In the world of big data, the world is your oyster. With so many jobs available, the role you choose comes down to your current skillset and your career goals. While some entry level positions allow you to learn on the job, others require a more defined set of skills and years of experience. Not everyone knows which role is right for them. What looks good on paper may not align with what you want out of your job. That’s why it’s important to know more about the positions before you apply.
Education and Prerequisites
It’s important to understand the educational requirements. Most mid and senior-level positions do require a college degree. Some accept a BA while others demand a masters in the field. If you’re just starting out or wanting to change careers, another degree may be required. Since tuition can add up quickly, you can look for scholarships for college students online. A simple search will bring up plenty of options, many of which can be tailored to your educational needs and timeline for studies. However, it’s best to weigh the pros and cons of each one prior to applying. Some scholarships require a certain GPA while others may require full-time attendance. The world is big data is vast. There is definitely no shortage of jobs where you can apply your skills and know-how.
Data scientists are those who want to turn a love of tech into a career and basically have three jobs in one. They’re experts at mathematical equations, they know their way around technology and they can easily spot a successful trend. Interestingly enough, data scientists have been around for many years, but due to the limitation’s technology had back in the day, you really didn’t hear much about them. However, times have drastically changed, and even since technology has become more accessible, so too has the demand for data scientists. Their job is somewhat demanding, but the tasks they perform aren’t what you’d expect them to be. Here’s a quick list of the tasks you can expect to complete as a data scientist:
- Gaining insight into digital trends and formulating plans to capitalize on them
- Predicting outcomes using handmade data models and algorithms
- Discuss recommendations and suggestions to the higher-ups
- Perform in-depth data analysis for various purposes
Becoming a data scientist can be quite the journey, but it’s more than worth the time and effort. To start, you must first acquire a BA in data science, which is a four-year program. From there, you’ll need to start gaining hands-on experience by applying for entry-level positions. Job experience is what employers really want to see, so try to gain as much knowledge as you can. Of course, we wouldn’t want to squander the chance of you getting a higher level of education either. Despite not exactly being a requirement, landing an interview might be much easier with a master’s degree on your resume. A master’s degree in data science, however, can be very expensive ranging from $55,000 to as much as $100,000.
Paying for this might not be possible for you, but luckily, you won’t have to. There are plenty of scholarships for college you can choose from. Each one is tailored to specific needs, so take your time researching. The search and application platforms you’ll be using come with personalized matching and filtering, which makes it easier to find what you need.
Business Intelligence Developer
A business intelligence developer is somewhat similar to a data scientist, except their services are exclusively geared towards streamlining business operations. The main role of a BI developer is to design, create and implement software that helps a business come up with effective strategies, solve difficult problems, and make projects easier to manage. Another role of theirs is to gather data and information, which is then simplified, so everyone can understand it. Similar to a data scientist, you must graduate with a bachelor’s degree in data science.
As a statistics analyst, you help organizations make business decisions based on numerical data. Statistical analysts are skilled at the collection, organization, analysis, and presentation of data in various contexts. Their role is to help key decision makers, such as managers and shareholders, make informed decisions based on relevant information. Statistical analysts need a background in statistics, data visualization, data cleaning, and languages like SQL, Python, and R. Specializing in big data and AI can help you further excel in your career.
Big Data Architect
A big data architect crafts important infrastructure to store a company’s data. They plan, design, develop, and maintain databases throughout an organization. Their primary job is to ensure that large quantities of data are properly collected and stored efficiently while protecting a company’s confidentiality. There are many ways a big data architect helps a business, and their greatest role is helping companies stay organized and compliant with data regulations. You’ll need a background in data architecture, and big data technologies like Hadoop, Cassandra, and MongoDB.
Machine learning is the heart of AI and technological innovation. Everything from home appliances to airplanes are becoming more efficient and responsive to humans’ needs thanks to machine learning experts. A machine learning engineer specializes in designing the algorithms that power artificial intelligence. Essentially, they put the smart in smart technology, giving it the ability to collect, analyze, interpret, and apply data in meaningful ways. Machine learning engineers need to know programming languages, like Python, Java, and NLP. They also need to have a strong background in computer science, AI, data modeling and analysis, and communication skills.