machine learning Archives - General Assembly

So you’re thinking of a career in data science, but you’re not sure if it’s the right fit for you. Here is your data science guide, where we break down what data science is, day in the life of a data scientist, tips from GA’s data science alumni, career opportunities, and much more.

WHAT IS DATA SCIENCE?

According to Berkeley, data science is the ability to take data, understand it, extract value from it, visualize it, and communicate the findings. The term “data science” was coined in 2008 when companies realized the need for data professionals to analyze immense amounts of data.

As a data scientist, “you need to be able to digest big data samples, clean the data, organize and analyze it, to then be able to look into the data and gather meaningful insights for the end-user, as well as various stakeholders,” explained Neal Manahan, GA Alumni, now an Analytical Framework Consultant, at Neilson IQ.

Today, data science is one of the most in-demand career paths. According to DevSkiller’s recent Top IT Skills Report, companies have recorded a 154% increase in the demand for skills like Python by companies in 2021.

However, due to the shortage of talent globally, the demand for data scientists is increasing faster than there is supply, that’s why data science is now one of the best-paid tech careers. According to Fortune Education, data science is a hot job market, with data scientists making a median salary of $164,500 in 2020 (an 8% increase from the $152,500 median pay in 2019).

DAY IN THE LIFE OF A DATA SCIENTIST.

The spectrum of tasks for a data scientist can vary hugely day-to-day. Data scientists tend to work closely with data analysts, hence why a data scientist’s range of skills can be a mixed bag. To narrow down the daily tasks of a data scientist, we asked Kenny Evans, GA Alumni, now a Principal Machine Learning Engineer at Vizio, to break it down for us.

Some of the day-to-day responsibilities of a data scientist include:

Preparing large data sets, and running models to interpret the data, using tools like Python, SQL, and SAS. You are using these tools to plot and graph your data and to understand what type of data you are dealing with, and what information you need to get out of the data.

Coding to gain further insights into the data, using tools like R, Python, JavaScript (Programming Languages).

Daily, “you see a lot of code, it doesn’t necessarily look pretty, but it’s fun if you know what you’re trying to get out of the data,” said Kenny.

Meetings with various stakeholders to present and further analyze specific findings from the data. It’s essential to collaborate well with others and understand each end-users needs to make sure you are extracting the correct information from the data.

WHAT SHOULD YOU ASK YOURSELF TO FIGURE OUT IF DATA SCIENCE IS RIGHT FOR YOU?

Data science may be a very well-paid and sought-after position by employers globally, but this shouldn’t be the only deciding factor in becoming a data scientist. Here are four questions you should be asking yourself to determine if data science is the right career choice for you.

Q1. Are you a critical thinker not afraid of a challenge?

As a data scientist, you need to be able to make sense of and organize large data sets. This tends to be challenging and time-consuming and requires critical thinking skills. Extracting knowledge from data is vital, as it enables data scientists to reveal actionable insights and solutions.

Q2. Are you able to work well with others?

As we mentioned earlier, collaboration is a critical component of a data scientist’s daily life. So you need to be able to collaborate closely with teams from various backgrounds and experience levels, such as the data analytics and engineering teams and multiple stakeholders and users of the data.

Q3. Are you prepared to work hard to fine-tune your technical skills?

Data science is one of those careers where you need to be able to use your tech stack well. Otherwise, you may extract and misinterpret data. This is why it’s so critical you are an expert in tools like Python and are prepared to keep learning. “My biggest tip for aspiring data scientists is Python. Spend a week doing a deep dive into Python, so you hit the ground running”, said Kenny.

Hot tip: If you’re interested in joining a data science bootcamp, we recommend you brush up on your foundational data knowledge to rock the course. Spend a few weeks prior manipulating data sets and coding with tools like Python, SQL, and JavaScript.

Q4. Are you quick on your feet?

Sometimes you will need to be able to make data-driven conclusions fast and be able to connect the dots before others. If you’re someone who has an insatiable curiosity, enjoys solving a challenging puzzle, and focuses on the source of the issue rather than the symptoms, you might enjoy the challenge.

Still not sure if Data Science is the right career choice for you? Check out our ebook, “Landing Work You Love,” for more helpful insights and tips.

DATA SCIENCE CAREER PATHWAY.

During our Data Science Immersive, you are taught by industry experts, experience one-on-one career coaching, and connect with top employers worldwide to get hired. Here are some typical beginner roles our GA graduates tend to land post bootcamp graduation:

1. DATA SCIENCE ASSOCIATE

Job Role: A data science associate is an academic program offered at the undergraduate level. It gives students the basic skills and fundamental knowledge to progress to a postgraduate level if they want to progress to a data scientist level. The main responsibility of a data science associate is to support the lead data scientist on the team, this would involve assisting in the production of statistical models, tools, and processes.

Skills Required: Data Mining, Data Visualization, Statistics, Algorithms, Data Acquisition and Cleaning, Python (basic level).