Data Science & ML for Python-Python & Data Science Made Easy

Leverage Python for application development and data processing, from batch data loading and web scraping to unstructured and semi-structured data analysis using neural networks and natural language processing.

Explore how to access and use a Jupyter-based application (via Anaconda) to write and run Python code in a single web-based notebook, with integrated input/output, line editing, and saving notebooks.

Learn the basics of Python commands, creating objects and elements, using hash for single-line comments and triple quotes for multi-line comments, and how to execute code with run or ctrl+enter.

Learn how multi-element objects such as tuples and lists hold heterogeneous data, compare creation methods and mutability, and use indexing and length to inspect and update elements.

Analyze sets as unordered collections of unique elements that remove duplicates and convert lists to sets; compare dictionaries with key-value pairs and list values.

Explore mutable and immutable data in Python, identify single-element types like integers, floats, booleans, and strings, and distinguish lists, sets, and dictionaries with their applicability.

Explore Python basics and commands, learn how None represents missing values, build and inspect lists, view objects with dir, and delete objects with del while avoiding name errors.

Learn how python packages group modules and objects, import them with prefixes or aliases, and selectively import functions from modules using the math package as examples.

Learn how packages contain modules and objects, how to import and configure them, alias package names, and selectively import functions using from and star import.

Explore indexing in python tuples with zero-based left-to-right position, using brackets to access elements, and apply negative indexing for right-to-left access, including slicing from start to end.

Learn to index lists and nested lists, access multi-level elements, and replace values within hierarchical data, such as language topics and employee counts.

Explore how to index sets and dictionaries in Python by converting sets to lists for element access, then converting back to sets, and index dictionaries with keys to retrieve values.

Explore list operations in Python, including list comprehension, appending elements, removing items by value or index, and joining lists with the plus operator.

Explore how to apply and/or logic to a customer eligibility scenario, combining income, debit cards, and credit score conditions to decide who qualifies for a credit card.

Explore how to implement if-else logic in Python using two conditions to categorize users by age (minor vs adult), including syntax, indentation, and sequential evaluation.

Learn to implement more than two conditions using an if-else chain to assign grades A, B, or C from marks, with >90 for A, 80–90 for B, and otherwise C.

Create user defined, non-parameterized functions with valid names, proper indentation, and a constant output, then call the function with parentheses to display its result.

Demonstrates parameterized functions that accept user inputs, concatenate dynamic data such as dates and temperatures, and compute client affluence from income, retirement years, and assets.

Define a Python user defined function with an if condition to check age, and use default parameter values for missing input, returning minor or adult.

Explore while loops in depth with a practical savings example that increments current savings by 500 until it reaches a 10,000 target, showing condition checks, iteration flow, and output.

Explore the numpy package and array in data science and machine learning, covering one-, two-, and three-dimensional arrays, matrix operations, and statistics like standard deviation, median, min, and max.

Explore NumPy arrays in Python, focusing on 1D and 2D structures, their shape and size, and setting dtypes like int32 and int64 with a customer age and income example.

Explore the pandas package overview, focusing on data frames and series, their columns and data types, and how to clean, concatenate, and merge data for unified datasets.

Explore creating a Pandas Series, adding a scalar, handling missing values, and understanding implicit type coercion and explicit dtype conversion for numeric computations.

Explore pandas dataframe indexing in two dimensions, using row and column indices or names, including selecting all columns, specific rows, and mixed name and number references.

Learn data wrangling with data frames, handling missing values by averaging, selecting and filtering columns using indices and names, and preparing the Boston housing dataset for regression.