Any data science book which you loved so far?

Important Note

Over the recent days, I've observed several Instagram influencers and Telegram channels endorsing a platform that claims to provide data science and data analyst certificates for 399 INR. Unfortunately, many individuals unwittingly fall into this trap.

I strongly advise against succumbing to such schemes, as these certificates hold little to no real value. Instead, channel your efforts into skill development through hands-on projects, leveraging the wealth of available online resources. If you're considering an investment, I recommend directing it towards high-quality books.

Feel free to share your thoughts in the comments, whether you agree or have a different perspective.

©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.

🔐"Key Python Libraries for Data Science:

Numpy: Core for numerical operations and array handling.

SciPy: Complements Numpy with scientific computing features like optimization.

Pandas: Crucial for data manipulation, offering powerful DataFrames.

Matplotlib: Versatile plotting library for creating various visualizations.

Keras: High-level neural networks API for quick deep learning prototyping.

TensorFlow: Popular open-source ML framework for building and training models.

Scikit-learn: Efficient tools for data mining and statistical modeling.

Seaborn: Enhances data visualization with appealing statistical graphics.

Statsmodels: Focuses on estimating and testing statistical models.

NLTK: Library for working with human language data.

These libraries empower data scientists across tasks, from preprocessing to advanced machine learning."

Today's question: What do you want to achieve in this life? 🥹

🟢 7 valuable resources that I used to prepare for data science interviews!

🟢 One of the most important factors to get data science jobs in the best companies is success in job interviews.

🗂 I have put here 7 valuable resources that helped me a lot while preparing for data science interviews. I hope these resources can help you succeed in data science interviews


1️⃣ machine learning
📕 Link: Machine Learning


2️⃣ Python programming language
📕 Link: Python Programming Language


3️⃣ SQL programming language
📕 Link: SQL Programming Language


4️⃣ R programming language
📕 Link: R Programming Language


5️⃣ Pandas library
📕 Link: Pandas Python Library


6️⃣ NumPy library
📕 Link: NumPy Python Library


7️⃣ Matplotlib library
📕 Link: Matplotlib Python Library

Enjoy 👍

I like your data science project channel. I have suggestion for you, please create a WhatsApp channel too. Because many students are not in telegram but everyone uses WhatsApp.

For those of you who are more active on WhatsApp can join Data Science Projects channel 👇👇
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y

Sharing quality content here as well 😄❤️

200+ followers completed ❤️

Time to bring more quality content on WhatsApp as well 😄

Here are some project ideas for a data science and machine learning project focused on generating AI:

1. Natural Language Generation (NLG) Model: Build a model that generates human-like text based on input data. This could be used for creating product descriptions, news articles, or personalized recommendations.

2. Code Generation Model: Develop a model that generates code snippets based on a given task or problem statement. This could help automate software development tasks or assist programmers in writing code more efficiently.

3. Image Captioning Model: Create a model that generates captions for images, describing the content of the image in natural language. This could be useful for visually impaired individuals or for enhancing image search capabilities.

4. Music Generation Model: Build a model that generates music compositions based on input data, such as existing songs or musical patterns. This could be used for creating background music for videos or games.

5. Video Synthesis Model: Develop a model that generates realistic video sequences based on input data, such as a series of images or a textual description. This could be used for generating synthetic training data for computer vision models.

6. Chatbot Generation Model: Create a model that generates conversational agents or chatbots based on input data, such as dialogue datasets or user interactions. This could be used for customer service automation or virtual assistants.

7. Art Generation Model: Build a model that generates artistic images or paintings based on input data, such as art styles, color palettes, or themes. This could be used for creating unique digital artwork or personalized designs.

8. Story Generation Model: Develop a model that generates fictional stories or narratives based on input data, such as plot outlines, character descriptions, or genre preferences. This could be used for creative writing prompts or interactive storytelling applications.

9. Recipe Generation Model: Create a model that generates new recipes based on input data, such as ingredient lists, dietary restrictions, or cuisine preferences. This could be used for meal planning or culinary inspiration.

10. Financial Report Generation Model: Build a model that generates financial reports or summaries based on input data, such as company financial statements, market trends, or investment portfolios. This could be used for automated financial analysis or decision-making support.

Any project which sounds interesting to you?

Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science

Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.

1. Basic python and statistics

Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset

2. Advanced Statistics

Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset

3. Supervised Learning

a) Regression Problems

How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview

b) Classification problems

Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking

4. Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

5. Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Share with credits: https://t.iss.one/sqlproject

ENJOY LEARNING 👍👍

Sharing 20+ Diverse Datasets📊 for Data Science and Analytics practice!


1. How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview

2. Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand

3. Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction

4. Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data

5. Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction

6. Iris Dataset: https://archive.ics.uci.edu/ml/datasets/iris

7. Titanic Dataset: https://www.kaggle.com/c/titanic

8. Wine Quality Dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality

9. Heart Disease Dataset: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

10. Bengaluru House Price Dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data

11. Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

12. Credit Card Fraud Detection: https://www.kaggle.com/mlg-ulb/creditcardfraud

13. Netflix Movies and TV Shows: https://www.kaggle.com/shivamb/netflix-shows

14. Trending YouTube Video Statistics: https://www.kaggle.com/datasnaek/youtube-new

15. Walmart Store Sales Forecasting: https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting

16. FIFA 19 Complete Player Dataset: https://www.kaggle.com/karangadiya/fifa19

17. World Happiness Report: https://www.kaggle.com/unsdsn/world-happiness

18. TMDB 5000 Movie Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata

19. Students Performance in Exams: https://www.kaggle.com/spscientist/students-performance-in-exams

20. Twitter Sentiment Analysis Dataset: https://www.kaggle.com/kazanova/sentiment140

21. Digit Recognizer: https://www.kaggle.com/c/digit-recognizer


💻🔍 Don't miss out on these valuable resources for advancing your data science journey!

Python Projects for your portfolio 👇👇
https://www.linkedin.com/posts/sqlspecialist_python-projects-data-activity-7181930981203787776-mrGF?utm_source=share&utm_medium=member_android

Like for more

5️⃣ responses that you can use when you don’t know the answer to an interview question!

✅ Option 1 (Shows that you are motivated)
Thank you for asking this question. However, I am not very well acquainted with this subject. But, I can assure you I will definitely do some research around this.

✅ Option 2 (Shows that you are a fast-learner)
Right now I won’t be able to provide you with an exact answer. But I can assure you that I am a fast learner and will learn very quickly under your mentorship.

✅ Option 3 (Best for technical questions)
I can’t think of an exact answer. I would request you to allow me some time and we can come back on this later.

✅ Option 4 (If you don’t understand the question)
I did not understand the question properly. Could you please simplify and rephrase it? I don’t want to misinterpret it.

✅ Option 5 (Redirect the conversation to the topic you are confident about)
While I don’t have much experience in X skill, I do have proper knowledge of Y. If the job requires me to learn X skill I will be excited to expand my knowledge.

Join for more: https://t.iss.one/englishlearnerspro

Remember that Interviewers don’t even expect you to answer every question perfectly. However, simply saying ‘I don’t know’ could leave a bad impression. These responses will help you to tackle tricky interview questions!

Data Science Roadmap 👇👇
https://www.linkedin.com/posts/sql-analysts_data-science-roadmap-in-2024-data-science-activity-7186569032685273088-_18e