Tech Talk
Welcome
This page is dedicated to learnings, ideas and information that I have collected about technology topics I’m currently interested in such as:
- Product Management
- Data Science
- Analytics
- Data Engineering
- Cryptocurrencies
Product Management
Quality PM Attributes
- Leadership
- Self Starter
- Organization
- Collaborative
- Analytics & Data Skills
- Empathy for Customers
- Design Skills
- Technical Skills
- Vision
PM Metrics
- Am I balancing the needs of the customer, my team and the company’s vision
- Am I pointing my team in the right direction
- Are we hitting out timelines effectively
- Is my team staying motivated
- What roadblocks do we see ahead, how can they be removed
Types of Product Managers
Internal PM
- Builds products internally for the company
Consumer PM
- Builds products for a wide world of individuals
Business to Business PM
- Can be labeled a Software as a Service Product Manager. Clients tend to be other business.
Data Science & Analytics
What is Data Science?
Data science is a field that involves using scientific methods, algorithms, and tools to extract insights and knowledge from data. It combines elements of statistics, computer science, and domain expertise to analyze complex data sets and uncover hidden patterns, trends, and relationships.
Data science is used in a variety of industries and fields, including healthcare, finance, marketing, and sports. It is used to solve a wide range of problems, such as predicting customer behavior, identifying fraud, optimizing supply chain management, and improving medical diagnoses.
Data science is impactful because it allows organizations to make data-driven decisions and solve complex problems more efficiently and effectively. By analyzing large amounts of data, data scientists can identify patterns and trends that would be difficult or impossible to detect through manual analysis. This can lead to improved decision-making, increased efficiency, and better outcomes in various domains.
For example, in healthcare, data science is being used to develop predictive models that can identify patients at risk of developing chronic diseases or experiencing adverse events, enabling clinicians to intervene early and improve patient outcomes. In finance, data science is being used to detect fraudulent transactions and identify investment opportunities. In marketing, data science is being used to personalize marketing campaigns and optimize advertising spend. Overall, data science is a powerful tool that can enable organizations to gain a competitive edge and achieve their goals more effectively.
When You Need Help With TheWhen, Why, What, Where, How, WhoOf Data
This chart is a quick and easy way to visuals all different types of information and methods to consider when dealing with data or data science. I often find the word data science to be a large umbrella, similar to AI where the term morphs significantly depending on whom you are talking with. One way to get specific and utilize the right terminologies when referring to data or data science is to use this chart as a way to determine the topic or process to execute.
IS YOUR DATA TYPESTRUCTURED, UNSTRUCTURED
Use the chart below to determine the type of data that you are dealing with when working on a data science project.
Cryptocurrencies
What is Python?
Python is a popular programming language that was first released in 1991 by Guido van Rossum. It is an interpreted, high-level language that emphasizes code readability and simplicity. Here is an example of a simple Python code:
- Ease of Use: Python is known for its readability and simplicity, which makes it easier for developers to write and maintain code.
- Large Community and Support: Python has a large community of developers who contribute to the language, develop libraries and frameworks, and offer support through forums and documentation.
- Versatility: Python is versatile and can be used for a wide range of applications, such as web development, data analysis, machine learning, scientific computing, and more.
- Popularity in Data Science and Machine Learning: Python is widely used in the field of data science and machine learning, with many popular libraries and frameworks, such as NumPy, Pandas, TensorFlow, and PyTorch, available for these domains.
The future of Python looks bright, as it continues to gain popularity and support. Python 3, the latest version of the language, was released in 2008 and is expected to be supported for many years to come. Python is also being used in emerging technologies, such as blockchain and quantum computing, which could further increase its popularity and relevance.
Python Coding Videos
Know the 3 V's of Big Data
The three V’s of Big Data are:
- Volume: This refers to the amount of data that is being generated and collected. The volume of data can be massive, often exceeding the capacity of traditional data processing systems.
- Velocity: This refers to the speed at which data is being generated and collected. In today’s world, data is being generated and collected at an unprecedented rate, and it is important for systems to be able to process it quickly in order to extract value from it.
- Variety: This refers to the diversity of data types and sources. Data can come in many different forms, including structured, semi-structured, and unstructured data. It can also come from various sources, such as social media, sensors, and machines.
In summary, the three V’s of Big Data are:
- Volume: The amount of data being generated and collected.
- Velocity: The speed at which data is being generated and collected.
- Variety: The diversity of data types and sources.
