6 answers
Kang
Student
Fremont, California
5
Questions
Asked
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What does a day in the life of a data scientist look like?
Looking to potentially become a data scientist in the future.
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6 answers
PRASANJIT PASWAN
Service
112
Answers
Bengaluru, Karnataka, India
Updated
PRASANJIT’s Answer
Data scientists spend much of their time gathering data, looking at data, shaping data, but in many different ways and for many different reasons.
I appreciate you taking the time to answer this.
Kang
Adelyn Arens
Data Analyst Intern
5
Answers
Columbus, Ohio
Updated
Adelyn’s Answer
Hey Kang, the biggest part of being a data scientist is cleaning data. Real world data is often not in a good or useable form, so you must spend a lot of time making it look pretty. Once you do this, you can then run it through a model but that is a lot less time since the computer will do the majority of the work. An important skill of a data scientist is to be a creative thinker. You must determine what inputs to the model will give the desired result.
Pooja Shah
Software Engineer
2
Answers
Pune, Maharashtra, India
Updated
Pooja’s Answer
Being a data scientist is equal to being an overthinker. A data scientist has to consider a number of scenarios under consideration. The maximum time a data scientist spends is in data discovery and then applying correct statistics.
Thank you for sharing your perspective.
Kang
Ben Gof
Finance
29
Answers
Wayne, New Jersey
Updated
Ben’s Answer
I am actually more of a data analyst that is in a Business Intelligence role. Some people use data analyst and data scientist synonymously.
The majority of the time is spent working with the Business Partners I support in my company and trying to address their data needs. For example, they may ask to see if their is a correlation between the amount of money spent on equipment vs average life of the equipment. Or does more spend on maintenance, really show that the equipment breaks down less. A data scientist can also work with the marketing teams to try and determine the cause and effect of spending money on ad campaigns and how that may affect sales. There's dozens of these types of scenarios that are analyzed on a daily basis in business. So the data scientists role is crucial.
Going back to the original question of a typical day, it could look like this:
- Meet with the business partner to determine their needs. What are they trying to do, solve or analyze.
- Determine where you can access the data needed.
- Once obtaining the data, review it and clean it up. For example, may need to remove or replace null values, remove duplicates, etc.
- Start creating charts and arranging the data that addresses your business partners request.
- Revisit with the BP to review your initial findings. Take notes.
- Work on revising your initial findings after revisiting with the Business Partner.
The goal of the team I am on is as follows:
• Automate any rule based reports. Use tools like Python, Alteryx, Power BI, Tableau, Qlik
• Build Dashboards providing insight to the Business partners, which allow them to make sound informed business decisions.
• Respond to requests that are either new or to revise existing reports and dashboards.
• Provide deep dive analysis wherever applicable.
There's never a dull moment and if you enjoy statistics, working with numbers, charts, tables, you'll enjoy this career as I do.
The majority of the time is spent working with the Business Partners I support in my company and trying to address their data needs. For example, they may ask to see if their is a correlation between the amount of money spent on equipment vs average life of the equipment. Or does more spend on maintenance, really show that the equipment breaks down less. A data scientist can also work with the marketing teams to try and determine the cause and effect of spending money on ad campaigns and how that may affect sales. There's dozens of these types of scenarios that are analyzed on a daily basis in business. So the data scientists role is crucial.
Going back to the original question of a typical day, it could look like this:
- Meet with the business partner to determine their needs. What are they trying to do, solve or analyze.
- Determine where you can access the data needed.
- Once obtaining the data, review it and clean it up. For example, may need to remove or replace null values, remove duplicates, etc.
- Start creating charts and arranging the data that addresses your business partners request.
- Revisit with the BP to review your initial findings. Take notes.
- Work on revising your initial findings after revisiting with the Business Partner.
The goal of the team I am on is as follows:
• Automate any rule based reports. Use tools like Python, Alteryx, Power BI, Tableau, Qlik
• Build Dashboards providing insight to the Business partners, which allow them to make sound informed business decisions.
• Respond to requests that are either new or to revise existing reports and dashboards.
• Provide deep dive analysis wherever applicable.
There's never a dull moment and if you enjoy statistics, working with numbers, charts, tables, you'll enjoy this career as I do.
I am really grateful you took the time to answer this question.
Kang
Nicole Baillie
Data Analyst
484
Answers
Atlanta, Georgia
Updated
Nicole’s Answer
Hi Kang L. Thanks so much for the forward-looking question!
In my experience, a day in the life of a data scientist includes some initial understanding that the work of a data science is much more building block-oriented than it is task oriented. What I mean by that is there are jobs where an individual will be given a task and that task may get completed on that same day. The work of a data scientist typically takes a longer time to complete only because the data scientist has a broad range of tools to assess whether or not their outcomes are "right". In order to make good and sustainable assessments, the work that a data scientist does requires time, patience, space for trial and error and collaboration. In other words, one day may be spent pounding out some code, another day may be spent running and adjusting code and analyzing results, another day may be spent sharing those results with others who maybe have a deeper understanding of the business initiative at hand. Their feedback becomes very important and sometimes their feedback requires a new set of ideas/logic to be included in the work of the data scientist.
It is also my experience that the work of a data scientist is never boring :)..If one is truly interested in learning and impacting results in a positive way, the role of a data scientist can be incredibly helpful towards creating improvement. Additional reward can come from the fact that the data scientist can use their tools to help get to great answers faster.
Hope you find this answer helpful. Best of luck to you!
In my experience, a day in the life of a data scientist includes some initial understanding that the work of a data science is much more building block-oriented than it is task oriented. What I mean by that is there are jobs where an individual will be given a task and that task may get completed on that same day. The work of a data scientist typically takes a longer time to complete only because the data scientist has a broad range of tools to assess whether or not their outcomes are "right". In order to make good and sustainable assessments, the work that a data scientist does requires time, patience, space for trial and error and collaboration. In other words, one day may be spent pounding out some code, another day may be spent running and adjusting code and analyzing results, another day may be spent sharing those results with others who maybe have a deeper understanding of the business initiative at hand. Their feedback becomes very important and sometimes their feedback requires a new set of ideas/logic to be included in the work of the data scientist.
It is also my experience that the work of a data scientist is never boring :)..If one is truly interested in learning and impacting results in a positive way, the role of a data scientist can be incredibly helpful towards creating improvement. Additional reward can come from the fact that the data scientist can use their tools to help get to great answers faster.
Hope you find this answer helpful. Best of luck to you!
Thank you Nicole! This is really helpful!
Kang
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity.
3702
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Hi Kang,
Let's dive into the exciting world of a data scientist! The day-to-day activities of these professionals can differ greatly depending on the industry they're in, the company they work for, and the projects they're tackling. But don't worry, we'll cover the common tasks and responsibilities that most data scientists have. We'll also touch on the skills needed, potential challenges, and rewards that come with this profession.
Collecting and Preparing Data:
A key job for a data scientist is to collect and prepare data for analysis. This means finding the right data sources, pulling data from different databases or APIs, cleaning and transforming the data to make sure it's good quality and consistent, and organizing it so it's ready for analysis. They also have to handle any missing or incomplete data, outliers, and other data quality issues.
Exploring the Data (EDA):
Once the data is ready, data scientists explore it to find insights and understand the patterns or relationships within the data. They use statistics, visualizations, and other tools to find trends, correlations, anomalies, or any other interesting patterns that might be hidden in the data. This helps them understand the data better and guides them when they're making hypotheses or planning further analyses.
Developing Models:
After getting a good understanding of the data through EDA, data scientists start developing models. They choose the right machine learning algorithms or statistical models based on the problem they're trying to solve and the type of data they have. They need to know a lot about different modeling techniques like regression, classification, clustering, time series analysis, natural language processing (NLP), deep learning, and more. They also need to check how well different models are performing using the right metrics and fine-tune them to get the best results.
Engineering Features:
Feature engineering is a crucial step in developing models where data scientists turn raw input variables into meaningful features that can make the models perform better. This might involve creating new features, selecting the right features, scaling or normalizing features, handling categorical variables, and dealing with high-dimensional data. Feature engineering needs domain knowledge and creativity to get the most informative features from the data.
Training and Evaluating Models:
Once the features are ready, data scientists train the chosen models using part of the dataset called the training set. They use techniques like cross-validation to make sure the models will work well with new data. They check how well the model is performing using the right evaluation metrics like accuracy, precision, recall, F1 score, and more. They keep developing the model by adjusting hyperparameters, trying different algorithms, or adding more features until they get the results they want.
Deploying and Monitoring Models:
After developing a successful model, data scientists work on getting it into production systems or integrating it into existing workflows. They work with software engineers or IT teams to make sure the model is integrated smoothly and can scale. They might also need to monitor how the model is performing in real-time, find any issues or changes in model behavior, and make necessary updates or improvements.
Communicating and Collaborating:
Data scientists often work with a team of different experts, business stakeholders, software engineers, and other data professionals. They need good communication skills to understand business needs, explain complex technical concepts to people who aren't technical, and present their findings or recommendations clearly and concisely. They might also need to document their work, write reports or research papers, and take part in conferences or industry events to keep up with the latest advances in their field.
Learning Continuously:
The field of data science is always changing with new algorithms, tools, and techniques being developed regularly. So, data scientists need to keep learning to stay up-to-date with the latest trends and advances in their field. This might involve reading research papers, going to workshops or training programs, taking part in online courses or webinars, and contributing to the data science community through open-source projects or knowledge sharing platforms.
To become a data scientist, you need a strong background in mathematics, statistics, and programming. Being good at programming languages like Python or R is essential for manipulating data, analyzing it, and developing models. Also, knowing SQL for querying databases and being familiar with big data technologies like Hadoop or Spark can be helpful. A solid understanding of machine learning algorithms, statistical techniques, and data visualization tools is also important.
When it comes to education, many data scientists have advanced degrees like a Master's or Ph.D. in fields like computer science, statistics, mathematics, or engineering. But there are also successful data scientists who have learned through self-study and practical experience.
The top 3 authoritative references used in answering this question are:
1. Towards Data Science - towardsdatascience.com
2. DataCamp - www.datacamp.com
3. Kaggle - www.kaggle.com
Let's dive into the exciting world of a data scientist! The day-to-day activities of these professionals can differ greatly depending on the industry they're in, the company they work for, and the projects they're tackling. But don't worry, we'll cover the common tasks and responsibilities that most data scientists have. We'll also touch on the skills needed, potential challenges, and rewards that come with this profession.
Collecting and Preparing Data:
A key job for a data scientist is to collect and prepare data for analysis. This means finding the right data sources, pulling data from different databases or APIs, cleaning and transforming the data to make sure it's good quality and consistent, and organizing it so it's ready for analysis. They also have to handle any missing or incomplete data, outliers, and other data quality issues.
Exploring the Data (EDA):
Once the data is ready, data scientists explore it to find insights and understand the patterns or relationships within the data. They use statistics, visualizations, and other tools to find trends, correlations, anomalies, or any other interesting patterns that might be hidden in the data. This helps them understand the data better and guides them when they're making hypotheses or planning further analyses.
Developing Models:
After getting a good understanding of the data through EDA, data scientists start developing models. They choose the right machine learning algorithms or statistical models based on the problem they're trying to solve and the type of data they have. They need to know a lot about different modeling techniques like regression, classification, clustering, time series analysis, natural language processing (NLP), deep learning, and more. They also need to check how well different models are performing using the right metrics and fine-tune them to get the best results.
Engineering Features:
Feature engineering is a crucial step in developing models where data scientists turn raw input variables into meaningful features that can make the models perform better. This might involve creating new features, selecting the right features, scaling or normalizing features, handling categorical variables, and dealing with high-dimensional data. Feature engineering needs domain knowledge and creativity to get the most informative features from the data.
Training and Evaluating Models:
Once the features are ready, data scientists train the chosen models using part of the dataset called the training set. They use techniques like cross-validation to make sure the models will work well with new data. They check how well the model is performing using the right evaluation metrics like accuracy, precision, recall, F1 score, and more. They keep developing the model by adjusting hyperparameters, trying different algorithms, or adding more features until they get the results they want.
Deploying and Monitoring Models:
After developing a successful model, data scientists work on getting it into production systems or integrating it into existing workflows. They work with software engineers or IT teams to make sure the model is integrated smoothly and can scale. They might also need to monitor how the model is performing in real-time, find any issues or changes in model behavior, and make necessary updates or improvements.
Communicating and Collaborating:
Data scientists often work with a team of different experts, business stakeholders, software engineers, and other data professionals. They need good communication skills to understand business needs, explain complex technical concepts to people who aren't technical, and present their findings or recommendations clearly and concisely. They might also need to document their work, write reports or research papers, and take part in conferences or industry events to keep up with the latest advances in their field.
Learning Continuously:
The field of data science is always changing with new algorithms, tools, and techniques being developed regularly. So, data scientists need to keep learning to stay up-to-date with the latest trends and advances in their field. This might involve reading research papers, going to workshops or training programs, taking part in online courses or webinars, and contributing to the data science community through open-source projects or knowledge sharing platforms.
To become a data scientist, you need a strong background in mathematics, statistics, and programming. Being good at programming languages like Python or R is essential for manipulating data, analyzing it, and developing models. Also, knowing SQL for querying databases and being familiar with big data technologies like Hadoop or Spark can be helpful. A solid understanding of machine learning algorithms, statistical techniques, and data visualization tools is also important.
When it comes to education, many data scientists have advanced degrees like a Master's or Ph.D. in fields like computer science, statistics, mathematics, or engineering. But there are also successful data scientists who have learned through self-study and practical experience.
The top 3 authoritative references used in answering this question are:
1. Towards Data Science - towardsdatascience.com
2. DataCamp - www.datacamp.com
3. Kaggle - www.kaggle.com