• 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)

• 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

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:

1. Getting Started With The Fundamentals of Python Programming: Begin your journey with a solid foundation in Python, understanding its syntax, variables, and data types.
2. How to Create Project Files with Basic Python Syntax: Learn to set up and organize your Python projects efficiently, ensuring a smooth workflow.
3. Strings In Python Programming: Manipulate and handle text data effectively with Python’s powerful string methods.
4. Operators In Python Programming: Master various operators, including arithmetic, relational, and logical operators, to perform complex operations in your programs.
5. List In Python Programming: Work with lists to store, access, and manipulate collections of data.
6. Tuple In Python Programming: Learn about tuples and how to use them for immutable sequences of data.
7. Set In Python Programming: Explore sets and their applications for storing unique elements.
8. Dictionary In Python Programming: Understand dictionaries for key-value pair storage and retrieval.
9. Decision Making Statements In Python Programming: Implement conditional statements like if, else, and elif to control the flow of your programs.
10. Loop Systems In Python Programming: Automate repetitive tasks with for and while loops to enhance your program’s efficiency.
11. Functions, Lambda, and Arrays: Create reusable code blocks with functions, utilize lambda expressions for short functions, and work with arrays for efficient data storage.
12. Iterators In Python Programming: Learn how to use iterators to traverse through all elements of a collection.
13. File Handling In Python Programming: Read from and write to files, enabling data persistence and advanced data management.
14. Python Programming Concepts: Delve into advanced Python concepts to enhance your programming skills.
15. String Formatting: Format strings for better readability and presentation of your data.
16. 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.
17. Introduction to Python for Data Science: Transition into data science with an introduction to its core concepts and applications.
18. Python Libraries for Data Science: Explore essential Python libraries for data science, including NumPy, Pandas, and Matplotlib.
19. NumPy Library: Learn to perform numerical operations and handle arrays with NumPy.
20. Pandas Library: Master data manipulation and analysis using the Pandas library.
21. Matplotlib Library: Visualize data effectively with Matplotlib’s powerful plotting capabilities.
22. Sampling Data in Data Science: Understand the importance of sampling and how to sample data for analysis.
23. How to Read Data: Learn various methods to read data from different sources.
24. How to Sample Data: Implement sampling techniques to work with subsets of your data.
25. Read Data from External Files: Import data from external files into your Python programs.
26. Data to CSV and TXT Formats: Save and export your data in CSV and TXT formats for easy sharing and analysis.
27. Convert and Read Data in CSV Format: Convert your data into CSV format and read CSV files in Python.
28. Convert TXT File to Table: Transform text files into tabular data for easier analysis.
29. Data Preparation in Data Science: Prepare your data for analysis by cleaning, transforming, and organizing it.
30. Series Data Structure: Work with Pandas Series for one-dimensional labeled data.
31. Data Frame Structure: Master Pandas DataFrames for two-dimensional labeled data structures.
32. 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:

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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.