Data science involves a blended process of data extraction, interpretation, transformation, algorithm development, and mining insights. It’s a process of fusing statistics and data together to pull useful information from the troves of unstructured data being collected. In the business world, it’s about using scientific methods to solve problems with data.
Data science is a highly-valued skill set with a wide array of applications. If you’re considering putting your programming skills and analytical mindset to work, then let’s walk through the steps of securing an entry-level data science position.
What Is A Data Scientist?
Data scientists are the ones who accumulate and analyze large sets of structured and unstructured data. Knowledge of statistics, computer science and math is crucial to a data scientist’s work. Their primary role is to find certain trends and patterns hidden within vast amounts of data. They collect and analyze information and use data visualization tools and techniques to build models that are used to make high-level business decisions.
How Can You Become A Data Scientist?
Before venturing into a career in data science, you should work on foundational skills and gain a solid understanding of statistics and probability. So it’s advisable to get a specialized, skills-based education in data science, as well as polish up your math, computer science, and statistics knowledge.
Thinkful’s Data Science course will teach you everything you need to know and more to kickstart your career in data science. Not only will you learn all of the necessary skills to thrive as a data scientist, but you’ll also have 1-on-1 mentorship and unrivalled support on your journey. Thinkful’s all about creating courses in tech that’ll set you up for a job you’ll love, which is different from other educational options who leave you to fend for yourself post-graduation.
Another option is to get an undergraduate degree in a related field. As far as a master’s degree in data science is concerned, it goes by many names, some of which are:
- Master of Science in Data Science
- Master of Computational Data Science
- Master of Information and Data Science (MIDS)
- Master of Science in Statistics: Data Science
If you want to pursue a graduate program, you need to have one of the following subjects as your undergraduate major:
- Computers science
- Applied mathematics
- Applied statistics
Some programs accept students with other undergraduate majors. However, they should have done an undergraduate program in one of the following subjects:
- Computer programming
- Calculus I & II
- Linear algebra
Data Science Portfolio
If you don’t create a portfolio, you’re missing out on an opportunity to highlight your best work, skills, accomplishments, and potential. Unlike a resume that provides bullet points, a portfolio visually showcases examples of work. So, it’s important to maintain an impactful portfolio that demonstrates your coding and data science skills. In fact, you should start a portfolio before you graduate. You can start by highlighting your accomplishments from when you were in school.
Specializations within Data Science
You need to identify your skills in data science areas such as data visualization, database management, exploratory analysis, and machine learning. Specialization helps you remain focused to increase your potential in a particular field. It’s suggested that you work on your skills and gain expertise in machine learning, statistics, data mining, visualization to draw insights, building predictive models and so on.
At the heart of a data scientist’s career lies trending technologies, data interpretation, statistics, and high-level programming languages like Python and SQL. You should have a technical skill within data science, such as:
- R programming
- Computer Vision
- Computer Vision
Creating The Ideal LinkedIn Profile
LinkedIn is often described as the Facebook for professionals. If you don't have a LinkedIn profile, it’s time you create one! Describe your past work experience in at least two paragraphs. Optimize your profile by adding relevant skills and accomplishments. And remember to keep your profile updated as you gain more skills. Search ‘data scientist hiring’ in LinkedIn to begin to develop connections.
Responsibilities of a Data Scientist
If you want to understand the role of a data scientist, Josh Wills explains it best, ‘A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statisticians.’ With that being said, let’s go through a summarized list of roles and responsibilities that a data scientist has:
- Extract huge amounts of structured and unstructured data to gain insights from relational databases using programming languages such as SQL. The unstructured data is gathered by web scraping, APIs, and surveys.
- Employ predictive analysis to foresee how sales are going to be affected.
- Employ statistical methods to prepare data for use in predictive and prescriptive modeling.
- Clean data to avoid irrelevant information and prepare the data for modeling.
- Develop an understanding of the data framework and how it relates to business use.
- Perform EDA (exploratory data analysis) to handle missing data and to look for patterns and trends.
- Discover new algorithms to solve business related problems and to automate repetitive work.
- Suggest cost-effective changes to existing procedures and strategies.
Note: Every company has a different take on data science jobs. It is often seen that some companies hire data scientists that are entrusted with the work of data analysts.
Characteristics of A Successful Data Scientist
It is imperative to know that the roles of a data scientist and a data analyst may combine together. For instance, if you are extracting data one day, you may be expected to clean it the next.
- A successful data scientist is capable enough to work with a variety of data and review large amounts of data.
- To make a mark as a successful data scientist, you must be creative in making new algorithms to organize data.
- Since you’ll have to explain your views to your team and communicate with clients and stakeholders, you must have efficient communication skills.
Must-Have Data Science Skills
You have to master at least one of the programming languages such as R. R is specifically designed for data science needs, and it’s used to solve any problem encountered in data science. It’s been estimated that 43% of data scientists use R.
Machine Learning And AI
If you want to stand out in a crowd of data scientists, machine learning is the key. You can learn techniques such as supervised machine learning, logistic regression, and decision trees. These are the skills which will help you to solve big data problems which are otherwise difficult to handle.
Data visualization is like storytelling which makes complex data easier to comprehend. This is basically a translation of the difficult data into a format that is accessible using graphical representations, charts, and pictures. As an aspiring data scientist, you should be able to use data visualization tools such as ggplot, d3.js and Tableau. Often people do not understand serial correlation or p values; therefore, to make them understand, you’ll need to show these values visually.
Non-Technical Skills for A Data Scientist
As you know, data science is a field that includes a lot of research and curiosity. In data science, intellectual curiosity is important since you have to solve real world problems for the industries and bring predictive and thoughtful solutions to the table.
If you’re an aspiring data scientist, companies usually look for someone who can clearly translate technical information to non-technical teams. Marketing or sales departments are not aware of tech jargon; therefore, a data scientist needs to speak a language that the company understands.
A data scientist cannot work alone. You’ll have to work with different teams to develop strategies and statistical solutions for business needs. For that reason, you’ll need to be willing to work in a team.
The journey of research and data science involves a lot of dead ends and wrong turns. You may also have to deal with unexpected issues on a daily basis, so you should be flexible and patient. You cannot show your frustration or walk off when an issue comes up.
Data Scientist Salaries
The earning capacity of a data scientist depends on their work experience, education, and skills. An associate level data scientist’s compensation package is around $118,370 per year and that of a senior data scientist could be around $171,755 per year.
The packages offered may vary throughout the United States. However, the table provided below will give you a rough idea.
IT System Analysts$68,807.Healthcare Data Analysts $61,438.Operations Analysts$7500Quantitative Analyst$82,879.Data Analytics Consultant $78,264.Digital Marketing Manager$97,000.Transportation Logistics Specialist$79,000
How Long Does It Take to Become A Data Scientist?
The answer to this question depends on your career goals. The quickest way to begin your career as a data scientist is by taking our Data Science course. This is an accelerated course, especially in the full-time format which preps you to start your career in as little as 5 months. Another bonus from this course is that you don’t have to pay a cent towards your education until you’re hired in your new career.
Hopefully this article has given you some clarity on the basic information about how to become an entry level data scientist. If you need more information and guidance, our team is available to answer any questions or concerns you may have. There’s also an abundance of useful tech-related articles available that you can browse through to get you excited about starting your new career in tech.
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