Opposed to the mounting speculations and fear surrounding data science jobs getting replaced or obsolete with the meteoric emergence of AI, data science still stands as an excellent career path. Amidst layoffs, data science skills have gained more relevance with the surge of AI, with data scientists playing a crucial role in the integration of technological advancements into various disciplines. Data science skills such as mastery in programming languages, statistics and probability, ML and Deep learning, APIs, etc are indispensable for understanding, developing, and innovating AI and AI-powered projects. So there’s no contest to answering ‘Is data science a good career‘. Data Science is a promising career path leading to employment across diverse fields.

If you are interested in the prospect of a data science career,  let’s delve into the intricate details of building a data science career through this post. The article covers the fundamental information you need to navigate the challenging and competitive career path. We shall outline the basic ideas such as eligibility, difficulty level, job opportunities, salary, etc. 

What is Data Science and What do Data Scientists do?

Data science is an umbrella term governing the practice of extracting insights from data by large sets of data by employing several scientific methods that require the amalgamation of computer science, mathematics, statistics, and artificial intelligence, at its core. The Data Science field pivots on the systematic knowledge extraction from data in various forms by processing the full spectrum of the data science lifecycle. This process is executed by employing combined tools and techniques such as data analysis, data modelling, developing algorithms and analysing human-machine interaction, etc. 

Crux of Data Science Life Cycle

Data Collection: This involves gathering data from various sources such as online platforms, databases, manual entry, etc. Aggregating relevant and accurate data is essential for a robust foundation for data analysis. 

Storage and Maintenance: Ensuring a secure, organized and efficient data storage entails cleaning and organizing the data by removing errors and annomalies. 

Data processing: Next comes the processing of data by employing combined techniques of statistical analysis and machine learning. 

Data Analysis: Various systematic and scientific methods are employed in this step including regression, predictive analysis, qualitative techniques, etc. 

Communication: This step entails the clear and concise communication of the insights drawn from the data analysis. 

Data science experts like Data Scientists are thus expected to have mastery of the complete spectrum of the data science life cycle to carry out their tasks. Their job involves collecting data from diverse data sources, organizing that data, translating the information, converting data into solutions, and communicating their findings in the best manner to effect positive change in the organization. 

Data Scientists are expected to be well-rounded professionals with high-level technical and analytical skills and the potential to build complex quantitative algorithms, be adept at organizing and synthesizing large amounts of data, capable of extracting insights and employing them to build solutions and drive business strategies. 

Your desire to practice the professional life of a data scientist must be bolstered by your qualities of being inquisitive and result-oriented, having excellent industry-relevant skills and knowledge and exceptional interpersonal qualities. Your regular tasks as a data scientist will entail formulating questions around specific sets of data, finding patterns by using advanced data analytics, developing predictive models and generating insights to inform organizations or businesses for smarter well-researched and data-driven decisions. 

Who is eligible for Data Science?

Individuals with the zeal and passion for wrangling data can get into a data science profession, provided they have the requisite skills and educational background. The data science field can be pursued by a wide range of individuals across multiple backgrounds including Computer science, Statistics, Physics, Mathematics, Engineering, etc. While a bachelor’s degree is the basic criterion, the data science field demands proficiency across various skills (both technical and interpersonal). Mathematical areas of Linear Algebra, Probability, Calculus, Statistics, database and several other software. 

• Proficiency in Coding and Programming Languages
• Techniques and Tools Adeptness
• Domain knowledge including Mathematics, Computer Science and statistical analysis
• Data Manipulation and Cleaning Skills
• Machine Learning and Deep Learning 
• Statistical knowledge
• Cloud Computing
• Internet of Things (IoT)
• Quantum Computing
• Data Management
• Data Visualization
• Database modeling

Soft Skills Requirement for Data Science Field

• Business Intuition
• Inquisitiveness
• Analytical Thinking
• Critical Thinking
• Logical Reasoning
• Teamwork and Collaborative skills
• Excellent Communication
• Problem-Solving

Which data science course is best?

The high relevance and demand for data science skills have resulted in the importance and popularity of data science courses. Becoming a data scientist or choosing a data science career path calls for in-depth training and pursuing a well-structured study program. A data science course trains and orients an individual with the prowess and necessary skills needed for the field. There are well-designed courses available for prospective students focused on building programming, data analytical and statistical competencies. 

Data science courses are available in multiple degree programs and certification courses. Identifying the best data science course depends upon your desired level of training. There are Undergraduate and Masters level degree programs. Aspirants starting afresh in the data science field may begin their journey from the foundational degree programs at the University level. However, this is not a mandatory route to entering the field of Data science. With a Bachelor’s degree in any of the relevant domain disciplines, an individual may pursue a master’s degree in data science with a specific specialization. There are several data science certifications, boot camps and short-term courses, that allow aspirants to build data science skills. 

Selecting the best data science course involves checking certain parameters:

• Individual Goals: Identify what you expect to achieve by pursuing the course. Determine the level of training you seek, whether a beginner’s or foundational course or a specialization level. Several data science courses focus on specializations like Data science in Machine Learning, Business Analytics, Deep Learning, Database Administration, Data Engineering, Data Warehousing, Business Intelligence, Data Mining, etc. 
• Explore Course Options: There are enumerable institutions and platforms that provide data science courses. Many universities also offer data science degree programmes and certification courses on campus and online. Your trick is to identify which course aligns with your goals and interests.
• Research Course Content and Syllabus: Regardless of the mode of learning you choose, make sure you go through the program content and syllabus. Reviewing the topics and curriculum is essential in identifying whether your expectations will be met through the course. Validating the quality and credibility of the course through alumni and seniors is another crucial aspect. 
• Budget: Make sure the investment you make for the data science course is worth it and that it should not burn a hole in your pocket. 

While a structured formal education is a foundational necessity, data science certifications today play a huge role in honing industry-specific skills. It opens a platform for aspirants to build practical skills that are otherwise difficult to harness in a traditional setting. A data science course leading to certification has become a competitive advantage whether you are a new graduate seeking experience or data science students seeking advanced skills, data science courses with certifications can open doors to several opportunities. 

Some of the popular and job-market reigning certifications in data science include:

• Certified Analytics Professional (CAP)
• Senior Data Scientist (SDS) certification by DASCA
• Principal Data Scientist (PDS) Certification by DASCA
• Data Science Professional Certificate- Available across several vendors offering Data science course
• Azure AI Fundamentals certified by Microsoft
• Azure Data Scientist Associate Certified by Microsoft
• Open Certified Data Scientist 
• SAS Certified Professional: AI and ML 
• SAS Certified Advanced Analytical Professional
• SAS Certified Data Scientist

Is data science a good career?

Data Science has risen to an atypical career path with unprecedented demand. Businesses today are charging towards and focused on employing AI-powered technologies, regardless of their size, leading to massive demand for data science experts who can help them leverage the power of data. Looking a decade back, the data science discipline was in its nascent stage. There was no well-defined career path for professionals who could analyze data and help businesses program their data power. 

Fast-forward to the current time, the data science discipline has evolved in several ways, large or small. The field has evolved into a better institutionalized and more redefined scope. The present state and affluence of the data science field are consequent to massive technological innovations, particularly the growth and rise of AI power. Businesses and organizations today need to muster their data power and AI integration. All these demand data science experts. The resurgence of AI, in fact, underscores the vitality of data science skills.  So yes, data science is not just a good career but a thriving career path that will present exciting career opportunities in data science for capable and interested individuals. Developing the right mix of knowledge and skills in data science will lead to several employment opportunities. A degree in data science means a golden ticket to lucrative jobs. 

To validate why data is a good career, here are a few advantageous points you may consider:

• Massive career opportunities: Its applicability and relevance across industries such as business, finance, healthcare, manufacturing, retail, etc. make it a highly valuable degree.
• Lucrative earning potential: A data science degree commands high remuneration and it grows with specialized expertise and experience
• Impactful and influential field: Contributes to the changing and growing society and enables organizations to drive dynamic growth 
• Versatile and highly transferable skills: data science skills are applicable across industries and can lead to employment in diverse organizational roles
• Evolving Career: The career path evolves along with the emergence of technology and innovations

Is data science hard to study?

The data science field may be challenging given its interdisciplinary nature and the demand to be adept at domain-specific knowledge. To master data science, one must build a robust foundation in quantitative skills and programming. The data science field also requires proficiency across multiple tools and technologies including machine learning libraries, Python, and SQL, all of which demand a deep learning curve. 

It can be daunting and challenging to get a solid grasp of handling diverse sets of applications and languages. The challenges to learning and mastering data science are further compounded by the need to integrate the constant demand for innovation, adeptness at industry-specific tools, and the inherent combination of soft and hard skills. 

Learning and mastering data science typically requires constant effort and a deep learning curve. Mastering the basic data science fundamentals normally demands six to 12 months and specialized expertise demands years. 

Some of the top reasons that designate data science a difficult field include:

• Requirement of high technical skills
• The complexity of dealing with real-world data
• Requirement of domain expertise
• Demand for continuous learning

No doubt, data science can be a challenging field, yet there are solid solutions that make the learning process easier. Passionate individuals seeking to build careers in data science can overcome difficult challenges by pursuing specialized training and courses. Numerous platforms today cater to these increasing demands and enable aspirants with the ease of mastering data science skills. Giving yourself exposure to practical experience can enhance your skills substantially. You can gain mastery of handling complex real-world problems by engaging in projects and hackathons or by pursuing opportunities. 

Another excellent way to improve your learning path is by networking and connecting with peers and professionals in similar fields who can give you insights and guidance. Attending networking events and being a part of online forums can be a great boost to your learning journey. 

You may choose to learn data science on your own given the wide availability of free resources online, like videos, blogs and study materials. However, mastering key skills demands in-depth and intensive training led by expert instructors. You may use the resources available online to supplement your learning through a well-structured instructor-led study program. 

What is a data scientist’s salary?

Your degree in data science is a ticket to several lucrative job opportunities. A Data Scientist job is one of the most highly paid among data science professionals. According to the salary analysis of the US Bureau of Labor Statistics, a data scientist’s salary in the US is $108,020 in a year. 

Data science is among the best career paths that one can pursue in a technologically evolving era. If you are passionate and equally capable of meeting the challenging and exciting career path, there’s no stopping. Focus on developing the high-in demand skills today and proceed with your dream of landing this highly lucrative job. 

Conclusion: We have thoroughly discussed the fundamental insights into building a data science career and how it stands as a competitive career path amidst the technological revolution and volatile job market. Investing in a data science career translates into playing an impactful role in enabling a tech-driven world. Building a career in data science or becoming a data science expert requires in-depth knowledge about the professional path, smart navigation and conscious efforts. With the above information at hand, you will establish a solid foundation on how to go about your career breakthrough.