Data science is everywhere right now. One after the other, companies worldwide are turning to data science to solve the most diverse problems out there. This situation has put data scientists in an advantageous position regarding employment and payment.
Naturally, a lot of people are becoming interested in learning how to become a data scientist. If you are reading this article, you may be one of those people. Here, we will show everything you need to know about becoming a data scientist, including an effective and attractive way to learn what you need to get started in the data science world.
But first…
What is 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.
This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.
Data science is important because it combines tools, methods, and technology to generate meaning from data. Modern organizations are inundated with data; there is a proliferation of devices that can automatically collect and store information. Online systems and payment portals capture more data in the fields of e-commerce, medicine, finance, and every other aspect of human life.
Who is a data scientist?
Data scientists are analytical experts who extract meaning from and interpret data to solve complex problems. They use industry knowledge, contextual understanding, and skepticism of existing assumptions to uncover solutions to business challenges.
A data scientist’s role combines computer science, statistics, and mathematics to collect and organize data from many different data sources, translate results into actionable plans, and communicate their findings to their organizations. Successful data scientists must be effective communicators, leaders, team members, and high-level analytical thinkers.
As a specialty, data science is young. It grew out of the fields of statistical analysis and data mining. The Data Science Journal debuted in 2002, published by the International Council for Science: Committee on Data for Science and Technology. And by 2008, the title of data scientist emerged, and the field quickly took off.
However, there remains a shortage of data scientists, even though more and more colleges and universities have started offering data science degrees.
What Does a Data Scientist Do?
A data scientist’s duties can include developing strategies for analyzing data, preparing data for analysis, exploring, analyzing, and visualizing data, building models with data using programming languages, such as Python and R, and deploying models into applications.
Essentially, data scientists work to deeply understand and analyze data to provide actionable insights.
In a business environment, they work across multiple teams to lay the foundations for robust analytics. This means developing strategies to capture, gather, and clean data from a range of sources. After organizing and exploring this data, they can then build solutions and communicate their findings to the wider business.
Data scientists often work with other professionals, including data engineers, analysts, IT architects, application developers, and business stakeholders. In addition to a data scientist, this team might include a business analyst who defines the problem, and a data engineer who prepares the data and how it is accessed.
It might also include an IT architect who oversees the underlying processes and infrastructure and an application developer who deploys the models or outputs of the analysis into applications and products.
Other responsibilities of Data Scientists
- Solve business problems through undirected research and framing open-ended industry questions.
- Extract huge volumes of structured and unstructured data. They query structured data from relational databases using programming languages such as SQL. They gather unstructured data through web scraping, APIs, and surveys.
- Employ sophisticated analytical methods, machine learning, and statistical methods to prepare data for use in predictive and prescriptive modeling.
- Clean data to discard irrelevant information and prepare the data for preprocessing and modeling.
- Perform exploratory data analysis (EDA) to determine how to handle missing data and to look for trends and/or opportunities.
- Discovering new algorithms to solve problems and build programs to automate repetitive work.
- Communicate predictions and findings to management and IT departments through effective data visualizations and reports.
- Recommend cost-effective changes to existing procedures and strategies.
Every company will have a different take on a data scientist job description. Some treat their data scientists as data analysts or combine their duties with that of data engineers, others need top-level analytics experts skilled in intense machine learning and data visualizations.
As data scientists achieve new levels of experience or change jobs, their responsibilities invariably change. For example, a person working alone in a mid-size company may spend a good portion of the day in data cleaning and munging. A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products.
What a data scientist needs in a platform
If you are ready to explore the capabilities of data science platforms, there are some key capabilities to consider:
- Choose a project-based UI that encourages collaboration. The platform should empower people to work together on a model, from conception to final development. It should give each team member self-service access to data and resources.
- Prioritize integration and flexibility. Make sure the platform includes support for the latest open-source tools, common version-control providers, such as GitHub, GitLab, and Bitbucket, and tight integration with other resources.
- Include enterprise-grade capabilities. Ensure the platform can scale with your business as your team grows. The platform should be highly available, have robust access controls, and support a large number of concurrent users.
- Make data science more self-service. Look for a platform that takes the burden off of IT and engineering, and makes it easy for data scientists to spin up environments instantly, track all of their work, and easily deploy models into production.
- Ensure easier model deployment. Model deployment and operationalization are some of the most important but often disregarded steps of the machine learning lifecycle. Make sure that the service you choose makes it easier to operationalize models, whether it’s providing APIs or ensuring that users build models in a way that allows for easy integration.
What Skills Do Data Scientists Need?
Essentially, to succeed in data science, you’ll need an assortment of hard and soft skills.
Hard Skills
These are the technical skills you’ll need to develop to become a fully-fledged data scientist:
- Python
- R
- Statistics and math
- SQL and NoSQL
- Data visualization
- Machine learning
- Deep learning
- Natural language processing
- Big data
- Cloud computing
Soft Skills
These are now usually referred to as ‘power skills’ or ‘human skills,’ and they are cross-functional skills that aren’t job-specific but are still necessary. For a data scientist, these include:
- Communication
- Storytelling
- Critical thinking
- Business acumen
- Problem-solving
- Teamwork
How to Become a Data Scientist
Learn data wrangling, data visualization, and reporting
As you work to become a data scientist, you’ll often encounter large and complex datasets. To make sense of this information (for yourself and others), you’ll need to learn how to deal with it. This is where skills in data wrangling come in handy. This is where you’ll clean, organize, and transform raw data into a format you can analyze and draw conclusions from.
There are various tools you can use with data wrangling, but libraries like pandas in Python are often the go-to choice. When it comes to presenting your data with impact, you’ll need to master reporting and data visualization.
Work on your statistics, math, and machine-learning skills
While you don’t necessarily need a degree that covers these fields, you do need a functional knowledge of these areas. Generally, you’ll want to cover areas such as calculus, linear algebra, and statistics. However, it’s worth assessing your intent behind learning these things.
For example, learning calculus can help you learn to create optimization algorithms for machine learning, although there are functions in both Python and R that can take care of the calculus for you. As a specific example, understanding a topic like gradient descent can help you measure the change in a function’s output when you tweak the inputs, which can help refine machine learning models.
Learn to code
Two of the most obvious choices for data scientists are Python and R, given their versatility and ubiquity. Of course, working with data also means working with databases, so SQL is another essential programming language. Thankfully, it’s a relatively straightforward language once you’ve learned Python and R.
Julia is a good choice once you’ve learned Python, R, and SQL. With Julia, you get a language that’s built from the ground up for data science, making it fast and intuitive. If you start working with very big data sets, you may need other languages, but until then, these four will be sufficient.
With a language like Java, you get an open-source language known for its efficiency and performance. For data science, Java Virtual Machines provide a solid and efficient framework for popular big data tools, such as Hadoop, Spark, and Scala.
Other coding languages worth learning for data science with very large data sets include Scala, C/C++, JavaScript, Swift, Go, MATLAB, and SAS.
Understand databases
Relational databases allow data scientists to store structured data in a fast and efficient way. When collecting and organizing data, you’ll often find that SQL is your preferred tool.
SQL allows you to handle structured data, query databases, wrangle, prepare, and experiment with data, and more. What’s more, SQL is often deployed alongside Python, with libraries such as SQLite, PostgreSQL, and MySQL helping you connect different data sources.
Learn to work with big data
In an era where everything and everyone is producing more data than ever before, these data sets are increasingly huge, making them harder to collect, maintain, and process.
Yet from these huge data sets, a skilled data scientist can extract new and detailed insights. So, learning to use cloud platforms such as AWS, Microsoft Azure, and Google Cloud can all benefit your career as a data scientist. Similarly, tools like Apache Spark can help with big data processing, analysis, and machine learning.
Get experience, practice, and meet fellow data scientists
As with any career, to become a data scientist, you’ll need as much experience and practice as possible. Luckily, there are many ways that you can get involved with communities, work on projects, and develop your data science skills.
For example, DataCamp Workspace provides a collaborative cloud-based notebook that allows you to analyze data, collaborate with others, and share insights. Workspace is designed to take you from learning data science to doing data science. It also has built-in datasets, so you can start analyzing data within minutes.
You can also apply your knowledge to various data science projects, allowing you to solve real-world problems from your browser.
Take an internship or apply for a job
Once you’ve developed all of the skills mentioned above, you’ll want to start applying them in more professional settings. Once you’re confident you have the data scientist skills needed to meet the expectations of a role, you can start applying for internships or jobs.
Follow and engage with the community
If you want to become a data scientist, you’re going to need to keep up-to-date with a fast-paced industry. There is no better way to stay informed about developments in data science than by engaging with what can often be a generous and dedicated community.
As well as social media sites such as LinkedIn, Twitter, Discord, and Reddit, there are all kinds of niche sites, blogs, and data science leaders you can follow. Look for people who are interested in the same areas as you, reach out for advice, contribute to discussions, and get involved with what’s going on.
Professional Steps to Become a Data Scientist
Here are six common steps to consider if you’re interested in pursuing a career in data science:
Step 1: Pursue an undergraduate degree in data science or a closely related field
You will generally need at least a bachelor’s degree in data science or a computer-related field to get your foot in the door as an entry-level data scientist. However, some data science careers require a master’s or doctoral degree. Degrees add structure, internships, networking, and recognized academic qualifications to your résumé.
However, if you’ve received a bachelor’s degree in a different field, you may need to focus on developing skills required for the job through continued education, like online short courses or boot camps.
Step 2. Consider a specialization
Data scientists may specialize in a particular industry or develop strong skills in areas such as artificial intelligence, machine learning, research, or database management. Specialization may be a good way to increase your earning potential and do work that is meaningful to you.
Step 3. Get your first entry-level job as a data scientist
Once you have acquired the necessary skills and specialized knowledge, you can begin the search for your first data science role. Creating an online portfolio is a valuable way to display a few projects and showcase your accomplishments to potential employers. You may want to consider a company with room for growth since your first data science job may not have the title data scientist but could be more of an analytical role.
You will likely learn how to work within a team and master best practices that can prepare you for more senior positions.
Step 4. Advance your skills with a data science boot camp (optional)
Data science boot camps are short-term, immersive educational programs that teach critical data science skills and programming languages such as Python, R, and SQL. There are lots of boot camps online; some may take a couple of weeks to complete, while others may take up to a couple of months.
Boot camps can help you expand your network. As a student in a boot camp, you can also take advantage of dedicated career services offered through the program to help with job placement after completion. Data science boot camps typically cover various topics such as machine learning, natural language processing, data analytics, data visualization, and more.
Step 5. Review additional data scientist certifications and post-graduate learning (optional)
Here are a few certifications that focus on and help improve valuable skills:
- Certified Analytics Professional (CAP): CAP was created by the Institute for Operations Research and the Management Sciences (INFORMS) and is targeted towards data scientists. During the certification exam, candidates must demonstrate their expertise in the end-to-end analytics process. This includes framing business and analytics problems, data and methodology, model building, deployment, and life cycle management.
- SAS Certified Predictive Modeler using SAS Enterprise Miner 14: This certification is designed for SAS Enterprise Miner users who perform predictive analytics. Candidates must have a deep, practical understanding of the functionalities for predictive modeling available in SAS Enterprise Miner 14.
Step 6. Earn a master’s degree in data science
Academic qualifications could play a crucial role in the selection process for positions in the field. However, a master’s degree in data science depends on the job.
Some professionals have a bachelor’s or graduated from a data science boot camp. However, because some employers prefer that candidates have a graduate degree, a master’s could expand your career prospects.
What is an Average Data Scientist’s Salary?
- Average salary data differs between sources. For example, in the US, Glassdoor gives an average salary of $117,212 per year based on a sample of 18,000 salaries, whereas PayScale estimates $97,318 per year, based on 7,280 salary profiles.
- Data science managers are generally the top earners, with a salary range of $113,000 – $230,000 per year.
- Data scientists and data engineers tend to have a similar salary, ranging from $82,000 – $167,000 per year for scientists and $76,000 – $166,000 per year for engineers.
- Company size matters; with companies of more than 1,000 employees paying a median salary ranging from $90,000 to $110,000 per year, compared to around $80,000 for medium-sized companies (26 to 1,000 employees) and $60,000 for small companies and startups (2-25 members).
Data Analyst vs Data Scientist: What’s the Difference?
- Data analysts. Typically work on interpreting existing data to provide actionable insights. They may use simpler models and tools.
- Data scientists. Take on a broader role, often formulating the questions that need answering. They use advanced tools and models, including machine learning algorithms, to derive insights.
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