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DECODE DSA WITH PYTHON JAVA C++ SKILLS
@decode_dsa_with_python_java
DECODE DSA WITH PYTHON JAVA C++ SKILLS (English)
DECODE DSA WITH PYTHON JAVA C++ SKILLS is a Telegram channel dedicated to helping individuals enhance their Data Structures and Algorithms (DSA) knowledge using Python, Java, and C++. Whether you are a beginner looking to grasp the basics or an experienced programmer aiming to deepen your understanding, this channel provides valuable resources and support. The channel offers a wide range of educational content, including tutorials, code snippets, practice exercises, and tips for optimizing DSA solutions using Python, Java, and C++. By joining this channel, you will have access to a community of like-minded individuals who are passionate about coding and eager to improve their DSA skills. Stay updated with the latest trends and techniques in DSA, ask questions, participate in discussions, and collaborate with fellow members to elevate your programming proficiency. Start your journey towards mastering DSA with Python, Java, and C++ today by joining DECODE DSA WITH PYTHON JAVA C++ SKILLS!
DECODE DSA WITH PYTHON JAVA C++ SKILLS
10 Nov, 14:22
How Git Works - From Working Directory to Remote Repository
[1]. Working Directory:
Your project starts here. The working directory is where you actively make changes to your files.
[2]. Staging Area (Index):
After modifying files, use git add to stage changes. This prepares them for the next commit, acting as a checkpoint.
[3]. Local Repository:
Upon staging, execute git commit to record changes in the local repository. Commits create snapshots of your project at specific points.
[4]. Stash (Optional):
If needed, use git stash to temporarily save changes without committing. Useful when switching branches or performing other tasks.
[5]. Remote Repository:
The remote repository, hosted on platforms like GitHub, is a version of your project accessible to others. Use git push to send local commits and git pull to fetch remote changes.
[6]. Remote Branch Tracking:
Local branches can be set to track corresponding branches on the remote. This eases synchronization with git pull or git push.
[1]. Working Directory:
Your project starts here. The working directory is where you actively make changes to your files.
[2]. Staging Area (Index):
After modifying files, use git add to stage changes. This prepares them for the next commit, acting as a checkpoint.
[3]. Local Repository:
Upon staging, execute git commit to record changes in the local repository. Commits create snapshots of your project at specific points.
[4]. Stash (Optional):
If needed, use git stash to temporarily save changes without committing. Useful when switching branches or performing other tasks.
[5]. Remote Repository:
The remote repository, hosted on platforms like GitHub, is a version of your project accessible to others. Use git push to send local commits and git pull to fetch remote changes.
[6]. Remote Branch Tracking:
Local branches can be set to track corresponding branches on the remote. This eases synchronization with git pull or git push.
DECODE DSA WITH PYTHON JAVA C++ SKILLS
01 Oct, 14:37
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.
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.
DECODE DSA WITH PYTHON JAVA C++ SKILLS
01 Oct, 14:37
©How fresher can get a job as a data scientist?©
Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
DECODE DSA WITH PYTHON JAVA C++ SKILLS
16 Sep, 19:51
◾HANDWRITTEN NOTES ✍️ ◾️
🔺DATA STRUCTURE SHORT NOTES
🔺DATA STRUCTURE
INTERVIEW SERIES 🔹(PART - 1)
🔺DATA STRUCTURE
INTERVIEW SERIES 🔹(PART - 2)
🔺DATA STRUCTURE
INTERVIEW SERIES 🔹(PART - 3)
🔺DBMS (DATABASE MANAGEMENT SYSTEM)NOTES
🔺C PROGRAMMING SHORT NOTES
🔺DATA STRUCTURE SHORT NOTES
🔺DATA STRUCTURE
INTERVIEW SERIES 🔹(PART - 1)
🔺DATA STRUCTURE
INTERVIEW SERIES 🔹(PART - 2)
🔺DATA STRUCTURE
INTERVIEW SERIES 🔹(PART - 3)
🔺DBMS (DATABASE MANAGEMENT SYSTEM)NOTES
🔺C PROGRAMMING SHORT NOTES
DECODE DSA WITH PYTHON JAVA C++ SKILLS
07 Sep, 13:08
Here's a short roadmap to crack an IT job with a non-CS background 🚀
1. 📚 Learn basics of CS and programming.
2. 🎯 Choose a specialization (e.g., web dev, data analysis).
3. 🏆 Complete online courses and certifications.
4. 🛠️ Build a portfolio of projects.
5. 🤝 Network with professionals.
6. 💼 Seek internships for experience.
7. 📚 Keep learning and stay updated.
8. 🧠 Develop soft skills.
9. 📝 Prepare for interviews.
10. 💪 Stay persistent and positive! Good luck!
1. 📚 Learn basics of CS and programming.
2. 🎯 Choose a specialization (e.g., web dev, data analysis).
3. 🏆 Complete online courses and certifications.
4. 🛠️ Build a portfolio of projects.
5. 🤝 Network with professionals.
6. 💼 Seek internships for experience.
7. 📚 Keep learning and stay updated.
8. 🧠 Develop soft skills.
9. 📝 Prepare for interviews.
10. 💪 Stay persistent and positive! Good luck!
DECODE DSA WITH PYTHON JAVA C++ SKILLS
07 Sep, 13:08
𝗚𝗶𝘁 𝘃𝘀 𝗚𝗶𝘁𝗛𝘂𝗯: What's the Difference?
Ever mixed up Git and GitHub? You’re not alone—they’re related but serve distinct purposes!
𝐆𝐢𝐭: A powerful version control system that tracks changes in your code. It’s your local toolkit for managing versions, rolling back changes, and collaborating.
𝐆𝐢𝐭𝐇𝐮𝐛: A cloud-based platform that hosts Git repositories online. It enhances collaboration by letting you share, review, and manage code—think of it as a social network for developers.
In short:
Git = Local version control tool
GitHub = Cloud-based hosting service for Git repositories
Understanding the difference can significantly improve your workflow and collaboration in software development!
Ever mixed up Git and GitHub? You’re not alone—they’re related but serve distinct purposes!
𝐆𝐢𝐭: A powerful version control system that tracks changes in your code. It’s your local toolkit for managing versions, rolling back changes, and collaborating.
𝐆𝐢𝐭𝐇𝐮𝐛: A cloud-based platform that hosts Git repositories online. It enhances collaboration by letting you share, review, and manage code—think of it as a social network for developers.
In short:
Git = Local version control tool
GitHub = Cloud-based hosting service for Git repositories
Understanding the difference can significantly improve your workflow and collaboration in software development!
DECODE DSA WITH PYTHON JAVA C++ SKILLS
07 Sep, 13:08
What Is MERN?
MERN Stack is a Javascript Stack that is used for easier and faster deployment of full-stack web applications. MERN Stack comprises of 4 technologies namely: MongoDB, Express, React and Node.js. It is designed to make the development process smoother and easier.
MongoDB
MongoDb is a NoSQL DBMS where data is stored in the form of documents having key-value pairs similar to JSON objects. MongoDB enables users to create databases, schemas and tables.
ExpressJS
ExpressJS is a NodeJS framework that simplifies writing the backend code. It saves you from creating multiple Node modules.
ReactJS
ReactJS is a JS library that allows the development of user interfaces for mobile apps and SPAs. It allows you to code Javascript and develop UI components.
NodeJS
NodeJS is an open-source Javascript runtime environment that allows users to run code on the server.
MERN Stack is a Javascript Stack that is used for easier and faster deployment of full-stack web applications. MERN Stack comprises of 4 technologies namely: MongoDB, Express, React and Node.js. It is designed to make the development process smoother and easier.
MongoDB
MongoDb is a NoSQL DBMS where data is stored in the form of documents having key-value pairs similar to JSON objects. MongoDB enables users to create databases, schemas and tables.
ExpressJS
ExpressJS is a NodeJS framework that simplifies writing the backend code. It saves you from creating multiple Node modules.
ReactJS
ReactJS is a JS library that allows the development of user interfaces for mobile apps and SPAs. It allows you to code Javascript and develop UI components.
NodeJS
NodeJS is an open-source Javascript runtime environment that allows users to run code on the server.
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