Some useful PYTHON libraries for data science

NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++

SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.

Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.

Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.

Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.

Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.

Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.

Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.

Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.

SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.

Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.

Additional libraries, you might need:

os for Operating system and file operations

networkx and igraph for graph based data manipulations

regular expressions for finding patterns in text data

BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run

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You don't need to buy a GPU for machine learning work!

There are other alternatives. Here are some:

1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks
12. Ola kutrim

Spend your time focusing on your problem.💪💪

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How to Land a Software Development Role in Top Tech Companies.

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Dreaming of landing a software development role at top tech companies? Your journey to Silicon Valley’s elite starts here!
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Your biggest enemy 𝐅𝐄𝐀𝐑 𝐨𝐟 𝐑𝐞𝐣𝐞𝐜𝐭𝐢𝐨𝐧

People hesitate to apply for many opportunities just because of fear of rejection.

However, not applying means you are automatically rejecting yourself. They usually think I will start applying after 6-8 months with full preparation.

Do you really think it will work ??? Interview calls usually take months 😅

My suggestion would be to start applying after 10 days to 1 month of preparation . Try to give as many interviews as you can. In this way, you will learn 👇🏻

🌴 Frequently asked questions
🌴 Interview pattern
🌴 How to tweak your answers?

Give a try ,even in the worst scenario, you will get some interview experience. That experience will eventually help you in the future

All the best 👍👍


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Some Useful Top Ai Tools

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20 programming languages that changed their original names👇

1. JavaScript (Originally: Mocha)
2. Python (Originally: Molder)
3. Java (Originally: Oak)
4. C++ (Originally: C with Classes)
5. Ruby (Originally: DLite)
6. PHP (Originally: Personal Home Page Tools)
7. Perl (Originally: Pearl)
8. Rust (Originally: Graydon)
9. Swift (Originally: Bob)
10. Kotlin (Originally: Jet)
11. ECMAScript (Originally: MochaScript)
12. TypeScript (Originally: Script#)
13. Go (Originally: Go Lang)
14. Scala (Originally: Scalable Language)
15. Julia (Originally: Cathy)
16. Haskell (Originally: ISWIM)
17. Lua (Originally: Lua Script)
18. Pascal (Originally: P-System)
19. Visual Basic (Originally: BASIC Interpreter)
20. Delphi (Originally: Turbo Pascal)

Other notable mentions:

C# (Originally: COOL)
F# (Originally: FSharp)
TypeScript (Originally: Script#)
ActionScript (Originally: Flash Script)

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Reasons for name changes:

1. Trademark issues
2. Rebranding
3. Expanded capabilities
4. Avoiding confusion
5. Reflecting language features
6. Merging with other languages
7. Changing language focus
8. Improving marketability

Top 10 programming languages & frameworks for beginner web developers:

1. HTML/CSS – Basics of web structure & styling
2. JavaScript – Adds interactivity
3. Python – Backend & versatility
4. PHP – Server-side scripting
5. SQL – Database management
6. Ruby on Rails – Easy backend framework
7. Node.js – JavaScript backend runtime
8. React – Popular frontend library
9. Angular – Framework for building dynamic UIs
10. Bootstrap – Simplifies responsive design

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Python Tip

WHAT FOR WHAT?
🖼 Frontend
HTML + CSS
Javascript
React
VueJs
Angular
Svelte

🔙 Backend:
Nodejs/Express
Python/Django
PHP/Laravel
Java
C#

💽 Database
MongoDB
MySQL
Postgres
Redis

🖥 Desktop
Electron
Tairi
PyQt

📱 Mobile:
React Native
Flutter
Swift
Kotlin

🖥 System
Go
C++
Rust

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Mastering Java: Roadmap to Complete Mastery

1. Fundamentals
2. Control Structures
3. Object-Oriented Programming (OOP)
4. Advanced Java
| |-- Collections Framework
| | |-- List (ArrayList, LinkedList)
| | |-- Set (HashSet, TreeSet)
| | |-- Map (HashMap, TreeMap)
| | |-- Queue (PriorityQueue, LinkedList)
5. .
6. .
7. .
Detailed Roadmap Here: https://techurl.in/pIsqP

TOP 20 Essential Algorithms

https://x.com/tech_psyche/status/1844360697167348230

Python interview questions 😍😍

What is Python?

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics, automatic memory management.

What's the difference between a tuple and a list?

Both tuples and lists are data structures in Python and hold a list of values. Unlike lists, tuples are immutable - they can't be changed.


What is a dict and what's its most important limitation?

A dict is a structure akin a hash map. It stores key-value pairs, where keys are unique and it has O(1) access time. The most important limitation for a dict is that the keys must be hashable/immutable. Meaning, we can use a tuple as a key, but not a list.


What is pickling/unpickling?

Pickling is converting an object to a string representation in python. Generally used for caching and transferring objects between hosts/processes.

Is Python a Scripting Language?

Python is capable of scripting, but it is more than that. It is considered as a general-purpose programming language.

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𝗚𝗢𝗢𝗚𝗟𝗘 𝗗𝗢𝗥𝗞𝗦 - 𝗢𝗡𝗘 𝗦𝗧𝗢𝗣 📌

•Shodan Search
•API (WSDL)
•GIST github search
•Apache Config Files
•Install/Setup Files
•Apache Struts RCE
•htaccess/phpinfo()
•Security Headers
•Source Code PublicWWW
•Search GitLab and Github
•Find .php (WayBack)
•Find Subdomains (Google)
•Digital Ocean Space
•Sub-Subdomains (Google)

Link 🔗:-

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Free Labs to Elevate your skills and practice 🎯 #Security

* Academy Hackaflag BR - hackaflag.com.br
- Attack-Defense - attackdefense.com
* Alert to win - alf.nu/alerti
* CTF Komodo Security - ctf.komodosec.com
* CMD Challenge - cmdchallenge.com
* Exploitation Education - exploit.education
* Google CTF-capturetheflag.withgoogle.com
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* Hack TheBox - hackthebox.com
* Hackthis - hackthis.co.uk
* Hacksplaining - hacksplaining.com/lessons
* Hacker101-ctf.hacker101.com
* Hacker Security - capturetheflag.com.br
* Hacking-Lab - hacking-lab.com
* HSTRIKE - hstrike.com
* ImmersiveLabs - immersivelabs.com
* NewbieContest - newbiecontest.org
* Resources - t.me/TechPsyche
* OverTheWire - overthewire.org
* Practical Pentest Labs - lnkd.in/esq9Yuv5
* Pentestlab - pentesterlab.com
* Penetration Testing Practice Labs - amanhardikar.com/mindmaps/Pract…
* PentestIT LAB - lab.pentestit.ru
* PicoCTF - picoctf.com
* PWNABLE - pwnable.kr/play.php
* Root-Me-root-me.org
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Simple 12-Week Program for you.


⭕️ 1-2 Weeks:
Basics of Programming
* Choose a programming Language: Java/Cpp
* Learn about the concepts of OOPs from
Geeksforgeeks
* Practice basic programming from
Geeksforgeeks
* Practice basic Algorithms for Searching, Sorting & Recursion
* Practice basic questions of Strings and Numbers on Leetcode.
* Keep practicing the easy level questions until the basics are clear.

⭕️ 3-4 Weeks:
Complexities & Basic DSA
* Learn about Time and Space complexities
* Start with DSA from YouTube
Abdul Bari / Strivers A2Z DSA Playlist
* Only practice the basic data structures-
Arrays, Stacks, Queues, Linked Lists
* Practice Easy level questions on Leetcode
* Do this until you’re Confident

⭕️ 5-6 Week
Practicing the basics
* Solve Medium Level Leetcode questions for the topics you've prepared.
* This week aims to solidifying your basic DSA concepts.
* Give contests on Hackerrank to gain speed and getting prepared for Interviews.

Telegram: t.me/TechPsyche

⭕️ 7-10 Weeks:
Web Development / Machine Learning
* Start with Web Development or Machine Learning or any domain which will help you in building Projects.
* Complete playlists or any other courses available on it from Youtube or similar.
* Learn by building Projects. This will help you add it to your Resume as well.
* Simultaneously start with the medium level problems of the topics you’ve covered in DSA.

⭕️ 11-12 Weeks:
Advanced DSA
* Start with advanced DS like Trees & Graph
* Practice Easy & Medium level questions on these topics on Leetcode
* Learn more algorithms like Backtracking, Sliding Window, Two pointers and more
* Practice as much as you can.

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KENYA
WordPress Developer Needed.
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Ready to start work immediately.

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’Simply studying others’ code won’t make you a great developer. You need to build your own.

- Reading doesn't make you a great writer.
- ⁠Eating doesn't make you a great chef.
- Watching tutorials won’t turn you into a dev / engineer.
- Only practice makes you proficient.( make projects)
> Consumption can increase taste, but not proficiency.

t.me/TechPsyche

Remote Project Manager Needed at Africa of Our Dream Education Initiative (AODI)

- Location: Remote
- Job Type: Full-time

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