The field of technology has a huge demand for data scientists and other data professionals. Usually, when people enter the tech industry, they go for a profession in software development. But those who are already interested in data will find that training in data from a data science Bootcamp and getting into a data science career is easier and more rewarding.
The best thing about being a data scientist is that you aren’t limited to working in one industry. Data plays a vital role in all businesses, irrespective of the industry, so filling the role of a data science expert has become necessary. A data scientist analyses big data for an organization and helps implement artificial intelligence in the systems to make operations quicker and more efficient.
But being a data scientist is not just about what you learn in training. To be an expert in data science, you need to pick up data scientist skills through a data science internship. An internship will give you all the necessary practical knowledge you need to have for this role.
In this article, you will learn about the technical skills one needs to become a data scientist.
Top 10 technical skills for data scientists
If you aspire to become a data scientist, you must possess these technical skills in data science:
- Probability and Statistics
The only way you can read and understand data and make informed decisions based on the data is if you have an understanding of probability and statistics. Data science requires you to create interfaces and estimate and predict results based on collected data. So you need to be good at probability and statistics to make predictions close to the actual result.
You will get data science jobs quickly when you are good at probability and statistics.
- Multivariate Calculus and Linear Algebra
In addition to statistics, calculus is another critical area of mathematics you will delve into during your data science internship. Dealing with several predictors and unknown variables becomes part of your daily tasks when those tasks involve machine learning.
If you are working on building a machine learning model, you need to have an understanding of linear algebra and multivariate calculus.
- Programming Languages
Although a data scientist’s role differs from a software engineer’s, an expert in data needs to know at least the basic programming languages. The results they get from the data should be good enough to practice.
So if you are just starting and know little to nothing about programming, start with Python or R.
- Data Wrangling
When organizations receive data for modeling, it is usually full of imperfections. Therefore, this data needs to be reworked before it can be used to gain insights.
Data wrangling is the skill that helps you prepare the data for further analysis. You start with the raw data, analyze it and find all the imperfections; then, you change the factors that do not work and cleanse the data up to your needs.
- Database Management
To be a full-stack data scientist, you must be excellent at every element of data science. And database management is a huge part of data science. You will be preparing a lot of data for processing in your organization. All this data needs to be tracked and managed properly, or your efforts will be vain if you don’t have a proper track of things.
- Software Engineering
You don’t need to be a software engineer to get into data science, but it is an easy transformation to make. Knowing the basics of software engineering will help you communicate better with the developers.
The data you collect must align with the code they write. Your work goes hand in hand with software engineers, so knowing project lifecycles, data types, and time-space complexities will help you in your work.
- Data Visualization
Data is not easy to read for everyone. So a data scientist, it is your responsibility to present it so that it becomes understandable for everyone. Showing people data in graphs and charts will help them see the drastic differences that just numbers won’t show.
So you should have data visualization skills to make data comprehension easier.
- Web Scraping
Many organizations require their data scientists to scrape data off the internet and increase the company’s productivity. This data that needs to be scraped can be in the form of texts, images, videos, or anything informational.
So a data scientist must also know how to perform web scraping and find data that can be useful.
- AI and ML
Information Technology is a field heavily influenced by artificial intelligence and machine learning. So to work with innovative tools and develop new programs implementing AI and ML, a data scientist must know about AI and ML.
If you get a data science certificate, it will teach you everything you would need to know about these technologies.
- Big Data
If you have the phrase ‘big data’ on your data science resume, you are guaranteed to get a job as a data scientist.
Any data scientist can handle a data set, but your ability to manage big data sets you apart from others. Big data is where all the data sets meet. Only an expert in data science can handle all that hefty amount of data.
What is the average Data Science salary?
According to PayScale, the average data scientist’s salary in the US is $97,680 per year. The more experienced you are, the better pay you get. Also, knowing Python and Machine learning will be necessary if you want to earn well in this field.
To gain technical knowledge and become skilled in data science, you need some hands-on experience. Leading or even being part of data science projects will help you achieve these skills. Upskilling yourself will mean getting better data science jobs and a better salary.
