Data science is one of the hottest and fastest growing fields today. With massive amounts of data being generated every day, companies are looking to hire data scientists who can help them analyze and extract value from their data. This has created a huge demand for data science skills in the job market.
So can a fresher with no prior experience work as a data scientist right after college? Let’s take a deeper look at what it takes to become a data scientist and whether it is realistic for a fresher to enter this field.
What is a data scientist?
A data scientist is someone who has the technical skills to analyze massive amounts of structured and unstructured data, the statistical and machine learning expertise to build predictive models, and the business acumen to understand what insights are the most valuable.
In general, a data scientist’s day-to-day work involves:
- Collecting, cleaning and organizing data
- Performing exploratory data analysis to uncover patterns and meaningful relationships
- Applying statistical and machine learning techniques like regression, classification and clustering to build predictive models
- Developing and optimizing data pipelines and architectures
- Communicating technical results to stakeholders with non-technical backgrounds
Data scientists work across various industries like finance, e-commerce, healthcare, social media and more. Their high impact work provides key business insights that help drive strategy and growth.
What are the prerequisites to become a data scientist?
To become an effective data scientist, there are some key technical skills required:
- Coding: Python and R are the most popular programming languages used for data science. SQL is also an important skill for working with databases.
- Statistics: Having a solid grasp of statistical concepts like distributions, hypothesis testing, regression, etc. is critical for modeling data.
- Machine learning: Data scientists need to be proficient with machine learning techniques like classification, clustering, neural networks, etc.
- Math: Linear algebra, calculus and optimization are important for building and implementing algorithms.
- Data visualization: The ability to create compelling data visualizations and present insights is key.
Apart from these technical skills, data scientists also need soft skills like curiosity, storytelling, collaboration and creativity.
Can a fresher have all these skills?
The short answer is – it is highly unlikely for a fresher to possess the full range of technical and non-technical skills required to be job-ready as a data scientist.
Let’s take a closer look at some of the key challenges:
- Limited coding experience: While college curriculums have some programming and statistics courses, freshers typically lack real world coding experience in Python, R or SQL at a level required for complex data tasks.
- No hands-on machine learning practice: Classroom theory about machine learning algorithms is not enough. Companies look for demonstrated ability to build, validate and optimize models.
- Math gaps: The math taught in most undergraduate programs does not cover some advanced concepts like multivariate calculus, linear algebra, optimization theory etc. which are regularly applied in data science roles.
- Domain expertise: Understanding the business context and specifics of an industry vertical takes time to develop and most freshers do not have this.
- Soft skills: Data science is an interdisciplinary field requiring collaboration with stakeholders from multiple teams. Most freshers need time to develop communications, storytelling and other soft skills.
As a result of these gaps, freshers can feel overwhelmed and struggle to contribute meaningfully as data scientists right after graduation.
Paths to gain data science skills
However, this does not mean fresher cannot enter the data science field. Here are some of the paths that freshers can take to build their skills and prepare for data science roles:
Pursue a post graduate degree
Enrolling for a specialized master’s program in data science is an excellent way to build a strong foundation. Reputed programs offer rigorous courses, hands-on projects and internship opportunities that can equip freshers with the required skills within 1-2 years.
Complete bootcamp certifications
There are many coding bootcamps that offer intensive 3-6 months training programs focused specifically on data science skills. For freshers looking for a quick ramp-up, these can provide job-ready training through hands-on learning.
Online certifications
Freshers can also consider pursuing online certifications in areas like Python, machine learning, deep learning, big data analytics etc. Platforms like Coursera, edX, Udemy offer affordable and flexible courses that can be completed part-time.
Build real projects
Freshers can do self-learning and build their own data science projects to create an impressive portfolio. Analyzing open datasets from Kaggle, DrivenData, data.gov etc. or creating projects around machine learning operations are great ways to apply skills.
Interning at a company while pursuing their education also provides freshers with valuable hands-on experience in a business context.
Entry-level data science roles
Rather than aiming directly for a data scientist role right after graduation, freshers are often better suited for associate or junior level positions that require some data science skills while also providing on-the-job training and mentorship.
Some examples of entry-level roles include:
- Data analyst
- Business analyst
- Data engineer
- Junior data scientist
- Research analyst
- Data science intern
These positions allow freshers to get their foot in the door and start building specialized data science skills required for more advanced roles later on. The work experience and training also helps them determine what areas of data science they are most interested in pursuing.
Tips for transitioning into data science
Here are some tips for freshers looking to eventually transition into data science roles:
- Develop programming, math and statistics core competencies
- Build diverse projects for your portfolio using real data
- Learn storytelling and communications skills
- Understand business needs and key performance metrics
- Make connections within the data community
- Keep learning – data science evolves quickly
- Consider specialized master’s programs or certifications
- Bring passion and curiosity to stay motivated
Conclusion
While it is challenging for freshers to directly start as data scientists, there are clearly defined paths they can follow to gain the required technical and business skills. Pursuing higher education, certifications, building projects and starting in associate roles can provide them with the experience needed to transition into data science positions within a few years.
With the right combination of learning initiatives and on-the-job training, freshers can successfully embark on rewarding data science career paths.