✅Introducing the Crypto Exchange

🔴Buying cryptocurrency is just like buying foreign currency for a holiday. It is just an exchange of one currency for another at an agreed rate - e.g Euros for BTC (the currency symbol for bitcoin) - which is why the most common place to buy cryptocurrency is called an Exchange. 

🔴It might feel confusing when cryptocurrency like bitcoin is talked about as having a price, whereas for dollar, euro etc we are used to talking about an exchange rate. 

➡️These two terms - price/exchange rate - are interchangeable and simply reflect the fact that currency values - especially crypto - are constantly changing. 

🔴The price simply reflects the interaction between buyers and sellers on each Exchange, which organically maintain parity with each other. (again explained in detail elsewhere). An overall representation of price can then be reached by aggregating the price from the main exchanges.

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Who is Data Scientist?

He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.

A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:

Determines correct datasets and variables.

Identifies the most challenging data-analytics problems.

Collects large sets of data- structured and unstructured, from different sources.

Cleans and validates data ensuring accuracy, completeness, and uniformity.

Builds and applies models and algorithms to mine stores of big data.

Analyzes data to recognize patterns and trends.

Interprets data to find solutions.

Communicates findings to stakeholders using tools like visualization.

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Tableau vs Power BI: Must-Know Differences

Tableau:
- Usage: Best suited for advanced data visualization and analytics with complex datasets.
- Best For: Users who need highly customized, detailed visualizations and are working with large datasets across different industries.
- Data Handling: Performs well with large datasets and offers strong data handling capabilities.
- Visuals: Known for its superior, highly customizable visuals, making it perfect for complex and artistic data representations.
- Integration: Easily connects with a wide range of data sources but may require more technical skills for setup.
- Sharing: Sharing options are available, but often require paid licenses for viewers.
- Cost: Tableau tends to be more expensive, especially at the enterprise level.
- Automation: Supports automated data refreshes, but the setup might be more complex than Power BI.

Power BI:
- Usage: Designed for data analysis and creating interactive, dynamic reports that integrate seamlessly with other Microsoft tools.
- Best For: Users who need to combine data from multiple sources and create reports with interactive dashboards.
- Data Handling: Efficiently handles large datasets without performance issues and integrates smoothly with Microsoft platforms.
- Visuals: Offers interactive dashboards and visualizations, with built-in themes that are user-friendly.
- Integration: Easily integrates with Microsoft products like Excel, Azure, and SQL Server, making it a natural choice for Microsoft users.
- Sharing: Built-in cloud sharing features allow for real-time collaboration and automatic updates.
- Cost: Power BI is more affordable, with a free version available and competitive pricing for the Pro version.
- Automation: Offers strong automation features with real-time data refreshes and scheduling, making it ideal for dynamic reporting.

Tableau is a great choice for users who prioritize advanced visualizations, while Power BI is better for those who need easy integration with Microsoft tools, affordability, and real-time collaboration.

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1. Presentation

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Types Of Cypto Currency Analysis

Most Tokens Go to Zero in the Long Run

Many crypto tokens are created primarily to benefit a small circle of insiders and VCs. These project's tokenomics focus on enriching their founders rather than delivering long-term value, leading to a lack of genuine commitment to the success in the long period.

Most of these tokens lack intrinsic value and aren't truly connected to the technology they claim to represent. Without real utility or solid fundamentals, they're prone to losing value over time and will eventually become worthless by underperforming market leaders for long enough.

During bull markets, even weak assets can experience significant price increases due to overall market enthusiasm—the "rising tide lifts all boats" effect. This can give the illusion that these tokens are successful when they're simply riding the wave of market optimism

When the market corrects, investors typically shift their capital from speculative, overhyped tokens to solid projects with proven track records. Funds flow back into fundamental blue-chip crypto assets like BTC, ETH, and SOL, which have demonstrated resilience

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DATA ENGINEERING

Understand the power of Data Lakehouse Architecture for 𝗙𝗥𝗘𝗘 here

🚨𝗢𝗹𝗱 𝘄𝗮𝘆
• Complicated ETL processes for data integration.
• Silos of data storage, separating structured and unstructured data.
• High data storage and management costs in traditional warehouses.
• Limited scalability and delayed access to real-time insights.

✅𝗡𝗲𝘄 𝗪𝗮𝘆
• Streamlined data ingestion and processing with integrated SQL capabilities.
• Unified storage layer accommodating both structured and unstructured data.
• Cost-effective storage by combining benefits of data lakes and warehouses.
• Real-time analytics and high-performance queries with SQL integration.

The shift?

Unified Analytics and Real-Time Insights > Siloed and Delayed Data Processing

Leveraging SQL to manage data in a data lakehouse architecture transforms how businesses handle data.

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14 Uses Of Data Science

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