Data Analytics A-Z with Python

Install Anaconda to get Python and all libraries in one distribution for data analytics; download the appropriate installer for Windows, Mac, or Linux and choose the correct version.

Explore the Jupyter notebooks interface, an interactive computing environment for writing Python code, managing cells, running code with shift+enter, and saving or exporting notebooks as HTML or ipynb files.

Explore multi-paradigm programming languages, comparing imperative, functional, procedural-oriented, and object-oriented styles with real-world language examples.

Python is open source, downloadable freely, and its code is available for contributions. You can customize it, contribute to the community, and add functionality through libraries and frameworks.

Explore how Python serves as a diversified programming language across disciplines, with Phosphine Zango for backend development, data analysis, machine learning, and libraries like Fadiga for behavior testing.

Compare interpretation-based and compilation-based programming languages, revealing how interpretation lets you check code line by line and catch errors during execution in data analytics with Python.

Discover how Python makes data analytics easy and simple by requiring fewer lines of code than C or Java, often a single statement.

Compare static typing and dynamic typing: static typing requires declaring a variable's type before use (as in C or Java), while dynamic typing assigns type from its value.

Explore Python's large standard library and thousands of external libraries that simplify complex tasks, reducing code from scratch and accelerating data analytics work.

Explore Python numbers in a Jupyter notebook, focusing on integers and floating point types, and learn basic operations like addition, division, and modulo.

Explore Python numeric operations, including exponentiation, roots, and basic arithmetic, then apply the correct order of operations and parentheses to control calculation outcomes.

Explore dynamic typing in Python, where variables do not need prior declaration and values like integers or strings are assigned automatically based on the data.

Explore strings in Python, a sequence of characters used to store text data. Learn declaring with single or double quotes, including quotes inside strings, indexing, and printing.

Master string operations in Python by measuring length, indexing with zero-based positions, and slicing with steps to extract substrings, including negative and backward indexing.

Discover Python string methods such as split and replace, learn to split by spaces or a custom keyword, and control breakpoints for parsing.

Explore the immutability of strings in Python, showing that once created, characters cannot be changed; practice concatenation, repetition, combining strings with numbers, and building matrices of strings.

Learn three Python string formatting methods—percent-based formatting, the format method, and a third approach—and use placeholders and dictionaries to dynamically inject variables in data analytics tasks.

Explore Python lists as mutable, heterogeneous sequences created with square brackets. Access elements via zero-based indexing and slicing, measure length with len, and extend lists by concatenation.

Learn Python list methods and properties to manipulate data, including concatenation, element removal, sorting, indexing, mapping, and converting lists into matrices.

Explore tuples in python: immutable, heterogeneous data collections. Learn indexing, slicing, counting occurrences, and using index, while noting that values cannot be changed once created.

Learn how Python dictionaries map keys to values, index by keys, and nest lists as values, performing common operations to manipulate and access dictionary data.

Explore Python dictionary methods and operations, including key access, value manipulation, declaring dictionaries, nesting, and retrieving keys and values.

Explore sets in Python, focusing on unique element storage, duplicate handling, and basic set operations; then learn booleans and None values for true/false logic.

Explore Python conditional statements, including if, elif, and else, and learn how indentation governs code blocks, enabling logical branching without semicolons.

Learn to use for loops to access and iterate over lists, printing values and applying conditional logic to display even numbers.

Explore how the while loop repeats a statement while a condition holds, using x < 5 and increments to stop when the condition is met in data analytics with Python.

Learn how functions in Python enable reusability by defining and calling functions, passing parameters, and printing output, with examples of function definitions and calls.

Decorators are functions that modify the functionality of other functions, enabling behavior changes without altering the original code, and the video shows how a decorator wraps a function.

learn how generator functions yield values and resume execution, and how they differ from normal functions, while generating values from a range without allocating all results and saving memory.

Explore object oriented programming in Python, where everything is an object, and learn how classes serve as blueprints for creating and storing data.

Explore how Python attributes work within classes and objects, using self, default methods, and instance variables to define, assign, and access data such as names and breeds.

Learn how methods inside a class operate on attributes using self and init, with a practical example calculating a circle's area and circumference from a radius.

Discover how inheritance lets a subclass reuse properties and methods from a parent class, using an animal and a dog to illustrate polymorphism.

Learn how polymorphism lets different classes share the same method and call it from the same place, without inheritance, using object examples to show methods working across diverse objects.

Explore NumPy, the numerical Python library that simplifies mathematical operations, enables vector computations, and offers interoperable, optimized, open-source tools with Python syntax for data analytics.

Install NumPy, import numpy as np, and create and print one-dimensional and two-dimensional arrays, exploring dimensions and dtype options, including handling complex numbers in NumPy arrays.

Explore NumPy data types, including boolean, integers (8/16/32/64), floating point and complex numbers, and practice declaring variables, arrays, and one-dimensional structures with Indian notation.

Explore NumPy array attributes such as shape, dimensions, and itemsize, and learn how reshaping arrays and printing dimensions works.

Learn to use NumPy to perform common mathematical operations, including square roots, floor values, and combining expressions with simple syntax.

Learn to use pandas, a Python package for flexible data handling of tabular and real-time datasets, enabling streamlined data analytics and machine learning.

Learn to read data with pandas, load the iris dataset from a local file or link, inspect the dataframe, adjust headers, and remove duplicates for clean analysis.

Explore pandas data manipulation by inspecting a dataset's columns and shapes, renaming columns, and using describe to get quick statistics on numeric columns and unique values.

Master pandas data manipulation by dropping rows or columns, inspecting with head, using loc to access labels, and adding new columns to a 150-row dataset.

Explore Matplotlib to visualize data with figures and graphs, including line charts and pie charts, and learn styling for clearer analytics and insights.

Plot arrays with matplotlib by creating line and scatter plots, customizing color, size, and alpha, and exploring random values and simple formulas like y = mx + b.

Learn to customize plots in matplotlib by applying colors, markers, line styles, and labels, and explore combining styles for clearer data visualizations.

Learn to customize plots in matplotlib by adding x and y axis labels, a title, and explicit axis ranges, while experimenting with colors, line widths, and various options.

Explore variants of graphs by styling axes, enabling grids, configuring linear and logarithmic scales, and creating pie charts and histograms with labeled segments, start angles, and explode effects.

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