Data Science 101: Python Plus Excel
Published 5/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.56 GB | Duration: 8h 15m
Learn excel and python with real world case study.
What you'll 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
Requirements
Python basics (data types, loops, functions etc.)
Install Microsoft 2016, 2013 or 2010
Description
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 basics2. Excel Functions3. Excel Visualizations4. Excel Case study (Financial Statements)5. Python numpy and pandas6. Python Implementations of Excel functions7. Python matplotlib and pandas visualizationsThe 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.
Overview
Section 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?
Section 2: Download Resources for ExcelMPORTANT
Lecture 8 Download Excel Lecture content!
Section 3: Introduction to Basics of Excel
Lecture 9 Structure of Excel sheets
Lecture 10 The Ribbon
Lecture 11 Rows and Columns
Lecture 12 Enter, Edit, Delete in Excel
Lecture 13 Excel basic formatting: border, font, color
Lecture 14 Align Left, Right, Center
Lecture 15 Arithmetic operations
Lecture 16 Excel formulas introduction
Lecture 17 Copy and Paste
Lecture 18 Formatting cell
Lecture 19 Formatting worksheet
Lecture 20 Moving and selecting contents in Excel sheets
Lecture 21MPORTANT Fixing cell references
Lecture 22 ALT+ENTER
Lecture 23 Text to Column
Lecture 24 Wrap Text
Lecture 25 Select special
Lecture 26 Dynamic Naming
Lecture 27 Custom Formatting 1
Lecture 28 Custom Formatting 2
Lecture 29 Multiple Formats
Section 4: Excel Tools and Tips
Lecture 30 Macros
Lecture 31 Data Validation
Lecture 32 Sort and Filter
Lecture 33 Hyperlinks
Lecture 34 Freeze Panes
Lecture 35 Tell me what you want to do
Lecture 36 Keyboard Shortcuts
Section 5: Excel Functions
Lecture 37 Count, countif and countifs
Lecture 38 Sum, sumif and sumifs
Lecture 39 average and averageif
Lecture 40 Text functions
Lecture 41 max and min functions
Lecture 42 round function
Lecture 43 vlookup function[IMPORTANT}
Lecture 44 hlookup function
Lecture 45 index and match function
Lecture 46 iferror function
Lecture 47 pivot tables
Lecture 48 data tables
Section 6: Excel Visualizations
Lecture 49 Excel charts
Lecture 50 Basic formatting for charts
Lecture 51 Designing charts
Lecture 52 Bridge charts
Lecture 53 Treemap
Lecture 54 Spark Lines
Section 7: Excel Case Study
Lecture 55 Introduction to data
Lecture 56 Preprocessing data
Lecture 57 Create unique code (primary key)
Lecture 58 Creating database
Lecture 59 Populate database 1
Lecture 60 Populate database 2
Lecture 61 Mapping each row to category
Lecture 62 Income statement
Lecture 63 Format statement
Lecture 64 Format statement more
Lecture 65 Populate Income (P&L) statement
Section 8: Excel Functions in Python
Lecture 66 vlookup function in excel
Lecture 67 Implement vlookup functionality in Python
Lecture 68 Pivot tables in excel
Lecture 69 Implement pivot tables functionality in Python
Lecture 70 Pivot tables using pandas
Lecture 71 IF function in Excel
Lecture 72 IF functionalities in python
Lecture 73 Text manipulation in Excel
Lecture 74 Text manipulation in Python
Lecture 75 count, countif, countifs, sum, sumif, sumifs
Lecture 76 count, countif, countifs, sum, sumif, sumifs in Python
Section 9: Python Visualizations
Lecture 77 pivot charts in Excel
Lecture 78 Python pandas visualization
Lecture 79 Matplotlib
Lecture 80 Formatting charts
Lecture 81 More on matplotlib
Lecture 82 matplotlib and pandas together
Excel users curious about automating their work using python,Python Developer wanting career switch in Data Science
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