Is a Data Science Certificate Worth It?
The answer to the question “is data science certification worth it?” Absolutely yes! Did you know data scientists are among the world’s most in-demand and highly compensated IT roles? One of the main reasons data scientists hold so much value is because they dig through volumes of data, analyze trends and monitor changes that impact key business decisions, ultimately helping the companies they work for stay competitive. Businesses are proactively trying to improve efficiencies with data science, but one of the biggest hurdles is the lack of skills within their company to help them meet their data goals. There has never been a better time to earn data science certification online than now.
In the age of endless reporting capabilities thanks to innovative software-as-a-service programs, accessing and organizing data from multiple platforms to build a narrative is in demand more than ever. That’s because regularly reporting on hundreds to thousands of data points can be an enormous challenge for businesses. To alleviate the increasing demand for professionals who are trained to research and decipher data, the emerging field of data science can be a rewarding career path.
Earning a data science certificate and acquiring relevant skills are vital for young professionals and businesses to get ahead — and stay ahead — in today’s digital economy.
What Is Data Science?
The term “data science” was coined in 2001 by William S. Cleveland, a distinguished professor who specializes in computer science and statistics at Purdue University.
Data science is a board term and incorporates a blend of tools, business acumen, algorithms and machine learning to extract insights and intelligence from raw data. The tools used to analyze and generate predictions range from SAS, Apache Spark, and Tableau. SAS is a leader in business analytics, and the demand for SAS Data Scientists is on the rise. Apache Spark is popular alternative especially companies that are using machine learning models to understand data.
In describing why data science is vital to how information is collected and processed, Investopedia states that: “Data science provides meaningful information based on large amounts of complex data or big data. Data science, or data-driven science, combines different fields of work in statistics and computation to interpret data for decision-making purposes.”
From a company’s internal data collection of customers, patients, products or services, to the collection of external data such as traffic on social media, blog posts and product pages, information must be gathered in a way that accelerates business growth. Data science helps to support the corporate vision, strategy, and roadmap.
Professionals interested in Data Science as a career can wear many different titles, such as data analyst, data engineers, and data administrator. There are many data science certification online courses available to help individuals build the skill sets needed to start a successful Data Scientist career. The top Data Science certification online courses are Cloudera Data Scientist, SAP- Fundamentals of Data Science, SAS- Strategies and Concepts for Data Scientists and Business Analysts, and AWS-Practical Data Science with Amazon SageMaker.
Given the speed at which information is accessed and the impact of this on business operations, data science is certainly the wave of the future in 2020 and beyond.
The Future of Big Data
Data management is an evolving process, and demonstrating proficiency in adapting to future trends of data science is critical for several industries.
According to an IDC report, big data is expected to grow exponentially in 2020:
- The big data and business analytics market is projected to hit $203 billion by 2020, up from $130.1 billion in 2016.
- This market growth will be led by banking, discrete manufacturing, process manufacturing, federal/central government and professional services.
- More than half the projected revenue will come from U.S.-based companies, with larger firms driving the most growth.
Big data will have an increasing impact on how businesses integrate the processing of information in operational systems. To emphasize this point, insideBIGDATA outlines the key benefits of big data to businesses:
- Better decision making with quantifiable evidence, enabling smart, strategic growth
- Improving the relevance of your product and offering solutions your customers need
- Recruiting the best, most qualified talent
- Training staff to use insights to drive more business
- Finding your target audience by understanding your customers more effectively
Businesses and organizations need to adapt to the changing technological trends in how big data is gathered and compartmentalized because it can be the difference between a business that is struggling and one that is thriving.
Responsibilities of a Data Scientist
Data scientists do more than just look at numbers on a spreadsheet. The role encompasses a range of skillsets geared toward deciphering large amounts of information or data that can be used to shape quarterly strategies to help businesses grow.
According to Glassdoor, data scientists use their analytical, statistical and programming skills to collect, analyze and interpret large datasets. They then use the information they’ve learned to develop data-driven solutions to overcome difficult business challenges.
One core focus of a data scientist is ensuring businesses have the information they need to effectively communicate to their target audience. This involves storytelling, presentation skills and advanced problem solving.
GetSmarter identifies the following as common responsibilities of a data scientist:
- Gather large amounts of unstructured and structured data, then condense it all into a more understandable format
- Utilize various programming languages, such as SAS, R and Python, to evaluate performance and other insights from data
- Identify trends and patterns in data that may impact profitability
- Solve complex challenges using data-driven techniques
- Communicate and collaborate with IT and the business as the point of contact
- Stay up to date with analytical techniques, such as machine learning, deep learning and text analytics
Earning a Data Science Certificate
As an emerging field, data science is still only in its infancy. The world is expected to generate 50 times more data in 2020 than in 2011, according to SpringPeople. “Considering this, we have no denial to accept the fact that we need conjurers who can maneuver data and create magic with it to drive business growth and innovation.”
Studying to become a data scientist is the first step to embarking on a rewarding career. Earning a data science certificate provides legitimacy that you are sufficient in key skills that will inevitably lead to full-time employment in the field.
Qualifications of a Certified Data Scientist
A data science certificate ensures you possess a broad range of skills that are relevant to countless industries. Throughout a certification program, you’ll learn the following areas of data science to help you become job-ready:
MATH AND STATISTICS
- Machine learning
- Statistical modeling
- Experimental design
- Bayesian inference
PROGRAMMING AND DATABASE
- Computer science fundamentals
- Scripting language: Python
- Statistical computing package: R
- NoSQL and SQL databases
DOMAIN KNOWLEDGE AND SOFT SKILLS
- Passion for the business
- Data curiosity
- Influence without authority
- Hacker mindset
- Problem-solving techniques
COMMUNICATION AND VISUALIZATION
- Ability to engage with senior management
- Storytelling skills
- Translating data-driven insights into decisions
In addition to playing an influential role in helping businesses grow, data scientists command competitive salaries and have plenty of career advancement opportunities.
In the U.S., the average salary for data scientists is $90,801. Students who are still deciding on a career path or IT professionals who want to transition into another career should consider data science, as they will certainly have a bright future to look forward to as a force for change in how data is consumed in business and society.