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5 Ways To Gain Hands-On Experience In Data Science

5 Ways To Gain Hands-On Experience In Data Science

Are you all set to take the leap into the dynamic world of data science? How do you get a competitive edge in a field that already has thousands of aspiring data science practitioners?

Being book-smart is not enough to break into a field as competitive as data science. The key is to get hands-on experience so you can become a viable candidate.

A chief factor that carves your identity in the market, your hands-on experience can help you understand your strengths, skills, and most importantly, enrich your experience in real-world settings. Besides, hands-on experience is a necessity to fully comprehend the professional challenges in data science.

Here are 5 ways for you to gain hands-on experience in data science:

1. Personal Projects
Working on personal projects is a good way to start. They need not necessarily be complete end-to-end projects, but the idea is to focus on perfecting a specific skill. You can also showcase your projects on platforms such as GitHub.
Here are some small projects you can work on:

  • Collecting and cleaning data sets - This type of project will expose you to different data formats, data collection methods, and data cleaning tasks.
  • Making predictions - Work on logistic regression and linear regression models to learn predictions.
  • Exploratory Data Analysis (EDA) - EDA is one of the best types of projects to exhibit your skills in framing and investigating the right questions, using different plots, and visualizing your inferences.

2. Hackathons
Hackathon participants are required to build projects that have real-world applications. These events demand the conversion of hypotheses and theories into actions, thus helping participants put their theoretical skills to the test. Hackathons simulate real working environments and deadlines, thereby giving contestants a peek into the real settings of a data science job. They also stimulate business thinking; apart from creating data models, contestants need to leverage their critical thinking skills to understand what data strategies would benefit a business.

3. Tutorials
By creating tutorials, you tell your potential employers that you have what it takes to tackle the role of a data science professional. Tutorials can help you demonstrate your in-demand data science skills as well as communication skills. For instance, you can clean a dataset while you walk your audience through the steps. Or you could create a detailed blog post where you explain the relevance of a certain in-demand skill and how one can execute it.

4. Open-Source Projects
Participating in open source projects is one of the best ways to enrich your coding and technical skills. You can also become familiar with data science libraries, version control, and git by contributing to such projects. These projects also further your communication skills as you would be required to stay constantly in touch with other data science professionals involved in the project. In short, you get to create codes that benefit other projects and also expand your professional network.

5. Data Science Bootcamps
Data science bootcamps prioritize practical skills over theories. Here, learners receive job-oriented training wherein they are exposed to all the challenges of a data science job. With the optimal usage of individual and group exercises, projects, simulations, and discussions, candidates are more aligned to industry demands. Bootcamp curriculum is industry-vetted; learners are given hands-on training in all in-demand data science skills.

Until you gather experience with data science tools, real-world data sets, and develop a sound data intuition, you cannot make your way into data science. In short, building your career in data science is never about degrees but about how well you can wield your practical skills. This is why acquiring ample hands-on experience is inevitable to your data science career.

Join OdinSchool’s Data Science Bootcamp to Gain Hands-on Experience