Idea: Explore books by ideas.

I'm used to search for books in two modes. In the 1st mode, I follow recommendations I receive from various sources (podcasts, individuals I follow, etc.). In the 2nd mode, I identify what is interesting for me right now, and search for the books that elaborate on the topics I'm currently curious about.

An idea that I propose is to create a place where one could see the essential ideas, excerpts, quotes from various books in the Tinder-like manner (or not): choose what you like right now, so the system reveals a book where this idea/quote originated from.

It's not a book summary app. The goal here is to have a possibility to dive deeper into the topics a person is currently interested in.

STRONG PERSONALITIES HAVE THEIR PRINCIPLES

I am telling you a must have trait if you want to build your personality.
You must live with your STRONG PRINCIPLES!

If you decided to not to smoke or drink, NEVER DO IT whatever the condition is.

THIS IS YOUR PRINCIPLE.

If you decided not to eat non-veg, then never do it.

YOU CAN HAVE YOUR OWN STRONG PRINCIPLES IN LIFE!

The thing that matter is to follow them no matter what the condition is!!!

50 random niches that may not be so popular and you may find some where you have experience to kickstart a business:

1. Custom pool design and construction
2. Rare book dealing
3. Luxury pet accessories and clothing
4. Medieval armor reproduction
5. Heirloom seed farming
6. Taxidermy
7. High-end audio equipment manufacturing
8. Vintage and collectible toy restoration
9. Gourmet mushroom cultivation
10. Exotic and rare plant nursery
11. Custom doll-making
12. Luxury chicken coops
13. Mechanical keyboard manufacturing
14. Classic car restoration
15. Handcrafted artisanal candles
16. Sustainable and eco-friendly home products
17. Bespoke leather goods and accessories
18. Organic honey production
19. Artisanal cheese-making
20. Antique furniture restoration
21. Personalized wedding and event planning
22. Vintage watch repair and restoration
23. Customized interior design for small spaces
24. Premium handcrafted chocolate and confectionery
25. Customized bicycle design and assembly
26. Rare gemstone and jewelry design
27. Specialty tea and herbal blend curations
28. Artisanal soap and skincare products
29. Eco-conscious fashion and apparel
30. Bespoke yacht and boat building
31. Handcrafted artisanal pottery and ceramics
32. Vintage vinyl record restoration and sales
33. Custom-made leather footwear and boots
34. Specialty coffee roasting and brewing equipment
35. Organic and sustainable farming practices consultancy
36. Luxury watch customization and modification
37. Artisanal pasta and gourmet food production
38. Handmade sustainable home decor
39. Eco-friendly and custom-designed bicycles
40. Bespoke wedding gown and bridal attire design
41. High-quality handmade paper and stationery
42. Personalized fitness and wellness coaching
43. Exotic pet breeding and care services
44. Customized mobile app development
45. Vintage and antique restoration services
46. Rare and collectible coin dealing
47. Premium quality beard grooming products
48. Customized automotive detailing and modification
49. Bespoke furniture upholstery and restoration
50. Specialty outdoor adventure tours and experiences

Today we share with you 5 Tips for a successful business.

1. Passion
Passion is what drives you to succeed.
Don’t do it for the money do it for the passion and have fun. The money will come.

2. Determination
Work hard, stay focused, stay determined, and keep going.
Your determination will separate you from your competitors.

3. Overcome your limits reach out.
Overcome the limits that you put on your success. Step out of the comfort zone to accomplish great things.
Take action toward your goals and confront the limiting beliefs.

4. Create added value
Whenever you do something give your added value. Offer better quality and convenience.
Understand what drives value for your customers.

5. Listen to the market
Understand the needs, likes, and interests of your target market.

Listen to your customers and learn. Customer feedback and satisfaction is a great way to determine if you meet or surpass your customer expectations.

15 Best Project Ideas for Python : 🐍

🚀 Beginner Level:
1. Simple Calculator
2. To-Do List
3. Number Guessing Game
4. Dice Rolling Simulator
5. Word Counter

🌟 Intermediate Level:
6. Weather App
7. URL Shortener
8. Movie Recommender System
9. Chatbot
10. Image Caption Generator

🌌 Advanced Level:
11. Stock Market Analysis
12. Autonomous Drone Control
13. Music Genre Classification
14. Real-Time Object Detection
15. Natural Language Processing (NLP) Sentiment Analysis

👉✔️Here are Data Analytics-related questions along with their answers:

1.Question: What is the purpose of exploratory data analysis (EDA)?

Answer: EDA is used to analyze and summarize data sets, often through visual methods, to understand patterns, relationships, and potential outliers.

2. Question: What is the difference between supervised and unsupervised learning?

Answer: Supervised learning involves training a model on a labeled dataset, while unsupervised learning deals with unlabeled data to discover patterns without explicit guidance.

3.Question: Explain the concept of normalization in the context of data preprocessing.

Answer: Normalization scales numeric features to a standard range, preventing certain features from dominating due to their larger scales.

4. Question: What is the purpose of a correlation coefficient in statistics?

Answer: A correlation coefficient measures the strength and direction of a linear relationship between two variables, ranging from -1 to 1.

5. Question: What is the role of a decision tree in machine learning?

Answer: A decision tree is a predictive model that maps features to outcomes by recursively splitting data based on feature conditions.

6. Question: Define precision and recall in the context of classification models.

Answer: Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to all actual positives.

7. Question: What is the purpose of cross-validation in machine learning?

Answer: Cross-validation assesses a model's performance by dividing the dataset into multiple subsets, training the model on some, and testing it on others, helping to evaluate its generalization ability.

8. Question: Explain the concept of a data warehouse.

Answer: A data warehouse is a centralized repository that stores, integrates, and manages large volumes of data from different sources, providing a unified view for analysis and reporting.

9. Question: What is the difference between structured and unstructured data?

Answer: Structured data is organized and easily searchable (e.g., databases), while unstructured data lacks a predefined structure (e.g., text documents, images).

10. Question: What is clustering in machine learning?

Answer: Clustering is a technique that groups similar data points together based on certain features, helping to identify patterns or relationships within the data.

Is accuracy always a good metric?

Accuracy is not a good performance metric when there is imbalance in the dataset. For example, in binary classification with 95% of A class and 5% of B class, a constant prediction of A class would have an accuracy of 95%. In case of imbalance dataset, we need to choose Precision, recall, or F1 Score depending on the problem we are trying to solve.

What are precision, recall, and F1-score?

Precision and recall are classification evaluation metrics:
P = TP / (TP + FP) and R = TP / (TP + FN).

Where TP is true positives, FP is false positives and FN is false negatives

In both cases the score of 1 is the best: we get no false positives or false negatives and only true positives.

F1 is a combination of both precision and recall in one score (harmonic mean):
F1 = 2 * PR / (P + R).
Max F score is 1 and min is 0, with 1 being the best.

5 Data Analytics Project Ideas for your Resume

©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.

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