Learn data science and analytics from scratch

Master the select statement in sql by retrieving data from tables using select * or select specific columns from a table, as shown with the person table's id and email.

Learn how select distinct in SQL returns only unique values and can be used with aggregate functions. See example counting distinct emails from a person table to illustrate removing duplicates.

Explore how the group by clause in SQL groups rows with the same values into summary results, often used with aggregate functions to remove duplicates.

Demonstrates how to use the max and min functions in SQL to return largest or smallest values from a column, with the syntax max or min plus the column name.

Learn how the having clause filters aggregated results in SQL using aggregate functions and how it differs from the where clause, with example that returns emails appearing at least twice.

Explore how the in operator works in SQL to specify multiple values in a where clause, acting as a shorthand for multiple or conditions and illustrating subqueries with location table.

Learn how the datediff function in SQL computes the difference between two dates, with examples filtering the last 30 days in a transaction table.

Master the cast function in SQL to convert data types, using select cast column as data type, with examples like salary to number and enrolled timestamp to date.

Explore how SQL window functions use the over clause with partition by and order by to compute rankings, including row_number, rank, and dense_rank, with examples like the third-highest salary.

Explore subqueries in SQL to restrict main queries, using where and in operators. Compare two tables and even replace joins, with weather temperature and country examples.

Explore complex join techniques to combine two tables, the person and vote tables, using group by, aggregate and window functions, plus order by and limit to identify the winning candidate.

Master join and aggregate techniques to report department sizes by joining person and department tables, choosing the right aggregate (count) and grouping by department name.

Learn how to combine having and where clauses to filter aggregated data using where, group by, and aggregate functions such as sum and count. Uses movie and order tables.

Learn how to find and remove duplicates using window functions, partition by and order by, with row_number or rank, plus alternatives like having clause, join, group by, and distinct.

Learn how to find the nth number using window functions, limit and offset, or max, with steps to order by height and identify the highest, second highest, or nth value.

Learn to boost SQL query efficiency by reducing dataset size, using where filters, selecting specific columns, and understanding the execution order start with from and join to limit.

Define success metrics by business needs and data. Use two positive metrics like loss numbers and loss rate, plus a guardian metric such as net profit, with dashboards and alerts.

Master AB testing design, data collection, and analysis while addressing single vs multiple variant tests, precision and false discovery rate, novelty and primacy effects, and network interference.

Explore payment risk basics across buyer and seller roles. Distinguish chargeback from refund and note fraud and non-fraud risks to inform analytics models.

Explain card payments from buyer to merchant, naming issuer, card network, acquirer, and interchange and discount fees, and contrast ACH transfers through originator, ODFI, ACH operator, and RDFI.

Learn how Python uses statements, indentation, and comments to structure code, with examples of newline, line continuation, and semicolon usage in a Jupyter Notebook.

Explore Python data types, including numbers (integer, float, complex) and strings, and core collections—lists, sets, tuples, and dictionaries with mutability. Convert between types using int and float functions.

Learn how Python functions group related statements to perform tasks, enable reuse by calling with arguments, and see how a greet function demonstrates parameters and indentation.

Explore Python operators, including plus and minus, the percentage remainder, the double back slash for division, and the double star for exponent, plus in, not in, and assignment operators.

Explore how Python if else statements make logic judgments using if, elif, and else. Understand nesting and practical examples with numbers to determine positive, zero, or negative outcomes.

Learn how to use Python for loops to iterate over lists, tuples, and strings, accumulate sums in the loop, and use range to control start, stop, and step.

Explore the while loop in Python, which runs as long as a condition stays true, compare it with the for loop, and learn to use break and increment.

Explore python list 101, where lists are ordered, mutable collections in square brackets. Access elements with zero-based and negative indices, and handle duplicates, multiple data types, and nested lists.

Access and slice tuple elements by index and range, note immutability, and delete the entire tuple. Demonstrate concatenation with plus, repetition with multiply, and negative indexing.

Explore Python sets as unordered collections that disallow duplicates, support multiple data types, and enable operations like union and intersection, while distinguishing them from dictionaries and lists.

Explore how Python sets, mutable and unordered, work in set 201: add and update elements, and remove with discard, remove, or pop, then perform union, intersection, difference, and symmetric difference.

Master Python dictionaries, an unordered collection of key value pairs with no duplicates, in curly brackets. Access values with keys, use for loops and the get function in Jupyter Notebook.

Explore how Python dictionaries are mutable and ordered, and learn to add, update, and remove keys using assignment, update, delete, and pop, with comparison using == and !=.

Discover how numpy enables fast, memory-efficient array calculations in Python. Learn to create, index, and slice arrays, compare arrays with lists, and work with data types using numpy.

Explore numpy 201: build and inspect n-dimensional arrays, understand 1d, 2d, and 3d shapes, and master indexing and slicing with practical examples.

Explore numpy basics: reshaping 1d arrays to 2d, using shape and indexing, iterating with the n-dimensional iterate function, sorting rows, and filtering with boolean indexing in a Jupyter notebook.

Master joining NumPy arrays with the NumPy concatenate function, selecting axis 0 for rows or axis 1 for columns, and ensure compatible shapes for successful concatenation.

Master Python pandas 101 by importing pandas, working with a dataframe, and loading data with read CSV and read JSON while indexing and mutating a two-dimensional table.

Explore Python pandas 301 by analyzing a two-column dataframe (height and weight) using info, length, and columns; filter with loc and sort height in ascending or descending order.

Explore Python matplotlib 101 for data visualization, learning to create plots, histograms, and 3D surfaces with easy syntax, and navigate charts with different settings.

Apply a three-step thinking process to data: show the overall trend with sales and average price, segment by house type for insights, and make data-driven recommendations with statistics and SQL.

Analyze the Nashville housing overall trend with sql by computing gross merchandise value, total sales, and price metrics from the house sale, house details, and seller information tables.

Query Nashville housing data by joining the house, house details, and seller tables to analyze sale prices, total values, and hotspots. Track trends in bedrooms, year built, and seller activity.

Analyze Nashville housing trends to identify Goodlettsville as a prime buy area, with 4–5 bedroom single-family homes built after 1960 delivering strong selling prices, based on data-driven insights.

Analyze year-over-year revenue and cost using SQL and Python, identify drivers behind net revenue changes, and propose actionable solutions to improve business performance.

Load subscription business data into a Python dataframe, explore with pandas and NumPy, perform groupby analytics and histogram visualizations to examine default versus fraud across years.

Celebrate completing the course and recognize mastery of statistics, analytics, SQL, Python, modeling, and a case study, with an introductory overview of data-driven problem solving.