4 answers
Hugo
Student
Riverside, California
4
Questions
Updated
837 views
People who work in the field of statistics, what are some fun things you got to do for your jobs??
I'm a first-year college student and it's almost the end of the school year. Over the summer I would like to work on a project and I have a few fun ideas I would like to try out. I asked this question to see if my ideas could relate to others.
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4 answers
Taylor Bolt, PhD
Data Scientist
2
Answers
Atlanta, Georgia
Updated
Taylor’s Answer
Hi Hugo,
The benefit of a job in statistics is that your specialty is analyzing data, and that data can come from anywhere. I have found myself analyzing data collected from surveys, comments on a social media site, government public health data, anonymized health record data, as well as data collected from neuroimaging of the brain. The key is to keep an open mind and not be afraid to jump into an area that you may not have much subject-specific experience in.
For the summer, I would consider doing some exploratory work on what sort of data is out there in the questions you're interested in. A good place to start is Kaggle that hosts a lot of different analysis-ready datasets already collected for you. Google has a 'public data explorer' if you're interested in govt related statistics. Google also has a 'dataset search' page that can look all over the internet for data that you might be interested in.
Also, you may be aware of this, but the field of statistics (and data science) has moved towards open-source programming languages, Python or R, as the primary tools. I would strongly encourage you to do your statistics project using these tools. It will be worth it!
The benefit of a job in statistics is that your specialty is analyzing data, and that data can come from anywhere. I have found myself analyzing data collected from surveys, comments on a social media site, government public health data, anonymized health record data, as well as data collected from neuroimaging of the brain. The key is to keep an open mind and not be afraid to jump into an area that you may not have much subject-specific experience in.
For the summer, I would consider doing some exploratory work on what sort of data is out there in the questions you're interested in. A good place to start is Kaggle that hosts a lot of different analysis-ready datasets already collected for you. Google has a 'public data explorer' if you're interested in govt related statistics. Google also has a 'dataset search' page that can look all over the internet for data that you might be interested in.
Also, you may be aware of this, but the field of statistics (and data science) has moved towards open-source programming languages, Python or R, as the primary tools. I would strongly encourage you to do your statistics project using these tools. It will be worth it!
Shyam Varan Nath
Computer Consulting / Management Consulting
6
Answers
Tracy, California
Updated
Shyam Varan’s Answer
You can participate in hackathons, some of which are on fun topics like related to sports and other topics to make it a fun way for learning AI/Analytics an application of Statistics. Joining industry events or Meetups are also a fun way to learn.
Look at Kaggle for hackathons
Key an eye for free industry events, many will have free tickets for the expo part of the event.
Shyam Varan recommends the following next steps:
Violaine Gonzalez
Senior Associate
176
Answers
New York, New York
Updated
Violaine’s Answer
Hello Hugo! The field of statistics is a treasure trove of exciting opportunities:
**Painting with Data**
The art of crafting engaging visual narratives from data, such as interactive dashboards and infographics, is a blend of creativity and insight.
*Real-World Heroics**
Statistical models are powerful tools for tackling tangible problems in diverse sectors like healthcare, finance, sports, and environmental science.
**Future Gazing**
Constructing predictive models allows you to anticipate trends, customer behaviors, or market shifts, acting as a crystal ball for the future.
**Team Adventures**
Join forces with a vibrant mix of scientists, engineers, and business professionals on multidisciplinary projects, enriching your knowledge and experience.
**Data Exploration**
Dive into the depths of large datasets to unearth hidden patterns and insights, often leading to surprising and fascinating discoveries.
. **Knowledge Sharing**
Be a beacon of knowledge by teaching, mentoring, or showcasing your findings at conferences and workshops, inspiring others along your journey.
**Software Experimentation**
Play around with the latest statistical software and programming languages like R and Python, innovating new methods and techniques.
These facets make a career in statistics not just challenging, but a thrilling ride of discovery and innovation.
**Painting with Data**
The art of crafting engaging visual narratives from data, such as interactive dashboards and infographics, is a blend of creativity and insight.
*Real-World Heroics**
Statistical models are powerful tools for tackling tangible problems in diverse sectors like healthcare, finance, sports, and environmental science.
**Future Gazing**
Constructing predictive models allows you to anticipate trends, customer behaviors, or market shifts, acting as a crystal ball for the future.
**Team Adventures**
Join forces with a vibrant mix of scientists, engineers, and business professionals on multidisciplinary projects, enriching your knowledge and experience.
**Data Exploration**
Dive into the depths of large datasets to unearth hidden patterns and insights, often leading to surprising and fascinating discoveries.
. **Knowledge Sharing**
Be a beacon of knowledge by teaching, mentoring, or showcasing your findings at conferences and workshops, inspiring others along your journey.
**Software Experimentation**
Play around with the latest statistical software and programming languages like R and Python, innovating new methods and techniques.
These facets make a career in statistics not just challenging, but a thrilling ride of discovery and innovation.
Iqra Bismi
Data Scientist
10
Answers
Milpitas, California
Updated
Iqra’s Answer
Here are some fun and rewarding tasks they might do as part of their jobs:
A/B Testing: This involves comparing two versions of a variable (like a web page or a marketing email) to determine which one performs better. It's a practical application of hypothesis testing and can be very satisfying when you see clear results.
Exploratory Data Analysis (EDA): During EDA, statisticians use visual and quantitative methods to understand the underlying structure of the data. This includes:
Normality Tests: Checking if the data follows a normal distribution using tests like the Shapiro-Wilk test or Q-Q plots.
Distribution Analysis: Assessing the distribution of data to understand its spread and central tendency. This can involve plotting histograms, box plots, and density plots.
Correlation Analysis: Investigating relationships between variables using correlation coefficients and scatter plots. This helps in identifying potential predictors for machine learning models.
Designing and Analyzing Experiments: Statisticians often design experiments to test hypotheses and analyze the results. This can include everything from clinical trials to marketing experiments.
Machine Learning Projects: Involvement in building and validating machine learning models. This includes:
Feature Selection and Engineering: Identifying and creating the most relevant variables for the models.
Model Validation: Using statistical techniques to validate the performance of machine learning models, such as cross-validation and bootstrapping.
Data Visualization: Creating informative and aesthetically pleasing visual representations of data. This can be both a creative and technical challenge, requiring good knowledge of tools like R, Python (Matplotlib, Seaborn), Tableau, or Power BI.
Predictive Analytics: Using statistical methods to make predictions about future events based on historical data. This is often used in fields like finance, marketing, and healthcare.
Consulting and Collaboration: Working with other departments or clients to solve problems using statistical methods. This often involves a lot of communication and can be very rewarding when you help others make data-driven decisions.
Teaching and Mentoring: Sharing knowledge with others, whether through formal teaching, workshops, or mentoring junior colleagues.
These activities not only involve rigorous analytical thinking but also offer opportunities for creativity and problem-solving, making the field of statistics both challenging and enjoyable.
A/B Testing: This involves comparing two versions of a variable (like a web page or a marketing email) to determine which one performs better. It's a practical application of hypothesis testing and can be very satisfying when you see clear results.
Exploratory Data Analysis (EDA): During EDA, statisticians use visual and quantitative methods to understand the underlying structure of the data. This includes:
Normality Tests: Checking if the data follows a normal distribution using tests like the Shapiro-Wilk test or Q-Q plots.
Distribution Analysis: Assessing the distribution of data to understand its spread and central tendency. This can involve plotting histograms, box plots, and density plots.
Correlation Analysis: Investigating relationships between variables using correlation coefficients and scatter plots. This helps in identifying potential predictors for machine learning models.
Designing and Analyzing Experiments: Statisticians often design experiments to test hypotheses and analyze the results. This can include everything from clinical trials to marketing experiments.
Machine Learning Projects: Involvement in building and validating machine learning models. This includes:
Feature Selection and Engineering: Identifying and creating the most relevant variables for the models.
Model Validation: Using statistical techniques to validate the performance of machine learning models, such as cross-validation and bootstrapping.
Data Visualization: Creating informative and aesthetically pleasing visual representations of data. This can be both a creative and technical challenge, requiring good knowledge of tools like R, Python (Matplotlib, Seaborn), Tableau, or Power BI.
Predictive Analytics: Using statistical methods to make predictions about future events based on historical data. This is often used in fields like finance, marketing, and healthcare.
Consulting and Collaboration: Working with other departments or clients to solve problems using statistical methods. This often involves a lot of communication and can be very rewarding when you help others make data-driven decisions.
Teaching and Mentoring: Sharing knowledge with others, whether through formal teaching, workshops, or mentoring junior colleagues.
These activities not only involve rigorous analytical thinking but also offer opportunities for creativity and problem-solving, making the field of statistics both challenging and enjoyable.