio, and video types, including missing value handling and text data mining.

Big data appears across industries. Facebook's diverse user data and real time analytics show variety, velocity, and volume, with financial trading data and stock prices every second illustrating big data.

Blend data skills with business knowledge to explain past performance and answer key questions, using observations, measures, metrics, KPIs, and business intelligence dashboards.

Use BI to optimize pricing in real time based on demand and historical data. Apply BI to inventory management by analyzing past sales for seasonality and stock optimization.

Explore traditional data science methods for predictive analytics, including linear and logistic regression, cluster and factor analysis, and time series applications for business decisions.

Explore real-world applications of traditional statistical methods in ux, including cluster analysis by continent, A/B/n testing, and time series sales forecasting.

Explore machine learning as a predictive analytics technique that learns from data through a model, an objective function, and an optimization algorithm, using trial-and-error training to improve predictions.

Explain and compare the three major machine learning types—supervised, unsupervised, and reinforcement—highlighting labeled versus unlabeled data, training goals, and practical examples.

Discover how supervised, unsupervised, and reinforcement learning blend with natural language processing and self-supervised learning, leading to transformers, llms, and real-world data science applications.

Explore how machine learning powers data science in banking and retail, using supervised learning on labeled credit card data to detect fraud in real time and boost customer retention.

Explore how data science relies on programming languages like R and Python alongside software such as SQL, MATLAB, Excel, Tableau, and Hadoop to support BI and predictive analytics.

Explore common data science job titles and roles, from data architect and data engineer to bi analyst and machine learning engineers, and trace how data flows to analysis.

Debunk common data science misconceptions by clarifying that big data hinges on volume, variety, and velocity, not just rows, and emphasize BI as past-event interpretation while SWOT remains qualitative.

Explore how probability measures the chance of events, using outcomes, sample space, favorable results, and simple examples like coins and dice to compute P(A) and expected values.

Learn how to compute expected values using experimental and theoretical probabilities, with examples from coin flips, card draws, and target scoring, and see why this aids future predictions.

Explore how the expected value relates to outcomes, build a frequency distribution for two dice sums, and transform frequencies into probabilities to identify the most probable interval.

Explore how complements complete the sample space, calculate P(A') = 1 - P(A) with coin and die examples, and note that sums of probabilities equal one.

Explore combinatorics and probability by examining permutations, variations, and combinations, and learn to determine sample spaces and favorable outcomes under various restrictions.

Learn how permutations count the different ways to arrange a set of elements, with the race podium example, and how n factorial multiplies from n down to 1.

Explore factorials by defining n! as 1×2×…×n, note zero factorial equals 1 and negatives have no factorial, and apply recurrence and extensions to n±k with examples.

Explore variations with repetition and compare to permutations, using n^p to count two-letter codes from A, B, C and 26 letters, and preview variations without repetition.

Explore variations without repetition by counting ordered selections of four runners from five, using the formula n!/(n-p)!, which yields 120 possible lineups.

Learn how combinations count distinct groups by ignoring order, and apply the formula N!/(P!(N-P)!) to compute examples like 10 choose 3 equals 120 and 10 choose 4 equals 210.

Learn the symmetry of combinations, where selecting p from n equals selecting n minus p, and see how this simplifies factorial calculations with examples like fruit baskets and employee picks.

Multiply the options for each separate position to obtain total combinations, from lunch menus (sandwich, side, drink) to online ads (heading, thumbnail, description, button).

Explore combinatorics in real life by calculating lottery probabilities: use combinations without repetition to pick five numbers from 69 and a Powerball from 26, yielding about 1 in 300 million.

Summarizes combinatorics by comparing permutations, variations, and combinations, and shows when to use each for full arrangements or selecting subsets, with and without repetition and present P, V, C formulas.

Apply combinatorics to a menu, using the multiplication principle to compare Domino's deals and illustrate combinations without repetition, variations with repetition, and combinations with repetition.