Hello, Welcome to my first blog post!

Introduction

My name is Marc Aradillas!, and I have successfully completed the first week at Codeup in the Data Science Cohort class. This week has been an exciting journey into the world of Data Science, where I have gained a deeper understanding of the field and the opportunities it holds for aspiring Data Scientists. Codeup has provided us with a wealth of resources to help us succeed, including career projections, information about various data science roles, tools, presentations, and insights into the Data Science pipeline.

Learning Experience

I feel privileged to learn from two experienced instructors who have helped numerous students succeed in their careers as data analysts, data scientists, data engineers, and Machine Learning engineers. Their expertise and guidance have been invaluable, and I am grateful for the opportunity to learn from them.

Key Takeaway: The Data Science Pipeline

The most significant takeaway from the first week is the Data Science pipeline. As a data scientist, the journey begins with planning, which involves asking relevant questions and understanding the objectives that need to be achieved for a given problem. Next comes Data Acquisition, where we retrieve and gather information from various sources such as databases, web scraping, or data importing.

Once we have the data, we move on to Data Preparation, which includes tasks like tidying, cleansing, and wrangling the data into usable formats, including the creation of train, validate, and test sets. Following Data Preparation, we delve into Exploratory Data Analysis (EDA), where we visualize and analyze the data to gain insights, perform feature engineering, and select the most impactful features for modeling.

Modeling is the process of leveraging tools like Python and its libraries to build models that map features to target outcomes. Finally, we reach the Delivery phase, where we materialize our findings and communicate the results in a clear and actionable manner to clients, stakeholders, and others involved, with the aim of driving results and solving problems.

Command Line Interface (CLI) and Terminal

In addition to the Data Science pipeline, we also delved into the world of command line interfaces using the terminal (specifically zshell). Understanding how to navigate and interact with the file system using keyboard commands in the command line is a valuable skill. The terminal empowers us to automate tasks, create a robust file system, and use Git actions to streamline workflow with repositories on platforms like GitHub.

Conclusion

As I reflect on the first week, I feel a sense of accomplishment and pride in calling myself a Data Scientist. The wealth of new material we have covered, coupled with hands-on practice and further readings, has given me the confidence to dive deeper into this course. I am excited to share my experiences from the second week, where we will explore MySQL and further expand on the foundations of Data Science.

Stay tuned for more updates and insights from my data science journey!