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What would a career in data science look like?
I'm a HS Junior and I love data representation and statistics, making lists, etc, so I'm considering going into data analytics/data science, but I'm not sure what a career in that field would actually look like.
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6 answers
Updated
Goodera’s Answer
If you enjoy working with numbers, a career in data science could be a lot of fun for you. It might not even feel like work! This field is in high demand and will stay important because data helps us understand our complex world. You can use your skills to make a difference in an organization, in society, and even globally.
Thank you so much! This is really helpful.
Indigo
Updated
Avin’s Answer
Hello Indigo,
First of all congratulations on getting ahead and starting to think about your career. Data science is a great field to go into. At this time, data science would involve machine learning, analytics and applied data science. Learning Python would be a great addition if you want to get into data science as most data intensive applications seem to use Python as it has the most extensive support. A career in data science would involve training ML models, working with statistics, linear algebra, experimentation and iteration. It could involve fine tuning language models specific to a field/company and applying that to real-world problems.
First of all congratulations on getting ahead and starting to think about your career. Data science is a great field to go into. At this time, data science would involve machine learning, analytics and applied data science. Learning Python would be a great addition if you want to get into data science as most data intensive applications seem to use Python as it has the most extensive support. A career in data science would involve training ML models, working with statistics, linear algebra, experimentation and iteration. It could involve fine tuning language models specific to a field/company and applying that to real-world problems.
Thank you so much! This is so helpful and I really appreciate the detail.
Indigo
Updated
Goodera’s Answer
Great question! A career in data science is very rewarding. It combines technology, business strategy, and continuous learning. This field uses scientific methods and algorithms to extract insights from all types of data. As a data scientist, you'll focus on solving problems and communicating solutions. You'll need to understand data, find key messages, and explain these findings to others in simple terms.
This is so helpful, I really appreciate it!
Indigo
Updated
Jugal’s Answer
Imagine a data scientist as a digital detective and storyteller.
Today, companies like Netflix, Spotify, Amazon, and even sports teams gather tons of information every second. Netflix tracks what you watch, when you pause, and what you skip. Spotify knows your favorite songs and the ones you skip.
Sports teams know every player's speed, pass accuracy, and heart rate. All this data is just a big, messy pile of numbers. A data scientist dives into this pile, finds hidden patterns, and uses them to make smart choices.
They create systems that recommend your next Netflix show or suggest a new Spotify song you'll love. They help businesses understand why customers leave, assist hospitals in predicting high-risk patients, and guide sports teams in choosing new players.
A Day in the Life: The Data Detective's Journey
A data scientist's job is more than just coding. It's like solving a puzzle, and it usually goes like this:
Ask the Right Question: It begins with a problem. A manager might ask, "Why did our sales drop last month?" or "What video should we create next?"
Go on a Data Hunt: The data scientist gathers all the data needed to answer that question. This could involve pulling records from a company database, collecting info from a website, or using public data like weather patterns.
Clean Up the Mess: This is a big part of the job. The data is often messy, like finding torn and stained diary pages. You have to piece them together, fix errors, and fill in gaps before understanding the story.
Explore the Clues: Here, you look for patterns. You might create graphs and charts to see things visually. For example, "Sales only dropped in cold-weather states," or "Most people stop watching a video after 30 seconds if it has a long intro."
Build a "Crystal Ball": This is the part you often hear about. Data scientists use statistics and machine learning to build "models." A model is a piece of code that uses past data to predict the future.
Prediction: "Based on these 10,000 other customers, this new customer will probably love this movie."
Classification: "This email looks like spam."
Tell the Story: This is the most crucial skill. You might have the best model, but if you can't explain it to your boss, it's useless. Data scientists create presentations and reports to show what they found, why it matters, and what the company should do next.
Today, companies like Netflix, Spotify, Amazon, and even sports teams gather tons of information every second. Netflix tracks what you watch, when you pause, and what you skip. Spotify knows your favorite songs and the ones you skip.
Sports teams know every player's speed, pass accuracy, and heart rate. All this data is just a big, messy pile of numbers. A data scientist dives into this pile, finds hidden patterns, and uses them to make smart choices.
They create systems that recommend your next Netflix show or suggest a new Spotify song you'll love. They help businesses understand why customers leave, assist hospitals in predicting high-risk patients, and guide sports teams in choosing new players.
A Day in the Life: The Data Detective's Journey
A data scientist's job is more than just coding. It's like solving a puzzle, and it usually goes like this:
Ask the Right Question: It begins with a problem. A manager might ask, "Why did our sales drop last month?" or "What video should we create next?"
Go on a Data Hunt: The data scientist gathers all the data needed to answer that question. This could involve pulling records from a company database, collecting info from a website, or using public data like weather patterns.
Clean Up the Mess: This is a big part of the job. The data is often messy, like finding torn and stained diary pages. You have to piece them together, fix errors, and fill in gaps before understanding the story.
Explore the Clues: Here, you look for patterns. You might create graphs and charts to see things visually. For example, "Sales only dropped in cold-weather states," or "Most people stop watching a video after 30 seconds if it has a long intro."
Build a "Crystal Ball": This is the part you often hear about. Data scientists use statistics and machine learning to build "models." A model is a piece of code that uses past data to predict the future.
Prediction: "Based on these 10,000 other customers, this new customer will probably love this movie."
Classification: "This email looks like spam."
Tell the Story: This is the most crucial skill. You might have the best model, but if you can't explain it to your boss, it's useless. Data scientists create presentations and reports to show what they found, why it matters, and what the company should do next.
Updated
Lin’s Answer
1. What Does a Career in Data Science Look Like?
It's fantastic that you're exploring careers that match your interests in statistics, data representation, and organization. Data science is a vast and exciting field, and your skills are a perfect starting point.
The best way to think of a data professional is as a data detective 🕵️. The data science job in general is to start with a question or a mystery, gather clues (data), find hidden patterns, and then present a solution in a way that everyone can understand.
2. A Day in the Life: The Data Science Process
While every day is different, most data-related jobs involve a cycle of common tasks.
* Asking the Right Questions: It all begins with curiosity. A data professional works with others to define a problem, like "Why are our sales down this month?" or "Can we predict which of our website visitors are most likely to buy something?"
* Gathering and Cleaning Data: Data in the real world is messy! A significant part of the job is hunting down data from different sources and cleaning it up to make it accurate and usable. This is where a love for organization and "making lists" is a superpower.
* Exploring and Analyzing: This is where your love for statistics shines. You'll dive into the clean data to find trends, test hypotheses, and uncover insights that aren't obvious on the surface.
* Modeling and Predicting: For many roles, this is the next step. It involves using programming and statistical techniques (machine learning) to build models that can forecast what might happen in the future.
* Telling the Story: This is where data representation is crucial. A discovery is only useful if you can communicate it effectively. This means creating clear charts, interactive dashboards, and compelling presentations to share your findings and help drive decisions.
3. Exploring Different Data Careers
"Data Science" is an umbrella term. Just like there are many types of doctors, there are many types of data professionals. Here are a few of the most common roles:
📊 Data Analyst
Main Focus: Answering the question, "What happened?" They are masters of describing and summarizing past data.
Day-to-Day: Querying databases for information, analyzing the results, and building reports and dashboards to track key business metrics. This role is perfect for someone who loves finding patterns in data and presenting them visually.
Analogy: A business historian who tells you the story of what has already occurred, backed by clear evidence.
🔬 Data Scientist/Machine Learning Scientist
Main Focus: Answering the questions, "Why did it happen?" and "What will happen next?"
Day-to-Day: They do everything an analyst does but add a layer of predictive modeling and more advanced statistics. They use programming languages (like Python or R) to build machine learning models that can forecast future events or behaviors.
⚙️ Machine Learning Engineer
Main Focus: Building and maintaining the systems that run machine learning models at a massive scale.
Day-to-Day: This is a very software-engineering-heavy role. They write clean, efficient code to make sure that the predictive model a data scientist designed can be used reliably by thousands or even millions of people in a real-world application (like a recommendation engine on Netflix).
🧪 Research Scientist
Main Focus: Inventing entirely new methods and algorithms for data analysis and machine learning.
Day-to-Day: This role is often found in large tech company research labs. It involves deep knowledge of mathematics and computer science, developing new algorithm, writing papers to push the boundaries of what is possible.
Analogy: The atmospheric physicist who discovers the new scientific principles that make better weather forecasting possible in the first place.
4. Your Next Steps
The great news is that you're already building the right foundation. Continue to lean into your math and statistics classes. If your school offers computer science or programming, definitely give that a try. Most importantly, stay curious and have fun with data!
It's fantastic that you're exploring careers that match your interests in statistics, data representation, and organization. Data science is a vast and exciting field, and your skills are a perfect starting point.
The best way to think of a data professional is as a data detective 🕵️. The data science job in general is to start with a question or a mystery, gather clues (data), find hidden patterns, and then present a solution in a way that everyone can understand.
2. A Day in the Life: The Data Science Process
While every day is different, most data-related jobs involve a cycle of common tasks.
* Asking the Right Questions: It all begins with curiosity. A data professional works with others to define a problem, like "Why are our sales down this month?" or "Can we predict which of our website visitors are most likely to buy something?"
* Gathering and Cleaning Data: Data in the real world is messy! A significant part of the job is hunting down data from different sources and cleaning it up to make it accurate and usable. This is where a love for organization and "making lists" is a superpower.
* Exploring and Analyzing: This is where your love for statistics shines. You'll dive into the clean data to find trends, test hypotheses, and uncover insights that aren't obvious on the surface.
* Modeling and Predicting: For many roles, this is the next step. It involves using programming and statistical techniques (machine learning) to build models that can forecast what might happen in the future.
* Telling the Story: This is where data representation is crucial. A discovery is only useful if you can communicate it effectively. This means creating clear charts, interactive dashboards, and compelling presentations to share your findings and help drive decisions.
3. Exploring Different Data Careers
"Data Science" is an umbrella term. Just like there are many types of doctors, there are many types of data professionals. Here are a few of the most common roles:
📊 Data Analyst
Main Focus: Answering the question, "What happened?" They are masters of describing and summarizing past data.
Day-to-Day: Querying databases for information, analyzing the results, and building reports and dashboards to track key business metrics. This role is perfect for someone who loves finding patterns in data and presenting them visually.
Analogy: A business historian who tells you the story of what has already occurred, backed by clear evidence.
🔬 Data Scientist/Machine Learning Scientist
Main Focus: Answering the questions, "Why did it happen?" and "What will happen next?"
Day-to-Day: They do everything an analyst does but add a layer of predictive modeling and more advanced statistics. They use programming languages (like Python or R) to build machine learning models that can forecast future events or behaviors.
⚙️ Machine Learning Engineer
Main Focus: Building and maintaining the systems that run machine learning models at a massive scale.
Day-to-Day: This is a very software-engineering-heavy role. They write clean, efficient code to make sure that the predictive model a data scientist designed can be used reliably by thousands or even millions of people in a real-world application (like a recommendation engine on Netflix).
🧪 Research Scientist
Main Focus: Inventing entirely new methods and algorithms for data analysis and machine learning.
Day-to-Day: This role is often found in large tech company research labs. It involves deep knowledge of mathematics and computer science, developing new algorithm, writing papers to push the boundaries of what is possible.
Analogy: The atmospheric physicist who discovers the new scientific principles that make better weather forecasting possible in the first place.
4. Your Next Steps
The great news is that you're already building the right foundation. Continue to lean into your math and statistics classes. If your school offers computer science or programming, definitely give that a try. Most importantly, stay curious and have fun with data!
Thank you so much for giving so much detail! This is so helpful and I really appreciate it!
Indigo
Updated
Goodera’s Answer
A career in data science is valued and rewarding. It helps companies turn bad decisions into good ones. While it might seem dull to just look at numbers, data science is exciting because it combines statistical skills with a deep understanding of business and management decisions. It involves working with numbers, but sharing the results is the most satisfying part.
Thanks for your encouragement!
Indigo