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Deep Learning into Python with Numpy, Pandas and Matplotlib

Name: Deep Learning into Python with Numpy, Pandas and Matplotlib
Rating: 4.5 (219 reviews)

Deep Learning into Python Programming for Beginners with Introduction to Numpy, Pandas and Matplotlib for Data Science

Created byEmenwa Global, George Steve, Juliet Rona

Last updated 4/2026

English

English [Auto],

What you'll learn

Use Python for Data Science and Machine Learning
Learn to use NumPy for Numerical Data
Learn to use Pandas for Data Analysis
Learn to use Matplotlib for Python Plotting
Master the basic syntax in python programming
Master the Data Types in Python programming
You will understand Variables in Python programming
Master the Loop systems in Python programming
You will understand Operators in Python programming
You will understand File Input/Output in Python programming
Get the in-depth knowledge in the use of OOP (Object Oriented Programming)

Course content

26 sections • 155 lectures • 16h 35m total length

Download and installation of Python IDE9:08
Download and install the PyCharm community edition, then create a new Python project and configure the interpreter with essential packages like numpy, matplotlib, and OpenCV.
How to create project files with basic python syntax5:37
Learn to create Python project files, write syntax with a hello world using the print function, and run the program; explore variables, comments, data types, dictionaries, tuples, and lists.
Ask Questions, Get Support & Achieve Your Dream!0:49
Comments7:30
Explore how Python comments explain code, boost readability, and support testing and maintenance. Learn single-line comments with # and multi-line comments with triple quotes, and how to place them.
Variables11:48
Learn how Python treats variables as containers for data with no declaration required; assign values to create variables, explore integers, floats, and strings, and inspect types and casting.
Rules of naming a variable5:42
Learn the rules for naming variables in Python: valid characters are letters, digits, and underscores; names are case sensitive and cannot start with a number.
How to assign multiple values to variables6:36
Learn how to assign multiple values to Python variables, print them on one line, and unpack a collection into x, y, and z, including lists and tuples.
Global Variable8:58
Explore global and local variables in Python, showing how a function uses outer variables, how local scope shadows globals, and how to define and call a function with def.
Global Keyword5:42
Demonstrates using the global keyword to turn a local variable into a global one in Python, via a hello function and printing Paul versus Peter.
Data Types9:22
Explore Python data types, including strings, integers, floats, booleans, sequences (lists and ranges), mappings (dict), sets (set and frozenset), and binary types (bytes, memoryview), with type checks and casting.
Casting5:40
Learn how casting in Python converts values to int, float, or string using constructor functions, ensuring correct data types for numerical and string operations.

Introduction To String6:30
Explore Python strings, using single or double quotes, print statements, and assigning text to variables. Learn to create multi-line strings with triple quotes and manage line continuation with backslashes.
String Arrays12:42
Explore string arrays in Python; learn that strings are sequences of Unicode characters, index with zero-based positions, measure length with len, and use a for loop to traverse text.
String Slice7:54
Learn Python string slicing with the slice syntax, using start and end indices, positive and negative indexing, and understand that end is exclusive through examples like ETA, Peter, and Paul.
String Modifiers7:16
Explore Python string modifiers, using built-in methods like upper, lower, strip, replace, and split to transform text and manage whitespace.
String Concatenation3:19
Explore string concatenation in Python using the plus operator to join x and y and print the result. Learn to add spaces and practice fundamental Python string concepts.
String Format7:18
Learn how to format strings in Python using the format method, curly braces, and zero-based placeholders. Explore combining integers and strings with indexing 0, 1, 2 and casting when needed.
Escape Character6:18
Explore escape characters in Python, learn how backslashes manage quotes, newlines, and spaces, and see how raw strings and print handle text.

Arithmetic Operators4:53
Learn arithmetic operators in Python, including addition, subtraction, multiplication, division, modulus, floor division (fraud division), and float division, with x=6 and y=4 to show results.
Assignment Operators4:58
Explore Python assignment operators, including +=, -=, *=, /=, % and //, with step-by-step examples showing how they update variables and relate to arithmetic operators.
Comparison Operators4:55
Explore Python comparison operators that compare two values and return boolean results, using ==, !=, >, <, >=, and <= with x=6 and y=3 as examples.
Logical Operator5:00
Explore logical operators in Python, including and, or, and not. Learn how these operators combine conditions like x > 5 and x < 10 to produce true or false.
Identity Operator5:26
Learn how the identity operator 'is' checks object identity versus equality in Python, illustrated with arrays, and how in and not in test membership.

Introduction To List7:36
Discover how Python lists work, including ordering, mutability, and duplicates; compare lists with tuples, sets, and dictionaries, use square brackets or the list constructor, and check length.
Access List Items8:15
Access list items in Python by indexing and slicing, using positive and negative indices and the in operator for membership checks.
Change List Items7:12
Explore changing elements in a list by selecting an index or a range (for example 0 to 2), and inserting new values with the insert method to modify the list.
Add List Items8:20
Learn how to manipulate Python lists with insert, append, and extend, inserting at an index, appending items, and extending lists with other lists or objects.
Remove List Items4:17
Learn how to remove items from a Python list using remove, pop, del, and clear. Remove by value or by index, including the last item, and clear the entire list.
List Loop7:58
Learn to loop through list items in Python using for loops, while loops, and list comprehensions, printing each item and using range and length to access indices.
List Comprehension18:31
Explore list comprehension in Python to compress multi-line loops into a single expressive line, using for loops, conditions, and practical examples like filtering by letters, uppercasing, and range.
List Sorting11:27
Explore how to sort lists in Python by alphabetical and numerical order, control ascending or descending order, and apply a custom function, while addressing spaces and case sensitivity.

Introduction To Tuple10:22
Discover how Python tuples store multiple items in a single variable with ordered, immutable elements, support mixed data types, and create single item tuples with a trailing comma.
Access Tuple5:10
Learn how to access items in a Python tuple using positive and negative indexing, slice ranges, and print specific elements like pen, book, and eraser.
Tuple Update10:10
Learn how to update a Python table by converting it to a list, then append items, update by index, remove entries, or delete the table.
Unpack Tuples5:38
Learn python tuple packing and unpacking by assigning items to variables, using extended unpacking for remaining values as a list, and printing outcomes to understand sequence unpacking.
Tuple Loop7:41
Explore looping through tuples in Python with for and while loops, printing items by value or by index using range and length, and practice indexing the hello variable.
Join Tuples4:31
Join tables in Python by concatenating strings and numbers, then learn how to add and multiply table data, printing results to verify the combined outputs.

Introduction To Set8:22
Explore Python sets, a built-in unordered data type that stores unique items using curly braces or the set constructor, with examples of length and basic operations.
Access Set3:47
Learn to assess Python set items with a for loop, recognizing that sets are unordered and unindexed, and use in for membership checks; adding items may keep the set unchanged.
Add Set5:15
Learn how to add new items to a set in python using add and update, including handling unordered results and mixing in lists, while distinguishing set syntax with curly braces.
Remove Set5:06
Learn how to remove items from a Python set using remove, discard, pop, and del, including behavior for unordered sets and clearing an empty set.
Join Sets9:41
Master how to join two or more sets in Python using union and update, and explore intersection, symmetric difference, and creating new versus updating existing sets.

Introduction To Dictionary8:23
Learn how dictionaries store data as key-value pairs, access items by key, stay unordered and not indexed, avoid duplicates, and use length and mixed data types in Python.
Access Dictionary Items6:43
Explore accessing dictionary items in Python with key indexing, the get method, and keys, values, and items, and learn to check key existence with in.
Add And Update A Dictionary4:50
Learn how to add new items to a Python dictionary and update existing ones, using keys like surname and name, with update and print to verify results.
Remove Dictionary Items5:04
Remove dictionary items in Python using pop, pop item, delete keyword, and clear. Learn how these methods affect keys such as name and the dictionary, and note Python 3 behavior.
Loop Dictionary Items7:44
Explore looping over Python dictionaries by printing keys, values, and items, using for loops and methods like keys(), values(), and items(), plus copying dictionaries with copy() or dict().

if statement6:15
Explore the Python if statement, a core decision tool, with operators like equal, not equal, less than, less than or equal, greater than, and nesting, including indentation, pass, and elif.
Elif and Else statements5:47
Explore how elif and else extend Python's if statements to handle multiple conditions, using x and y comparisons (equal, greater than, less than) and the else fallback.
Ternary Operator9:05
Explore Python's ternary operator and one-line shorthand for if statements, using x and y comparisons, elif and else in single lines, and combining and or for multiple conditions.

While Loop7:49
The lecture explains the while loop in Python, including printing numbers from 1 to 10, incrementing x, and control flow with break, continue, and else.
For Loop7:18
Master Python for loops to iterate over lists, tuples, sets, and dictionaries, using break, continue, and range to print items and control flow.
More on for loop4:54
Explore Python for loops with range, start and stop values, and printing sequences, including break and else logic for controlling loop flow.

Introduction To Functions5:43
Learn how to define and call functions in Python using the keyword 'Dave', create function blocks with indentation, and use print and for loops with lists.
Function, Argument and Parameter20:40
Explore functions, arguments, and parameters in Python, clarifying how parameters become arguments, and illustrate default, keyword, and positional usage with returns and the pass statement.
Lambda In Python Programming10:42
Explore how lambda creates anonymous functions in Python, taking multiple arguments and parameters with a single expression, and learn to use lambdas inside other functions.
Arrays6:54
Learn how Python uses lists as arrays, access and modify elements by index, and manage length; explore for loops, append, pop, and remove, and preview numpy import.

Requirements

No basic knowledge of computer programming is required for this course
You must have a good computer system. A computer (Windows/Mac/Linux)
You’ll need to install Anaconda. We will show you how to do that step by step

Description

Unlock the world of Python programming and data science with our comprehensive course, “Deep Learning into Python with Data Science for Absolute Beginners.” Designed specifically for beginners, this course takes you from the basics of Python to the foundations of data science. Through detailed lessons, hands-on projects, and expert guidance, you’ll gain the skills and confidence to excel in Python programming and data analysis.

What You’ll Learn:

Getting Started With The Fundamentals of Python Programming: Begin your journey with a solid foundation in Python, understanding its syntax, variables, and data types.
How to Create Project Files with Basic Python Syntax: Learn to set up and organize your Python projects efficiently, ensuring a smooth workflow.
Strings In Python Programming: Manipulate and handle text data effectively with Python’s powerful string methods.
Operators In Python Programming: Master various operators, including arithmetic, relational, and logical operators, to perform complex operations in your programs.
List In Python Programming: Work with lists to store, access, and manipulate collections of data.
Tuple In Python Programming: Learn about tuples and how to use them for immutable sequences of data.
Set In Python Programming: Explore sets and their applications for storing unique elements.
Dictionary In Python Programming: Understand dictionaries for key-value pair storage and retrieval.
Decision Making Statements In Python Programming: Implement conditional statements like if, else, and elif to control the flow of your programs.
Loop Systems In Python Programming: Automate repetitive tasks with for and while loops to enhance your program’s efficiency.
Functions, Lambda, and Arrays: Create reusable code blocks with functions, utilize lambda expressions for short functions, and work with arrays for efficient data storage.
Iterators In Python Programming: Learn how to use iterators to traverse through all elements of a collection.
File Handling In Python Programming: Read from and write to files, enabling data persistence and advanced data management.
Python Programming Concepts: Delve into advanced Python concepts to enhance your programming skills.
String Formatting: Format strings for better readability and presentation of your data.
Object Oriented Programming In Python (OOP): Dive deep into OOP concepts such as classes, objects, inheritance, polymorphism, and encapsulation to create modular and reusable code.
Introduction to Python for Data Science: Transition into data science with an introduction to its core concepts and applications.
Python Libraries for Data Science: Explore essential Python libraries for data science, including NumPy, Pandas, and Matplotlib.
NumPy Library: Learn to perform numerical operations and handle arrays with NumPy.
Pandas Library: Master data manipulation and analysis using the Pandas library.
Matplotlib Library: Visualize data effectively with Matplotlib’s powerful plotting capabilities.
Sampling Data in Data Science: Understand the importance of sampling and how to sample data for analysis.
How to Read Data: Learn various methods to read data from different sources.
How to Sample Data: Implement sampling techniques to work with subsets of your data.
Read Data from External Files: Import data from external files into your Python programs.
Data to CSV and TXT Formats: Save and export your data in CSV and TXT formats for easy sharing and analysis.
Convert and Read Data in CSV Format: Convert your data into CSV format and read CSV files in Python.
Convert TXT File to Table: Transform text files into tabular data for easier analysis.
Data Preparation in Data Science: Prepare your data for analysis by cleaning, transforming, and organizing it.
Series Data Structure: Work with Pandas Series for one-dimensional labeled data.
Data Frame Structure: Master Pandas DataFrames for two-dimensional labeled data structures.
And Many More: Continue to build your skills with additional topics and projects designed to reinforce your learning and prepare you for real-world challenges.

Why Enroll in This Course?

Comprehensive Curriculum: Covering all essential topics from Python basics to data science, ensuring a thorough understanding and skillset.
Hands-On Projects: Gain practical experience with real-world projects that solidify your learning.
Beginner-Friendly: No prior programming experience required, making this course accessible to everyone.
Expert Instruction: Learn from experienced instructors who provide clear explanations and step-by-step guidance.
Lifetime Access: Revisit course materials anytime and learn at your own pace.
Community Support: Join a community of learners to share knowledge, seek help, and collaborate on projects.

By the end of this course, you’ll have the confidence and skills to tackle any Python programming and data science challenge, positioning you for success in the industry. Enroll now and start your journey to becoming a Python programming and data science expert!

Knowlegde Base:

Python programming course, learn Python programming, data science for beginners, Python basics, Python data structures, Python strings, Python operators, Python loops, Python functions, Python OOP, Python file handling, Python data science libraries, NumPy, Pandas, Matplotlib, data sampling, data preparation, Python data analysis, master Python programming, beginner to advanced Python programming.

Python is an interpreted, high-level and general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented, and functional programming. Python is often described as a "batteries included" language due to its comprehensive standard library.

Python was created in the late 1980s as a successor to the ABC language. Python 2.0, released in 2000, introduced features like list comprehensions and a garbage collection system with reference counting.

Python 3.0, released in 2008, was a major revision of the language that is not completely backward-compatible, and much Python 2 code does not run unmodified on Python 3.

The Python 2 language was officially discontinued in 2020 (first planned for 2015), and "Python 2.7.18 is the last Python 2.7 release and therefore the last Python 2 release." No more security patches or other improvements will be released for it. With Python 2's end-of-life, only Python 3.6.x and later are supported.

Some Fundamentals of Python programming that were covered in this course are as follows:

1. Basic Python programming Syntax

2. Data Types

3. Variables

4. Loops

5. Operators

6. Decision Making Statement

7. File Input/Output

8. Sample Projects

9. Object Oriented Programming

10. Error Handling

11. Functions, lambda and Arrays

The advancement of technology has brought about an explosion in data collection and usage. Many industries rely on data science to develop more innovative and advanced products. In the last decade, the volume and variety of available data have increased dramatically, necessitating the development of new skills and the creation of entirely new occupations.

I am guessing you saw the hike too, and want in on the juicy tech space. You are in for a big treat. But this introduction will not be an introduction if we don’t know what we are dealing with. Allow me to introduce Data Science.

Data Science is a combo of several fields in IT where we use algorithms and scientific processes to extract facts from data and use them to create insights.

Data science entails using various techniques to draw conclusions from accumulated data. A data scientist's job is to take an intricate business issue, distill the relevant information into data, and apply that data to the problem. You may wonder what this means for you personally and where to begin.

All that's required is a head for ideas and a solid grasp of the ins and outs of a particular industry, both of which you undoubtedly possess. In data science, fraud, particularly online fraud, is a hot topic. Data scientists employ their expertise in this area by developing algorithms to monitor and prevent fraudulent activity. This data science beginner course will provide an excellent place to begin.

This comprehensive guide will teach you everything you need to know to get started in data science, from the various job opportunities available to data scientists to the practical applications of data science. You should begin this data science tutorial by reading up on the job description for a data scientist.

Many businesses and individuals are shifting their attention to big data and AI. It's shocking to think that over 2.5 exabytes of data are produced and extracted by individuals and institutions daily. Since then, there has been a meteoric rise in the quantity of data. Most businesses have shifted to rely heavily on data to make decisions. As a result, some companies have established dedicated data-analysis divisions.

Statisticians conduct quantitative historical data analyses, which is still insufficient because the analysis's findings would be limited to the present. Analysis was previously performed manually, but this task has been automated mainly with the advent of robust computing processes, cloud technology, and analytical tools. They started working on data analysis models.

Before delving into the many facets of data science, let's grasp what it actually is. Data science, in its simplest definition, is the application of mathematics and statistics to large datasets to draw meaningful conclusions about patterns and relationships within the data. Using your programming, business, and analytical skills, you can manage and process the data set. You have to admit, this sounds challenging. Most people lack the knowledge and understanding necessary to work effectively with data science and improve their skills in this area.

Why Must I Take This Course And What Benefit Is It To ME As A Python Programmer?

This is the only course on the internet that will help you to become a certified and successful programmer with an in-depth knowledge of the entire aspect of Python programming and prepare you with the required skills necessary to build you to face job interviews and get employed as a full stack Software developer.

Who this course is for:

Anyone who wants to learn to code.
The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
You should take this course if you want to become a Data Scientist or if you want to learn about the field

Deep Learning into Python with Numpy, Pandas and Matplotlib

What you'll learn

Explore related topics

Course content

Getting Started With The Fundamentals of Python Programming11 lectures • 1hr 17min

Strings In Python Programming7 lectures • 51min

Operators In Python Programming5 lectures • 25min

List In Python Programming8 lectures • 1hr 14min

Tuple In Python Programming6 lectures • 44min

Set In Python Programming5 lectures • 32min

Dictionary In Python Programming5 lectures • 33min

Decision Making Statements In Python Programming3 lectures • 21min

Loop Systems In Python Programming3 lectures • 20min

Functions, Lambda and Arrays4 lectures • 44min

Requirements

Description

Who this course is for: