How to Become a Data Scientist 2024, Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
Becoming a Data scientist is a relatively new career trajectory that merges statistics, business logic, and programming knowledge. Given the exponential amount of data being churned out via our smartphones, desktops, and the vast array of IoT devices throughout the world, governments and private enterprises are interested in gleaning insight from their extensive data collection processes. At first glance, one may assume that data analysts and data scientists are interchangeable – meaning there is a mutual one-to-one correspondence between the two, but this is not the case.
Read more: New Google Algorithm Update
The disciplines of statistical modeling and data science are not simply interchangeable. Modeling, for example, consists of selecting a mathematical optimization technique, applying it to a data set, and then analyzing the resulting predictive performance. A data scientist, by contrast, specializes in gathering, cleaning, transforming, and transforming index data into the useful form that is most interpretable. In this regard, data science is often compared to engineering, with a focus on data quality and predictive performance. The analogy is not perfect, however. While much software engineering is concerned with automated testing, automated deployment, fault tolerance, continuous integration, robust components, and virtualization, data science requires a similar set of attributes in order to effectively analyze and communicate data. Let’s dive into the three foundational skills of a data scientist: data collection, basic coding skills, and exploring data. Prior to graduate school, many of my programming projects involved filling out various forms for a university application process. This included aspects of data collection, parsing data from a spreadsheet, and plotting different data points. Regardless, these numeric data entry and spreadsheet tasks took at least 20 minutes of my time in addition to my final project review.
While submitting my applications to graduate programs, I realized that I had not perfected my spreadsheet skills. Therefore, I had to go beyond simple data entry by completing my final project in Data-Driven Design. This project required constructing an alternative data collection strategy on which to base decision-making. The rationale for this alternative was as follows: The key to my alternative data collection approach proved to be the ability to create different data visualization options. One simple visualization option I was able to leverage was a bar-plot. A bar-plot is a standard chart which plots multiple independent sample means against their medians. An example of a bar chart, which was properly developed using D3.js, can be seen below. The visualization environment I chose for my project required a bar chart library (.default). These libraries are often implemented using the d3.geom library, which executes standard statistical methods on SVG charts. What this means for the programming skills required to create the bar chart is that I only needed to know how to code basic SVG graphics. This does not sound too difficult, however. I focused my study on foundational skills such as Git, basic Python, R programming, and web development. To review: The ability to learn these skills relatively quickly was a huge benefit in my acceptance into and success in graduate school. However, it should also be noted that explaining these skills to future employers will serve as a major barrier to employment.
One career path focuses on the creation of unique data to solve a research problem; the other analyzes raw data to discover patterns and trends, ultimately producing a report with actionable data to help better serve a business goal. Many folks decide to study fields that intersect with their core competency, but these typically turn out to be more technical fields that require additional activities. As an industry, we like to create buzzwords that give us exciting-sounding titles and we overall are very proud of our analytical prowess and love for data. However, becoming a data scientist means cutting many of these buzzwords from your resume. Being a data scientist is job aspirational. But let’s be honest: it is harder than it looks. How to Become a Data Scientist A good place to start is by familiarizing yourself with your chosen discipline of data science. This can be tricky; most career guides recommend getting an undergraduate degree in statistics or another relevant statistical subject, which prepares you to prepare for the stats PhD. Therein lies a problem with this. Introverts are notoriously bad at mathematical logic and logical certifications, and this also prevents many humans from truly understanding the analytical side of a discipline, especially when sharing the word “science” with others. Getting PhDs frequently requires at least 2-3 years of stellar thesis work and lab work, and unfortunately, many people never make it past the first year, because it can be extremely unrewarding. To help folks out of the statistical rut, aspiring data scientists can focus on study building, programming, or even getting an advanced degree in some other communication-related field, like political science or journalism. This allows people to partial out the knowledge and pedigrees needed for the stats PhD, mostly by studying the work of individuals who have already achieved previously. To some, this might seem like cherry-picking – the industry standard is often around 7 years or more of statistical work to become a true statistics PhD. My experience working as a consultant prior to founding my own company makes me pretty confident in this. While we may not be taught anything about statistical logic in high school, coding was a requirement, and for those with a project-based background, Python is well-suited to the hardest parts of our Alchemy API.