I've always been fascinated by the way technology shapes our world. From getting a ride with a few taps on an app to having Netflix suggest the perfect show, technology has been a driving factor shaping our daily lives. Although not as appreciated as much, data science is part of this impact, driving extraction of meaningful insights which help businesses, new products, and even users. For instance, navigation systems such as Google Maps take data from traffic and road systems and analyze that data to cater an optimal route for users. To gain a more total understanding of data science, I watched a YouTube video called “What is Data Science? | Introduction to Data Science | Data Science for Beginners | Simplilearn” by Simplilearn that offered an introduction to this exciting domain. (https://www.youtube.com/watch?v=KxryzSO1Fjs)

The video made me realize how much data science is already integrated into our everyday lives. Data science has an impact on software you may not realize: self-driving cars, continually optimized airline routes, and delivery services that can predict when your package will arrive. Data science is the force behind these advancements, transforming raw data into insights that help make smarter decisions.

At its core, data science is like a detective story where clues are hidden within mountains of data. Data scientists are tasked with finding these clues, using specialized tools to find patterns and trends. It crossed my mind how similar this process is to how users shop online. To find the perfect product, we analyze reviews, compare prices, and consider various features. Data scientists do this on a much larger scale, taking on challenges across industries like entertainment, healthcare, and finance to power new and important projects which continuously change the way humans across the world live.

The video detailed a specific approach data scientists often use when developing new projects. It follows a structured approach:

• Asking the right questions is where it all begins. What's the issue you're trying to solve? This varies across industries and tasks a data scientist is given; however, it should give a solid foundation and direction in which you may continue.

• Exploring and cleaning data is the next step. Data scientists need to clean, organize, and refine raw data before it's ready for analysis. This may include clearing empty data cells, reformatting data, and making sure it is easily interpretable.  

• After cleaning data, modeling data is the next. Data scientists select and apply algorithms, such as regression models and other machine learning models to find comprehensive findings. This could also not include algorithms and simply be visualizations that reveal trends within the data.

• After the model or visualizations are made, it is much easier to identify expressible trends. It's crucial to present the findings in a clear way. For example, visualizations like charts and graphs can make complex insights understandable for everyone including people who do not have a strong background on the specific topic.

One interesting distinction the video described was between data science and business intelligence (BI). BI is mostly focused on analyzing past data to track business performance. Data science goes a step further by utilizing statistics, machine learning, and predictive modeling to not only understand the past but also peek into the future.

As someone who wants to become a data scientist in the future, I noted some skills that would be optimal as a data scientist. 

• Curiosity: It's all about asking questions and having the drive to find answers.

• Communication Skills: You need to be able to turn complex results into easy-to-understand stories.

• Technical Skills: Programming in Python or R, a solid grasp of statistics, and familiarity with machine learning algorithms are important.

• Database knowledge: Not only being familiar with the specific data set you have, but also familiarity with overall data structures, and cataloging/querying databases are essential.

This introduction has definitely informed my knowledge of data science and fueled my passion for the subject. While the path to becoming a data scientist may be challenging, the potential rewards are huge. Harnessing the power of data to solve real-world problems and shape the innovations of tomorrow seems like a very interesting path. I’m excited for the future of data science and learning more about this topic!