Data Science – is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.
Data Science has taken the world by storm and is one of the most in-demand career choices today. Opportunities in the field are endless, and job roles in Data Science promise high-paying salaries. Certifications that can help you move into data science domain include:
- Data Scientist Master’s Program
- Business Analytics Expert
- Data Science with R Programming
- Data Analyst Master’s Program
Who Should Take This?
Earning a Data Science certification is a great step for anybody interested in learning how to use data in decision-making. Experts in computer science, data analysis, business management, and data science enthusiasts all fall under this category.
What are the Top Companies and Industries Hiring?
Jobs for data scientists may be found in every sector, from banking and finance to insurance and media to healthcare and retail to telecommunications and even the automotive industry. Microsoft, Amazon, Adobe, Accenture, Deloitte, and IBM are some of the most prestigious employers of data scientists.
What’s the Career Path?
Data scientists work in many fields, and their career paths are as diverse as the people who choose to pursue them. Data Analyst, Data Scientist, and Chief Data Officer are just a few examples of the many possible stops along the way of becoming an industry leader in the use of data.
What does a data scientist do?
Data scientists determine the questions their team should be asking and figure out how to answer those questions using data. They often develop predictive models for theorizing and forecasting.
A data scientist might do the following tasks on a day-to-day basis:
- Find patterns and trends in datasets to uncover insights
- Create algorithms and data models to forecast outcomes
- Use machine learning techniques to improve the quality of data or product offerings
- Communicate recommendations to other teams and senior staff
- Deploy data tools such as Python, R, SAS, or SQL in data analysis
- Stay on top of innovations in the data science field
Data analyst vs data scientist: What’s the difference?
The work of data analysts and data scientists can seem similar—both find trends or patterns in data to reveal new ways for organizations to make better decisions about operations. But data scientists tend to have more responsibility and are generally considered more senior than data analysts.
Data scientists are often expected to form their own questions about the data, while data analysts might support teams that already have set goals in mind. A data scientist might also spend more time developing models, using machine learning, or incorporating advanced programming to find and analyze data.
What’s the Demand?
By 2026, the number of data scientist employment is expected to grow by 27.9 percent, as reported by the BLS. The Bureau estimates that by 2026, the market would be worth USD 322.9 billion. As more and more businesses depend on data to make crucial decisions, the need for data scientists is expected to keep rising.
What are the Eligibility Requirements?
Courses leading to a Data Science certification are open to participants of any experience level, from complete beginners to seasoned experts. The completion of an undergraduate degree is the one and only requirement needed to sign up for a full- or part-time course. A previous background in coding is not required. The minimum admission requirement would be an honours degree or post-graduate diploma in Information and Knowledge Management, IT management, Information Systems, Marketing or a cognate discipline at NQF level 8 with an overall average of 65% or above.
Roles and Responsibilities
Data Scientists are responsible for collecting both structured and unstructured data, locating appropriate data analytics tools, promoting data-driven decision-making, and completing data-related tasks such as cleaning, wrangling, analyzing, and segmenting. In certain cases, they may also be tasked with designing and executing machine learning models, sharing findings with relevant parties, and keeping abreast of the latest developments in the field.
