Data Analysis using Python
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sql-basics-cheat-sheet-a4.pdf
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SQL Basics Cheat Sheet
LearnSQL, 2022
LearnSQL, 2022
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Admin: @love_data
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How to be a Prompt Engineer 101
The shortest and most comprehensive guide
1. start with an explanation
Make a description and character situation at the beginning of the Prompt
Error example:
Please help me read the following code:
{your input here}
Correct example:
2. Prompt to describe the situation
In the prompt, it is necessary to describe the context, result, length, format and style as much as possible
Error example:
Write a short story for kids
Correct example:
3. gives output in the format
If you are doing data analysis, please give the input template of the format
Error example:
Extract house pricing data from the following text.
Text: """
{your text containing pricing data}
"""
Correct example:
4. Add some example questions and answers
Sometimes adding some question and answer examples can make GPT more intelligent
Correct example:
The question and answer example is also a standard template example in fine-tune
5. Simplify the sentence and clarify the purpose
Keep your words as short as possible and don't say useless content
Error example:
ChatGPT, write a sales page for my company selling sand in the desert, please write only a few sentences, nothing long and complex
Correct example:
6. Good at using introductory words
Error example:
Write a Python function that plots my net worth over 10 years for different inputs on the initial investment and a given ROI
Correct example:
The shortest and most comprehensive guide
1. start with an explanation
Make a description and character situation at the beginning of the Prompt
Error example:
Please help me read the following code:
{your input here}
Correct example:
Now let's play the role, you are a senior information security engineer, I will give you a piece of code, please help me read the code and point out where there may be security vulnerable.
Text: """
{your input here}
"""2. Prompt to describe the situation
In the prompt, it is necessary to describe the context, result, length, format and style as much as possible
Error example:
Write a short story for kids
Correct example:
Write a funny soccer story for kids that teaches the kid that persistence is the key for success in the style of Rowling.3. gives output in the format
If you are doing data analysis, please give the input template of the format
Error example:
Extract house pricing data from the following text.
Text: """
{your text containing pricing data}
"""
Correct example:
Extract house pricing data from the following text.
Desired format: """
House 1 | $1,000,000 | 100 sqm
House 2 | $500,000 | 90 sqm
... (and so on)
"""
Text: """
{your text containing pricing data}
"""4. Add some example questions and answers
Sometimes adding some question and answer examples can make GPT more intelligent
Correct example:
Extract brand names from the texts below.
Text 1: Finxter and YouTube are tech companies. Google is too.
Brand names 2: Finxter, YouTube, Google
###
Text 2: If you like tech, you'll love Finxter!
Brand names 2: Finxter
###
Text 3: {your text here}Brand names 3:The question and answer example is also a standard template example in fine-tune
5. Simplify the sentence and clarify the purpose
Keep your words as short as possible and don't say useless content
Error example:
ChatGPT, write a sales page for my company selling sand in the desert, please write only a few sentences, nothing long and complex
Correct example:
Write a 5-sentence sales page, sell sand in the desert.6. Good at using introductory words
Error example:
Write a Python function that plots my net worth over 10 years for different inputs on the initial investment and a given ROI
Correct example:
# Python function that plots net worth over 10
# years for different inputs on the initial
# investment and a given ROI
import matplotlib
def plot_net_worth(initial, roi):π2
Most Demanding Data Analytics Skills!
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β³ Dive into the essential skills and tools that are shaping the future of data analytics. From SQL and Python to Tableau and PowerBI, discover which technologies are crucial for advancing your data analysis capabilities.
β³ Explore the importance of machine learning techniques like linear regression, logistic regression, SVM, decision trees, random forests, K-means, and K-nearest neighbors, and how they can enhance your analytical prowess.
β³ Understand why soft skills such as communication, collaboration, critical thinking, and creativity are just as important as technical skills in the data analytics field.
β³ Get a comprehensive overview of the skills and technologies that can propel your career forward and make you a standout in the competitive world of data analytics.
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π 2000+ Students Placed
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