Data science is a growing field with many possibilities for people interested in it. It’s not just a job title or buzzword, but rather a way of approaching problems and thinking about how data can be used to solve them. If you’re looking to get into data science, this guide will walk you through all the steps from learning what kind of work is involved in the field to getting started on your first project as an entry-level data scientist.
Data science is a broad field and can be described as the process of extracting knowledge from data to make better decisions. There are many definitions of data science, but they all have one thing in common: they define it as something that applies computer science, statistics, and mathematics to solve problems in the real world.
Data scientists work with large amounts of structured and unstructured data from various sources including databases, spreadsheets, text files, social media feeds, or web browser histories. Regarding programming languages, the most used by data scientists are R and Python.
The skills required to become a data scientist are many and varied. The ones most in demand include statistical analysis, machine learning, coding (in Python, Java, R, etc.), and business acumen.
- Statistical analysis helps you gain a better understanding of your data set. This knowledge can help you determine which variables have the strongest impact on other variables as well as what interests people on your website or social media platforms.
- Machine learning is used to analyze large amounts of data and find patterns in it. It can be used, for example, to flag instances of fraud and identify credit card purchases that might not be legitimate based on how they compare with other accounts opened by the same customer.
- Coding is one of the most in-demand skills across many data roles, not only for data science. Data scientists should know Python and R programming languages to be able to explore and process data. They should also have a basic understanding of SQL. If you want to become a data science professional, it’s crucial that you know how to use these languages and tools.
- Business acumen is the ability to understand and apply business concepts, processes, and strategies. Business acumen also helps identify opportunities for improvement within your organization and facilitates communication between departments that might not have direct interaction with each other (e.g., engineers working on new features may not always know what salespeople need from them).
There are a number of resources available to you that can help you learn data science.
- Books: There are many books on data science out there, but not all of them are created equally. You may want to research before deciding which book is right for you. There are some fantastic options available, like Python for Data Analysis, or Practical Statistics for Data Scientists. If you prefer video tutorials and courses, Coursera and Udemy have some solid offerings that should get the job done at a fraction of the cost.
- Online courses: The advent of online learning platforms has opened up new opportunities for anyone interested in learning something new — but it’s important to make sure your chosen course actually provides value for its price tag. We recommend starting with one or two introductory-level classes before spending money on more advanced ones; if they don’t provide enough information or instruction along the way, consider looking elsewhere! A fantastic example is the Data Science Dojo, an online course with a 16-week BootCamp with instructor-led live classes and continued support.
There are many tools available for data science and machine learning, some free and some paid. Below are the ones that data scientists use most frequently:
- RStudio — one of the favorites IDE for writing code in R, which is a free statistical programming language.
- Python Anaconda — a free platform for scientific computing that includes many useful packages (I usually use Jupyter Notebook).
- GitLab CE — an open-source alternative to GitHub that allows you to host your own Git repository server if you don’t want to pay for private repositories (you can also use Code Ocean but it is not quite as elegant). It’s also possible to set up your own private GitLab instance at home using Docker or Docker Compose with this guide.
You’ll need to learn at least one programming language. There are many options, but we recommend Python or R. Both languages are great for data science, and they’re often used together in courses and tutorials.
Python is a general-purpose programming language that’s easy to pick up and has a lot of support from the community. It’s also very popular with data scientists, so you’ll be able to find plenty of resources online if you get stuck on something.
R is an open-source statistical programming language built for statistical analysis and modeling tasks such as analyzing data sets or making predictions based on them (hence “r” being short for “regression”). Because it was designed specifically with statistics in mind, R can do things like automatically plot graphs based on the data that was entered into it — something that most other programming languages don’t offer out of the box (though this isn’t always desirable!).
Data science is a hot field that is attracting both young graduates and experienced professionals from other fields. But how do you get started?
It all starts with learning the skills for data science and then putting them into practice by working on projects, building your portfolio, and getting experience in the industry.
As we’ve shown here, there are many different ways to get started with this exciting career path, so don’t be afraid if you don’t have formal training or certification yet!
Take a look at our list of recommended resources below that will teach you more about data science.
Remember that the most important skill you can have is your ability to learn.