Data is a big deal today. We’re producing more data than ever before in history, and there’s a great need for professionals who can successfully work with it and turn it into something useful. That’s why a data science career is a strong choice for anyone who wants to be part of the IT industry and have job security and a good challenge.
This article shows you how to build a career in data science. You will read about what a data scientist is, what a data science career path looks like, the requirements, roles, and responsibilities of a data scientist, and how an online data science course can help you gain the necessary skills.
Let’s begin our data science career journey with some definitions.
What is Data Science?
Data science is the process of analyzing vast amounts of data and predicting future business growth by employing mathematical and statistical concepts and techniques, deep learning and machine learning algorithms, probability, and many other advanced technologies and tools. Data science aims to find hidden patterns and market trends from the given business data, allowing organizations to make better, more well-informed business decisions in the future.
Let’s build off this definition and explain what a data scientist is.
What is a Data Scientist?
A data scientist is a highly skilled IT professional who functions as a statistician, mathematician, trend-spotter, and computer scientist. The data scientist’s primary purpose is deciphering and decrypting vast quantities of data, then analyzing them to find and extract patterns to acquire valuable insights.
Data scientists can be found in information technology, business, and many other industries, analyzing complex data sets to obtain actionable, valuable insights that will benefit their organizations.
Now, we’ll explore the specific responsibilities and roles of data scientists.
Data Science Roles and Responsibilities
Here is a sample of the roles and responsibilities of a data scientist. Organizations have different expectations for data scientists, so bosses and managers in these companies may emphasize some of these responsibilities and roles over others. Some of these roles may not even show up.
- Extracting data from multiple sources
- Using machine learning tools to organize raw data
- Process, clean, and validate the data
- Analyze data and look for information and patterns
- Develop prediction systems
- Investigate additional tools and technologies for creating innovative data strategies
- Work with team members and leaders to develop data strategies
- Present the cleaned and analyzed data clearly, using various visualization tools and techniques
- Devise comprehensive analytical solutions, ranging from data gathering to display, and assist in building data engineering pipelines
- Collaborate with product teams and partners to create data-driven solutions designed with original concepts
- Design, implement, and oversee data pipelines while conducting knowledge-sharing sessions with team members and peers to ensure effective data use
- Create analytics business solutions by combining different tools, applied statistics, and machine learning
- Assess the feasibility of implementing AI/ML solutions for the organization’s business processes and outcomes
- Propose actionable solutions and strategies based on findings extracted from the data
That’s a lot of responsibility! Consequently, data scientists must be well-trained, skilled professionals to handle and accurately discharge these responsibilities. This leads us to our next section, which covers requirements.
Requirements for Becoming a Data Scientist
Since much is asked of data scientists, their training must equip them to deal with any demands and issues. Here’s a list of typical data scientist requirements, although not every organization will expect you to be an expert in every one of the following.
- Although not mandatory, most data scientists have at least a bachelor’s degree in computer science, mathematics, statistics, or a related field. Most recruiters are attracted to a degree.
- The following requirement is optional, although the truth is that many data scientists have a master’s degree, typically in a field like mathematics or data science.
- Regardless of your ultimate education level, learn statistics and mathematics.
- Possess excellent skills in R, Java, Python, or any other related programming language (ideally, learn more than one programming language).
- Acquire hard skills such as analysis, machine learning, statistics, and Hadoop.
- Possess soft skills like critical thinking, persuasive communication, and problem-solving.
- Comprehend the extract, transfer, and load (ETL) concept and its processes.
- Understand machine learning algorithms and tools.
- Acquire experience in data exploration, data wrangling, and data visualization. In the latter case, this involves familiarity with data visualization tools such as Excel, PowerBI, and Tableau.
- Learn deep learning concepts and techniques.
- Gain industry experience in dealing with and solving big-data-related problems.
A Typical Data Science Career Path
Data scientists can take their careers in two directions. They can either focus heavily on technical deliverables, which includes mentoring teams and staff and reviewing their deliverables, or adopt a more business-focused approach, managing projects from start to finish and liaising with the business teams.
Regardless of your direction, here is a standard data science career path.
- Entry-Level Data Science Positions. This level involves executing tasks delegated by lead and senior data scientists. This position’s primary goal is to learn, explore areas of interest, solidify analytical skills, and grow technical skills in languages like R and Python.
- Typical job titles: analyst, business analyst, business intelligence analyst, data analyst I, data scientist I, junior data analyst, and junior data scientist.
- Mid-Level Data Science Positions. This level is like entry-level positions but with added seniority and ownership. You should decide if you will focus more on deliverables or business at this level.
- Typical job titles: data architect, data engineer, data mining engineer, data scientist, senior business analyst, and senior data scientist.
- Senior-Level Data Science Positions. Senior-level data scientists have a proven track record of leading projects, dealing with crises, and possessing a high level of ownership. In addition, senior-level data scientists must work alongside business leaders and C-suite executives.
- Typical job titles: chief data scientist, chief information officer, chief operations officer, chief technology officer, director of data science, lead data scientist, principal data scientist, and vice president of data science.
Building a Career in Data Science
So, let’s take all the information we’ve covered thus far and sum up the steps for building a rewarding career in data science.
- Get your requirements in place. This step includes a degree (or equivalent) and a basic understanding of programming languages and the appropriate hard skills.
- Secure an entry-level position. The next step is getting your foot in the door, typically a ground-floor position.
- Continue expanding your education and skill sets. Sharpen your skills by tackling projects, learning from senior data scientists, and engaging in continuing education via online classes, data science bootcamps, and certificates.
- Decide on your future path. Will you focus on the technical side or the business side?
- Consider branching out into another position. Data science-related career opportunities include:
- Analytics Consultant
- Business Analyst
- Data Analyst
- Data Scientist
- Data Science Engineer
- Machine Learning Engineer
- Product Analyst
- Stay current. Finally, make sure to remain up to date on the latest data science-related methods, technologies, and innovations.
The Data Scientist Career Outlook
According to the data recently provided by the U.S. Bureau of Labor Statistics, data scientist employment is projected to increase 35 percent between 2022 and 2032, a rate that is considerably faster than the average for other occupations.
This growth figure translates into about 17,700 new openings for data scientists annually, on average, throughout the decade. Many of these openings are expected to come from the need to replace workers who either left the labor force (e.g., retired) or who decide to move on to different occupations.
According to Indeed.com, data scientists working in the United States may earn an annual average of $125,254, with figures as high as $189,359.
So, the future looks bright for data scientists. As long as our society relies on data, there will be a need for data scientists. Spoiler alert: modern society will always rely on data!
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Q: How do you build a career in data science?
A: Secure your degree in data science or related fields, learn programming languages, sharpen the required skills, and apply for an entry-level position. Once you’re in place, expand your skill set and experience, work your way up the ladder, and keep up your training through self-learning methods, online classes, and bootcamps.
Q: Who is suited to a career as a data scientist?
A: Obviously, it helps if you like working with vast reams of data. Data scientists generally enjoy critical thinking, problem-solving, and analyzing disparate collections of information. It also helps to be detail-oriented, persistent, and a good communicator.
Q: How long does it take to become a data scientist?
A: This figure varies wildly. Suppose you have no experience in coding or a mathematical background. In that case, landing an entry-level data scientist position will take a half year to a full year of intensive study. If you want a degree, add three to four years. The bottom line is that the more of the required skills you already have, the less time it will take.
Q: Does data science require coding?
A: Traditionally, data science roles require coding. Although there are now technologies introduced that significantly reduce or eliminate the need for coding, it’s wiser to assume that coding will be part of the job description until the no-coding technology becomes more widely available.