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

Explore a high-level tour of data science and machine learning with Python, covering Python basics, data frames, statistics essentials, and core machine learning algorithms, plus a Titanic case study.

Discover why Python is an integrated, object-oriented, high-level, interpreted language, and how it processes structured data for data science and machine learning, including churn prediction and business statistics.

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.

Download and install Anaconda with Python 3.7, choose the 64-bit or 32-bit installer, then use Spyder or Jupyter to write code and view results in the console and variable explorer.

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.

Save python codes by creating a folder, writing commands in a python notebook, and saving as an ipynb file in the datasets folder, then upload it to the Jupiter application.

Learn about single-element Python objects, including integers, floats, strings, and booleans, and use the type function to inspect them, create copies, and print results.

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 how object memory locations link lists so a copied list shares the same memory location; changes to one affect the other, demonstrated with id() checks.

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.

Explore creating a pandas series with homogeneous elements and verify its data type, while installing and importing the pandas package and checking integer 64 and floating types.

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 converting Python object types by turning tuples, lists, and sets into other objects via list, set, and dict conversions, including dictionaries from keys and 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 set operations in Python, including union, intersection, and difference, update sets, add or remove elements, and use discard to avoid errors.

Explore python operators, including arithmetic and membership operators, alongside logical operators. Learn how in and not in membership checks, and how = differs from == and from is in comparisons.

Explore Python operators, including arithmetic, division and modulo, exponentiation, and logical and or, with scenarios showing how and requires both conditions true and or requires at least one.

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.

Learn how nesting in if-else conditions builds a layered decision flow, using age and iq level to output minor, minor plus intelligent, minor plus normal, or adult.

Explore builtin functions, package-specific functions such as the math and statistics packages, and user defined functions, and learn to compute mean, standard deviation, and min/max.

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 Python loops, including for and while loops, with live examples using range to iterate and apply tasks like adding marks to student scores.

Explore for loops in Python with range-based iterations, printing values and iterating over lists of products, incrementing each item by five, and applying to student marks.

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.

Build a three-dimensional data cube from two related tables, linking customer age and income with mobile network service and broadband on its dimensions.

Explore array computations and functions, including element-wise operations, broadcasting, converting lists to arrays, and Python and NumPy tools like min, max, sum, unique, and nan-aware handling of missing values.

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.

Learn how to build pandas data frames in Python by using a dictionary to define columns and observations, create multiple rows, and customize indexes.

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.

Set and switch your working directory to a datasets folder to import and export data using CSV files, and understand how importing creates a data frame from the dataset.

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.