The emerging Artificial Intelligence and Machine Learning technologies have drastically revolutionized and transformed the world. However, modern AI and ML solutions need heaps of data for processing and training models. This enormously growing data requires advanced handling and managing strategies which resulted in the formation of the data science field.
Data science is a branch of AI that facilitates examining input, identifying useful information, and finding unusual patterns from the data. This field has earned rapid popularity by offering efficient data handling and effective decision-making solutions for real-world industries to boost productivity and escalate their revenues.
Moreover, data science tools and techniques support acquiring, maintaining, processing, analyzing, visualizing, and presenting data on a large scale. No IT person with a general knowledge of the data handling processes can conduct the tasks. Professionals or field experts with complete domain knowledge perform these operations with astute observation and proficient skills.
In this article, we will cover key concepts related to data science and highlight field experts’ importance. In addition, we will specifically discuss the role and responsibilities of a data scientist.
What Is a Data Scientist?
Data Scientist is an individual with technical expertise in data analytics and curiosity about discovering and solving complex real-world problems. Data scientists are capable of studying input data to explore previously undetected or unknown findings. They can identify interesting patterns and observe the statistics to draw valuable conclusions.
How Did Data Scientists Emerge?
Interestingly, very few data scientists worked in the field a decade ago. Professionals in engineering, networking, and information technology were common. With the excessive developments in the technological world, data has become the central hub of focus.
Organizations have realized the significance of exploring and processing data to escalate productivity. This result in increased demand for data scientists ready to begin the data exploration journey. Universities recognized the trend and started offering special degree programs for candidates interested in pursuing the field of data science.
The advancing impact of big data on businesses has paved the way for data scientists and opened enormous opportunities for their growth. Furthermore, the abundance of unstructured data has forced businesses to opt for modern data handling and analysis techniques.
Therefore, data scientists must have a strong grip on diverse fields like business and IT to dig into this data and derive meaningful business insights. The findings of data scientists help enterprises understand their infrastructure, track progress, improve performance, and accomplish set targets. Analyzing the facts and statistics is useful for companies to make mindful decisions and take informed steps toward accelerating their services.
What Skills Are Needed To Become a Data Scientist?
Data science skills are hiking demand due to the big data industry producing large volumes of data that is an asset for the organization. Data scientists must have deep technical knowledge and the ability to perform critical analysis. This role requires expertise in various interlinked domains such as mathematics, statistics, computer science, and data mining.
Technologies like machine learning, deep learning, advanced analysis, and prediction are frequently used by data scientists. Therefore, expertise in a range of big data tools and command over diverse programming languages, such as Python, R, C/C++, Java, etc., is essential for data scientists.
In addition to the conventional technological background, the candidate must also develop some soft skills. Interpersonal skills and effective communication are important for client handling. Leadership skills help in tasks like risk prediction and decision-making processes.
In a nutshell, for an individual to become a data scientist, proficiency is required in:
- Programming languages
- Data cleaning, analysis, and visualization
- Communication skills
Who Can Become a Data Scientist?
The job of a data scientist involves technical/non-technical skills, critical thinking, complex planning, and analysis in real-time. Therefore, the candidate aspiring to a career as a data scientist should have a solid educational background to be familiar with multi-disciplinary fields.
Candidates with a strong technological background like software engineering, computer science, or IT are ideal for becoming data scientists. The report states that 88% of data scientists hold a master’s degree, and 48% have a Ph.D. The majority of data scientists come from scientific or technical programs like computer science, engineering, statistics, mathematics, and economics.
Candidates with bachelor’s degrees in business and related disciplines can also become data scientists by setting a strong technical foundation and acquiring the necessary skills. In general, the aspirant should have the capacity and motivation to learn different branches of knowledge and tools.
How To Become a Data Scientist?
Becoming a data scientist is all about learning and developing expertise in the business and IT sector, which takes time and patience. Therefore, a properly structured roadmap is vital to ensure heading in the right direction and tracking gradual progress. The following five steps can guide any candidate (whether a student or a professional looking to shift career) aspiring to become a data scientist.
Phase 1: Planning and Preparation
The first step towards becoming a data scientist is planning your learning journey and preparing all essentials beforehand. Therefore, candidates interested in becoming data scientists should begin preparing before enrolling in the relevant degree programs or courses by learning basic skills or programming languages like Python, R, etc.
Furthermore, revising and renewing mathematics and statistics concepts can help fresh data scientists to have a smooth start. Entering an institute with pre-established concepts and skills accelerates the learning rate. Most of all, early exposure and knowledge of the field requirements can help candidates decide whether the career choice is the right match for them or not.
Phase 2: Enrollment In Undergraduate Program
The recommended approach for beginners is to complete an undergraduate program majoring in any available options like computer science, statistics, data science, information technology, and mathematics. Students enrolled in different undergraduate programs who can’t switch should consider minoring in any of the suggested fields. Learning programming languages and practicing SQL/databases should be continued along with academics.
Phase 3: Secure an Entry-Level Data Science Position
For practical hands-on experience and industrial exposure, applying for entry-level data science jobs is an ideal opportunity. Different companies hire fresh candidates with a technical educational background as data analysts, junior data scientists, or even interns. As a result, applicants with relevant training and certifications are more likely to be selected. The key is to prepare for the interviews and sharpen relevant skills.
Phase 4: Sign Up for a Master’s or a Ph.D. Degree
Candidates with advanced master’s or Ph.D. degrees are preferred for the data scientist roles. However, graduate-level degrees in computer science, statistics, mathematics, information technology, and data science are in-demand. Some recruiters even accept applicants with STEM degrees (physics, engineering, biotechnology).
Phase 5: Learning on-the-go
With technological advancements and evolution, continuous self-learning is critical for survival in the industry. Attending conferences, seminars, workshops, bootcamps, and training programs help in learning about the changing trends and latest innovations. Staying up-to-date with modern research, reading blog posts, and signing up for relevant clubs can keep the knowledge levels high.
What Are the Responsibilities of a Data Scientist?
The data scientist’s role includes varying responsibilities depending upon the tasks and projects under consideration. Data scientists extract different forms of data (structured, semi-structured, and unstructured), clean them, and prepare them for analysis. Building, testing, and deploying models is a part of their job.
Typical responsibilities that data scientists are tasked with include:
- Gathering huge amounts of data
- Pre-processing and cleaning data for analysis
- Finding patterns and observing trends from the dataset
- Creating effective solutions for business-related challenges
- Building analytical tools and contributing to existing architectures
- Applying pre-built tools and machine learning models to problems
- Deriving useful insights from the data
- Anticipating outcomes and predicting future trends/demands
- Visualizing data and generating reports
- Devising a reasonable and cost-effective plan for improvement
- Communicating results and recommending alternate solutions
- Collaborating with technical and business teams
In addition to these responsibilities, a key aspect of a data scientist’s role is ensuring the security and integrity of data. Given the sensitivity and value of data in various industries, understanding and adhering to data security standards is crucial. We cover this topic in our detailed article: Top 10 Data Security Standards Every Data Professional Must Know.
What are the Important Tools and Technologies Used by Data Scientists?
The tools commonly used by data scientists are:
- Frameworks such as MATLAB, Apache Spark, Hive, MapReduce, and Hadoop Platform
- Databases like SQL, MYSQL, Postgres, etc.
- Programming languages include Python, R, C/C++, Java, SAS, etc.
- Visualization tools like PowerBI, Tableau, and Excel.
Data scientists mainly perform data-related tasks such as collection, cleaning, analysis, visualization, prediction, etc. Some of the techniques needed to practice these tasks include:
- Machine Learning
- Deep Learning
- Data Mining
- Big Data
What Is the Average Salary of a Data Scientist?
The increased demand and inadequate availability/supply of data scientists is the major reason behind the handsome salaries offered by top-notch companies. The attractive packages are drawing attention from different spheres of industry.
The overall income of a data scientist depends on multiple factors like experience, qualification, position, region/location, and most importantly standing of the employer. Therefore, there’s no benchmark or fixed salary range for the designation worldwide. However, according to Glassdoor, the annual income of a data scientist in December 2021 was recorded as around $119,000 in the U.S.
Payscale reports the average salary of a data scientist to be around $97,000. For the entry-level position, the expected income is $86,000. Data scientists with 1-4 years of experience earn around $96,000. The payroll increases with the progress in the experience level of the employee.
Is Coding Expertise Required to Become a Data Scientist?
Coding is the core of data science tasks and projects to sort, process, handle and analyze data. In addition, nearly all data science models need data processing at some level. Therefore, programming expertise is a must for data scientists.
Basic to intermediate level coding expertise in languages like Python and R is required for fresh data scientists applying for entry-level jobs. The progress in task complexity requires advanced coding expertise to create synthesized data models and manage heaps of data. In addition, proficiency in working with databases like SQL and frameworks like Hadoop is essential for data scientists.
What Mathematical Concepts Should the Data Scientist Know?
Mathematics is an important branch of knowledge that helps solve complex computational problems. It plays a significant role in data science projects and combines statistics concepts for creating statistical data models.
- Linear Algebra
- Probability and Statistics
- Multivariable Calculus
- Computational Algorithms
- MATLAB Toolbox
What Programming Languages should a Data Scientist Learn?
Programming skills are highly essential for a data scientist. Regular tasks like cleaning, arranging, managing, and analyzing data demand highly curated techniques from modern programming languages.
Popular data science coding languages are:
Programming languages used less frequently include:
Is the Data Scientist Role In Demand in 2022?
Yes. The role of a data scientist is ranked third among the best technology jobs and sixth in the best STEM jobs. According to the U.S. Bureau of Labor Statistics, computer systems design in the industry with the maximum positions for data scientists. Moreover, it predicts a 30-35% spike in the employment rate of data scientists during 2020-2030. Top-tech companies like IBM, Amazon, Microsoft, etc., are rapidly employing data scientists.
Data is the fuel of the modern world, enabling organizations to drive major revenues. Organizations have to deal with structured, semi-structured, and unstructured data on a daily basis. Data scientists are professionals who can easily process, sort, and analyze all these types of data. This trend is opening positions for data scientists in public and private sectors, like retail, manufacturing, media, entertainment, education, healthcare, legislation, banking, finance, etc.
When Should a Company Hire a Data Scientist?
Ideally, companies should look for a data scientist if their internal operations produce huge amounts of data and they have to deal with complex issues regularly. This is more common in some industries where data growth is massive, and its analysis is crucial for management.
Different organizations are bound to provide different products or services. However, all such setups follow some practices that comply with their targets and goals. In the past decade, technology has impacted all industries in various ways. The current data explosion rates have forced businesses and service providers of all shapes and sizes to revise their strategies and easily incorporate modern technologies.
Secondly, suppose an organization’s culture values and promotes data or the environment supports analytics and evaluation. In that case, it needs a data scientist to dive deep into the data and help find useful insights. Companies willing to adopt change, discover ways to improve their services, and maximize their outputs should consider hiring data scientists.
What do Recruiters Look for While Hiring a Data Scientist Candidate?
Generally, while hiring, candidates with relevant and higher data science experience are preferred. Recruiters look for a couple of traits that can add value to their organization. A list of important technical skills is given below:
- Machine learning
- Data modeling
Data Scientist vs. Data Analyst
The growing data needs are opening opportunities for different roles in the industry. The positions of data scientists and analysts are often mixed up and confused with each other. With a great similarity in the tasks, there are big differences as well.
The data analyst post is an entry-level position in the analytics team. Usually, the role of a data analyst is defined by the company depending on its criteria, requirements, and policies. Typically, data analysts are responsible for gathering, processing, and statistically analyzing data using standard methods and tools.
Data analysts use datasets for translating entries, recognizing patterns, and correlating data for estimating trends to help companies improve services. A data analyst should be an expert in handling and modeling data, along with Python coding skills. With gradual progress and reasonable experience, data analysts can upskill to being data engineers and then rank to data scientists.
A data scientist is a senior position in a team with additional responsibilities than that of a data analyst. These professionals must have the skills of both data analysts and data engineers. They should be extremely proficient in machine learning, data handling, and statistical techniques.
Data scientists work exhaustively on the inputs data analysts and engineers provide to derive actionable business insights. A data scientist applies advanced methods like clustering, decision trees, etc., to understand and explore data for improving business products and services.
Is It Worth Working as a Data Scientist?
In a data-driven world, industries are drawing in data abundance. With approximately 63,000 Google searches every second, countless stories posted on Instagram, and videos uploaded on Youtube every nano-second, it’s more important than ever for businesses to explore and dig into the data they’re generating. As a result, the rapid emergence of big data is fueling the data science industry to grow and progress.
A report predicts the data science industry size to expand to $33.9 billion in 2026 from $95.3 billion recorded in 2021. Data scientists have become a part of the fastest growing and tech-leading industry, helping in better decision-making. Data scientists are generally offered a handsome income worldwide. Field experts can get a chance to work with top tech companies like Amazon, Microsoft, and Accenture.
Practices Data Scientists Should Follow
Data scientists must follow certain ethics while performing typical functions such as:
- Decision Making: The role of data scientists involves large-scale decision-making. Data scientists should never take decisions or any actions concerning clients on their own.
- Task Transparency: Data scientists should keep the clients well-informed and updated about their project development and data handling.
- Data Confidentiality: Data scientists work closely with different types of information. Therefore, the confidentiality of client data must be protected while working with third parties.
- Client Dealing: Data scientists should communicate clearly with clients to understand project goals and requirements effectively.
- Information Handling: Data scientists are responsible for detecting faulty data or requirements and informing clients about possible implications.
What are some of the challenges faced by Data Scientists?
Regardless of the diversity and multifunctionality of modern technologies and features, no method can be entirely fool-proof. Similarly, the field of data science also has some complications when it comes to projects.
Although data scientists are field experts and known professionals equipped with skills and expertise of the latest tools, they still face challenges at different levels. These challenges can be tackled smartly with a clear understanding and the right approach.
Listing down the challenges faced by data scientists:
- Boundless Technological Evolution: With each passing day, knowledge grows stale. The current evolution demands learning new tools, going through the latest research, and analyzing unique requirements. Data scientists must constantly stay updated and upgraded, which is hard to practice. Keeping up with the latest trends and ideas is challenging for data scientists.
- Analysis Paralysis Loop: While analyzing data, scientists can easily lose track and remain stuck in the phase of trying to improve the results, aiming for accuracy. It is important to know when to stop and record the findings. Chasing for perfection can cause more harm than benefit.
- Ensuring Data Quality: Data scientists interact with different kinds of datasets having different features and entries. Cleaning, pre-processing, and organizing every dataset is a challenge.
The technological evolution and data explosion has compelled industries to change typical forms of operation and persuaded them to adopt a modern data-oriented environment. Top-tech companies following a data-driven approach create more opportunities for data scientists worldwide. As a result, data scientists are highly paid, and most demanded in the modern industry.
Data scientists collect, explore, inspect, and report complex information organizations produce to help derive useful insights. Data scientists are expected to take over a major chunk of enterprise-level procedures. Hence, individuals seeking highly rewarding careers should learn data science essentials to set foot in this enormously expanding field.