Python for Data Analysis & Business Intelligence

Explore why Python accelerates analytics with scalability, open source versatility, and automation that connects a database to Excel, empowering data prep, analysis, machine learning, and visualization using key libraries.

Explore the Python analytics ecosystem, starting from base Python fundamentals to powerful libraries like Pandas, visualization tools for charts, and machine learning libraries such as scikit-learn, statsmodels, and TensorFlow.

Learn how Python powers data roles—from analysts to data scientists and engineers—by automating workflows, manipulating data with pandas, and powering visualizations with Tableau and Power BI.

Install Anaconda and launch Jupyter notebooks, explore the notebook interface, and learn basic Python syntax, including comments, markdown, and the print function, with Google Colab notes.

Launch Jupyter Notebook from Anaconda Navigator, create a Python notebook titled Jupyter Practice in a Python course work folder, then save for easy access from the file system.

Explore the code cell as the coding environment, write in edit mode, run code with the play button or shift-enter, see input and output, and manage cells with shortcuts.

Learn how to use comments to explain code, choose useful inline and block comments, avoid clutter, and convert code cells to markdown to document workflows with headers, lists, and formatting.

Explore the print function in Python to display text and numbers, using quotes and commas for multiple values, and access function docstrings in the notebook.

Google Colab offers a cloud-based alternative to Jupiter notebooks, letting you run and store notebooks in Google Drive with internet access, and easily transfer files to and from Jupiter.

Explore Python data types, learn to identify and convert between them, and understand mutability and the type function across numeric, text, boolean, none, sequence, mapping, and set types.

Explore intervals and mutability in Python. Discover how iterables like lists, dictionaries, sets, and strings can be traversed, modified, or kept immutable.

Learn how to name, assign, overwrite, and delete variables in Python, and keep track of values using intuitive names, proper spacing around the equals sign, and any data types.

assign variables in python by setting price to five and tax to two, then compute total as price plus tax. print the total to verify the result.

Learn how to overwrite variables in Python by assigning new values, understand the underlying value versus the variable name, use placeholders for safe testing, and avoid memory waste.

Understand how the delete keyword removes variables from memory in Python and why printing a deleted name triggers an error. See when deletion helps free resources after merging datasets.

Learn how to overwrite and delete variables in Python by assigning new values, printing current values, storing backups in old variables, and using the delete keyword to remove names.

Define variable names by starting with a letter or underscore, using only letters, numbers, and underscores, avoid spaces and reserved keywords; follow snake_case like price_list_2019.

Demonstrates naming and tracking variables in Python with who and whos, shows variable cleanup by deletion or restart, and advocates snake_case naming with valid examples.

rename variables to the format price_list_2018, price_list_2019, and price_list_2020, clean up unnecessary names, and review the price list notebook’s three price lists for 2018–2020.

Demonstrates proper variable naming in Python by assigning price lists for 2018, 2019, and 2020 using underscores and avoiding numbers, spaces, hyphens, and reserved keywords.

Master how variables function as memory-stored containers for values of any data type. Overwrite values, use placeholders or new variables, and follow Python naming rules with intuitive snake_case names.

Explore python's numeric data types, convert between them, and perform arithmetic operations and numeric functions to review data type handling and calculations.

Explore Python's numeric data types, including integers (whole numbers), floats, and complex numbers, with examples and notes on their use in data analytics and the rare role of complex numbers.

Explore Python arithmetic operators: addition, subtraction, multiplication, division, floor division, modulo, and exponentiation, and learn how parentheses and the order of operations produce correct results.

Practice arithmetic operators through a financial calculations notebook to compute gross profit, gross margin, price for a 70% margin, sales tax, and one-year investment return on five snowboards.

Explore arithmetic operators in Python by calculating gross profit and gross margin, sales tax, and the amount after one year from reinvested profit, using parentheses to control order of operations.

Explore how to use Python's numeric functions to round numbers, compute absolute values, find minimum and maximum in lists, and sum scores with practical golf examples.

Apply Python numeric functions to find the lowest and highest prices from a seven-item list and compute the total cost of two of every item, rounded to the nearest dollar.

Learn to use Python numeric functions like min, max, sum, and round to compute costs from a price list, with optional variable assignments for two of every item.

Master numeric data types—integers and floats—while noting complex numbers are rarely used; apply Pandas order of operations with parentheses and built-in functions like round, abs, min, and max.

Explore strings, data type used to store text, creating from scratch or converting numbers, and manipulating with indexing, slicing, and string methods; use triple quotes for multi-line text and f-strings.

Learn to create strings in Python with single, double, or triple quotes, use escape characters for apostrophes, write multi-line strings, perform string arithmetic and concatenation, and apply basic methods.

Learn to index strings with zero-based and negative indexing to access characters and data points in columns, handle the last character, and avoid index errors in Python for data analysis.

Learn string indexing with zero-based and negative indices to access first and end characters, counting apostrophes, and apply indexing to lists, tuples, and Pandas rows.

Index one letter from each of five text messages to spell Maven, using string indexing as shown in the attached notebook. Learn how this hidden message reveals a password.

Master string slicing by specifying start, stop, and step to extract parts of a string, including defaults and negative indices, and apply this to data analysis and pandas-style subsetting.

Master Python string slicing to extract testimonials and craft marketing copy, assigning phrases like 'I love skiing. It's my life.' and 'I love my life!' from a notebook.

Explore the length function to count characters in strings and elements in lists, and apply it to any iterable, enhancing indexing practice.

Compute the length of each message to index an alphabet and assemble a password. See how string addition and careful line breaks guide Python code toward spelling the word maven.

Demonstrates how to use Python string methods like upper, lower, strip, lstrip, rstrip, replace, split, and join, and shows method chaining for text cleaning.

Explore essential Python string methods for data analysis and business intelligence, including join, strip, lower, replace, find, slicing, and count, plus a preview of f-strings.

Learn how to create dynamic text in Python by embedding variables in strings with f-strings and curly braces, as shown by examples using name and role.

Demonstrate f-strings by embedding a movie variable into text, printing dynamic updates as the variable changes, and using uppercase and repetition to display list outputs.

Use f-strings to format a string that takes product and price variables and prints each item's cost with a dollar sign, e.g., snowboard 9999 and scarf 1999.

Strings store text and sequences of characters; use indexing and slicing to access them, apply string methods guided by Python documentation, and use f-strings to create dynamic strings.

Explore the boolean data type, boolean operators, and conditional logic to control program flow using true or false expressions.

Learn how boolean operators combine multiple comparisons using and and or, explore short circuiting, and see how parentheses control the evaluation order in complex conditions.

Use boolean operators to test inventory levels, price display, and a customer flag in a notebook, exploring zero, five, and conditions like greater than five and Chris with Super Snowboard.

Learn to use boolean operators to compare inventory against zero and five, display price when inventory is above five, and combine conditions with and/or logic.

Master Python control flow by using if statements and indentation to run code only when conditions are true, with examples like price > 100 and inventory checks.

Apply if, elif, and else statements to compare prices against expert, intermediate, and luxury thresholds, emphasizing proper order to avoid unintended matches and selecting a single, correct board.

Explore control flow by implementing inventory-based conditions that print out of stock for zero or less, low stock for one to five, and in stock for over five, with tests.

Learn to implement nested if statements that compare budget to price and check inventory, revealing when you can afford this and when it's in stock or out of stock.

Use nested if statements in the Jupiter Notebook inventory query to show 'you can have the display models' for Chris with zero inventory, otherwise 'out of stock'.

Demonstrate nested logic to handle a special case: inventory less than or equal to zero and customer named Chris, printing a display model or otherwise showing out of stock.

Master conditional logic by using the boolean data type, true and false, and one and zero, with comparison operators, membership tests, and boolean operators to drive if, elif, and else.

Learn list operations in Python, including membership tests, indexing with zero-based positions, slicing with endpoints and steps, and unpacking lists into variables, noting potential value errors.

Convert text customer ids from jupyter notebook into string list, check for target id to return 9999 or 0.0, and slice for fifth, sixth, last two, and every third customer.

Practice python list operations: use membership tests and slicing to select customers, compute prices, and extract every third and last two items, following pap eight guidelines.

Add customers from the Saturday list to the customer list, using the Jupyter Notebook results preview to verify Customer 00009 appears at the end and observe any misorder.

Explore how to add elements to a Python list with append and insert, control order via indices, and merge lists using the addition operator to extend the existing one.

Remove the last seven Saturday customers and delete customer four from the combined list used for Black Friday sales. Start from the attached Jupyter notebook 'Updated Updated Customer List'.

Remove elements from a Python list using slices and the remove method to drop the last seven items or keep the first 14 with a sliced reassignment.

Master Python list modification by updating items via index assignment, adding with append or insert, and removing elements with delete or remove by index or by value.

Explore list methods and functions in Python, including count, len, append, sort vs sorted, indexing, and slicing, to analyze transaction data and build VIP customer lists with ratios.

Calculate the average subtotal, count a customer's transactions, use index mapping to link IDs to subtotals, locate the first transaction value, and print a non-destructive sorted list of IDs.

Learn to work with nested lists by indexing outer and inner lists, appending values, and counting elements. Compare assignment, shallow copy, and deep copy to copy nested structures safely.

Explore nested lists, access details with indices, and compare copy methods to avoid shared references. Practice copying item details and using deep copy to prevent cross-list updates.

Build a vip list containing nested transaction lists, index and slice to view specific orders, and use the copy method to replace an entire nested list without altering the original.

Demonstrate working with tuples like lists, including indexing, slicing, and membership tests; show immutability, converting to lists, unpacking, and basic arithmetic after cleaning data.

Practice creating and unpacking a tuple of transactions to compute sales tax at 8%, rounding to the nearest cent, using Python in a Jupyter notebook.

Discover how ranges generate integers with start, stop, and step; stop is not inclusive. Ranges are memory efficient and commonly used with loops, convertible to lists for display.

Create a range from zero to 100 with a ten-step increment to generate an x axis of percentiles, and access elements by index. Understand memory efficiency and looping with ranges.

Use python ranges to generate evens from two to ten, odds from one to nine, and every seventh from seven in the first hundred; extract values then print.

Use ranges to build even numbers 2–12 with step 2, odd numbers 1–11 with step 2, and multiples of seven 7–100; pull lists for looping.

Learn how to use for loops in Python to iterate over items in iterables (strings, lists, tuples, dictionaries and sets) with proper indentation and indexing, including currency conversions.

Learn to use a for loop to clean a list of string ratings, extract the numeric portion, convert to integers, and build a numeric ratings list for the average rating.

Loop over indices with Python ranges that start at zero and end before the list length to access elements by index, paving the way for multi-list pairing with enumerate.

Enumerate iterates over items and indices, returning index-element tuples to print or access elements, offering a clearer, more efficient alternative to range when looping over multiple lists.

Modify the tax calculator to apply the correct location-specific tax rates, mapping Sun Valley 8%, Stoli 6%, and Mammoth 7.75% using the attached location list and notebook.

Learn to pair transactions with indices using enumerate, then apply location-based tax logic (8% for Sun Valley, 6% for Snowy, 7.75% otherwise) and validate results with quick sanity checks.

Explore while loops that run while a boolean expression is true, using counters and addition or subtraction assignment with examples like stock, revenue, and bank balance; interrupt loops if needed.

Learn how to use a while loop to remove one star ratings from a customer list, counting removals with a counter, compare with for loops, and avoid infinite loops.

Demonstrates a while loop that tracks inventory by deducting 84 skis per month and prints the inventory after each month. It also demonstrates nested loops for more complex inventory scenarios.

Explore nested loops in Python by combining items and sizes, revealing how an inner loop runs fully for each outer loop iteration to produce all item-size combinations.

Apply a 10% discount to each transaction within a nested list of multi-order customers, and return the updated totals in a list preserving the original structure.

Learn how break, continue, and pass control statements manage Python loops, exit loops when conditions are met, skip iterations, and reserve unfinished logic as placeholders.

Master for loops with known length and while loops with unknown iterations, apply loop control statements to avoid infinite loops, and use placeholders, skip iterations, and error handling.