Should I learn data science or big data?
Before deciding whether to pursue data science or big data as a career, make sure you know how they differ.
Big data is the mass of structured, unstructured and semi-structured information that data professionals use to analyze insights leading to better organizational decisions and strategic outcomes. Data science is the broader field that comprises tools, data cleansing, analysis, algorithms and machine-learning principles to uncover patterns within raw data.
Big data features prominently in data science. If you’re a current or aspiring big data professional, consider a data science qualification to broaden your horizons.
What is the role of big data in business?
Big data has revolutionized business practices by:
- Enabling companies to fully understand the needs of their customers by analyzing social media data, browser logs and text data mining.
- Reducing costs with predictive analytics to reduce guesswork. Businesses can predict inventory requirements and plan their production.
- Improving cyber security to detect cyber attacks in combination with machine learning and artificial intelligence tools and techniques. This provides a great opportunity within the cyber security space for those with big data skills, as we’re currently experiencing a global cyber skills shortage.
Is big data necessary for a career in data science and machine learning?
Big data is grouped under data science, which is the umbrella term. It is fundamental to data science and machine learning. In everyday work, a data scientist encounters big data and needs to know how to use it.
Machine learning, a branch of artificial intelligence (AI), automates analytical model building. By learning from big data, it identifies patterns and makes decisions with minimal human intervention.
Are data scientists or big data professionals in demand?
In recent years, some of the world’s largest companies owe their successes to big data and the tools data scientists use for analysis. This development has generated a booming demand for data professionals, with almost every industry employing those with the relevant skills to enhance their business practices. Market leaders continue to look for skilled data professionals in America to ensure they keep their positions in a competitive market, meaning now might be the perfect time to upskill.
If you’re a big data professional, it’s a great time to become qualified in data science, expand your resume and become a more dynamic, future-focused employee. Businesses are looking for data professionals with practical skills in big data and also with training in data science, machine learning and artificial intelligence.
What skills and experience can help me become a data scientist?
If you’re thinking of making the switch to a prosperous and fulfilling career filled with opportunities in data science, but don’t know where to start, you’re in the right place.
Here are some key skill areas to focus on:
- Begin refining your programming skills or gain a base understanding of required skills such as C/C++, Python, R, Java languages, SAS and Pearl.
- Refine or gain some business skills such as industry knowledge, communication, and knowledge of database systems.
- Become familiar with Apache Spark and the Hadoop platform.
- Learn more and push your limits.
- Work on your teamwork skills: data scientists must collaborate with colleagues to solve complex problems – strong communication is essential.
The career outlook of a data scientist vs a big data professional in Australia
If you’re new to the industry, the professions of data science and big data professional may seem synonymous, but they do differ.
Data scientist
- Main responsibility is data analysis.
- Expert in technologies such as Hadoop, Apache Spark, Pig, Python and Java.
- Tends to specialize in a niche and commonly works in groups within an organization.
- Interprets and manages data to help solve complex problems.
- Average annual salary of USD$102k.
Big data professional:
- Involved in data scrapping and maintaining the quality of data.
- Summarizes findings of trends and patterns within data.
- Understands the business and business objectives.
- Average annual salary of USD$131k.
Data science is in demand not only in Australia but also worldwide. Qualifying will boost your career prospects in Australia or abroad.
6 ways upskilling to data science can boost your career prospects
- You will become a more dynamic and valuable employee. In America, the demand for skilled data scientists outweighs supply. Companies are willing to reward skilled data scientists because they are seen as a valuable asset.
- You can help to optimize your business’s operations. Data scientists use data to increase business efficiencies to reduce costs and save money. For example, transport companies use route optimization for quick, cost-effective deliveries.
- You can identify fraud and assess risk. By upskilling to data science, you can help your company’s cybersecurity by aiding in fraud detection and potential cyber-attacks.
- You can target markets. Data science and analytics enable a company to target ads at consumers personally to increase sales.
- You can deploy predictive analysis. Data science plays a pivotal role in predictive analysis to plan and manage costs. You can predict accurately how much a consumer will pay for a product and forecast when production will need to increase.
- You can make more money. By upskilling to data science from big data, your salary could almost double.
Given the benefits, it’s clear to see why upskilling to data science makes so much sense. Although big data professionals are in demand, employees educated and trained in data science are even more sought after, as their skills are viewed as wide-ranging across the breadth of a company.
Keen to make the switch to data science? The Institute of Data full-time and part-time programs will help you achieve your career goals and connect you with thousands of industry partners seeking professionals with skills in data science. Talk to a career consultant now.