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Introduction
Data scientists are constantly navigating a changing field, along with its evolving technologies and techniques. The rapid growth and dynamic nature of this industry conspire to demand continuous learning and adaptation of participating professionals. Due to this constant growth, to be active and viable practitioners requires continued personal development. There are always more concepts, tools, and technologies to take up and master for both the novice and established data scientist.
And this is why we are here today. This article intends to provide practical advice for becoming a better data scientist by focusing on five different areas of proficiency. Whether you are starting out, or looking to get grounded after years as a practitioner, jump in and elevate your game.
1. Master the Mathematical Fundamentals
Understanding the fundamentals of the required mathematics is an elemental part of being able to work with data. The primary subjects of linear algebra, calculus, and probability are the grounding of so much of the modeling and algorithm work that data scientists do. The book Mathematics for Machine Learning is an excellent reference to start with, as are the courses in Coursera’s Mathematics for Data Science specialization. 3Brown1Blue’s YouTube videos are another fantastic resource for these topics. Putting these mathematical fundamentals into practice in real projects and exercises will ensure your knowledge stays solid.
2. Stay Updated with Industry Trends
Supposing one wishes to keep in-the-know and remain employable for the long-term in this field of both enormous breadth and depth, staying up-to-date on the latest tools, technologies, and methodologies can’t be overlooked. From technological innovations such as automated machine learning and interpretability processes, to large scale data technologies and state of the art machine learning algorithms, the landscape from “good to know” to “need to know” is in constant flux. This isn’t a frivolous concern: people and organizations want to be able to incorporate the latest where appropriate. What better place to keep on such topics as KDnuggets (you’re already here), along with our sister sites Machine Learning Mastery and Statology.
But there are other great resources as well: popular sites like Towards Data Science, DataCamp, MarkTechPost, and a whole host of others are worthy of your time as well. The myriad podcasts, webinars, and YouTube channels all provide alternative avenues, with something that fits everyone’s preferences. Communities and conferences, both online and in-person, can be great ways to both network and stay up in the latest trends.
3. Develop Strong Programming Skills
This can’t be overstated: proficiency in one or more of Python, R, and SQL — key programming languages in the field — is an absolute must for anyone wanting to be a useful data scientist. Libraries such as Pandas and Matplotlib (Python) and packages such as dplyr and ggplot2 (R) for data work are important skills to acquire. Learning the most efficient ways to approach writing SQL queries is equally important, as SQL remains one of the most used language worldwide, especially when it comes to data science. There are, of course, many other languages that could come in handy for data work — Java, Rust, C++, Go, Javascript, Ruby… the list goes on and on. You can pick and choose from these what makes sense for you, but don’t learn them to the neglect of The Big Three mentioned above; it just isn’t worth the risk.
Through online platforms like HackerRank or LeetCode, or through GitHub contributions, one can improve their coding skills. Working on group projects necessitates an understanding of Git, which a person can use for version control. In short, don’t buy into the hype that you don’t need to code. If you can’t, someone else will be needed to do so, and since there are so many data scientists that code, how do you positively differentiate yourself from them? Be a strong coder as a baseline, and then add on additional skills to set you apart.
4. Work with Real Datasets
Working with fresh facts and figures is a must for anyone wanting to be more than an academic in this field. There is nothing better than solving data issues on your own initiative and doing. Methods to do so include competing on Kaggle, taking on independent challenge projects, or even seeking out internships or volunteer work. By accurately solving a concern, including applying algorithms fittingly, understanding the various datasets, and recording all this work, people build up a robust portfolio.
The difference between sharing your portfolio project based on a reworking of the the Iris dataset and performing some in-depth analysis on robust and contemporary real-world data is night and day. Use real and valuable data.
5. Cultivate Communication and Collaboration Skills
In order to put complex analysis results in the hands of a non-academic audience, strong communication is key to success. Telling a complelling story with one’s data along with eye-catching visualizations, a captivating and well-crafted accompanying speech, and supporting artifacts intended to preemptively answer questions and fill in the blanks for listeners is what it takes to convey a message well. Several tools are available to assist in your data science story time, including Tableau, Power BI, and even PowerPoint or Google Slides.
Alongside this persuasive projection, an effective data scientist will also employ active listening and preemptive question-answering, essential in conveying your sense of domain authority. These same skills can also help improve team effectiveness and project output. Expressing your ideas and findings, and working well with both the analytical team and your eventual audience, is another critical component of an effective data scientist, and re-doubling your efforts on mastering this aspect can help you step up your game.
Final Thoughts
This article aimed to express how to improve various aspects of your data science role. In these five areas — comprehensive informational backing, staying enlightened about evolutions in the industry, coding fluently and capably, working hands-on with real data, and having a knack for working with others — we have looked for ways to help the average data professional improve their game. Learning and growth in data science is continuous and constantly changing, so make sure you are all aboard when it comes to this journey.
Matthew Mayo (@mattmayo13) holds a master’s degree in computer science and a graduate diploma in data mining. As managing editor of KDnuggets & Statology, and contributing editor at Machine Learning Mastery, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.
