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๐Ÿš€ ๐—ง๐—ผ๐—ฝ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—ฉ๐—ถ๐—ฟ๐˜๐˜‚๐—ฎ๐—น ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ๐˜€ โ€“ ๐—™๐—ฅ๐—˜๐—˜ & ๐—ข๐—ป๐—น๐—ถ๐—ป๐—ฒ๐Ÿ˜
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Forwarded from Artificial Intelligence
๐—™๐—ฅ๐—˜๐—˜ ๐—ข๐—ป๐—น๐—ถ๐—ป๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ง๐—ผ ๐—˜๐—ป๐—ฟ๐—ผ๐—น๐—น ๐—œ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ ๐Ÿ˜

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๐—ฆ๐˜๐—ฎ๐—ฟ๐˜ ๐—ฎ ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ ๐—ถ๐—ป ๐——๐—ฎ๐˜๐—ฎ ๐—ผ๐—ฟ ๐—ง๐—ฒ๐—ฐ๐—ต (๐—™๐—ฟ๐—ฒ๐—ฒ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ฃ๐—ฎ๐˜๐—ต)๐Ÿ˜

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Enjoy Learning โœ…๏ธ
โค1
10 Ways to Speed Up Your Python Code

1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)

2. Use the Built-In Functions
Many of Pythonโ€™s built-in functions are written in C, which makes them much faster than a pure python solution.

3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.

4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.

5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.

6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post.

7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.

8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.

9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.

10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you canโ€™t make use of dictionaries or sets.
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๐—–๐—œ๐—ฆ๐—–๐—ข ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€๐Ÿ˜

- Data Analytics
- Data Science 
- Python
- Javascript
- Cybersecurity
 
๐‹๐ข๐ง๐ค ๐Ÿ‘‡:- 

https://pdlink.in/4fYr1xO

Enroll For FREE & Get Certified๐ŸŽ“
Azure_Data_Factory_by_Example_Practical_Implementation.pdf
10.8 MB
Azure Data Factory by Example
Richard Swinbank, 2021
Azure Data Engineering Cookbook (SafefilekU.com).pdf
55.7 MB
Azure Data Engineering Cookbook
Nagaraj Venkatesan, 2022
Hands-on Guide to Apache Spark 3 (2024).pdf
11.2 MB
Hands-on Guide to Apache Spark 3
Alfonso Antolรญnez Garcรญa, 2023
๐Ÿ”ฅ2โค1
Forwarded from Artificial Intelligence
๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—™๐—ฅ๐—˜๐—˜ ๐—ฅ๐—ผ๐—ฎ๐—ฑ๐—บ๐—ฎ๐—ฝ ,๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ,๐—ฃ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜๐˜€ & ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐—š๐˜‚๐—ถ๐—ฑ๐—ฒ๐Ÿ˜

Roadmap:- https://pdlink.in/41c1Kei

Certifications:- https://pdlink.in/3Fq7E4p

Projects:- https://pdlink.in/3ZkXetO

Interview Q/A :- https://pdlink.in/4jLOJ2a

Enroll For FREE & Become a Certified Data Analyst In 2025๐ŸŽ“
Effective Communication of Data Insights (Very Important Skill for Data Analysts)

Know Your Audience:

Tip: Tailor your presentation based on the technical expertise and interests of your audience.

Consideration: Avoid jargon when presenting to non-technical stakeholders.


Focus on Key Insights:

Tip: Highlight the most relevant findings and their impact on business goals.

Consideration: Avoid overwhelming your audience with excessive details or raw data.


Use Visuals to Support Your Message:

Tip: Leverage charts, graphs, and dashboards to make your insights more digestible.

Consideration: Ensure visuals are simple and easy to interpret.


Tell a Story:

Tip: Present data in a narrative form to make it engaging and memorable.

Consideration: Use the context of the data to tell a clear story with a beginning, middle, and end.


Provide Actionable Recommendations:

Tip: Focus on practical steps or decisions that can be made based on the data.

Consideration: Offer clear, actionable insights that drive business outcomes.


Be Transparent About Limitations:

Tip: Acknowledge any data limitations or assumptions in your analysis.

Consideration: Being transparent builds trust and shows a thorough understanding of the data.


Encourage Questions:

Tip: Allow for questions and discussions to clarify any doubts.

Consideration: Engage with your audience to ensure full understanding of the insights.

You can find more communication tips here: https://t.me/englishlearnerspro

I have curated Data Analytics Resources ๐Ÿ‘‡๐Ÿ‘‡
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Like this post for more content like this ๐Ÿ‘โ™ฅ๏ธ

Share with credits: https://t.me/sqlspecialist

Hope it helps :)
โค1
๐—œ๐—ป๐—ฑ๐˜‚๐˜€๐˜๐—ฟ๐˜† ๐—”๐—ฝ๐—ฝ๐—ฟ๐—ผ๐˜ƒ๐—ฒ๐—ฑ ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐Ÿ˜

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๐Ÿ”ฐ Web Frameworks in Python
โค1
๐—ง๐—ผ๐—ฝ ๐—–๐—ผ๐—บ๐—ฝ๐—ฎ๐—ป๐—ถ๐—ฒ๐˜€ & ๐—Ÿ๐—ฒ๐—ฎ๐—ฑ๐—ถ๐—ป๐—ด ๐—–๐—ผ๐—บ๐—ฝ๐—ฎ๐—ป๐—ถ๐—ฒ๐˜€ ๐—ข๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿ˜

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Forwarded from Artificial Intelligence
๐Œ๐ข๐œ๐ซ๐จ๐ฌ๐จ๐Ÿ๐ญ ๐…๐‘๐„๐„ ๐‚๐ž๐ซ๐ญ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง ๐‚๐จ๐ฎ๐ซ๐ฌ๐ž๐ฌ!๐Ÿš€๐Ÿ’ป

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Donโ€™t waitโ€”start your journey to success today! โœจ
โค1
How To Code in Python 3
by Lisa Tagliaferri


๐Ÿ“„ 459 pages

๐Ÿ”— Book link
โค1
How_to_kickstart_an_azure_data_engineering_project_1751578967.pdf
393.7 KB
Dear Data Fam,

If you are looking to kick start Azure Data Engineering from Starch , check out this document !!

It will help you to understand a basic end to end prod flow
โค2
๐—™๐—ฟ๐—ฒ๐—ฒ ๐—”๐—œ & ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ๐˜€๐Ÿ˜

Want to explore AI & Machine Learning but donโ€™t know where to start โ€” or donโ€™t want to spend โ‚นโ‚นโ‚น on it?๐Ÿ‘จโ€๐Ÿ’ป

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