๐๐ข๐ฉ๐ฌ ๐๐จ๐ซ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐จ๐๐ข๐ง๐ ๐ข๐ง ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ:
๐ ๐จ๐ฆ๐ต ๐ด๐ฐ ๐ฎ๐ข๐ฏ๐บ ๐ฒ๐ถ๐ฆ๐ด๐ต๐ช๐ฐ๐ฏ๐ด ๐ง๐ณ๐ฐ๐ฎ ๐ฅ๐ข๐ต๐ข ๐ข๐ฏ๐ข๐ญ๐บ๐ต๐ช๐ค๐ด ๐ข๐ด๐ฑ๐ช๐ณ๐ข๐ฏ๐ต๐ด ๐ข๐ฏ๐ฅ ๐ฑ๐ณ๐ฐ๐ง๐ฆ๐ด๐ด๐ช๐ฐ๐ฏ๐ข๐ญ๐ด ๐ฐ๐ฏ ๐ฉ๐ฐ๐ธ ๐ต๐ฐ ๐จ๐ข๐ช๐ฏ ๐ค๐ฐ๐ฎ๐ฎ๐ข๐ฏ๐ฅ ๐ฐ๐ง ๐๐บ๐ต๐ฉ๐ฐ๐ฏ.
๐๐๐๐๐ซ๐ง ๐๐จ๐ซ๐ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ: Master Python libraries for data analytics, like
-pandas for dataframes,
-NumPy for numerical operations,
-Matplotlib/Seaborn for plotting,
-scikit-learn for machine learning.
๐๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐ ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ: Important concepts like list comprehensions, lambda functions, object-oriented programming, and error handling to write efficient code.
๐๐๐ฌ๐ ๐๐ซ๐จ๐๐ฅ๐๐ฆ-๐๐จ๐ฅ๐ฏ๐ข๐ง๐ ๐๐๐ญ๐ก๐จ๐๐ฌ: Apply data wrangling techniques, efficient loops, and vectorized operations in NumPy/pandas for optimized performance.
๐๐๐จ ๐๐จ๐๐ค ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Work on end-to-end Python analytics projectsโdata loading, cleaning, analysis, and visualization.
๐๐๐๐๐ซ๐ง ๐๐ซ๐จ๐ฆ ๐๐๐ฌ๐ญ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Review your previous Python projects to see where your code can be more efficient.
Stay with Us Here for more Tips ๐๐
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Hope you'll like it
Like and share this post if the information is helpful ๐โค๏ธ
๐ ๐จ๐ฆ๐ต ๐ด๐ฐ ๐ฎ๐ข๐ฏ๐บ ๐ฒ๐ถ๐ฆ๐ด๐ต๐ช๐ฐ๐ฏ๐ด ๐ง๐ณ๐ฐ๐ฎ ๐ฅ๐ข๐ต๐ข ๐ข๐ฏ๐ข๐ญ๐บ๐ต๐ช๐ค๐ด ๐ข๐ด๐ฑ๐ช๐ณ๐ข๐ฏ๐ต๐ด ๐ข๐ฏ๐ฅ ๐ฑ๐ณ๐ฐ๐ง๐ฆ๐ด๐ด๐ช๐ฐ๐ฏ๐ข๐ญ๐ด ๐ฐ๐ฏ ๐ฉ๐ฐ๐ธ ๐ต๐ฐ ๐จ๐ข๐ช๐ฏ ๐ค๐ฐ๐ฎ๐ฎ๐ข๐ฏ๐ฅ ๐ฐ๐ง ๐๐บ๐ต๐ฉ๐ฐ๐ฏ.
๐๐๐๐๐ซ๐ง ๐๐จ๐ซ๐ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ: Master Python libraries for data analytics, like
-pandas for dataframes,
-NumPy for numerical operations,
-Matplotlib/Seaborn for plotting,
-scikit-learn for machine learning.
๐๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐ ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ: Important concepts like list comprehensions, lambda functions, object-oriented programming, and error handling to write efficient code.
๐๐๐ฌ๐ ๐๐ซ๐จ๐๐ฅ๐๐ฆ-๐๐จ๐ฅ๐ฏ๐ข๐ง๐ ๐๐๐ญ๐ก๐จ๐๐ฌ: Apply data wrangling techniques, efficient loops, and vectorized operations in NumPy/pandas for optimized performance.
๐๐๐จ ๐๐จ๐๐ค ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Work on end-to-end Python analytics projectsโdata loading, cleaning, analysis, and visualization.
๐๐๐๐๐ซ๐ง ๐๐ซ๐จ๐ฆ ๐๐๐ฌ๐ญ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Review your previous Python projects to see where your code can be more efficient.
Stay with Us Here for more Tips ๐๐
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Hope you'll like it
Like and share this post if the information is helpful ๐โค๏ธ
Top Platforms for Building Data Science Portfolio
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
7 Websites to Learn Data Science for FREE๐งโ๐ป
โ w3school
โ datasimplifier
โ hackerrank
โ kaggle
โ geeksforgeeks
โ leetcode
โ freecodecamp
If you like this content share widely and encourage your friends to follow this channel for more. ๐
Data Science Resources: https://t.me/DataScienceResourcesTP
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Remember, I'm here for you๐I provide anything I think you might need as long as it's in Tech๐ฅณ
Let's share the link widely, the more audience I see, the more I'm motivated to find more & share more๐ฅณ๐ฅณ
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
7 Websites to Learn Data Science for FREE๐งโ๐ป
โ w3school
โ datasimplifier
โ hackerrank
โ kaggle
โ geeksforgeeks
โ leetcode
โ freecodecamp
If you like this content share widely and encourage your friends to follow this channel for more. ๐
Data Science Resources: https://t.me/DataScienceResourcesTP
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Remember, I'm here for you๐I provide anything I think you might need as long as it's in Tech๐ฅณ
Let's share the link widely, the more audience I see, the more I'm motivated to find more & share more๐ฅณ๐ฅณ
โค1
Coding Interview Preparation:
Top 10 Sites to review your resume for free:
1. Zety Resume Builder
2. Resumonk
3. Free Resume Builder
4. VisualCV
5. Cvmaker
6. ResumUP
7. Resume Genius
8. Resumebuilder
9. Resume Baking
10. Enhancv
Coding is tricky. Coding in interviews feels even harder. Itโs intimidating, uncertain and hard to prepare. Here are 4 ways to do it!
1. Interview Cake: I think it is some of the best prep available and it is targeted toward weaknesses many data scientists have in algorithms and data structures: https://www.interviewcake.com
2. Leetcode: While developed for software engineering interviews, it has a LOT of useful content for learning algorithms. For data science, I'd suggest focusing on Easy/Medium: https://leetcode.com/
3. Cracking the Coding Interview: Amazing book, sometimes referred to as CTCI. A classic and one you should have: https://cin.ufpe.br/~fbma/Crack/Cracking%20the%20Coding%20Interview%20189%20Programming%20Questions%20and%20Solutions.pdf
4. Daily Coding Problem: The book and the website are awesome. Work on a daily problem. This was my go to resource for when I was looking to stay sharp: https://www.dailycodingproblem.com/
Follow this channel for more Resources.
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Top 10 Sites to review your resume for free:
1. Zety Resume Builder
2. Resumonk
3. Free Resume Builder
4. VisualCV
5. Cvmaker
6. ResumUP
7. Resume Genius
8. Resumebuilder
9. Resume Baking
10. Enhancv
Coding is tricky. Coding in interviews feels even harder. Itโs intimidating, uncertain and hard to prepare. Here are 4 ways to do it!
1. Interview Cake: I think it is some of the best prep available and it is targeted toward weaknesses many data scientists have in algorithms and data structures: https://www.interviewcake.com
2. Leetcode: While developed for software engineering interviews, it has a LOT of useful content for learning algorithms. For data science, I'd suggest focusing on Easy/Medium: https://leetcode.com/
3. Cracking the Coding Interview: Amazing book, sometimes referred to as CTCI. A classic and one you should have: https://cin.ufpe.br/~fbma/Crack/Cracking%20the%20Coding%20Interview%20189%20Programming%20Questions%20and%20Solutions.pdf
4. Daily Coding Problem: The book and the website are awesome. Work on a daily problem. This was my go to resource for when I was looking to stay sharp: https://www.dailycodingproblem.com/
Follow this channel for more Resources.
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Interview Cake: Programming Interview Questions and Tips
Programming Interview Questions + Help Getting Job Offers | Interview Cake
Free practice programming interview questions. Interview Cake helps you prep for interviews to land offers at companies like Google and Meta.
Fundamental concepts in statistics for entry-level data analysts.
โก๏ธ Descriptive Statistics
๐ Mean: The average value of a dataset.
๐ Median: The middle value of a dataset.
๐ Mode: The most frequently occurring value in a dataset.
๐ Range: The difference between the highest and lowest values.
๐ Variance: Measures how much the values in a dataset vary from the mean.
๐ Standard Deviation: The square root of the variance, representing the average distance of each data point from the mean.
โก๏ธ Descriptive Statistics
๐ Mean: The average value of a dataset.
๐ Median: The middle value of a dataset.
๐ Mode: The most frequently occurring value in a dataset.
๐ Range: The difference between the highest and lowest values.
๐ Variance: Measures how much the values in a dataset vary from the mean.
๐ Standard Deviation: The square root of the variance, representing the average distance of each data point from the mean.
Confused about which field to dive intoโFront-End Development (FE), Back-End Development (BE), Machine Learning (ML), or Blockchain?
Here's a concise breakdown of each, designed to clarify your options:
### Front-End Development (FE)
Key Skills:
- HTML/CSS: Fundamental for creating the structure and style of web pages.
- JavaScript: Essential for adding interactivity and functionality to websites.
- Frameworks/Libraries: React, Angular, or Vue.js for efficient and scalable front-end development.
- Responsive Design: Ensuring websites look good on all devices.
- Version Control: Git for managing code changes and collaboration.
Career Prospects:
- Web Developer
- UI/UX Designer
- Front-End Engineer
### Back-End Development (BE)
Key Skills:
- Programming Languages: Python, Java, Ruby, Node.js, or PHP for server-side logic.
- Databases: SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data management.
- APIs: RESTful and GraphQL for communication between front-end and back-end.
- Server Management: Understanding of server, network, and hosting environments.
- Security: Knowledge of authentication, authorization, and data protection.
Career Prospects:
- Back-End Developer
- Full-Stack Developer
- Database Administrator
### Machine Learning (ML)
Key Skills:
- Programming Languages: Python and R are widely used in ML.
- Mathematics: Statistics, linear algebra, and calculus for understanding ML algorithms.
- Libraries/Frameworks: TensorFlow, PyTorch, Scikit-Learn for building ML models.
- Data Handling: Pandas, NumPy for data manipulation and preprocessing.
- Model Evaluation: Techniques for assessing model performance.
Career Prospects:
- Data Scientist
- Machine Learning Engineer
- AI Researcher
### Blockchain
Key Skills:
- Cryptography: Understanding of encryption and security principles.
- Blockchain Platforms: Ethereum, Hyperledger, Binance Smart Chain for building decentralized applications.
- Smart Contracts: Solidity for developing smart contracts.
- Distributed Systems: Knowledge of peer-to-peer networks and consensus algorithms.
- Blockchain Tools: Truffle, Ganache, Metamask for development and testing.
Career Prospects:
- Blockchain Developer
- Smart Contract Developer
- Crypto Analyst
### Decision Criteria
1. Interest: Choose an area you are genuinely interested in.
2. Market Demand: Research the current job market to see which skills are in demand.
3. Career Goals: Consider your long-term career aspirations.
4. Learning Curve: Assess how much time and effort you can dedicate to learning new skills.
Each field offers unique opportunities and challenges, so weigh your options carefully based on your personal preferences and career objectives.
Follow these channels to receive more updates๐
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
X: https://x.com/tech_psyche
Here's a concise breakdown of each, designed to clarify your options:
### Front-End Development (FE)
Key Skills:
- HTML/CSS: Fundamental for creating the structure and style of web pages.
- JavaScript: Essential for adding interactivity and functionality to websites.
- Frameworks/Libraries: React, Angular, or Vue.js for efficient and scalable front-end development.
- Responsive Design: Ensuring websites look good on all devices.
- Version Control: Git for managing code changes and collaboration.
Career Prospects:
- Web Developer
- UI/UX Designer
- Front-End Engineer
### Back-End Development (BE)
Key Skills:
- Programming Languages: Python, Java, Ruby, Node.js, or PHP for server-side logic.
- Databases: SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data management.
- APIs: RESTful and GraphQL for communication between front-end and back-end.
- Server Management: Understanding of server, network, and hosting environments.
- Security: Knowledge of authentication, authorization, and data protection.
Career Prospects:
- Back-End Developer
- Full-Stack Developer
- Database Administrator
### Machine Learning (ML)
Key Skills:
- Programming Languages: Python and R are widely used in ML.
- Mathematics: Statistics, linear algebra, and calculus for understanding ML algorithms.
- Libraries/Frameworks: TensorFlow, PyTorch, Scikit-Learn for building ML models.
- Data Handling: Pandas, NumPy for data manipulation and preprocessing.
- Model Evaluation: Techniques for assessing model performance.
Career Prospects:
- Data Scientist
- Machine Learning Engineer
- AI Researcher
### Blockchain
Key Skills:
- Cryptography: Understanding of encryption and security principles.
- Blockchain Platforms: Ethereum, Hyperledger, Binance Smart Chain for building decentralized applications.
- Smart Contracts: Solidity for developing smart contracts.
- Distributed Systems: Knowledge of peer-to-peer networks and consensus algorithms.
- Blockchain Tools: Truffle, Ganache, Metamask for development and testing.
Career Prospects:
- Blockchain Developer
- Smart Contract Developer
- Crypto Analyst
### Decision Criteria
1. Interest: Choose an area you are genuinely interested in.
2. Market Demand: Research the current job market to see which skills are in demand.
3. Career Goals: Consider your long-term career aspirations.
4. Learning Curve: Assess how much time and effort you can dedicate to learning new skills.
Each field offers unique opportunities and challenges, so weigh your options carefully based on your personal preferences and career objectives.
Follow these channels to receive more updates๐
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
X: https://x.com/tech_psyche
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 โpylab = 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 Pythonโs 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
Follow this Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
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 โpylab = 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 Pythonโs 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
Follow this Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
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.๐ช๐ช
Follow this Channel for more Tips.
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
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.๐ช๐ช
Follow this Channel for more Tips.
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
DESIGNERS
Master the Art of Color Blending with these Resources.
Follow this Channel for More.
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Master the Art of Color Blending with these Resources.
Follow this Channel for More.
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
How to Land a Software Development Role in Top Tech Companies.
๐ฅณ
Dreaming of landing a software development role at top tech companies? Your journey to Silicon Valleyโs elite starts here!
Whether you're a coding wizard or just getting started, this guide offers insider tips that will get you noticed by industry giants like Google, Amazon, and Apple. Ready to unlock the door to your dream job?
Check Out Here: https://techurl.in/lbVVO
๐ฅณ
Dreaming of landing a software development role at top tech companies? Your journey to Silicon Valleyโs elite starts here!
Whether you're a coding wizard or just getting started, this guide offers insider tips that will get you noticed by industry giants like Google, Amazon, and Apple. Ready to unlock the door to your dream job?
Check Out Here: https://techurl.in/lbVVO
KenyaTrends.co.ke
How to Land a Software Development Role in Top Tech Companies - Kenya Trends
If you're aiming for a role at a top top Tech Product company, it's essential to understand the skills and knowledge you need to succeed. Whether you're
Your biggest enemy ๐
๐๐๐ ๐จ๐ ๐๐๐ฃ๐๐๐ญ๐ข๐จ๐ง
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 ๐
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 ๐๐ป
๐ด Frequently asked questions
๐ด Interview pattern
๐ด 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 ๐๐
Channel Link: https://t.me/TechPsyche
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 ๐
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 ๐๐ป
๐ด Frequently asked questions
๐ด Interview pattern
๐ด 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 ๐๐
Channel Link: https://t.me/TechPsyche
A collection of 130 Tools Here:
https://mega.nz/folder/we1FCQiK#Pa0H9oxlP8kClmBxJ5_VOw
Follow this whatsapp channel for More:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
https://mega.nz/folder/we1FCQiK#Pa0H9oxlP8kClmBxJ5_VOw
Follow this whatsapp channel for More:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
mega.nz
File folder on MEGA
Some Useful Top Ai Tools
Follow this Channel for More Updates:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Follow this Channel for More Updates:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
20 programming languages that changed their original names๐
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)
I'm not sure about how true this is ๐น pasted as copied...but follow this channel for more Tech Updates๐
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
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
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)
I'm not sure about how true this is ๐น pasted as copied...but follow this channel for more Tech Updates๐
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
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 โ Basics of web structure & styling
2. JavaScript โ Adds interactivity
3. Python โ Backend & versatility
4. PHP โ Server-side scripting
5. SQL โ Database management
6. Ruby on Rails โ Easy backend framework
7. Node.js โ JavaScript backend runtime
8. React โ Popular frontend library
9. Angular โ Framework for building dynamic UIs
10. Bootstrap โ Simplifies responsive design
Follow Tech Psyche for More Updates: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1. HTML/CSS โ Basics of web structure & styling
2. JavaScript โ Adds interactivity
3. Python โ Backend & versatility
4. PHP โ Server-side scripting
5. SQL โ Database management
6. Ruby on Rails โ Easy backend framework
7. Node.js โ JavaScript backend runtime
8. React โ Popular frontend library
9. Angular โ Framework for building dynamic UIs
10. Bootstrap โ Simplifies responsive design
Follow Tech Psyche for More Updates: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
WHAT FOR WHAT?
๐ผ Frontend
HTML + CSS
Javascript
React
VueJs
Angular
Svelte
๐ Backend:
Nodejs/Express
Python/Django
PHP/Laravel
Java
C#
๐ฝ Database
MongoDB
MySQL
Postgres
Redis
๐ฅ Desktop
Electron
Tairi
PyQt
๐ฑ Mobile:
React Native
Flutter
Swift
Kotlin
๐ฅ System
Go
C++
Rust
Follow Tech Psyche Channel for More Updates:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐ผ Frontend
HTML + CSS
Javascript
React
VueJs
Angular
Svelte
๐ Backend:
Nodejs/Express
Python/Django
PHP/Laravel
Java
C#
๐ฝ Database
MongoDB
MySQL
Postgres
Redis
๐ฅ Desktop
Electron
Tairi
PyQt
๐ฑ Mobile:
React Native
Flutter
Swift
Kotlin
๐ฅ System
Go
C++
Rust
Follow Tech Psyche Channel for More Updates:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R