Python for Data Science: A Comprehensive Journey to Mastery

Master Python data types by exploring integers, floats, booleans, and strings, and learn to use them with basic operators and text processing.

Explore Python operators, their symbols and keywords, and how they act on operands to perform arithmetic, comparison, and logical operations, preview four operator types and list operators in future lessons.

Explore arithmetic operators in Python that perform basic math operations, including addition, subtraction, multiplication, division, modulus, exponentiation, and floor division, with simple examples.

Learn Python assignment operators that assign values to variables of types like integers, floats, booleans, and strings, including in-place updates such as x += 3 and type casting to int.

Compare two operands with relational operators to yield a boolean. Learn equals, not equals, greater than, less than, greater than or equal, and less than or equal.

Define strings with single or double quotes, and escape quotes with backslashes. Concatenate with plus operator, index with zero-based and reverse indexing, slice, and cast non-strings to strings for concatenation.

Discover Python string methods to manipulate and analyze text, including capitalize, lower, upper, title, strip, find, replace, split, join, count, and natural language processing applications.

Explore data structures such as lists, tuples, sets, and dictionaries, and master membership and identity operators, while learning list indexing, slicing, mutability, and methods like append and remove.

Explore tuples, an immutable, ordered data structure in Python data science. Learn to create them with or without parentheses, unpack values, and store related information like location coordinates and dimensions.

Explore sets as unordered collections of unique elements, used to remove duplicates and perform union, intersection, and difference; create sets with set() or curly braces, and check membership with in.

Explore dictionaries in Python for data science, learning how to map keys to values, check membership with in, access with get, and modify via del and updates.

Explore the identity operator in Python by comparing lists with is and ==, revealing when objects share memory identity versus when they hold the same values.

Explore compound data structures by using nested dictionaries and lists of dictionaries, access elements with zero-based indexing, and add elements like oxygen while managing student records and grades.

Dive into Python loops, focusing on for loops and comprehensions, and learn how to iterate over lists, tuples, sets, and strings with use cases for data science.

Use the Python range function with for loops to generate index sequences, using start, stop, and step; stop is exclusive. Apply to iterate over ranges, and modify lists and tables.

Use for loops and range to create and modify lists by appending elements, such as capitalizing city names and squaring numbers, demonstrating iterative data transformations.

Explore looping through dictionaries in Python by iterating keys, values, and key-value pairs with for loops, using examples like a student dictionary to print names, ages, and majors.

Explore the enumerate function to iterate over an iterable while accessing both index and element, using for loops instead of range, with examples of numbers and strings.

Explore list comprehensions in Python as a concise way to create lists using square brackets, capitalizing the first letter in each word of city names from a cities list.

Explore list comprehension with conditionals to filter elements and produce a list, such as squaring even numbers from 0 to 8 and adding three to odd numbers with else clause.

Explore Python functions as building blocks for reusable, modular code, covering definitions, parameters, return values, and lambda functions, with hands-on examples like a grid function printing a greeting.

Discover function parameters in Python, learning how to pass values to a function and define parameters within the parentheses in the function definition, then print personalized greetings like Hello, John.

Learn how functions return values using the return keyword, store results in variables, and return various data types or structures, illustrated by a simple add function.

Define a Python function with default parameters, assign a default value to a parameter, and call it with fewer arguments to produce greetings such as hello guest or hello Alice.

Explore variable length arguments in Python, using * for positional arguments and ** for keyword arguments, and learn to define functions that accept any number of inputs and key-value pairs.

Learn how docstrings, as string literals after function definitions, document a function's purpose and usage in Python. See a multiply function example that explains inputs and the resulting product.

Explain function annotations that attach metadata to parameters and return values, using colon for parameters and the dash greater than symbol for returns, with divides example for floats and documentation.

Explore nested functions by defining an outer function that contains an inner function to encapsulate functionality, then observe how the outer prints a message before calling the inner.

Apply decorators to modify or extend function behavior with the @ symbol and a wrapper function, enabling pre- and post-execution messages.

Discover NumPy, the Python library for efficient numerical computations and multi-dimensional arrays; learn to install, check versions, and create arrays from lists, tuples, arange, linspace, zeros, and identity matrices.

Explore NumPy array attributes such as shape, ndim, size, and dtype; see how a 2x3 array reveals its shape, dimensions, total elements, and data type.

Explore array indexing and slicing in numpy to access and modify elements, using zero-based indexing, first and last elements, and end-exclusive slices.

Explore NumPy array operations, performing element-wise arithmetic (add, subtract, multiply, divide) and leveraging statistical functions (min, max, mean, standard deviation, sum) to analyze data.

Learn how to reshape numpy arrays to change their shape without altering data, converting 1D arrays to 2D forms such as 3x3 or 2x6 with reshape and same element count.

Explore stacking and splitting NumPy arrays, using vertical and horizontal stacking, and learn how array shapes and dimensions determine when each method works.

Learn how to split arrays into multiple parts using the np.split function, splitting at specified indices to produce three arrays with three, three, and six elements.

Explore how broadcasting lets NumPy operate on arrays of different shapes and speed computations. The lecture shows a 2x3 and a 1x3 array, illustrating row-wise broadcasting during addition.

Explore numpy boolean indexing to select array elements that satisfy conditions and filter data efficiently. See how a boolean mask identifies even values and enables targeted extraction from arrays.

Explore advanced array manipulation in NumPy, including reshaping with -1, transposing, flattening, and concatenating matrices, and learn to split arrays along rows or columns.

Explore the pandas library for data manipulation in Python, introducing series and dataframe structures for labeled data. Learn to import pandas, handle missing data, and merge datasets.

Create a pandas series, a one dimensional labeled array, from a list or dictionary, customize its index, and access or modify elements by index.

Explore DataFrame as a mutable, two-dimensional, heterogeneous table with labeled rows and columns, learn to create from dicts, access and modify columns, and add or drop columns.

Learn how to load data into a dataframe from csv, excel, and json files, and from a sqlite database using sql queries and print the dataframe head.

Identify and count NaN values in a pandas dataframe with isnull, then fill missing values with zero or the column mean using fillna, or drop affected rows or columns.

Explore basic dataframe operations in Pandas: sort by column a in descending order, filter rows where a > 2, and apply a lambda function to square non-null values.

Group data in pandas with group by on single or multiple columns, then compute aggregates—sum, mean, and standard deviation—for each group of points by team and sister school.

Learn to merge and join datasets with Pandas, using the merge method to combine dataframes on a common id, and explore inner, left, right, and outer joins.

Learn practical data cleaning techniques in pandas, including removing duplicates with drop_duplicates, standardizing gender values with replace, and binning ages with cut to create age groups.

Master machine learning with scikit-learn in Python, covering supervised and unsupervised algorithms, data preprocessing, model evaluation, feature selection, and practical setup in Google Colab.

Perform data pre-processing with scikit-learn by loading, exploring, and transforming the iris dataset; identify features, target, and distributions to build a classifier for species.

Demonstrate handling missing values in real-world datasets using scikit-learn's simple imputer with the mean strategy to replace NaN with feature means, and show imputation results.

Apply feature scaling techniques such as standard scaler and minmax scaler to align feature ranges, improving distance-based models like k-nearest neighbor and support vector machines.

Encode categorical data for machine learning by comparing label encoding and one hot encoding, using sklearn.preprocessing to transform a color dataset into numeric features.

Explore how decision trees use non-linear, spatial learning to classify and regress data by recursively partitioning the feature space, starting with the most important feature and iterating with thresholds.

Explore support vector machines, a powerful supervised classifier that finds the optimal hyperplane to maximize margin in linear and non-linear high-dimensional data via the kernel trick.