Difference between list and tuple in python

🔸List is mutable ( you can modify the original list) and it's values are written in sqare brackets [ ]

🔸Tuple is immutable ( you can't modify it) and it's values are written in parentheses ( ) delimited by comma( , )

🔸To convert list to tuple - we use tuple() function
list1 = [1,2,3]
print(tuple(list1)) Output : (1,2,3)

🔸 For single element list
list1 = [1]
print(tuple(list1)) Output : (1, )

▪️a tuple is a tuple because of comma not because of parentheses

Here are some essential data science concepts from A to Z:

A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.

B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.

C - Clustering: A technique used to group similar data points together based on certain characteristics.

D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.

E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.

F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.

G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.

H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.

I - Imputation: The process of filling in missing values in a dataset using statistical methods.

J - Joint Probability: The probability of two or more events occurring together.

K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.

L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.

M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.

N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.

O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.

P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.

Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.

R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.

S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.

T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.

U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.

V - Validation Set: A subset of data used to evaluate the performance of a model during training.

W - Web Scraping: The process of extracting data from websites for analysis and visualization.

X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.

Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.

Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.

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📌 MACHINE LEARNING INTERVIEW QUESTIONS

Job trends for software developers in the next 5 years:

[1] Most startups are starting to realize that they need to control profits, not just revenues. Many unicorns who were on track to their IPO had to delay their IPOs due to lack of profitability.

For software developers: This means that startups may be less likely to offer high salaries to attract new talent. However, it also means that startups are becoming more focused on profitability, which is a good thing for the industry in the long term.

[2] All smart CEOs have started to focus on their personal brands. This is evident on LinkedIn as well. People like to buy from people, not from companies. Therefore, almost every 'Shark' now feature on their own advertisement. Why? Personal brand.

Take a cue and start cultivating your personal brand as well.

For software developers: Building a personal brand is a great way to attract new job opportunities and build a following. You can do this by writing blog posts, creating videos, or speaking at industry events.

[3] Gone are the days when you could depend on 1 job. Firms are not loyal to you. And, any smart employee can see through this. If you are not building backups already, you are doing yourself a disservice. And, the volatile job market is might one day hit you badly.

For software developers: Software developers are in high demand, so they have more flexibility to take on multiple jobs. This can be a great way to increase income and diversify your skills. For example, you could work as a full-time software engineer and also freelance as a software developer on the side.

[4] Rise of remote jobs:

To cut the long story short, if work could be done from home, why would you waste your time, effort, energy travelling? Not every job fits into 'work from home' culture, but many do.

For software developers: Software development is a job that can easily be done remotely, so many companies are now offering remote positions. This can be a great benefit for software developers who want more flexibility in their work-life balance.

[5] With the rise of financial & career education, most people would prefer decent money + family time (OVER) crazy money + no time.

And owning small businesses is one way of fulfilling this goal.

For software developers: Starting a small business can be a great way for software developers to have more control over their work and earn more money. Some examples of small businesses that software developers can start include developing and selling software products, providing software consulting services, or freelancing as a software developer.

6] As more and more businesses move online, the demand for software developers will continue to grow. Software developers are responsible for building and maintaining the digital infrastructure that businesses need to operate in the digital age.

P.S. I am not an expert and these are speculations

Want to get started with System design interview preparation, start with these 👇

1. Learn to understand requirements
2. Learn the difference between horizontal and vertical scaling.
3. Study latency and throughput trade-offs and optimization techniques.
4. Understand the CAP Theorem (Consistency, Availability, Partition Tolerance).
5. Learn HTTP/HTTPS protocols, request-response lifecycle, and headers.
6. Understand DNS and how domain resolution works.
7. Study load balancers, their types (Layer 4 and Layer 7), and algorithms.
8. Learn about CDNs, their use cases, and caching strategies.
9. Understand SQL databases (ACID properties, normalization) and NoSQL types (key–value, document, graph).
10. Study caching tools (Redis, Memcached) and strategies (write-through, write-back, eviction policies).
11. Learn about blob storage systems like S3 or Google Cloud Storage.
12. Study sharding and horizontal partitioning of databases.
13. Understand replication (leader–follower, multi-leader) and consistency models.
14. Learn failover mechanisms like active-passive and active-active setups.
15. Study message queues like RabbitMQ, Kafka, and SQS.
16. Understand consensus algorithms such as Paxos and Raft.
17. Learn event-driven architectures, Pub/Sub models, and event sourcing.
18. Study distributed transactions (two-phase commit, sagas).
19. Learn rate-limiting techniques (token bucket, leaky bucket algorithms).
20. Study API design principles for REST, GraphQL, and gRPC.
21. Understand microservices architecture, communication, and trade-offs with monoliths.
22. Learn authentication and authorization methods (OAuth, JWT, SSO).
23. Study metrics collection tools like Prometheus or Datadog.
24. Understand logging systems (e.g., ELK stack) and tracing tools (OpenTelemetry, Jaeger).
25.Learn about encryption (data at rest and in transit) and rate-limiting for security.
26. And then practise the most commonly asked questions like URL shorteners, chat systems, ride-sharing apps, search engines, video streaming, and e-commerce websites

Coding Interview Resources: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

Top 9 Http Methods-

GET 🧐 - Retrieve data from a resource.
HEAD 🎧 - Retrieve the headers of a resource.
POST 📮 - Submit data to a resource.
PUT 📥 - Update an existing resource or create a new resource.
DELETE 🗑️ - Remove a resource.
CONNECT 🔗 - Establish a network connection for a resource.
OPTIONS ⚙️ - Describe communication options for the target resource.
TRACE 🕵️‍♂️ - Retrieve a diagnostic trace of the request.
PATCH 🩹 - Apply a partial update to a resource.

Coding and Aptitude Round before interview

Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.

Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.

Resources for Prep:

For algorithms and data structures prep,Leetcode and Hackerrank are good resources.

For aptitude prep, you can refer to IndiaBixand Practice Aptitude.

With respect to data science challenges, practice well on GLabs and Kaggle.

Brilliant is an excellent resource for tricky math and statistics questions.

For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.

Things to Note:

Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!

In case, you are finished with the test before time, recheck your answers and then submit.

Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.

9 beginner-friendly coding project ideas to build confidence:

📅 Digital Clock — show real-time hours, minutes, seconds
🎲 Dice Roller — generate random numbers with UI
📋 Quiz App — multiple choice questions with score tracking
🔢 Number Guessing Game — apply loops and conditionals
💬 Message Encoder/Decoder — basic string manipulation
🖼️ Image Slider — work with DOM and transitions
🔐 Password Generator — use randomization and user input
📈 Temperature Converter — switch between Celsius and Fahrenheit
✏️ Notes App — add, delete, and save notes with local storage

#coding #projects