Few common problems with lot of resumes:
1. ๐๐ซ๐ซ๐๐ฅ๐๐ฏ๐๐ง๐ญ ๐ข๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง.
I understand that there are a lot of achievements that we are personally proud of (things like represented school/clg in XYZ competition or school head/class head etc), but not all of them are relevant to technical roles. As a fresher, try to focus more on technical achievements rather than managerial ones.
2. ๐๐๐๐ค ๐จ๐ ๐ช๐ฎ๐๐ฅ๐ข๐ญ๐ฒ ๐ฉ๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ.
Many resumes have the same common projects, such as:
Creating just the front-end using HTML and CSS and redirecting all the work to an open-source API (e.g., weather prediction and recipe suggestion apps).
Most common projects are: -
Tic-tac-toe game.
Sorting algorithms visualizers.
To-do application.
Movie listing.
The codes for these projects are often copied and pasted from GitHub repositories.
Projects are like a bounty. If you are prepared well and have quality projects in your resume, you can set the tempo of the interview. It is one of the few questions that you will almost certainly be asked in the interview.
I don't understand why we can spend 2 years preparing for data structures and algorithms (DSA) and competitive programming (CP), but not even 2 weeks to create quality projects.
Even if your resume passes the applicant tracking system (ATS) and recruiter's screening, weak projects can still lead to your rejection in interviews. And this is completely in your hands.
I feel that this topic needs a lot more discussion about the type and quality of projects that one needs. Let me know if you want a dedicated post on this.
3. ๐๐๐๐ค ๐จ๐ ๐ช๐ฎ๐๐ง๐ญ๐ข๐ญ๐๐ญ๐ข๐ฏ๐ ๐๐๐ญ๐.
For technical roles, adding quantitative data has a big impact.
For example, instead of saying "I wrote unit tests for service X and reduced the latency of service Y by caching," you can say "I wrote unit tests and increased the code coverage from 80% to 95% of service X and reduced latency from 100 milliseconds to 50 milliseconds of service Y."
1. ๐๐ซ๐ซ๐๐ฅ๐๐ฏ๐๐ง๐ญ ๐ข๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง.
I understand that there are a lot of achievements that we are personally proud of (things like represented school/clg in XYZ competition or school head/class head etc), but not all of them are relevant to technical roles. As a fresher, try to focus more on technical achievements rather than managerial ones.
2. ๐๐๐๐ค ๐จ๐ ๐ช๐ฎ๐๐ฅ๐ข๐ญ๐ฒ ๐ฉ๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ.
Many resumes have the same common projects, such as:
Creating just the front-end using HTML and CSS and redirecting all the work to an open-source API (e.g., weather prediction and recipe suggestion apps).
Most common projects are: -
Tic-tac-toe game.
Sorting algorithms visualizers.
To-do application.
Movie listing.
The codes for these projects are often copied and pasted from GitHub repositories.
Projects are like a bounty. If you are prepared well and have quality projects in your resume, you can set the tempo of the interview. It is one of the few questions that you will almost certainly be asked in the interview.
I don't understand why we can spend 2 years preparing for data structures and algorithms (DSA) and competitive programming (CP), but not even 2 weeks to create quality projects.
Even if your resume passes the applicant tracking system (ATS) and recruiter's screening, weak projects can still lead to your rejection in interviews. And this is completely in your hands.
I feel that this topic needs a lot more discussion about the type and quality of projects that one needs. Let me know if you want a dedicated post on this.
3. ๐๐๐๐ค ๐จ๐ ๐ช๐ฎ๐๐ง๐ญ๐ข๐ญ๐๐ญ๐ข๐ฏ๐ ๐๐๐ญ๐.
For technical roles, adding quantitative data has a big impact.
For example, instead of saying "I wrote unit tests for service X and reduced the latency of service Y by caching," you can say "I wrote unit tests and increased the code coverage from 80% to 95% of service X and reduced latency from 100 milliseconds to 50 milliseconds of service Y."
โค5
๐๐๐Preparing for a Data science/ Data Analytics interview can be challenging, but with the right strategy, you can enhance your chances of success. Here are some key tips to assist you in getting ready:
Review Fundamental Concepts: Ensure you have a strong grasp of statistics, probability, linear algebra, data structures, algorithms, and programming languages like Python, R, and SQL.
Refresh Machine Learning Knowledge: Familiarize yourself with various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
Practice Coding: Sharpen your coding skills by solving data science-related problems on platforms like HackerRank, LeetCode, and Kaggle.
Build a Project Portfolio: Showcase your proficiency by creating a portfolio highlighting projects covering data cleaning, wrangling, exploratory data analysis, and machine learning.
Hone Communication Skills: Practice articulating complex technical ideas in simple terms, as effective communication is vital for data scientists when interacting with non-technical stakeholders.
Research the Company: Gain insights into the company's operations, industry, and how they leverage data to solve challenges.
๐ง ๐By adhering to these guidelines, you'll be well-prepared for your upcoming data science interview. Best of luck!
Hope this helps ๐โค๏ธ:โ -โ )
๐๐Be the first one to know the latest Job openings
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Review Fundamental Concepts: Ensure you have a strong grasp of statistics, probability, linear algebra, data structures, algorithms, and programming languages like Python, R, and SQL.
Refresh Machine Learning Knowledge: Familiarize yourself with various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
Practice Coding: Sharpen your coding skills by solving data science-related problems on platforms like HackerRank, LeetCode, and Kaggle.
Build a Project Portfolio: Showcase your proficiency by creating a portfolio highlighting projects covering data cleaning, wrangling, exploratory data analysis, and machine learning.
Hone Communication Skills: Practice articulating complex technical ideas in simple terms, as effective communication is vital for data scientists when interacting with non-technical stakeholders.
Research the Company: Gain insights into the company's operations, industry, and how they leverage data to solve challenges.
๐ง ๐By adhering to these guidelines, you'll be well-prepared for your upcoming data science interview. Best of luck!
Hope this helps ๐โค๏ธ:โ -โ )
๐๐Be the first one to know the latest Job openings
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
โค1
1. What is the AdaBoost Algorithm?
AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. What this algorithm does is that it builds a model and gives equal weights to all the data points. It then assigns higher weights to points that are wrongly classified. Now all the points which have higher weights are given more importance in the next model. It will keep training models until and unless a lower error is received.
2. What is the Sliding Window method for Time Series Forecasting?
Time series can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem.
In the sliding window method, the previous time steps can be used as input variables, and the next time steps can be used as the output variable.
In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem.
3. What do you understand by sub-queries in SQL?
A subquery is a query inside another query where a query is defined to retrieve data or information back from the database. In a subquery, the outer query is called as the main query whereas the inner query is called subquery. Subqueries are always executed first and the result of the subquery is passed on to the main query. It can be nested inside a SELECT, UPDATE or any other query. A subquery can also use any comparison operators such as >,< or =.
4. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook?
Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.
5. How is a Random Forest related to Decision Trees?
Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.
6. What are some disadvantages of using Naive Bayes Algorithm?
Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. What this algorithm does is that it builds a model and gives equal weights to all the data points. It then assigns higher weights to points that are wrongly classified. Now all the points which have higher weights are given more importance in the next model. It will keep training models until and unless a lower error is received.
2. What is the Sliding Window method for Time Series Forecasting?
Time series can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem.
In the sliding window method, the previous time steps can be used as input variables, and the next time steps can be used as the output variable.
In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem.
3. What do you understand by sub-queries in SQL?
A subquery is a query inside another query where a query is defined to retrieve data or information back from the database. In a subquery, the outer query is called as the main query whereas the inner query is called subquery. Subqueries are always executed first and the result of the subquery is passed on to the main query. It can be nested inside a SELECT, UPDATE or any other query. A subquery can also use any comparison operators such as >,< or =.
4. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook?
Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.
5. How is a Random Forest related to Decision Trees?
Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.
6. What are some disadvantages of using Naive Bayes Algorithm?
Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
โค4
๐งฟ Boost React Performance
Performance bottlenecks in React often come from unnecessary re-renders and poor state management. Hereโs a straightforward guide to optimizing your React apps.
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Roadmap to become a Programmer:
๐ Learn Programming Fundamentals (Logic, Syntax, Flow)
โ๐ Choose a Language (Python / Java / C++)
โ๐ Learn Data Structures & Algorithms
โ๐ Learn Problem Solving (LeetCode / HackerRank)
โ๐ Learn OOPs & Design Patterns
โ๐ Learn Version Control (Git & GitHub)
โ๐ Learn Debugging & Testing
โ๐ Work on Real-World Projects
โ๐ Contribute to Open Source
โโ Apply for Job / Internship
React โค๏ธ for More ๐ก
๐ Learn Programming Fundamentals (Logic, Syntax, Flow)
โ๐ Choose a Language (Python / Java / C++)
โ๐ Learn Data Structures & Algorithms
โ๐ Learn Problem Solving (LeetCode / HackerRank)
โ๐ Learn OOPs & Design Patterns
โ๐ Learn Version Control (Git & GitHub)
โ๐ Learn Debugging & Testing
โ๐ Work on Real-World Projects
โ๐ Contribute to Open Source
โโ Apply for Job / Internship
React โค๏ธ for More ๐ก
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If you want to Excel at using one of the most powerful programming languages in the world, learn these essential Python features:
โข List Comprehensions โ [x for x in range(10) if x % 2 == 0]
โข Lambda Functions โ lambda x: x * 2
โข Map, Filter, Reduce โ Functional programming magic
โข F-strings โ f"Hello, {name}!" (Best way to format strings)
โข Enumerate & Zip โ Iterate smarter
โข Generators & Yield โ Efficient memory usage
โข Exception Handling โ try-except-finally for error-proof code
โข Decorators โ @
โข Pandas & NumPy โ Data manipulation & numerical computing
โข Async Programming โ Speed up tasks with asyncio
Free Python Resources: ๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Like it if you need a complete tutorial on all these topics! ๐โค๏ธ
โข List Comprehensions โ [x for x in range(10) if x % 2 == 0]
โข Lambda Functions โ lambda x: x * 2
โข Map, Filter, Reduce โ Functional programming magic
โข F-strings โ f"Hello, {name}!" (Best way to format strings)
โข Enumerate & Zip โ Iterate smarter
โข Generators & Yield โ Efficient memory usage
โข Exception Handling โ try-except-finally for error-proof code
โข Decorators โ @
staticmethod, @classmethod, @propertyโข Pandas & NumPy โ Data manipulation & numerical computing
โข Async Programming โ Speed up tasks with asyncio
Free Python Resources: ๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Like it if you need a complete tutorial on all these topics! ๐โค๏ธ
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HTML/CSS ๐จ โ Kevin Powell
JavaScript ๐ โ The Net Ninja
TypeScript ๐ โ Academind
React โ๏ธ โ Traversy Media
Angular ๐บ โ Academind
Vue. js ๐ฉ โ Vue Mastery
๐๐ฎ๐ฐ๐ธ๐ฒ๐ป๐ฑ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐ & ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ๐
Node. js ๐ โ Traversy Media
PHP ๐ โ PHP Academy
Ruby on Rails ๐ โ Drifting Ruby
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๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ & ๐๐ฒ๐๐ข๐ฝ๐
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React โค๏ธ for more
๐๐ฟ๐ผ๐ป๐๐ฒ๐ป๐ฑ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐ & ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ๐
HTML/CSS ๐จ โ Kevin Powell
JavaScript ๐ โ The Net Ninja
TypeScript ๐ โ Academind
React โ๏ธ โ Traversy Media
Angular ๐บ โ Academind
Vue. js ๐ฉ โ Vue Mastery
๐๐ฎ๐ฐ๐ธ๐ฒ๐ป๐ฑ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐ & ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ๐
Node. js ๐ โ Traversy Media
PHP ๐ โ PHP Academy
Ruby on Rails ๐ โ Drifting Ruby
Django (Python) ๐ โ Corey Schafer
Flask (Python) ๐ฅ โ Pretty Printed
ASP. NET (C#) ๐ฏ โ IAmTimCorey
๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ & ๐๐ฒ๐๐ข๐ฝ๐
SQL ๐๏ธ โ DataSimplifier
MongoDB ๐ โ MongoDB Official
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React โค๏ธ for more
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