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Data scientist skills require undivided attention as there are many complex problems that you will need to resolve. Data scientists review data and statistics and use computer algorithms to answer questions to help a business improve.
Unlike data analysts, data scientists do more than just identify trends. They try to help companies by predicting future outcomes. However, to do this job, you must master data scientist skills.
In this guide, we will go over all the soft and technical skills a data scientist needs to best perform their job.
What Is a Data Scientist and What Are Their Duties?
Data scientists design processes to create complex algorithms and models that will analyze data. A data scientist’s day-to-day duties include:
- Building data visualizations
- Developing predictive models
- Using programming languages such as Python, R, SQL, and Tableau
- Creating and testing algorithms
- Pulling, merging, and analyzing data
- Using machine learning
- Analyzing big data
Soft Skills for Data Scientists
In a field like data science, having soft skills is vital because the job can be complicated. These soft skills will prepare a data scientist for their job and will also help them develop technical skills. Key soft skills include:
This is an important skill because data scientists deal with complex problems and must be able to see things from all angles. A data scientist must analyze large amounts of information to find their employer’s desired results.
A data scientist should have general business knowledge because they will use their expertise to help influence business decisions. They should be very familiar with the business they are working for and how it is currently performing.
Because a data scientist works with complex algorithms, they must have solid communication skills to relay information accurately to management. If needed, they must communicate information in a non-technical way so that people can understand what the data means for the business.
In data science, problem-solving is a big part of the job. Any issues in the algorithms or software must be corrected, so data scientists need to have proactive problem-solving skills.
Technical Skills for Data Scientists
Like soft skills, technical skills are an important part of data science. These technical skills will demonstrate your experience and knowledge of data, algorithms, software, and programming. Below are some technical skills that every data scientist should know.
- Cloud computing
- Data wrangling
- Knowledge of data visualization tools
- Knowledge of data querying languages
- Programming languages such as Python, R, SQL, Tableau
- Knowledge of AI and machine learning
- Strong mathematical knowledge in statistics, calculus, and linear algebra
- Software engineering
- Data intuition
- Knowledge of Apache Spark
Data Science Courses
By taking a course, an aspiring data scientist can learn a lot about the field and gain many relevant skills to help prepare them for their future career. You may want to learn about data science at a four-year college, at a bootcamp, or from an online course. Consider these courses below to help you build your skillset.
This data visualization program teaches students storytelling techniques and how to visualize data. Courses cover data storytelling, data visualization, and dashboard design.
In this course, students learn Python, SQL, Command Line, and Git.
In the Exploratory Data Analysis course, students learn to use basic data analysis to summarize information. They will also learn about the plotting systems used in the programming language R and other basic principles.
Machine learning is commonly used by data scientists. This course will cover the basic functions of how to create and apply prediction functions. Students will learn how to use their algorithms for machine learning.
Difference Between a Data Scientist and Data Analyst
Data scientists are responsible for solving a business’s problems. They provide data visualization and explain how a business can use collected data to improve. This involves reviewing past and current data to predict future business outcomes.
Data analysts are responsible for analyzing current data to tell a business how well it is performing. They identify trends and patterns and interpret and analyze data to help create charts and graphs that will give the company insight.
Data Science Jobs
A data scientist can work in many roles. According to the Bureau of Labor Statistics, data scientists in the US make an average of $100,560 per year.
Someone who is looking to get into the data science field may work as a:
- Data scientist
- Data engineer
- Data architect
- Data and analytics manager
- Data mining specialist
- Machine learning engineer
- Database developer
- Business analyst
- Database administrator
- Computer programmer
Skills to Add to Your Data Scientist Resume
If you are looking for a data science job, you should set up your resume to showcase your experience and skills. Your employer must see these skills so that they will know your experience level. Below are some skills to add to your data scientist resume.
- Coding languages such as R, SQL, and Python
- Data visualization
- Grit and version control
- Data manipulation
- Data cleaning
- Machine learning
The more experience you gain, the more marketable you will become. You can learn relevant skills by doing internships, taking an entry-level job, attending an online course, or going to a hands-on learning lab. These experiences are important so that employers can see which applicant is the best fit for their company.
Conclusion: Do You Have The Skills to Become a Data Scientist?
You will need many skills to become a data scientist. In the data science field, you will manage data, write code in several programming languages, solve mathematical problems, and find solutions that will help businesses improve. These skills are vital to becoming a professional data scientist.
If you’re interested in starting your career as a data scientist, be sure that you understand all of the necessary skills so that you can quickly find career opportunities within the data science field.
Data Scientist Skills FAQ
What is the difference between a data analyst and a data scientist?
Data analysts analyze, interpret, and identify trends to help make business decisions. Data scientists use past data to predict future outcomes.
What soft skills are important to have in this field?
Communication, problem-solving, and critical thinking.
What technical skills are important?
Programming languages such as Python and R, as well as knowledge of software like Apache Spark, and data wrangling skills.
How much does a data scientist make?
A data scientist in the US makes an average salary of $100,560 per year.
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