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Data Science

Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.

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Topics to be covered

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Introduction to Data Science

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Data Collection and Cleaning

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Data Exploration and Visualization

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Data Manipulation and Analysis

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Machine Learning Basics

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Supervised Learning Algorithms

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Unsupervised Learning Algorithms

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Model Selection and Evaluation

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Natural Language Processing (NLP)

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Time Series Analysis

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How to learn data science for beginners?

Unlock the world of data science for beginners! Learn the ropes with beginner-friendly steps, discover valuable insights, and find tailored resources to initiate your data science education.

DeveLearn Technologies

3 minutes

March 14, 2024

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Exploring the path to becoming a data scientist is an insightful journey, and this guide aims to provide you with all the essential information to determine if it's the right career for you. 

In today's world, data science is in high demand as companies use it to solve various problems. This has made data scientists valuable and well-compensated professionals.

If you're interested in learning how to become a data scientist, you're in the right place. In this article, we'll break down everything you need to know about starting a career in data science. We've chosen data scientist as one of the top careers in analytics, and you can explore other roles in our full article.

What Does a Data Scientist Do? 

Ever wondered what a data scientist does? Simply put, they dive deep into data to uncover valuable insights that can be used to make informed decisions. In a business setting, they collaborate with different teams to set the groundwork for effective analytics.

Their tasks include developing strategies to collect and organize data from various sources. Once the data is ready, they analyze it to find solutions and then share their discoveries with the rest of the business.

Data scientists don't work alone; they often collaborate with different professionals like data engineers, analysts, IT experts, and business stakeholders to ensure a well-rounded approach to using data for decision-making.

Data Analyst vs Data Scientist: What's the Difference?

Curious about the difference between a data analyst and a data scientist? Here's a quick overview:

Data Analysts:

  • Focus on interpreting existing data to provide useful insights.

  • Use simpler models and tools for analysis.

Data Scientists:

  • Take on a broader role, often shaping the questions that need answers.

  • Utilize advanced tools and models, including machine learning algorithms, to derive insights.

Learn More - Data Analyst vs. Data Scientist: What's the Difference?


Why did you become a data scientist?

Choosing a career in data science offers numerous compelling reasons. This field not only provides financial rewards but also a challenging and stimulating environment that requires staying updated on the latest technological trends.

If you have a curious mind, love working with data and technology, and possess analytical skills, a career as a data scientist might be the perfect fit.

Moreover, the job market for talented data scientists is thriving. According to Statista, the big data market is expected to experience substantial growth, reaching an estimated $103 billion in 2027, up from $70 billion in 2022.

The U.S. Bureau of Labour Statistics predicts a significant 36% rise in data science industry jobs between 2021 and 2031, a stark contrast to the 4% national average job growth.

The popularity of data science careers is evident in prestigious rankings. U.S. News & World Report positions data scientist as a top job in technology, STEM, and overall. Glassdoor similarly recognizes it as one of the best jobs in America for 2022.

For those seeking financial incentives, the average data scientist salary is appealing, with a median annual income of $102k. Clearly, the demand for data scientists offers exciting career prospects and a generous compensation package.

What Qualifications do Data Scientists Need?

The qualifications needed to become a data scientist are a subject of debate. While many professionals have entered the industry through alternative routes, a university degree can certainly be advantageous.

Numerous graduate schemes and job opportunities seek individuals with qualifications in fields such as computer science, data science, mathematics, statistics, engineering, and even physics. Some programs are open to training anyone with a degree to become a data scientist.

While specific skills will be discussed in more detail later, having a working knowledge of programming languages like Python, R, SQL, and/or Julia can be beneficial.

Others may opt for self-directed learning, acquiring necessary skills and experience at their own pace, and impressing employers during interviews. For instance, our Data Science Course with Python track is designed to help you learn all the essential skills to kickstart a career in data science.

Additionally, you can pursue certification as a data scientist with platforms like DeveLearn, allowing you to demonstrate your data science knowledge to potential employers.

What skills are required for a Data Scientist?

To succeed as a data scientist, you need a mix of technical and non-technical skills. Whether you're aspiring to be a data scientist or leading a team, understanding these skills is essential for a successful career in this dynamic and growing field.

Let's look at the technical skills needed for a data scientist:

Technical Skills:

  1. Programming Languages

  2. Statistical Analysis and Mathematics

  3. Data Cleaning and Preprocessing

  4. Machine Learning

  5. Data Visualization

  6. Database Management

  7. Big Data Technologies

Non-Technical Skills:

  1. Problem-Solving

  2. Communication Skills

  3. Curiosity and Learning Agility

  4. Business Acumen

  5. Collaboration

For those looking to enhance their data management skills, platforms like Develearn offer comprehensive courses. Similarly, our Data Scientist Course accelerates the journey to becoming a proficient data scientist, enabling mastery of in-demand skills and real-world problem-solving.

What is an Average Data Scientist Salary?

The average data scientist salary varies based on several factors, and different sources provide slightly different figures. Here's a brief summary:

  • In the US, Glassdoor reports an average salary of $117,212 per year based on a sample of 18,000 salaries. On the other hand, PayScale estimates an average of $97,318 per year, drawing from 7,280 salary profiles.

  • Data science managers typically earn the highest salaries, ranging from $113,000 to $230,000 per year.

  • For data scientists and data engineers, salaries are often similar. Data scientists earn between $82,000 and $167,000 per year, while data engineers earn between $76,000 and $166,000 per year.

  • Company size plays a role in salary variations. Larger companies with more than 1,000 employees tend to pay a median salary ranging from $90,000 to $110,000 per year. Medium-sized companies (26 to 1,000 employees) offer around $80,000, and small companies/startups (2-25 members) provide a median salary of about $60,000 per year.

Keep in mind that these figures are general estimates, and actual salaries can be influenced by factors such as location, industry, experience, and education.

How to Become a Data Scientist?

If you're eager to kickstart your journey to becoming a data scientist, you're on the right track. Here are eight essential steps to guide you on the path from scratch:

1. Learn Data Wrangling, Data Visualization, and Reporting:

In your journey to become a data scientist, dealing with large and complex datasets is a common task. To make sense of this information for both yourself and others, it's crucial to develop skills in data wrangling. This involves cleaning, organizing, and transforming raw data into a format that allows for analysis and drawing conclusions.

Various tools can be used for data wrangling, with libraries like pandas in Python being a popular choice. For more information, you can explore resources like cheat sheets on data wrangling in Python with pandas.

Additionally, mastering reporting and data visualization is essential for presenting your data effectively. Discovering the best data visualization tools offers a range of options based on your specific goals. This skill ensures your findings have a meaningful impact when shared with others.

2. Work on Your Statistics, Math, and Machine Learning Skills:

While a formal degree in these fields isn't mandatory, acquiring a functional knowledge of statistics, math, and machine learning is crucial for a data scientist. Focus on areas such as calculus, linear algebra, and statistics. Assess your learning goals to tailor your approach.

Learning calculus can empower you to create optimization algorithms for machine learning. Although Python and R offer functions that handle calculus, understanding concepts like gradient descent aids in refining machine learning models.

Enhance your proficiency in linear algebra, crucial for working with vectors and matrices, solving equations, conducting eigenvalue/eigenvector analyses, and employing principal component analysis for dimension reduction. Consider courses like "Linear Algebra for Data Science in R" for practical applications.

Strengthen your statistical modeling skills with a focus on essential machine learning statistics topics. Courses like "Intermediate Statistical Modeling in R" provide valuable insights, aiding progress in key areas vital for applied mathematics and data science.

3. Learn to Code

Learning to code is an essential step for aspiring data scientists. In our exploration of the top programming languages for data scientists, we highlight Python and R as two standout choices due to their versatility and widespread usage. 

As data work often involves interacting with databases, mastering SQL is also crucial, although it becomes relatively straightforward after gaining proficiency in Python and R. Once you've mastered these foundational languages, consider adding Julia to your repertoire.

Julia is purpose-built for data science, offering speed and intuition in its approach. While these languages are sufficient for most tasks, you may need to explore additional languages like Java for handling very large datasets efficiently. Java, with its open-source nature and support for big data tools through Java Virtual Machines, proves to be a solid choice for data science endeavors. 

Additionally, languages like Scala, C/C++, JavaScript, Swift, Go, MATLAB, and SAS are worth exploring for managing extremely large datasets, each offering unique strengths and capabilities for data science tasks.

4.Understand Databases:

Understanding databases is a crucial aspect of a data scientist's skill set. Relational databases play a vital role in storing structured data efficiently, and SQL is a key tool for data scientists in this context. SQL enables the handling of structured data, querying databases, and performing tasks like wrangling, preparation, and experimentation with data. Moreover, SQL is frequently used in conjunction with Python, where libraries like SQLite, PostgreSQL, and MySQL facilitate the seamless connection of various data sources. Mastering SQL enhances your ability to work with structured data and efficiently manage databases in a data science environment.

5. Learn to Work with Big Data:

As a data scientist, dealing with large datasets is a common challenge in today's data-driven era. The sheer volume of data generated makes collection, maintenance, and processing more complex. However, skilled data scientists can extract valuable insights from these extensive datasets. To enhance your capabilities, it's beneficial to learn how to utilize cloud platforms such as AWS, Microsoft Azure, and Google Cloud. These platforms provide scalable solutions for handling large datasets. 

Additionally, tools like Apache Spark prove invaluable for big data processing, analysis, and machine learning, empowering you to navigate and derive meaningful insights from massive datasets in your data science career.

6. Take an Internship or Apply for a Job:

Once you've honed the skills mentioned earlier, it's time to apply them in professional settings. When you feel confident in your data scientist abilities, consider seeking internships or applying for jobs in the field.

Taking an internship or securing a job not only allows you to apply your skills in real-world scenarios but also provides valuable hands-on experience. This step is pivotal in solidifying your position as a data scientist and advancing your career in the dynamic and rapidly evolving field of data science.

7. Follow and Engage with the Community:

Staying current in the rapidly evolving field of data science is essential for aspiring professionals. Engaging with the vibrant and supportive data science community offers a valuable avenue for staying informed.

Explore various social media platforms such as LinkedIn, Twitter, Discord, and Reddit to connect with the community. Additionally, discover niche websites, blogs, and influential data science leaders who share insights and resources. Seek out individuals with similar interests, reach out for advice, actively contribute to discussions, and immerse yourself in the ongoing developments in data science.

Participating in the community not only keeps you well-informed but also provides opportunities to build meaningful connections, share experiences, and contribute to the dynamic conversations shaping the data science world.

Educational Paths to Become a Data Scientist

Exploring the journey to become a data scientist involves various educational paths, accommodating individuals from diverse backgrounds. While a bachelor's degree in data science, computer science, or a related field is a common starting point, there are multiple avenues to acquire the necessary skills.

Online courses and certifications, such as DeveLearn's data science tracks, provide an excellent way to build essential skills and work towards becoming a certified data professional. Additionally, pursuing an undergraduate degree in data science, computer science, statistics, or a related field offers a strong foundational knowledge.

For those seeking specialization or advanced skills, a master's degree in data science can be beneficial. Alternatively, data science bootcamps provide intensive training, enabling quick acquisition of practical skills for a successful career in data science.

Choosing the educational path aligned with your goals and interests allows you to tailor your journey to becoming a proficient data scientist.

Final Thoughts

Becoming a data scientist is an exciting and rewarding journey, especially in today's world where data science plays a crucial role in various sectors. The demand for data scientists is high, providing numerous opportunities for those interested in problem-solving, analytical thinking, and leveraging the power of data to make informed decisions.

This guide has explored different paths to enter the dynamic field of data science. Whether through formal education in computer science or statistics or self-directed learning via online courses and certifications like those offered by DeveLearn, you can tailor your journey to match your needs and preferences.

The job market for data scientists is thriving, with significant growth expected in the coming years. This growth not only includes a variety of job opportunities but also offers diverse roles and specializations within the field. Whether you are interested in data wrangling, machine learning engineering, or business intelligence analysis, there's a niche waiting for you.

If you possess an analytical mindset and a fascination for the potential of data, now is the perfect time to start your journey to become a data scientist. Take the first step today with our Data Scientist with Python career track.