In this project I have to find a best crypto currency for investment, but this dataset consist of 60+ crypto currencies with different price range. I am very confused that how to plot them and compare them like plotting their price with time or market capital. Don't worry about special characters in the columns I will remove them to convert them in float valus. Please drop suggestions I am stuck at this point. Also tell me what types of statistical methods should I use for the same. It's not real investment it's just the problem for this analysis.

I help you on Data Science related projects by Python in a different satisfactory way and competitive cost

A brief of the services based on my knowledge :

1- Knowledge of developing and debugging in Python.

2- Knowledge of one or more open-source Machine Learning framework.

3- Knowledge of probability and statistics.

4- Develop prototypes and validate the results.

5- Deploy validated algorithms and develop techniques.for monitoring and visualizing the performance of all deployed algorithms.

6- Natural Language Processing to understand text content including reviews, descriptions, and interactions between users.

7- Modeling demand and supply situation in real-time to optimize the overall market efficiency and give valuable insights.

8- Clustering of users and listings to enable more intelligent matching

https://www.cryptogrind.com/service/5eae9ae6dd546d78fc82ab4a

"I started learning data science two weeks ago, but now I feel bored with it. What should I do?"

Hi this is my first time being in Reddit, and I was wondering if there is anyone that can help me with my R studio project for my Intro to R class??

https://youtu.be/X400eIcV-So

Top Trending Data Science Certifications in 2022 

Many organizations are benefitting from using data. It is helping them in making better decisions and ensuring they keep up with the changing market trends. However, using available data and using it effectively are two different things. It is no secret now that only a knowledgeable data professional can make the most out of the data. For this reason, many organizations are looking to hire talented data professionals who can add value to the organization and help businesses grow.

There is now a huge demand in the market for Data Scientists, Data Analysts, Data Engineers, and other data professionals. However, there’s a large amount of competition too as more and more professionals are entering the data science industry. In this case, the best way to make a successful career and get hired by top organizations is by getting data science certifications. Becoming a certified data scientist offers you an edge over other applicants, helps you showcase your skills, serves as proof of your knowledge and determination making you the best choice for recruiters.

In this blog post, we are going to give you an overview of the Top 5 Data Science Certifications that are trending in 2022.

We will talk about the following Data Science Certifications in this blog post:

Top 5 Data Science Certifications for 2022

• Certified Analytics Professional (CAP)

• SAS Certified Data Scientist

• Microsoft Certified: Azure Data Scientist Associate

• Data Science Master Program

So, without much ado, let’s begin our list of Top 10 Data Science Certifications in 2022.

Python libraries for ML and DataScience You Should Know

pandas #TensorFlow #NumPy #matplotlib

https://datascience24.wordpress.com/2020/12/26/python-libraries-for-data-science-you-should-know/

https://youtu.be/L7YNQI6ZCSU

Hi there :)

I got something cool to share with you, over the past few months i have been running around trying to find a way to make a dream come true

Im creating a online hub for people in ai that care about technological innovation and having a positive impact by building and contributing on projects

This is hub will be a place to find like minded people to connect with and work on passion projects with.

Currently we are coding a platform so that everyone can find each other and get to know each other

After we got some initial users we will start with short builder programs where individuals and teams can compete in a online competition where the projects that stand out the most can earn some prize :)

Our goal is to make the world a better place by helping others to do the same

If you like our initiative, please sign up below on our website !

https://www.yournewway-ai.com/

And in some weeks, once we're ready we will send you a invite to join our platform :)

The need for data scientists is at an all-time high with striking salaries, yet they leave their jobs. What are the reasons? Find out here

https://medium.com/@emily.joe685/what-is-causing-data-scientists-to-quit-their-jobs-92de89b94b03

How can we overcome of following challenges in funding the solution of intersectional inequality using Network data science?

1. Quantitative analysis masks the truly dynamic associations between individuals’ characteristics and outcomes. There are some methodological challenges with applying a ‘inter-categorical’ approach to quantitative analysis on intersectional inequalities. The first concerns the categorisation of people into predefined categories.

2. Because of data constraints, important dimensions of inequality may be overlooked.

3. In such models the coefficient of an interaction term is not easily understood, and the true relation can also go the opposite way (positive or negative).

4. Next, the presentation and structure of analysis is important in communicating results from quantitative studies that focus on interactions between features, particularly when transmitting results for audiences less experienced in quantitative research interpretation.

5. Quantitative data are not often organized in such a way that intersection analysis is easily facilitated.

Hi community,

I just want to bring our federated learning system, Apache Wayang (wayang.apache.org), to your attention. Apache Wayang is the result of 5 years combined research research and has a dozen papers out, we are also publish our first release under the umbrella of the ASF soon. Under normal circumstances Wayang will be up to 30 times faster as Spark, since we combine different platforms and silos into one AI solution. With special tuning we reaching up to 50x better performance as Apache Flink and Apache Spark.

The commercial system is called Scalytics Data Platform (www.scalytics.io), its founded by the core team of Apache Wayang.

Hey! 👋

After 8 months of development, I'm launching Mertiql - an AI-powered analytics platform that lets non-technical teams query databases using plain English.

**The problem:** Data analysts spend 2-3 hours writing complex SQL queries. Product managers can't get insights without bothering engineers.

**The solution:** Just ask questions in plain English:
- "Show me top 10 customers by revenue"
- "What's our MRR growth last 6 months?"
- "Compare sales by region this quarter"

**What makes it different:**
✅ Auto-generates optimized SQL (no SQL knowledge needed)
✅ Creates charts/visualizations automatically
✅ Works with PostgreSQL, MySQL, MongoDB, Snowflake, BigQuery
✅ AI-powered insights and recommendations
✅ <3 second response time



Live at: https://mertiql.ai

Would love to hear your thoughts! Happy to answer any questions about the tech stack or building process.

I need help figuring out:

• How to collect or build this dataset (sources, APIs, or open datasets).

• Whether it’s better to scrape startup directories/news portals (e.g., Crunchbase, AngelList, CB Insights, GDELT, NewsAPI, etc.) or combine multiple sources.

• The best practices for structuring and cleaning the data (fields like startup name, founding year, domain, funding, location, etc.).

If anyone has experience in scraping, APIs, or curating startup datasets, I’d really appreciate your guidance or pointers to get started.

colab file

Here I have attached a link. I tried to analyze and predict a data set with four methods. (Linear regression, logistic regression, Naive Bayes, and decision tree analysis)

So please help me to write the correct code for getting accuracy, precision, and recall value for all methods. And please correct my coding. Help me as soon as possible.

thanks in advance.

Hey folks.

I am studying Data science and I have been given an assignment to improve vending machine algorithm based on real world data.

Data/vending machines are very similar to ones in McDonalds.

How would you approach this task ?

Are there any quick wins that I can achieve?

Thanks