Data Science 101: Python Plus Excel
Data Science 101: Python Plus Excel, available at $54.99, has an average rating of 5, with 82 lectures, based on 2 reviews, and has 102 subscribers.
You will learn about Write excel advanced conditional, text, and lookup functions Excel automation using python Learn Microsoft Excel 2016 and many of its advanced features Learn data science skills using Python and Excel Excel features using numpy and pandas Visualization using Excel and Python This course is ideal for individuals who are Excel users curious about automating their work using python or Python Developer wanting career switch in Data Science It is particularly useful for Excel users curious about automating their work using python or Python Developer wanting career switch in Data Science.
Enroll now: Data Science 101: Python Plus Excel
Summary
Title: Data Science 101: Python Plus Excel
Price: $54.99
Average Rating: 5
Number of Lectures: 82
Number of Published Lectures: 82
Number of Curriculum Items: 82
Number of Published Curriculum Objects: 82
Original Price: $149.99
Quality Status: approved
Status: Live
What You Will Learn
- Write excel advanced conditional, text, and lookup functions
- Excel automation using python
- Learn Microsoft Excel 2016 and many of its advanced features
- Learn data science skills using Python and Excel
- Excel features using numpy and pandas
- Visualization using Excel and Python
Who Should Attend
- Excel users curious about automating their work using python
- Python Developer wanting career switch in Data Science
Target Audiences
- Excel users curious about automating their work using python
- Python Developer wanting career switch in Data Science
For many years, and for good reason, Excel has been a staple for working professionals. It is essential in all facets of business, education, finance, and research due to its extensive capabilities and simplicity of use.
Over the past few years, python programming language has become more popular. According to one study, the demand for Python expertise has grown by 27.6 %over the past year and shows no indications of slowing down. Python has been a pioneer in web development, data analysis, and infrastructure management since it was first developed as a tool to construct scripts that “automate the boring stuff.”
Why python is important for automation?
Consider being required to create accounts on a website for 10,000 employees. What do you think? Performing this operation manually and frequently will eventually drive you crazy. It will also take too long, which is not a good idea.
Try to consider what it’s like for data entry workers. They take the data from tables (like those in Excel or Google Sheets) and insert it elsewhere.
They read various magazines and websites, get the data there, and then enter it into the database. Additionally, they must perform the calculations for the entries.
In general, this job’s performance determines how much money is made. Greater entry volume, more pay (of course, everyone wants a higher salary in their job).
However, don’t you find doing the same thing over and over boring?
The question is now, “How can I accomplish it quickly?”
How to automate my work?
Spend an hour coding and automating these kinds of chores to make your life simpler rather than performing these kinds of things by hand. By just writing fewer lines of Python code, you can automate your strenuous activity.
The course covers following topics:
1. Excel basics
2. Excel Functions
3. Excel Visualizations
4. Excel Case study (Financial Statements)
5. Python numpy and pandas
6. Python Implementations of Excel functions
7. Python matplotlib and pandas visualizations
The evidence suggests that both Excel and Python have their place with certain applications. Excel is a great entry-level tool and is a quick-and-easy way to analyze a dataset.
But for the modern era, with large datasets and more complex analytics and automation, Python provides the tools, techniques and processing power that Excel, in many instances, lacks. After all, Python is more powerful, faster, capable of better data analysis and it benefits from a more inclusive, collaborative support system.
Python is a must-have skill for aspiring data analysts, data scientist and anyone in the field of science, and now is the time to learn.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Python vs Excel
Lecture 3: Limitation of Excel
Lecture 4: Python
Lecture 5: who can benefit from learning Python?
Lecture 6: What makes Python a better option than Excel?
Lecture 7: Excel vs Python: Who wins?
Chapter 2: Download Resources for Excel [IMPORTANT]
Lecture 1: Download Excel Lecture content!
Chapter 3: Introduction to Basics of Excel
Lecture 1: Structure of Excel sheets
Lecture 2: The Ribbon
Lecture 3: Rows and Columns
Lecture 4: Enter, Edit, Delete in Excel
Lecture 5: Excel basic formatting: border, font, color
Lecture 6: Align Left, Right, Center
Lecture 7: Arithmetic operations
Lecture 8: Excel formulas introduction
Lecture 9: Copy and Paste
Lecture 10: Formatting cell
Lecture 11: Formatting worksheet
Lecture 12: Moving and selecting contents in Excel sheets
Lecture 13: [IMPORTANT] Fixing cell references
Lecture 14: ALT+ENTER
Lecture 15: Text to Column
Lecture 16: Wrap Text
Lecture 17: Select special
Lecture 18: Dynamic Naming
Lecture 19: Custom Formatting 1
Lecture 20: Custom Formatting 2
Lecture 21: Multiple Formats
Chapter 4: Excel Tools and Tips
Lecture 1: Macros
Lecture 2: Data Validation
Lecture 3: Sort and Filter
Lecture 4: Hyperlinks
Lecture 5: Freeze Panes
Lecture 6: Tell me what you want to do
Lecture 7: Keyboard Shortcuts
Chapter 5: Excel Functions
Lecture 1: Count, countif and countifs
Lecture 2: Sum, sumif and sumifs
Lecture 3: average and averageif
Lecture 4: Text functions
Lecture 5: max and min functions
Lecture 6: round function
Lecture 7: vlookup function [IMPORTANT}
Lecture 8: hlookup function
Lecture 9: index and match function
Lecture 10: iferror function
Lecture 11: pivot tables
Lecture 12: data tables
Chapter 6: Excel Visualizations
Lecture 1: Excel charts
Lecture 2: Basic formatting for charts
Lecture 3: Designing charts
Lecture 4: Bridge charts
Lecture 5: Treemap
Lecture 6: Spark Lines
Chapter 7: Excel Case Study
Lecture 1: Introduction to data
Lecture 2: Preprocessing data
Lecture 3: Create unique code (primary key)
Lecture 4: Creating database
Lecture 5: Populate database 1
Lecture 6: Populate database 2
Lecture 7: Mapping each row to category
Lecture 8: Income statement
Lecture 9: Format statement
Lecture 10: Format statement more
Lecture 11: Populate Income (P&L) statement
Chapter 8: Excel Functions in Python
Lecture 1: vlookup function in excel
Lecture 2: Implement vlookup functionality in Python
Lecture 3: Pivot tables in excel
Lecture 4: Implement pivot tables functionality in Python
Lecture 5: Pivot tables using pandas
Lecture 6: IF function in Excel
Lecture 7: IF functionalities in python
Lecture 8: Text manipulation in Excel
Lecture 9: Text manipulation in Python
Lecture 10: count, countif, countifs, sum, sumif, sumifs
Lecture 11: count, countif, countifs, sum, sumif, sumifs in Python
Chapter 9: Python Visualizations
Lecture 1: pivot charts in Excel
Lecture 2: Python pandas visualization
Lecture 3: Matplotlib
Lecture 4: Formatting charts
Lecture 5: More on matplotlib
Lecture 6: matplotlib and pandas together
Instructors
-
Sachin Kafle
Founder of CSAMIN & Bit4Stack Tech Inc. [[Author, Teacher]]
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- 4 stars: 0 votes
- 5 stars: 2 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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