Data science jobs are fast-growing with tremendous opportunities, and data scientists can expect a high-profile job. Demand also translates into the ability to cope with advancements in data science and its related fields, such as artificial intelligence and cybersecurity, because these are interdisciplinary fields.
If you want to build a successful career as a junior data scientist, learn a range of techniques and different approaches to solve a problem [because data science is a vast field], and see what you can bring to the table.
A data scientist derives insights from vast data and solves intricate business problems. Data scientists combine the knowledge of computer science, mathematics, statistics, modeling, and business acumen. In this article, let’s learn how to become a junior data scientist.
A step-wise guide to becoming a junior data scientist
Education
Let’s start with the educational qualifications.
· A bachelor’s degree in computer science, data science, or statistics.
· Having a master’s degree is a plus
Or
· Possess data science certification(s)
Let’s elaborate on data science certifications here.
Today, data is everything across sectors, including banking, e-commerce, logistics, and healthcare. As data boosts efficiency, drives innovation, and scales profit, every business is leveraging data to optimize processes. To stay ahead in the competitive job world, industry-recognized and vendor-neutral certifications validate your skills to potential employers and help you land a good-paying job.
We have listed the three best data science certifications for your reference.
1. Certified Data Science Professional (CDSP™): USDSI®
2. Data Science Inference and Modeling: Harvard University, Cambridge
3. Applied Data Science with Python: University of Michigan
You can visit the respective websites and learn more about each certification program's coverage, duration, fees, and benefits.
Technical skills
The list of technical skills never ends. One should upgrade their skills as new technology and tools emerge. However, we have listed the five basic technical skills and their importance here.
Mathematics
Mathematics is essential because it helps connect with computers, and most machine learning algorithms are based on mathematical expressions. It's necessary to be well-versed in vectors, matrices, and arithmetic operations on vectors, as well as probability concepts, namely conditional probability, random processes, and variables.
Statistics
Statistics is another crucial branch that a junior data scientist should master. Understanding descriptive statistics enables the interpretation of data by identifying central tendency, measuring dispersion, and assessing the spread. To comprehend machine learning algorithms, knowing about correlations is vital. Inferential statistics helps draw conclusions from data.
Coding skills
While there are many languages to learn, it is best to start with Python and R. You can learn other programming languages as needed, depending on your domain. The most important concepts in Python include file processing, which is necessary for reading and writing files, and then applying loops to the read data. A basic understanding of data types, loops, math functions, list functions, and methods for handling multi-dimensional lists is also recommended.
Data visualization
Build your data visualization skills using the Matplotlib library. Initially, you can use Python lists to create plots and later on use complex real-world data from extensive files. You may need to use chart customizations for presentations, and a basic understanding of chart customization is a must.
Machine learning
Being a data science professional, you may have to spend most of your time in data manipulation and data processing. Therefore, we recommend learning Pandas for reading data, converting text to numerical data, and categorizing data as test and training data.
Excellence in regression enables you to predict numerical values, reuse data processing templates, perform multiple linear regression, and polynomial regression. Likewise, classification methods help to predict categorical outcomes.
Knowledge of feature selection enables you to select the relevant dataset features for building and training a machine learning model.
Soft skills
Soft skills sharpen as you add years to experience. A few of the soft skills that need continuous improvement include problem-solving, critical thinking, communication, team collaboration, attention to detail, time management, advanced learning, business acumen, project management, and ethical decision-making.
Build a portfolio
Building a strong portfolio demonstrates your interests and skills for the junior data scientist role. It is best recommended to focus on projects solving real-world problems, host successful projects on the GitHub platform, or own a personal website or a portfolio site with an accessible link for recruiters.
Career pathway of a junior data scientist
A typical career progression of a data science professional is:
Data science intern
Data analyst [entry level, junior, senior]
Junior data scientist
Senior data scientist
Leadership roles [VP, Director, Chief Data Officer]
Salary of a junior data scientist
According to PayScale, an entry-level junior data scientist (< one year of experience) can earn an average total compensation of $75,760, whereas an early career junior data scientist (one to four years of experience) can expect $82,281.
In 2025, the average salary for a junior data scientist is $80,042. However, unique skills can affect the salary. Data science professionals specializing in machine learning, data analysis, and Python can expect 7%, 3%, and 2% hike, respectively.
Conclusion
One should be realistic and acknowledge that data scientist jobs have both challenges and opportunities. Although automation handles basic data work, high-value tasks such as optimization, strategy, and ethical AI in data need human intervention.
The demand for data scientists exists, but competition is rising. Focusing on niche skills and updating your portfolio is highly essential for earning a job in big companies.
Comments
0 comment