Essential Python Skills 👇👇 https://www.linkedin.com/posts/sql-analysts_essential-python-skills-for-data-analytics-activity-7162802367040630784-oGtP?utm_source=share&utm_medium=member_android

It's productive to not execute the idea that just came to your mind and sparked your great interest immediately.
—-
Years ago, when I haven't build a lot of solutions yet, I usually started to almost instantly create what came to my mind because I was driven by a huge energy of my fascination about problems I may solve. Also, I enjoyed building solutions with code.
Of course, I didn't finish many such things I've started to create because:
- the idea wasn't mesmerizing enough to continue building a solution. Coding 4 hours vs. coding a month is a significant difference.
- I understood that the idea didn't solve problems.
- I found out I'm interested in building other ideas.

And other reasons but they all come down to the absence of reasonable planning. We have limited energy and time, so it's more productive to allocate them to the projects that make sense in the long-term.

Of course, this statement is obvious but I fall into executing not properly planned projects sometimes.

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

Today's question
Which tool do you use for data visualisation?
Reply in comments 👇👇

For those who can't comment, you need to join the group 👇
https://t.iss.one/Kaggle_Group

Today's question
Which python libraries have you used so far?

Do you agree with Jensen?

https://www.instagram.com/reel/C4Hsx2UtK-1/?igsh=MW1yZ2ZtaDFkem1iZw==

Instagram and Facebook both down
Update: Working now

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 😄❤️