Git Merge vs Git Rebase

Learn JavaScript in 14 Days:

Part 1:

💻 Day 1 - Learn JavaScript Basics:
Start with understanding variables, data types, and basic syntax.

📊 Day 2 - Master Operators and Expressions:
Get comfortable using arithmetic, comparison, and logical operators.

⚖️ Day 3 - Dive into Conditional Statements:
Learn how to use if, else if, else, and switch for decision-making.

♻️ Day 4 - Explore Loops:
Understand how for, while, and do-while loops work.

🔧 Day 5 - Work with Functions:
Learn how to define and call functions, pass parameters, and return values.

📦 Day 6 - Introduction to Arrays:
Explore how to create arrays and manipulate them with methods like push(), pop(), and map().

📜 Day 7 - Object Basics:
Learn how to create and work with JavaScript objects, properties, and methods.

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Getting job offers as a developer involves several steps:👨‍💻🚀

1. Build a Strong Portfolio: Create a portfolio of projects that showcase your skills. Include personal projects, open-source contributions, or freelance work. This demonstrates your abilities to potential employers.👨‍💻

2. Enhance Your Skills: Stay updated with the latest technologies and trends in your field. Consider taking online courses, attending workshops, or earning certifications to bolster your skills.🚀

3. Network: Attend industry events, conferences, and meetups to connect with professionals in your field. Utilize social media platforms like LinkedIn to build a professional network.🔥

4. Resume and Cover Letter: Craft a tailored resume and cover letter for each job application. Highlight relevant skills and experiences that match the job requirements.📇

5. Job Search Platforms: Utilize job search websites like LinkedIn, Indeed, Glassdoor, and specialized platforms like Stack Overflow Jobs, GitHub Jobs, or AngelList for tech-related positions. 🔍

6. Company Research: Research companies you're interested in working for. Customize your application to show your genuine interest in their mission and values.🕵️‍♂️

7. Prepare for Interviews: Be ready for technical interviews. Practice coding challenges, algorithms, and data structures. Also, be prepared to discuss your past projects and problem-solving skills.📝

8. Soft Skills: Develop your soft skills like communication, teamwork, and problem-solving. Employers often look for candidates who can work well in a team and communicate effectively.💻

9. Internships and Freelancing: Consider internships or freelancing opportunities to gain practical experience and build your resume. 🏠

10. Personal Branding: Maintain an online presence by sharing your work, insights, and thoughts on platforms like GitHub, personal blogs, or social media. This can help you get noticed by potential employers.👦

11. Referrals: Reach out to your network and ask for referrals from people you know in the industry. Employee referrals are often highly valued by companies.🌈

12. Persistence: The job search process can be challenging. Don't get discouraged by rejections. Keep applying, learning, and improving your skills.💯

13. Negotiate Offers: When you receive job offers, negotiate your salary and benefits. Research industry standards and be prepared to discuss your expectations.📉

Remember that the job search process can take time, so patience is key. By focusing on these steps and continuously improving your skills and network, you can increase your chances of receiving job offers as a developer.

Useful websites

Python List Methods

Don't overwhelm to learn Git,🙌

Git is only this much👇😇

1.Core:
• git init
• git clone
• git add
• git commit
• git status
• git diff
• git checkout
• git reset
• git log
• git show
• git tag
• git push
• git pull

2.Branching:
• git branch
• git checkout -b
• git merge
• git rebase
• git branch --set-upstream-to
• git branch --unset-upstream
• git cherry-pick

3.Merging:
• git merge
• git rebase

4.Stashing:
• git stash
• git stash pop
• git stash list
• git stash apply
• git stash drop

5.Remotes:
• git remote
• git remote add
• git remote remove
• git fetch
• git pull
• git push
• git clone --mirror

6.Configuration:
• git config
• git global config
• git reset config

7. Plumbing:
• git cat-file
• git checkout-index
• git commit-tree
• git diff-tree
• git for-each-ref
• git hash-object
• git ls-files
• git ls-remote
• git merge-tree
• git read-tree
• git rev-parse
• git show-branch
• git show-ref
• git symbolic-ref
• git tag --list
• git update-ref

8.Porcelain:
• git blame
• git bisect
• git checkout
• git commit
• git diff
• git fetch
• git grep
• git log
• git merge
• git push
• git rebase
• git reset
• git show
• git tag

9.Alias:
• git config --global alias.<alias> <command>

10.Hook:
• git config --local core.hooksPath <path>

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Python vs C++ vs Java

Important AI Terms Explained

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.

Java for Everything: ☕

Java + Spring = Enterprise Applications

Java + Hibernate = Object-Relational Mapping

Java + Android = Mobile App Development

Java + Swing = Desktop GUI Applications

Java + JavaFX = Modern GUI Applications

Java + JUnit = Unit Testing

Java + Maven = Project Management

Java + Jenkins = Continuous Integration

Java + Apache Kafka = Stream Processing

Java + Apache Hadoop = Big Data Processing

Java + Microservices = Scalable Services

Best Programming Resources: https://topmate.io/coding/886839

All the best 👍👍

In 1994, people told me programming was for nerds and that I should become a doctor or a lawyer instead.

10 years later, they told me that someone from India would take my job for $5/hour.

Then, no code was going to doom my career.

In 2021, Codex, then Copilot, then ChatGPT, then Devin, then OpenAI o1...

People keep yelling that "Programming is Dead," and yet the demand for good Software Engineers has never been higher.

Stop listening to midwit people. Learn to build good software, and you'll be okay. (Credits: unknown)

Python Roadmap