So, it’s finally happened. After thinking about your options, you decided on data science as your next step. And after a few weeks or months of research, you’ve found that the best way to get into that field is by completing a bootcamp. You then received your acceptance letter but aren’t sure what to do to prepare. Because data science is such a broad field, getting ready for a data science bootcamp can be at least as tricky as getting prepared for a course in web development.
It’s completely understandable if you feel intimidated or unsure of where to begin. However, you can succeed if you use the appropriate approach. Here are the steps you can take to prepare for a data science bootcamp.
Understand the Basics of Data Science
A data science bootcamp is a great option if your main goal is to quickly get a job in the field of data science. Bootcamps are intensive courses that allow you to learn data essentials and develop essential data science skills, such as data cleaning and web scraping. It also helps you learn various algorithms, including Big Data, deep learning, machine learning, singular value decomposition and linear regression.
Pre-Master pandas, Python and Git
At some early point in the data science bootcamp, you need to use these technologies daily for your coursework:
Python is a computer programming language that you can use to conduct data analysis, automate tasks, and build websites or software. It’s a general-purpose language, meaning you can use it to create various programs. You can use Python to write logic to make your models.
pandas is an open-source software library built on top of Python. It allows you to automate time-consuming, repetitive tasks associated with working with data, including:
- Data fill
- Data cleansing
- Merges and joins
- Data normalization
- Loading and saving data
- Data inspection
- Statistical analysis
- Data visualization
Git is a version control tool that allows you to record, track and save any changes made to source code and to easily and quickly recover any previous state.
Brush Up On Your Statistics and Math Skills
You should be absolutely comfortable with basic concepts like standard deviation, variance and mean. It’s also essential to be acquainted with the null hypothesis or p-values statistical testing and learn how to interpret confusion matrices.
While several available resources could help you improve your statistics and math skills, taking online courses is perhaps the best one among them. It’d be even better if you could pursue a preparatory course in data science from the same education company where you plan to complete a data science bootcamp.
Learn About the Three Different Roles in Data Science
Just because you go to a data science bootcamp doesn’t mean you want to become a data scientist. It’s essential to know what type of job you want to get so that you can effectively select the type of capstone projects you build. Here are the primary data science roles you might be eligible for when you complete the program:
A data engineer works in various settings to build a system that collects, manages and converts raw data into usable information for business analysts and data scientists to interpret. Their primary goal is to make data accessible so companies can use it to assess and optimize their performance.
A data scientist works closely with business stakeholders to understand their goals and find out how they can use data to meet those goals. They create data modeling processes, build algorithms and predictive models to extract the data the company needs, help analyze the data and generate insights.
Data analysts collect data, organize and use it to reach meaningful conclusions. Generally, they use computer systems and calculation applications to determine specific factors. They’re responsible for digesting the data and generating a report to explain the findings. These resulting reports can help identify a variety of inefficiencies and other business issues that may exist.
Set Yourself Up for Success
A data science bootcamp is going to be hard. So in order to succeed, you need curiosity to analyze data, grit to keep going and passion for discovering the world. To get over the challenges at the bootcamp, it’s important to work hard, develop a positive attitude and be creative.
Choosing the Right Data Science Bootcamp for You
Here are the steps you can take to pick the right bootcamp for you:
1. Outline Your Career Goals
Although many data science bootcamps cover similar material, each has a unique focus that can make a difference when pursuing a specialized job in the field. To find a suitable bootcamp for yourself, consider identifying and outlining your career goals so that you can match a program to your professional interests.
2. Research Job Requirements
Consider researching the qualifications and essential data science skills to perform the role. Many data science roles require you to have a skill set specific to that position, which may vary somewhat from those you already have. Here are some of the most common workplace and technical skills you may find in the data science field:
- Big data frameworks
- Data visualization
- Machine learning
- Knowledge of programming languages, such as Python or R
Consider looking up job advertisements in your area to get a good idea of what skills you need to develop before applying.
3. Assess Your Current Skills
If you already know the basics of data science, you’ll have the most success with data science bootcamps. Bootcamp instructors move quickly, and you’ll likely be working on projects that require some background knowledge. Classes are often limited to building your career toolbox and developing essential data science skills.
Since there won’t be much time to review basic concepts, assessing your skills is important because this can help you determine what kind of bootcamp would be the best fit for you based on your skills. If you want to focus more on the basics, consider looking for a bootcamp for beginners or taking an online course to expand or brush up on your current skills.
4. Consider Structure and Location
When comparing data science bootcamps, consider whether you want an in-person, online or hybrid program. Each of these different educational approaches has unique benefits, depending on your personal circumstances, resources and goals.
- Online courses: These can be a convenient way to participate in a bootcamp without sacrificing a comprehensive education. Online courses, however, may not provide as many opportunities for team-building and networking as in-person programs.
- In-person: This provides more structure in a hands-on environment with an instructor ready to assist you as you need. It can give a lot of networking opportunities and a chance to develop people skills, such as collaboration and teamwork.
- Hybrid courses: This gives you the convenience of online learning and allows you to experience the immersion of in-person learning.