How to Build a Career in Data Science

Our daily decisions, from Google searches to where we travel, are pieces of data that, when combined, build a larger picture of patterns in behavior, activity, and demography. These insights may assist governments, corporations, hospitals, and other organizations in making better decisions to enhance efficiency, better serve clients, and even save lives.

While there is no single path to launching a career in data science, the approach includes learning relevant information and a comprehensive skill set that will allow you to contribute to solving some of the world’s most critical problems.

What is a Data Scientist?

Data scientists are a cross between mathematicians, trend analysts, and computer scientists. The data scientist must decode massive amounts of data and perform additional analysis to uncover trends and understand what it all means. The field of data science bridges business and IT, driving industries by analyzing detailed statistics to extract insights that firms can use to take action.

The Skills Required for Data Scientists

With the growing demand for data scientists, the field offers an appealing career path for students and current professionals. In addition, it includes people intrigued with data and data science, leading them to wonder what data science skills and considerable data abilities are necessary to seek jobs in data science.

Leveraging Big Data as an insight-generating engine has increased enterprise demand for data scientists across all industry verticals. Organizations increasingly depend on data scientists to survive, expand, and stay one step ahead of the competition, whether to enhance product development, boost customer retention, or mine through data to identify new business prospects.

Some data scientists hold a Ph.D. or an MS in statistics, computer science, or engineering. This educational background not only gives a solid basis for any aspiring data scientist but also teaches the critical data scientist abilities and Big Data skills required to excel in the industry.

Some colleges now offer specific programs suited to the educational requirements for pursuing a career in data science, allowing students to focus on the topic of study they are most interested in in a shorter amount of time.

Here are some of the essential technical skills necessary for a Data Scientist:

Skill-1: Programming

You must be familiar with various programming languages, including Python, Perl, C/C++, SQL, and Java, with Python being the most commonly needed coding language in data science professions. These programming languages help data scientists in structuring unstructured data collections.

Skill-2: Knowledge of SAS and Other Analytical Tools

Understanding analytical tools are essential for a data scientist’s ability to get meaningful information from a well-structured data source. Prominent data analytics tools used by data scientists include SAS, Hadoop, Spark, Hive, Pig, and R. 

Certifications and data science boot camps can help you achieve this crucial data scientist talent by establishing your knowledge of these analytical tools.

Skill-3: Ability to Work with Unstructured Data

Data scientists must have experience working with unstructured data from various channels and sources. For example, suppose a data scientist is working on a project to assist the digital or social media marketing team in providing insightful research. In that case, the expert should also be well versed in social media.

ML, AI, deep learning, probability, and statistics are some of the additional skills necessary.

Along with technical data scientist abilities, we will now concentrate on the non-technical talents necessary to become a data scientist. These personal abilities might be challenging to measure based on school credentials, certificates, etc. They are as follows:

Skill-1: Must be Business Savy

Data Scientists need strong business acumen to channel their technical skills. Without it, a prospective data scientist may be unable to identify the problems and possible obstacles they must address for a company to thrive. It is critical to assist the organization you work in exploring new business prospects.

Skill-2: Excellent Communication Skills

Communication comes next on the list of top data scientist talents. Data scientists know how to extract, comprehend, and analyze data. However, to be successful in your work and for a business to profit from your services, you must be able to communicate your findings with seniors and team members who do not share your professional experience.

Skill-3: Excellent Data Intuition

This is one of the essential non-technical data scientist abilities. In vast data sets, valuable data insights are not always obvious, and a competent data scientist has intuition and understands when going beneath the surface for insightful information. This makes data scientists more efficient in their work, requiring experience and training. But, again, boot camps are an excellent method to hone it.

Data Science Job Titles and Career options

These are some of the desired and sought-after data science job titles and careers:

Business Intelligence Analyst

A business intelligence analyst will analyze the data and create a clearer picture of where the company stands to help them figure out market and business trends.

Data Mining Engineer

The data mining engineer analyses not just their own company’s data but also data from external sources. A data mining engineer will design complex algorithms to aid in the analysis of data in addition to analyzing it.

Data Architect

Data architects collaborate closely with users, system designers, and developers to produce blueprints for data management systems to centralize, integrate, maintain, and protect data sources.

Data Scientist 

Data scientists begin by turning a business case into an analytics plan, then build hypotheses, evaluate data, and explore patterns to determine their influence on organizations. They also research and select algorithms to aid in data analysis. Finally, data scientists utilize business analytics to explain what effect data will have on a company in the future and to help create solutions to assist the organization in dealing with these consequences.

Senior Data Scientist 

A senior data scientist can forecast a company’s future requirements. Apart from acquiring data, they extensively evaluate it to quickly address highly complex business challenges. In addition to designing new standards, they may also lead to developing new methods for analyzing statistical data and creating tools to aid future data analysis.

The Data Scientist Job Market

With millions of Big Data job opportunities globally, the post of data scientist has emerged as the hottest job of the decade. In today’s data-driven world, businesses need the insights provided by data scientists to stay one step ahead of the competition while keeping overhead expenses low. As a result, IBM, Apple, Microsoft, Booz Allen Hamilton, State Farm, Walmart, and other well-known companies often list job openings for data scientists.

There are 218,250 Data Scientist jobs available worldwide, according to LinkedIn. Data Scientists are still one of the fastest-growing occupations, ranking second in the top 50 positions in the United States for 2021. Although employment in data science isn’t as widespread as they were a few years ago, demand remains robust and constant.

Companies will need Data Scientists as long as organizations rely on data to make educated decisions and design successful can’t-miss strategies.

Data Scientist Career Outlook

According to a report by the Bureau of Labor Statistics of the USA, the typical annual compensation for data scientists is $126,830. However, salary levels differ depending on the area, local cost of living, and professional choice. For example, data scientists working for software publishers get the greatest median compensation of $132,190, while data scientists working for colleges and universities earn the lowest median salary of $77,240.

Jobs for data scientists are concentrated in places with a strong tech presence, such as California, Maryland, Virginia, Texas, and Washington state. In addition, states such as Utah, Rhode Island, and New Mexico have a large proportion of this employment.

The US BLS stated in their recent report that the United States federal government is one of the leading employers of data scientists. Including some of the 70 who work in Las Cruces, a New Mexico county home to the White Sands Test Facility and White Sands Missile Range.

Most data scientist employment is in the Washington, DC, region, which has almost 2,600 data scientist roles with a mean annual pay of $130,970. Another 1,700 people are employed in the Maryland suburbs immediately outside of Washington, spanning up to Baltimore. The average wage is significantly lower, ranging from $108,820 to $114,110.

Silicon Valley and Seattle, home to large companies such as Microsoft and Amazon, employ numerous data scientists on the West Coast. These areas also have some of the highest average salaries, ranging from $129,600 to $144,530.

However, we may find data scientist positions in virtually any state in the USA. For example, according to the same report by the US Bureau of Labor Statistics, data scientists in the Huntsville, Alabama, area make an average salary of $144,580. In Melbourne, Florida, there are 50 data scientists.

Prerequisites for Becoming a Data Scientist

Every successful Data Scientist is skilled in various technical and non-technical abilities, some of which are necessary to become a Data Scientist. In contrast, others are desirable and will simplify your life as a Data Scientist. The amount of needed skill-specific competency for various occupational jobs varies. This guide will look at all the technical and non-technical criteria for a Data Science profession.

Education

An undergraduate or postgraduate degree in IT, Computer Science, Mathematics, Statistics, Business Information Systems, Information Management, or a related discipline is required to become a successful Data Scientist.

This will lay a solid basis for your Data Science career, allowing you to develop the necessary data processing and analysis skills and preparing you to enter the Data Science sector.

You will be exposed to required skills such as Coding, Data Structures & Algorithms, Business Intelligence, Data Warehousing & Mining, Machine Learning, Predictive Analysis, Scholastic Models, Optimization Techniques, Matrix Computations, and Statistics if you pursue a degree in any of the previously mentioned fields.

Statistics and mathematics (32 percent), business and economics (21 percent), computer science (19 percent), and engineering (16 percent) are the most prevalent degrees held by Data Scientists.

Mathematics

While it is doable to become a Data Scientist without a degree, Mathematical abilities are required. Data Science is all about working with massive datasets, discovering trends and patterns, analyzing data, and crunching numbers, all of which are drawn from the fields of Mathematics and Statistics.

Programming

Another talent required to become a Data Scientist is programming. Data Scientists generally employ programming languages such as Python, R, and SQL. Data Scientists, unlike Software Developers, do not require extensive programming experience. However, knowing the fundamentals of the language is sufficient to land a job in Data Science.

Technical

Let’s look at the technical skills necessary to master Data Science:

• Data Analysis
• Machine Learning
• Working with Unstructured Data

Non-Technical

After discussing the educational, mathematical, programming, and technological qualifications for becoming a Data Scientist, let us move on to the non-technical prerequisites. Because Data Scientists serve as the interface between business goals and product strategy, these non-technical skills are essential.

• Business Acumen
• Management Principles
• Communication
• Data Intuition

Online resources for learning data science might help you along the way. Education providers, such as GoLogica, provide Data Science online courses such as the Data Science Program, Data Analytics Program, and Data Science Bootcamp. These programs are far more career-focused and ultimately better than attempting to study everything on your own. 

These courses will teach you the most sophisticated skills and technologies data scientists use, such as CMS, Hadoop, R, SAS, Python, machine learning, and others.

Want to Become a Data Scientist?

We have the perfect handbook for you if you want to become a Data Science specialist. The next chapter will provide insights on the most popular technologies, the top firms recruiting, the skills necessary to begin or kick start your career in the growing field of Data Science, and a customized Data Scientist career path to success in this in-demand profession.