Common Types Of Cryptocurrency Scams & Tips To Avoid Them. Part 5

4. Ponzi and Pyramid schemes
Cryptocurrency Ponzi schemes are just like any other pyramid scheme. It involves paying returns to existing investors from the money invested by new investors and focus more on recruitment than the product itself.

The most famous cryptocurrency Ponzi scheme was OneCoin; they encourage people to buy a membership for entering the pyramid.

The membership fee is termed as an investment in the OneCoin token which can be only traded on the company’s exchange and nowhere else. They even control the trading volumes coins by putting restrictions on an individual’s buy and sell.

It clearly isn’t a cryptocurrency business but a multi-level marketing scheme.

They made a bunch of empty promises to the people that were invested in the scheme. And have also been dragged to the court multiple times and were accused of defrauding people.

How to Avoid This:

Do a background check on the founders of the company.
Go through all the past feedbacks of the company.
Confirm the legal status of the company.

Courtesy : COIN SWITCH - KUBER Cryptocurreny Exchange of India

Comments

Post a Comment

Popular posts from this blog

Resources and Links to learn Python for Data Science.

Python Basics: Official Python Documentation: Python.org Codecademy Python Course: Codecademy Python Libraries for Data Science: NumPy: numpy.org Pandas: pandas.pydata.org Matplotlib: matplotlib.org Data Visualization: Seaborn: seaborn.pydata.org Plotly: plotly.com Data Visualization with Matplotlib Tutorial: matplotlib.org/3.3.4/tutorials/index.html Statistical Analysis: SciPy: scipy.org Statsmodels: statsmodels.org Statistics for Data Science with Python: realpython.com Machine Learning: Scikit-learn: scikit-learn.org TensorFlow: tensorflow.org PyTorch: pytorch.org Advanced Topics in Data Science: Natural Language Processing with NLTK: nltk.org Time Series Analysis with Python: statsmodels.org/dev/examples/index.html#time-series-analysis Deep Learning with TensorFlow: tensorflow.org/tutorials Deep Learning with PyTorch: pytorch.org/tutorials Data Science Communities and Platforms: Kaggle (Data Science Competitions and Datasets): kaggle.com Data Science Stack Exchange: datascience.sta
Read more

Electroculture Benefits & Uses. Part 2

Let's dive deeper into electroculture and explore some specific techniques and applications: Electrostatic Fields : One aspect of electroculture involves the use of electrostatic fields. Plants naturally have a weak positive electrical charge on their surface, and applying a negative charge to the soil can create an electrostatic field. This field is believed to enhance root growth and nutrient uptake. It can also help repel certain pests, reduce soil compaction, and increase water infiltration. Electromagnetic Fields : Another approach in electroculture is the application of low-frequency electromagnetic fields. These fields can be generated through various means, such as buried wires, antennas, or specialized devices. When plants are exposed to these fields, it can stimulate cellular activities and biochemical reactions, leading to increased growth and yield. Electromagnetic fields have been shown to influence the expression of specific genes related to plant growth and stress re
Read more

10-week plan to learn Python for data science and data analysis, along with hands-on projects for each week.

Week 1: Introduction to Python and Data Manipulation Resources: Python Crash Course by Eric Matthes: book Codecademy Python Course: codecademy.com Hands-on Project: Perform basic Python exercises to get familiar with the language and practice data manipulation using Python lists. Week 2: Data Analysis with Pandas Resources: Pandas User Guide: pandas.pydata.org "Python for Data Analysis" by Wes McKinney: book Hands-on Project: Analyze a real-world dataset using Pandas. Practice loading data, performing data cleaning, filtering, and basic exploratory data analysis (EDA). Week 3: Data Visualization with Matplotlib and Seaborn Resources: Matplotlib Documentation: matplotlib.org Seaborn Documentation: seaborn.pydata.org Hands-on Project: Create various visualizations (line plots, bar charts, scatter plots, etc.) using Matplotlib and Seaborn with a dataset of your choice. Week 4: Statistical Analysis with SciPy and Statsmodels Resources: SciPy Documentation: scipy.org Statsmodels D
Read more