How to Build a Strong Data Science Career as a Fresh Graduate

Are you a fresh graduate with a passion for data and statistics? A mid-career professional looking for an exciting transition? Either way, you may be wondering how to get started in a data scientist career path.

Data Science is a rapidly growing field that involves computer science, statistics, and domain expertise to extract insights from data. With the explosion of data in every industry, businesses recognise data science's importance in driving growth and innovation.

What Does a Data Scientist Do?

Data scientists are professionals who apply Data Science principles, techniques, and tools to analyse, interpret, and draw insights from large and complex data sets. They use various methods, including data mining, statistical modelling, and machine learning, to transform data into actionable insights. Data scientists are skilled in programming languages like Python, specialised tools, and platforms like Hadoop, Spark, and SQL.

The role of a data scientist is essential to organisations looking to gain insights and competitive advantages from their data. With their data analysis and interpretation skills and expertise, data scientists can help drive business growth and innovation while ensuring data quality, security, and accuracy.

How to Become a Data Scientist

The field of data science is rapidly expanding, offering a wide range of opportunities for professionals with diverse backgrounds and skill sets. It’s an exciting and lucrative career to pursue fresh graduates and experienced professionals alike. However, becoming a data scientist requires formal education, practical experience, and ongoing learning and development.

Here are some key steps to becoming a data scientist:

Step 1: Acquire the educational requirements

Data scientists are expected to have at least a bachelor's degree in a quantitative field such as computer science, mathematics, and statistics. Many employers also prefer candidates with advanced degrees, such as a master's or PhD in data science or any related field.

Step 2: Develop the right skill set

Data scientists must possess technical and soft skills to succeed in their roles. By developing these skills, you can effectively analyse data and communicate your findings to help drive business decisions.

Skills Required to Become a Data Scientist

Communication

Data scientists need to communicate complex technical concepts to non-technical stakeholders in a way that is easy to understand. They also need to work collaboratively with other members of their team and be able to present their findings effectively.

Problem-solving

Data scientists must identify and solve complex problems using data analysis techniques to develop innovative solutions.

Attention to detail

Data analysis requires close attention to detail to ensure accurate and reliable data. They need to spot trends and anomalies in the data and be able to interpret what these patterns mean.

Curiosity

A strong sense of curiosity is essential for data scientists. They must ask the right questions, uncover data insights, and continuously learn and explore new techniques and technologies.

Business acumen

Data scientists need a strong understanding of the business goals and objectives to develop effective data-driven strategies and make recommendations.

Programming languages

Data scientists must have strong programming skills in Python or R, commonly used for data analysis and machine learning.

Machine learning

Machine learning is a crucial area of expertise for data scientists, as it involves using algorithms and statistical models to analyse and make predictions based on data. Data scientists must develop and implement machine learning models, evaluate their performance, and tune them for optimal performance.

Statistical analysis

Data scientists need to have a solid foundation in statistics and be able to use statistical models and techniques to analyse data, make predictions, and identify patterns.

Data visualisation

Data scientists need to visualise and communicate complex information that makes it easy to understand and interpret. They should be familiar with tools like Tableau or Matplotlib in Python.

Big data technologies

Data scientists work with large datasets, so they must be familiar with big data technologies, such as Hadoop, Spark, and Hive.

Step 3: Understand the responsibilities of the role

Data scientists are expected to do the following:

• Gather and manage large amounts of data from various sources, including databases, APIs, and web scraping.
• Identify and correct errors, remove duplicates, handle missing data, and transform the data into a format that can be used for analysis.
• Use statistical and machine learning techniques to analyse data, build predictive models and identify patterns and insights.
• Effectively communicate findings which include creating reports, presentations, and visualisations that are easy to understand.
• Use data insights to influence decision-making and identify new business opportunities that can be leveraged using data.

Step 4: Further your education

While formal education in data science or a related field is a common path to becoming a data scientist, many resources are also available for individuals interested in upskilling. Online courses, boot camps, and self-study programmes are all viable options for individuals who want to acquire the skills required to become data scientists. The most important thing is to have a strong foundation in quantitative analysis and computer programming. You will also need to be willing to continually learn and adapt as new technologies and techniques emerge.

Step 5: Build a portfolio to showcase your data science skills

Building a portfolio is an effective way for data scientists to showcase their skills and expertise to potential employers. Your portfolio should demonstrate your ability to work on real-world problems and provide evidence of technical proficiency, problem-solving skills, and communication abilities.

Step 6: Learn to navigate the field of data science

Look into salary insights

Data science is a growing field in Malaysia, which means data scientists can expect competitive salaries and opportunities for career growth. However, this can vary depending on several factors. Some industries, such as finance and healthcare, may offer higher salaries due to the complexity of the data they will handle.

According to JobStreet's Explore Salaries tool, an entry-level Data Scientist can expect to earn between RM 3,500 to RM 6,000 per month. This can increase with experience, skills, and responsibilities within the company. Those with 1-4 years of experience can earn around RM 85,000 annually. And professionals with 5-9 years of experience, the average salary increases to around RM 122,000 per year.

Network with professionals

Build relationships to know more about the industry you're trying to pursue. You can join data science groups on social media and engage in discussions or contact professionals. The more you know about the field of data science, the better.

Step 7: Apply for the role

Create a well-written, clear, and concise resume that highlights your skills, experience, and achievements. Highlight your technical and soft skills, and provide specific examples of how you have used them in previous positions or projects. Tailor your resume to the specific job description and requirements of the data scientist role you are applying for.

Becoming a data scientist as a fresh graduate requires patience and determination. As long as you are committed to improving your technical skills and problem-solving abilities, you'll soon enjoy a lucrative career as a data scientist. Keep your JobStreet profile updated with your latest skills and experience to increase your chances of being noticed by employers and landing your ideal job.

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