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Are you interested in a career in data science? If so, you’ve chosen a great industry to work in.
This is because careers in data science are popping up all over the place. In fact, the BLS estimates that there will be a 16 percent job growth in data science careers through 2028. This rate is much faster than the national average.
But, as a data scientist, what is it that you’ll actually do on a day to day basis?
Check out this data scientist job description guide to learn everything you need to know about what it’s like to work in this field.
What is a Data Scientist?
First things first, what exactly is a data scientist?
Data scientists are responsible for using massive amounts of unstructured and structured data to discover insights about a business that will help them meet their specific needs and goals.
As businesses start to rely more heavily on data analytics, the role of the data scientist is becoming increasingly important.
Data Scientist Role
As a data scientist, it’ll be your job to analyze and organize large amounts of data. Typically, you’ll use different types of software that are designed for each task.
In the end, you’ll need to be able to present the data in such a way that stakeholders can understand it, especially if those stakeholders work outside the area of IT.
How you approach your data analysis will depend on the type of industry you work in and the specific needs of the department or business that you’re working for.
Therefore, before you can find the meaning in the data you’re looking at, business leaders will need to communicate what they’re looking for.
Therefore, you’ll also need to have some level of expertise in the business industry in order to translate your data into business goals.
As a data scientist, your main responsibility will be data analysis. This is a process that begins with collecting data and ends with making business decisions based on your findings.
Oftentimes, the type of data you analyze will be referred to as big data. And, this big data will fall into two categories: structured data and unstructured data.
Structured data is data that is organized and that can be easily sorted by a computer. Types of structured data include website traffic, sales figures, and data collected from products and electronic devices.
Unstructured data, on the other hand, comes from human input. This data is typically harder to sort through and more difficult to manage. Types of unstructured data include customer reviews, social media posts, emails, and videos.
In order to make sense of unstructured data, businesses typically rely on specific keywords.
Typically, businesses will hire data scientists to handle the unstructured data, whereas they’ll hire IT professionals to handle the structured data.
Businesses are starting to become more and more aware of the importance of unstructured data, which means that they’re looking to hire more and more data scientists.
As a data scientist, you’ll also be responsible for the following:
- Working with stakeholders in the organization to identify opportunities to leverage company data in order to drive business solutions
- Analyzing and mining data from company databases in order to improve marketing techniques, product development, and business strategies
- Assessing the accuracy and effectiveness of new data sources
- Developing algorithms and custom data models
- Use predictive modeling to optimize customer experience, ad targeting, and revenue generation
- Coordinate with different teams in order to implement strategies and monitor outcomes
To become a successful data scientist, you will need both technical and nontechnical skills. Let’s start by talking about the technical skills that you’ll need.
Python coding is the most common coding language that’s required in data science roles.
This is because Python coding is extremely versatile and can be used at almost any step of the data analysis process.
As a data scientist, you’ll likely be expected to be able to write queries in SQL. This is a programming language that can help you carry out certain operations such as adding and deleting extract data from a database.
Machine Learning and AI
As a data scientist, you’ll need to be proficient in machine learning techniques. This includes things such as reinforcement learning, neural networks, and adversarial learning.
If you want to stand out from other data scientists, you’ll want to learn machine learning techniques such as decision trees, supervised machine learning, and logistic regression.
Other technical skills that are required for data scientists include:
- Data visualization
- Apache Spark
- Hadoop Platform
- R Programming
- Software engineering
- Data intuition
- Data wrangling
- Multivariable calculus
- Linear algebra
As a data scientist, the nontechnical skills you possess are just as important as the technical skills. The nontechnical skills you need include:
As a data scientist, you’ll need to regularly update your knowledge by reading relevant books and online content.
And, you’ll have to constantly be searching for insight into the data you collect. You’ll also need to have curiosity in regards to industry trends and news. You can learn more about this here.
As we mentioned earlier, you’ll need to be able to translate the data you collect into terms that other business professionals will understand.
You’ll need to understand how the business you’re working for operates so you can direct your efforts in the right direction.
As a data scientist, you’ll need to be able to fluently and clearly translate the technical data you find to a non-technical team.
You’ll be working with IT departments, marketing departments, and sales departments, and you’ll be expected to communicate well with each of them.
Many people envision data scientists working alone all day in front of a computer. However, this is not the case.
As a data scientist, you’ll work with company executives, product managers, and designers in order to create better products and services.
Data Scientist Job Description: Wrap Up
Now that you’ve read this data scientist job description, it’s time to decide whether a career in data science is right for you.
If you liked this job description guide, be sure to check back in with our blog for more job-related posts.
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