Fast-Track Data Analytics 3 in 1: Excel Python + ChatGPT 3.5

Fast-Track Data Analytics 3 in 1: Excel Python + ChatGPT 3.5, available at $19.99, has an average rating of 3.75, with 81 lectures, 44 quizzes, based on 93 reviews, and has 1233 subscribers.

You will learn about Learn Python's syntax, data types, variables, and operators to construct simple programs and execute basic functions. To manage program flow, use loops and conditional statements like if, elif, and else. Learn to use Python lists, dictionaries, tuples, and sets. Learn to access, change, and manipulate these structures for various programming needs. Understand and apply techniques for cleaning and preparing raw data in Excel. Learn to identify and handle missing data, outliers, and inconsistencies. Utilize Excel functions and tools for data validation and transformation. Explore fundamental statistical concepts and their application in Excel. Learn to perform descriptive statistics, inferential statistics, and hypothesis testing using Excel functions and tools. Understand how to interpret and communicate statistical results effectively. Design and build interactive dashboards in Excel for effective data visualization. Learn to use PivotTables, PivotCharts, and slicers to create dynamic and user-friendly dashboards. Explore various data visualization techniques available in Excel, including charts, graphs. Learn and apply the data analysis methodology, from data cleaning to hypothesis testing, in real-world applications. Increase your critical thinking and problem-solving skills for data analysis, decision-making, and recommendation. Use value counts, percentage, group by, pivot tables, correlation, and regression professionally and realistically. Solve over 60+ real-world data analytical questions to practice applying data analysis to various circumstances. Emphasize practical application to gain valuable insights from data and create educated judgments and suggestions. This course is ideal for individuals who are Data Enthusiasts and Aspiring Analysts or Python and Excel Enthusiasts It is particularly useful for Data Enthusiasts and Aspiring Analysts or Python and Excel Enthusiasts.

Enroll now: Fast-Track Data Analytics 3 in 1: Excel Python + ChatGPT 3.5

Summary

Title: Fast-Track Data Analytics 3 in 1: Excel Python + ChatGPT 3.5

Price: $19.99

Average Rating: 3.75

Number of Lectures: 81

Number of Quizzes: 44

Number of Published Lectures: 81

Number of Published Quizzes: 44

Number of Curriculum Items: 126

Number of Published Curriculum Objects: 126

Original Price: $27.99

Quality Status: approved

Status: Live

What You Will Learn

Learn Python's syntax, data types, variables, and operators to construct simple programs and execute basic functions.
To manage program flow, use loops and conditional statements like if, elif, and else.
Learn to use Python lists, dictionaries, tuples, and sets.
Learn to access, change, and manipulate these structures for various programming needs.
Understand and apply techniques for cleaning and preparing raw data in Excel.
Learn to identify and handle missing data, outliers, and inconsistencies.
Utilize Excel functions and tools for data validation and transformation.
Explore fundamental statistical concepts and their application in Excel.
Learn to perform descriptive statistics, inferential statistics, and hypothesis testing using Excel functions and tools.
Understand how to interpret and communicate statistical results effectively.
Design and build interactive dashboards in Excel for effective data visualization.
Learn to use PivotTables, PivotCharts, and slicers to create dynamic and user-friendly dashboards.
Explore various data visualization techniques available in Excel, including charts, graphs.
Learn and apply the data analysis methodology, from data cleaning to hypothesis testing, in real-world applications.
Increase your critical thinking and problem-solving skills for data analysis, decision-making, and recommendation.
Use value counts, percentage, group by, pivot tables, correlation, and regression professionally and realistically.
Solve over 60+ real-world data analytical questions to practice applying data analysis to various circumstances.
Emphasize practical application to gain valuable insights from data and create educated judgments and suggestions.

Who Should Attend

Data Enthusiasts and Aspiring Analysts
Python and Excel Enthusiasts

Target Audiences

Data Enthusiasts and Aspiring Analysts
Python and Excel Enthusiasts

Chapter 1: Introduction

Welcome to the comprehensive and dynamic course, “Data Analysis 2 in 1: Excel & Python for A-Z Data Analysis.” This meticulously crafted program is designed to empower learners with a versatile skill set, encompassing the efficient data manipulation capabilities of Excel, the scalability and coding flexibility of Python, and the intuitive coding assistance from ChatGPT. As technology continues to evolve, proficiency in multiple tools becomes essential. This course aims to provide a holistic understanding of the data analysis workflow, ensuring that learners can seamlessly transition from Excel to Python, while also adding a touch of AI for an enhanced coding experience.

Chapter 2: Excel Mastery

The course kicks off with a deep dive into Excel, teaching you to wield its powerful features for data cleaning, transformation, and visualization. From managing missing data and outliers to leveraging advanced Excel functions and tools for statistical analysis, you’ll gain a solid foundation in Excel’s capabilities. The focus on interactive dashboard creation using PivotTables, PivotCharts, and various visualization techniques will empower you to present insights in a compelling and user-friendly manner.

Chapter 3: Python Basics and Beyond

Building on your Excel skills, the course introduces Python programming basics. You’ll learn the syntax, data types, and control structures, enabling you to construct simple programs. The emphasis is on practical application – generating, copying/pasting, adjusting, and running code with ease. Python’s ability to handle large datasets becomes evident, making it the tool of choice for scenarios where Excel’s limitations are surpassed. This section ensures you’re proficient in both tools, providing adaptability in real-world data analysis scenarios.

Chapter 4: Statistical Analysis and Interpretation

As the course progresses, you’ll delve into fundamental statistical concepts, applying them using both Excel and Python. Descriptive statistics, inferential statistics, and hypothesis testing are covered comprehensively. You’ll learn not just how to perform these analyses but, crucially, how to interpret and communicate the results effectively. This knowledge forms the backbone of making informed decisions and recommendations based on data-driven insights.

Chapter 5: Real-world Application and Problem-solving

The final section of the course is dedicated to real-world application. You’ll tackle over 60+ data analytical questions, honing your skills in solving practical problems. Value counts, percentage calculations, grouping data, and utilizing advanced statistical techniques become second nature. Emphasis is placed on critical thinking and problem-solving, ensuring that you not only understand the tools and techniques but can confidently apply them to various circumstances. By the course’s conclusion, you’ll be equipped to navigate the complete data analysis workflow with mastery and confidence.

Course Curriculum

Chapter 1: Understanding the concept of data analysis

Lecture 1: Introduction to data analysis

Lecture 2: Steps in data analysis workflow

Lecture 3: Get special handbooks

Chapter 2: Understanding the concept of statistical analysis

Lecture 1: Introduction to statistical analysis

Lecture 2: Various aspects of hypothesis testing

Lecture 3: Complete hypothesis testing workflow

Chapter 3: Excel – Data Cleaning and Manipulation

Lecture 1: Identify and replacing missing values

Lecture 2: Practice file – Missing values

Lecture 3: Dealing with inconsistent values

Lecture 4: Practice file – Inconsistent values

Lecture 5: Dealing with outliers

Lecture 6: Practice data – Outliers

Lecture 7: Dealing with duplicated values

Lecture 8: Practice data – Duplicated values

Chapter 4: Excel – Exploratory Data Analysis

Lecture 1: Install Excel Data Analysis Tool pack (If Necessary)

Lecture 2: Frequency and percentage analysis

Lecture 3: Practice file – Frequency and percentage analysis

Lecture 4: Descriptive analysis (mean, std. dev., skewness, etc.)

Lecture 5: Practice file – Descriptive analysis

Lecture 6: Group by analysis in excel pivot table

Lecture 7: Practice file – Group by analysis

Lecture 8: Crosstabulation analysis in excel pivot table

Lecture 9: Practice file – Crosstabulation analysis

Chapter 5: Excel – Statistical Analysis and Hypothesis Testing

Lecture 1: Independent sample t-test

Lecture 2: Practice file – Independent sample t-test

Lecture 3: Paired sample t-test

Lecture 4: Practice file – Paired sample t-test

Lecture 5: Analysis of variance (ANOVA)

Lecture 6: Practice file – ANOVA

Lecture 7: Pearson correlation analysis

Lecture 8: Practice file – Correlation analysis

Lecture 9: Multiple linear regression analysis

Lecture 10: Practice file – Regression analysis

Chapter 6: Excel – Putting All Insights in One Place

Lecture 1: Creating canvas for dashboard

Lecture 2: Creating the final dashboard

Lecture 3: Practice file – Dashboard

Chapter 7: Setting Up Your Data Analysis Environment

Lecture 1: Installing Python and Jupyter Notebook

Lecture 2: Setting Up The AI Environment: ChatGPT

Lecture 3: Practice dataset and quizz instructions

Chapter 8: 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: Logical operators (and, or, not)

Chapter 9: Python – Programming Fundamentals Level 2

Lecture 1: Lists: creation, indexing, slicing, modifying

Lecture 2: Sets: unique elements, operations

Lecture 3: Dictionaries: key-value pairs, methods

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 10: Python – Cleaning Data from Scratch

Lecture 1: Importing dataset into Jupyter Notebook

Lecture 2: Imputing missing values with SimpleImputer

Lecture 3: Finding and dealing with inconsistent data

Lecture 4: Identify and assign correct dataset

Lecture 5: Dealing with duplicate values

Chapter 11: Python – Various Data Manipulation Methods

Lecture 1: Sorting and arranging dataset

Lecture 2: Conditional Filtering of dataset

Lecture 3: Merging extra data with the dataset

Lecture 4: Concatenating variables within dataset

Chapter 12: Python – Exploratory Data Analysis

Instructors

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

Rating Distribution

1 stars: 3 votes
2 stars: 1 votes
3 stars: 6 votes
4 stars: 7 votes
5 stars: 76 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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