Data Analysis Fundamentals | Python | ChatGPT 3.5

Data Analysis Fundamentals | Python | ChatGPT 3.5, available at $94.99, has an average rating of 4.2, with 41 lectures, 51 quizzes, based on 27 reviews, and has 2217 subscribers.

You will learn about Rapidly analyze data using Python, grasping essential concepts and techniques to efficiently extract insights from raw information. Configure and set up ChatGPT to enhance your Python programming experience, leveraging AI to streamline your coding tasks. Implement efficient data cleaning strategies, including handling various Python datatypes, addressing missing values, outliers, and duplicate entries. Leverage advanced data preprocessing techniques, such as sorting, filtering, merging datasets, etc. Navigate and manage data distribution effectively, ensuring your data is properly structured and ready for analysis. Confidently work with date variables, ensuring accurate and meaningful analysis of time-based data. Learn data visualizations using Python and ChatGPT, including bar charts, pie charts, line plots, histograms, box plots, and scatter plots. Apply the acquired skills through practical exercises and hands-on practice, reinforcing your understanding of each concept and technique. Gain understanding of Python's basic syntax, data types, variables, and operators, enabling you to write simple programs and perform basic operations. Learn to utilize control structures like loops and conditional statements such as use if, elif and else to manage program flow effectively. Acquire skills in working with fundamental data structures in Python, such as lists, dictionaries, tuples, and sets. Learn how to manipulate, access, and modify these structures for diverse programming needs. This course is ideal for individuals who are Beginner and aspiring data analysts. or Students or individuals entering the field of data analysis. or Python enthusiasts eager to apply their skills to real-world data scenarios. or Curious about using AI-assisted coding with ChatGPT for data analysis. It is particularly useful for Beginner and aspiring data analysts. or Students or individuals entering the field of data analysis. or Python enthusiasts eager to apply their skills to real-world data scenarios. or Curious about using AI-assisted coding with ChatGPT for data analysis.

Enroll now: Data Analysis Fundamentals | Python | ChatGPT 3.5

Summary

Title: Data Analysis Fundamentals | Python | ChatGPT 3.5

Price: $94.99

Average Rating: 4.2

Number of Lectures: 41

Number of Quizzes: 51

Number of Published Lectures: 41

Number of Published Quizzes: 51

Number of Curriculum Items: 92

Number of Published Curriculum Objects: 92

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Rapidly analyze data using Python, grasping essential concepts and techniques to efficiently extract insights from raw information.
Configure and set up ChatGPT to enhance your Python programming experience, leveraging AI to streamline your coding tasks.
Implement efficient data cleaning strategies, including handling various Python datatypes, addressing missing values, outliers, and duplicate entries.
Leverage advanced data preprocessing techniques, such as sorting, filtering, merging datasets, etc.
Navigate and manage data distribution effectively, ensuring your data is properly structured and ready for analysis.
Confidently work with date variables, ensuring accurate and meaningful analysis of time-based data.
Learn data visualizations using Python and ChatGPT, including bar charts, pie charts, line plots, histograms, box plots, and scatter plots.
Apply the acquired skills through practical exercises and hands-on practice, reinforcing your understanding of each concept and technique.
Gain understanding of Python's basic syntax, data types, variables, and operators, enabling you to write simple programs and perform basic operations.
Learn to utilize control structures like loops and conditional statements such as use if, elif and else to manage program flow effectively.
Acquire skills in working with fundamental data structures in Python, such as lists, dictionaries, tuples, and sets.
Learn how to manipulate, access, and modify these structures for diverse programming needs.

Who Should Attend

Beginner and aspiring data analysts.
Students or individuals entering the field of data analysis.
Python enthusiasts eager to apply their skills to real-world data scenarios.
Curious about using AI-assisted coding with ChatGPT for data analysis.

Target Audiences

Beginner and aspiring data analysts.
Students or individuals entering the field of data analysis.
Python enthusiasts eager to apply their skills to real-world data scenarios.
Curious about using AI-assisted coding with ChatGPT for data analysis.

Welcome to “Data Analysis Fundamentals in Python & ChatGPT,” a comprehensive course designed to empower learners with essential skills in data analytics. In this course, we will delve into the fundamental concepts and techniques of data analysis using the Python programming language, coupled with the integration of ChatGPT for streamlined coding experiences. This course aims to provide a holistic understanding of data analysis, from rapid data processing to effective visualization, ensuring participants are well-prepared to handle real-world data challenges.

Core Learning Objectives Throughout the course, you will develop proficiency in various critical areas. Firstly, you will rapidly analyze data using Python, gaining insights into essential concepts and techniques that facilitate efficient information extraction. Secondly, you will explore the integration of ChatGPT to enhance your Python programming skills, utilizing artificial intelligence to streamline coding tasks. Additionally, the course emphasizes the implementation of effective data cleaning strategies, addressing diverse Python datatypes, handling missing values, outliers, and eliminating duplicate entries.

Python Basics, Control Structures, and Data Structures To provide a solid foundation, the course covers Python basics, including syntax, data types, variables, and operators. Participants will gain proficiency in writing simple programs and performing basic operations. Furthermore, the course explores control structures such as loops and conditional statements, enhancing program flow management. A comprehensive understanding of fundamental data structures, including lists, dictionaries, tuples, and sets, is emphasized, enabling learners to manipulate, access, and modify data structures for diverse programming needs. By the course’s conclusion, participants will be equipped with a versatile skill set, ready to undertake data analysis projects confidently using Python and ChatGPT.

Techniques and Data Distribution Management Building on the foundational skills, participants will delve into advanced data preprocessing techniques, such as sorting, filtering, and merging datasets. This prepares the data for meaningful analysis, ensuring accuracy and reliability. The course also covers effective data distribution management, teaching participants how to structure and organize data for optimal analysis. Moreover, a dedicated section focuses on time-based data analysis, equipping learners to confidently work with date variables for accurate insights.

Data Visualizations and Practical Application An integral part of the course involves mastering data visualizations using Python and ChatGPT. Participants will explore various visualization techniques, including bar charts, pie charts, line plots, histograms, box plots, and scatter plots. The emphasis on practical application is maintained through hands-on exercises and real-world examples, allowing learners to apply their acquired skills in a practical setting. This approach reinforces understanding and ensures participants are well-prepared to tackle diverse data analysis challenges.

Course Curriculum

Chapter 1: Necessary foundation on Data manipulation

Lecture 1: Understanding complete data manipulation process

Lecture 2: Connect with my youtube channel

Lecture 3: Get my special handbooks

Chapter 2: Introducing ChatGPT for Easy Coding!

Lecture 1: Setting up ChatGPT

Lecture 2: Effortless python programming with ChatGPT (An Overview)

Chapter 3: Necessary foundation on Python

Lecture 1: Python for Data Analysis (An Overview)

Lecture 2: Installation and activation of Python for windows

Lecture 3: Installation and activation of Python for mac

Chapter 4: Understanding Python Programming Fundamentals – Level 1

Lecture 1: Your First Python Code: Getting Started

Lecture 2: Variables and naming conventions

Lecture 3: Data types: integers, float, strings, boolean

Lecture 4: Type conversion and casting

Lecture 5: Arithmetic operators (+, -, *, /, %, **)

Lecture 6: Comparison operators (>, =, <=, ==, !=)

Lecture 7: Lists: creation, indexing, slicing, modifying

Chapter 5: Understanding Python Programming Fundamentals – Level 2

Lecture 1: Sets: unique elements, operations

Lecture 2: Dictionaries: key-value pairs, methods

Lecture 3: Logical operators (and, or, not)

Lecture 4: Conditional statements (if, elif, else)

Lecture 5: Logical expressions in conditions

Lecture 6: Looping structures (for loops, while loops)

Lecture 7: Defining, Creating and Calling functions

Chapter 6: Complete Understanding & Application of Data Cleaning

Lecture 1: Loading dataset in Jupyter notebook environment

Lecture 2: Identifying and assigning correct data types

Lecture 3: Defining and imputing techniques of missing values

Lecture 4: Defining and removing abnormal values or harmful outliers

Lecture 5: Defining and removing duplicates values from dataset

Chapter 7: Complete Understanding & Application of Data Preprocessing

Lecture 1: Feature encoding: assigning numeric values to categorical variables

Lecture 2: Feature binning: converting numeric variables into categorical variables

Lecture 3: Creating dummy variables of categorical variables

Lecture 4: Sorting and filtering dataset following conditions

Lecture 5: Merging two datasets horizontally or adding extra variables

Lecture 6: Concatenating two datasets vertically or adding extra data

Lecture 7: Various methods of data transforming

Lecture 8: Extracting days, months and year from date variable

Chapter 8: Complete Understanding & Application of Data Analysis

Lecture 1: Exploring Categorical Data with Value Counts and Vibrant Bar Charts

Lecture 2: From Percentages to Pie Charts for Deeper Understanding

Lecture 3: Analyzing Categorical Data with Group By and Dynamic Line Plots

Lecture 4: Dive into the Distribution of Numeric Data with Histograms and Box Plots

Lecture 5: Discovering Relationships Between Numeric Variables with Scatterplot

Chapter 9: Practice Time!!!

Lecture 1: Download the attached datasets for Practice

Instructors

Analytix AI
Unleashing the Power of Data with AI for Informed Insights.

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1 stars: 1 votes
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3 stars: 1 votes
4 stars: 5 votes
5 stars: 20 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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