13. Beyond DS at Kode With Klossy

🎯 Learning Goals

Explore new opportunities in the field of data science

Understand the diverse array of career pathways in data science

Identify next steps in the learning journey for data science

Introduction

Welcome to the next step in your data science journey! Until now, you’ve learned about using SQL and Tableau to clean, explore, and visualize data, but there's so much more out there. In this lesson, we’ll explain different careers, share some inspirational stories, and introduce helpful tools and communities to support you as you explore your next steps in data science.

What’s Next in Data Science?

The world of technology is ever-changing and data science is evolving faster than ever. Data scientists are exploring exciting areas such as AI-powered predictive analytics, machine learning algorithms, big data processing, and innovative data visualization techniques. These trends indicate that data science will continue to evolve, offering more sophisticated, personalized, and insightful analyses for organizations and users.

As data continues to transform how we communicate and interact with the world, staying up-to-date with these trends will be crucial for delivering meaningful and effective insights. Whether it's integrating cutting-edge statistical methods or optimizing performance across different data platforms, the future of data science holds immense promise.

Career Pathways

A career in data science offers a wide range of opportunities. Here are some roles you might consider:

Data Analyst: Specialize in analyzing data sets to identify trends and create visualizations that tell compelling stories with data.

Data Scientist: If you enjoy more advanced statistical analysis and predictive modeling, this role combines statistics, programming, and domain expertise.

Data Engineer: Create and maintain the infrastructure that allows data scientists to access and analyze data efficiently.

Business Intelligence Analyst: Focus on creating dashboards and reports that help businesses make data-driven decisions.

Machine Learning Engineer: Develop algorithms and models that can learn from data and make predictions or decisions without being explicitly programmed.

Data Visualization Specialist: Combine creative design with technical skills to develop engaging and informative visual representations of complex data.

Each pathway has its own unique challenges and rewards, ensuring that there's a place for every interest and skill set in the data science landscape.

Industry Icons

There are so many powerful women in technology, but we’ll highlight a few women who have had a meaningful impact in the world of data science.

Marizza Delgado

Marizza Delgado is a data scientist, model, and current Master’s in Analytics candidate at Georgia Tech. She currently works at Rent the Runway as a data scientist, blending fashion, sustainability, and technology. Previously, she was a data scientist at Etsy. Beyond technology, she was crowned Miss New York USA 2024, making her the first Filipina-American to win this title. That same year, she finished in the top 20 in Miss USA 2024. She is passionate about advocating for women in STEM and currently serves as council leader at Girls Who Code.

Dr. Anima Anandkumar

Anima Anandkumar is an AI Pioneer and Professor at Caltech. She holds a B.Tech degree in Electrical Engineering from IIT Madras, a PhD in Electrical Engineering from Cornell University, conducted postdoctoral research at MIT, and was an assistant professor at UC Irvine. Prior to becoming the Bren Professor of Computing at Caltech, she was a senior director of AI research at NVIDIA and principle scientist at Amazon AI, developing next generation AI solutions. She has developed novel AI algorithms that have applications in various scientific domains, including weather forecasting and scientific simulations.

Dr. Latanya Sweeney

Latanya Sweeney is a computer scientist, leading voice in data privacy and algorithmic fairness, and the Daniel Paul Professor of the Practice of Government and Technology at Harvard University. She is the founder and director of the Public Interest Tech Lab at Harvard, which is on a mission to ensure tech products are socially responsible, and of the Data Privacy Lab at Harvard, a lab dedicated to teaching and research in the field of data privacy. She holds a PhD in Computer Science from MIT and is the first African American woman to do so. Prior to her position at Harvard, she was the CTO of the FTC. She is most known for her work on the theory of k-anonymity and the observation that “87% of the U.S. population is uniquely identified by date of birth, gender, and postal code.”

Data Science Tools

Now that you've learned SQL and Tableau, there are many more tools and technologies you can explore to continue your data science journey. Here are some options to consider:

Python & Libraries: Python is the most popular programming language for data science, with powerful libraries like Pandas for data manipulation, NumPy for numerical computing, and Matplotlib/Seaborn for data visualization.

R Programming: R is a statistical programming language specifically designed for data analysis and visualization, with packages like ggplot2 for creating beautiful visualizations.

Advanced Analytics Tools: Tools like Power BI and advanced features of Tableau can help you create interactive dashboards and perform more complex analyses.

Machine Learning Platforms: Platforms like scikit-learn (Python) and TensorFlow provide tools for implementing machine learning algorithms and building predictive models.

Data Cleaning Tools: OpenRefine and other specialized tools can help you clean and transform messy datasets before analysis.

This is only a small sampling of the many data science tools you may encounter, but exploring any of them is a great next step in your learning journey!

Resource Roundup

If you're looking for structured courses or detailed guides, we put together a list of suggestions for continuing to learn data science.

DataCamp offers interactive courses on Python, R, SQL, and data visualization that build on your existing knowledge of SQL and Tableau.

Kaggle, which we used during camps!, provides free datasets, competitions, and tutorials to practice your data science skills and learn from a community of data scientists.

Google's Data Analytics Professional Certificate on Coursera covers the entire data analytics process, from collecting and processing data to analyzing and sharing insights.

The Python Data Science Handbook is available for free online and covers essential tools for data analysis in Python.

Websites like Udacity, Coursera, and free YouTube tutorials offer step-by-step lessons on data science concepts and techniques.

Find a Community

Learning is even more rewarding when you do it with others! If your school offers computer science classes, take full advantage of them – you’re more than ready to tackle new challenges. Beyond your classes, consider exploring other opportunities:

Join a robotics or computer science club

Attend hackathons

Team up with a study buddy

Find local workshops or coding communities

Building relationships with other data science enthusiasts can provide invaluable mentorship, support, and inspiration as you continue developing your skills. The journey of learning never stops, and surrounding yourself with a supportive community will help you continue to grow and innovate.

💭

Take a few minutes to reflect on which next steps excited you most today.

Which data science role excites you the most, and why?

What tools or technologies are you eager to learn next?

How can you leverage community support to accelerate your learning?

By broadening your skill set and exploring the full spectrum of data science—from data analysis and visualization to machine learning and AI—you're well on your way to creating insights that have a global impact. Enjoy the journey and keep innovating!

For a summary of this lesson, check out the 13. Beyond DS at Kode With Klossy One-Pager!