How to Become a Data Scientist?
Data science is a field that offers both intellectual challenges and analytical rewards. It keeps you at the forefront of new technology. Nowadays, data scientists are in high demand as more organizations rely on big data. This increase in demand is because big data is vital for companies to make smart decisions.
As a data scientist, I analyze complex data sets to discover insights influencing these decisions. If you want to become a data scientist, you need to build a strong base in statistics, machine learning, and data analysis. It’s a job where you must keep learning, as the world of data and technology constantly changes.
Here, I’ll cover all you need to know to start a career in data science, including a compelling way to learn the essentials. I’ve chosen data science as one of the top careers in analytics. You can find more about this and other roles below, so let’s dive in…
What Does a Data Scientist Do?
Data scientists analyze data deeply to offer useful insights. They collaborate with various teams in a business setting to set up strong analytics foundations. This involves creating methods to collect, acquire, and clean data from different sources. Once they organize and examine the data, they develop solutions and share their discoveries with the larger business.
Data scientists also work closely with other professionals, such as data engineers, analysts, IT architects, application developers, and business stakeholders. Their role is crucial in bridging the gap between complex data and strategic business decisions by transforming raw data into actionable knowledge. This collaborative effort helps businesses make informed choices.
Data Analyst vs. Data Scientist: What’s the Difference?
Data analysts and data scientists both handle data but have different roles and responsibilities:
Data analysts usually focus on understanding existing data to give useful insights. They often use simpler tools and models for their analysis.
Data scientists have a broader role. They analyze data and come up with the questions that need answers. They use more complex tools and models, like machine learning algorithms, to get deeper insights.
While data analysts work with what is known and readily available, data scientists explore broader possibilities and dig deeper into data to uncover new information. This makes the role of a data scientist more about discovery and innovation.
If you are interested in learning more about the differences, read my article that compares between data collection and data analysis.
Why Become a Data Scientist?
There are several compelling reasons to consider a career in data science. This field offers great rewards, providing a challenging and exciting work environment. As a data scientist, you must stay updated with the latest technology trends and work in a dynamic atmosphere.
If you’re naturally curious, enjoy solving problems, and like working with data and technology, data science might be the right path. The job market for data scientists is booming. According to Statista, the big data market is expected to grow from $70 billion in 2022 to $103 billion by 2027. Similarly, the US Bureau of Labor Statistics predicts jobs in data science will increase by 36% from 2021 to 2031, much higher than the average 4% national job growth.
Data science careers are increasingly popular and highly ranked. U.S. News & World Report lists data scientists as the third-best tech job, sixth in STEM, and sixth overall. Glassdoor also ranks it as the third-best job in America for 2022.
Moreover, the financial rewards are attractive. The average salary for a data scientist is around $102,000 per year, reflecting the high demand and substantial rewards in this field.
What Qualifications Do Data Scientists Need?
There’s an ongoing discussion about whether a degree is essential for becoming a data scientist. While many professionals enter the field through various paths, having a degree can be helpful.
Most employers prefer candidates with degrees in computer science, data science, mathematics, statistics, engineering, or physics. Some programs even offer training to those with any degree to help them become data scientists. Essential skills for data scientists include a good understanding of programming languages such as Python, R, SQL, and Julia.
Many individuals learn data science through self-study, acquiring skills at their own pace. This self-directed approach can be practical, particularly when complemented by courses like our Data Scientist with Python track, which equips learners with essential data science skills.
Additionally, obtaining a certification can help prove your skills to potential employers. These certifications demonstrate your commitment and expertise in data science, making you a strong candidate for positions in this growing field.
What Skills Do Data Scientists Need?
To succeed in data science, you need different skills. Some are technical, like programming, while others are about how you work with people. Having both types of skills helps you understand data and work with others to solve problems and make decisions based on what you find.
Hard Skills
Here are the technical skills you’ll need to develop to become a fully-fledged data scientist (the exact set of skills varies):
- Statistics and math
- Python
- Machine learning
- Data visualization
- SQL and NoSQL
- Big data
- Natural language processing
- Cloud computing
- R
- Deep learning
Soft Skills
Here are the cross-functional skills, often called ‘power skills’ or ‘human skills,’ that are essential for a data scientist:
- Critical thinking
- Communication
- Problem-solving
- Teamwork
- Business acumen
- Storytelling
What is an Average Data Scientist’s Salary?
Data science salaries vary depending on where you look. For instance, Glassdoor reports an average of $117,212 annually based on 18,000 salaries, while PayScale suggests $97,318 yearly from 7,280 profiles.
Data science managers earn the most, from $113,000 to $230,000 yearly. Both data scientists and data engineers often make similar salaries, ranging from $82,000 to $167,000 for scientists and $76,000 to $166,000 for engineers.
Company size also affects pay, with large companies of over 1,000 employees offering median salaries of $90,000 to $110,000 annually. Medium-sized companies (26 to 1,000 employees) pay around $80,000, while small companies and startups (2 to 25 members) offer about $60,000 yearly.
How to Become a Data Scientist?
If you’re excited to begin your journey as a data scientist, you might wonder how to start. Below, we’ve outlined eight steps to help you become a data scientist. Remember that the specific requirements may vary, but these are some commonly mentioned steps.
1. Learn Data Wrangling, Data Visualization, and Reporting
Data wrangling is essential for data scientists. It involves cleaning and organizing raw data to analyze it better. Tools like pandas in Python are commonly used for this task. Reporting and data visualization are also crucial. They help present data effectively.
Mastering these skills is vital for making an impact with your data. There are various data visualization tools available to choose from, depending on your needs and goals. By learning data wrangling and visualization, you’ll be better equipped to handle large and complex datasets, and communicate your findings clearly to others.
2. Improve Your Understanding of Statistics, Math, and Machine Learning
To excel in data science, it’s important to strengthen your statistics, math, and machine learning skills. While you don’t necessarily need a formal degree, grasping calculus, linear algebra, and statistics is beneficial. Understanding calculus aids in creating optimization algorithms for machine learning, while linear algebra helps with tasks like working with matrices and vectors.
Statistics is crucial for building machine learning models and making data-driven decisions. There are resources available, like courses in R, that can help you improve in these areas. For example, you can learn about gradient descent for optimizing models or dive into statistical modelling for better insights from your data. You’ll be better equipped to tackle real-world data challenges in your data science journey by honing these skills.
3. Learn Handling Large-scale Data
You’ll often deal with massive amounts of data as a data scientist. Nowadays, with everyone generating loads of data, it’s getting even more extensive and trickier to handle. But don’t worry; big data holds tons of valuable insights for those who can use it.
You can use cloud platforms like AWS, Microsoft Azure, and Google Cloud to tackle big data. These platforms offer powerful tools for managing and processing large datasets. Apache Spark is another handy tool for big data tasks, including processing, analysis, and even machine learning.
If you’re keen to learn, our PySpark course covers the basics of handling large amounts of data for analysis. So, jump in and start mastering the world of big data!
4. Learn Coding
If you want to learn coding to deal with data, start with Python and R. They’re easy to use, and many people use them. SQL is essential for working with databases, and it’s easy to learn after Python and R. Julia is a good choice after Python and R. It’s made specifically for working with data and it’s fast and easy to understand.
Java is another good option, especially for big data projects. It’s fast and works well with tools like Hadoop and Spark. If you’re dealing with huge datasets, you should also learn Scala, C/C++, JavaScript, Swift, Go, MATLAB, or SAS. They all have their strengths in handling data.
5. Understand Databases
SQL, or Structured Query Language, is super important for data scientists. It helps them manage structured data quickly and effectively in relational databases. When you’re gathering and sorting data, SQL is your go-to tool.
With SQL, you can do many things like query databases, organize and prepare data, and even experiment. Plus, it often works hand in hand with Python. Some libraries like SQLite, PostgreSQL, and MySQL help you seamlessly link up different data sources. So, getting comfortable with SQL is a smart move if you’re into data science.
6. Meet Other Data Scientists and Gain Experience
To do well as a data scientist, gaining lots of experience and practicing is essential. You can learn a lot and get better by working with other data scientists. Joining groups where data scientists meet is an excellent way to find friends and learn new things. In these groups, you can work together on projects. This helps you use what you’ve learned to solve real problems.
You can also work on different data science projects online. This lets you tackle real-world issues right from your computer. Getting this practical experience is key to understanding the tools and methods used in data science.
7. Get an Internship or Apply for a Job
After building up your skills, it’s time to put them to work in real-world settings. Consider applying for internships or jobs once you are ready to tackle a data scientist role. A strong portfolio showcasing your skills is essential for catching the eye of potential employers. Additionally, prepare for data scientist interviews by practicing common questions and scenarios.
Internships offer valuable hands-on experience, while job applications provide opportunities to join teams and contribute to projects. With your skills and portfolio, you’ll be well-prepared to take the next step in your data science career.
8. Engage with the Community
To keep up with the ever-changing world of data science, staying connected with the community is crucial. Engage with various platforms like LinkedIn, Twitter, Discord, and Reddit, where a vibrant community shares insights and updates.
Follow data science leaders, niche sites, and blogs to stay informed about trends and developments. Participate in discussions, seek advice, and contribute your thoughts. Engaging with others who share your interests will expand your knowledge and network within the field.
Educational Paths to Become a Data Scientist
To become a data scientist, you have different paths to choose from:
Online courses and certifications: Many websites offer courses to help you learn data science skills. You can also get certified as a data professional.
Undergraduate degree: A bachelor’s degree in data science, computer science, or related fields gives you a solid foundation.
Master’s degree: If you want to specialize or learn advanced skills, you can pursue a master’s degree in data science.
Data science bootcamps: These intensive training programs help you quickly learn practical skills. They’re a good option for those who want hands-on experience. You can explore various data science bootcamps to find the best fit for you.
Becoming a data scientist is an exhilarating journey filled with rewards. In today’s data-driven world, the role of a data scientist is more vital than ever. Whether you’re curious, want a good salary, or like making data-based decisions, being a data scientist offers many chances.
Conclusion
In this guide, I’ve explained different ways to become a data scientist, like attending school or learning online. The job market for data scientists is growing fast, and you can do many different jobs, like working with data, machines, or business stuff.
If you like solving problems and working with numbers, being a data scientist might be perfect for you. Start your journey today and discover all the cool things you can do in this field!
Please comment below your thoughts and questions.
