In today’s digital ocean, data is the water we swim in. Our technology records and tracks every digital action. Companies can nowadays accumulate and analyze swaths of valuable business data and orient themselves and their products towards future trends. Because data is at the center of everything we do, there is a huge demand for data-related talent. Data Analysts and Data Scientists are tasked with making sense of the immense quantity of available data and creating value from it, analyzing, making predictions, and suggesting recommendations to optimize a company’s resources and potential. Openings for Data Analysts and Data Scientists overwhelm today’s job market. The European Commission estimates that 100,000 new jobs in the data field were created in 2020 alone. Glassdoor’s #1 job is Data Scientist. Data scientists and analysts are in demand in every industry, but at the moment, the biggest players for data professionals are the financial, insurance, and most obviously tech industry. The problem is that there are not enough Data Analysts and Data Scientists to fill all of those roles.
We’ve discussed at a high level the demand for these two popular data job roles, but what’s the difference between them? What do they do, and what skills does one need to have to perform each role successfully? A Data Analyst is essentially a business analyst with number crunching capabilities. Also known as Business Intelligence (BI) Analysts, their main task is to look into accumulated past data to identify trends and create data visualizations that inform business strategy. Data analysts need to be proficient in analytics software, data visualization software, and data management programs. They do not necessarily need to code, but they do need to be tech-savvy, and know how to plug into various data sources, and work with advanced software tools. Critical thinking, rigorous analysis, a good business acumen, and presentation skills are required skills for this job role. Data Scientists are also data interpretation pros, but they are also required to have coding and mathematical modeling skills. Data Scientists are essentially programmers; they conceptualize and build new processes for data modeling using prototypes, algorithms, predictive models, and custom analysis with the goal of anticipating future needs. Data Scientists’ main programming language is Python, and they work with various data analysis and cloud tools with cute names, such as Numpy, Scipy, Matplotlib, Hadoop and Kaggle. The main skills that Data Scientists need to attain are problem solving, teamwork and self-study.
Employed. Utilizing Big Data yields a competitive advantage so valuable that trained professionals in Data Science and Data Analytics are in sky-high demand. LinkedIn co-founder Allen Blue said, “there are very few data scientists passing out their resumes. Data scientists are almost all already employed, because they’re so much in demand.” According to a recent Villanova University survey, 82 percent of organizations in the U.S. say that they are planning to advertise positions that require data-analytics expertise. This is in addition to 72 percent of organizations that have already hired talent to fill open analytics positions in the last year. Still 78 percent of businesses say they have experienced challenges filling open data-analytics positions over the last 12 months. But universities simply aren’t producing trained professionals quickly enough.
Wawiwa Tech Training is an Israeli tech education provider that helps its partners around the world to address this talent shortage through two separate programs for the training of Data Scientists and Data Analysts. All Wawiwa programs emphasize hands-on practice, which is necessary to become a job-ready data professional.
Wawiwa’s Data Scientist program is an entry point to the world of Data Science for beginners and career changers with math skills and some background in programming. The program spans 340 academic hours, over 8 months of part-time studies. Students develop knowledge of data science frameworks (e.g. data collection and analysis, machine learning, deep learning), programming (Python), and cloud tools. The program establishes students’ skills in understanding data, modeling, and presentation, through data science exercises, labs, and a final project. By its conclusion, students can design and build a complete data prediction system, including writing a web scraping task to collect data from websites, storing and retrieving data from many data sources, designing and building prediction models from the data, and finally, deploy a complete model to the cloud.
Wawiwa’s Data Analyst program prepares students for the entry-level position as a Data Analyst through a 250-hour, 6-month part-time program. Students develop knowledge of market-leading technologies, ways to process information, data analysis capabilities, business intelligence, and more. The program establishes students’ technical, analytical, and business skills, through hands-on exercises and a final project. Graduates are more than ready to take on BI analyst roles that require the use of Excel, Tableau, Anaconda, and SQL.