Data science is an area of study in which data is evaluated using specified parameters and decisions are made based on the pattern and results created as a result of the investigation. It’s an interdisciplinary discipline that entails analyzing data and gaining knowledge utilizing scientific methods, algorithms, and processes. 

There are numerous paths to becoming a Data Scientist, but because it is typically high-level employment, Data Scientists have usually been well-educated, holding degrees in mathematics, statistics, and computer science, among other fields. This, however, is beginning to change.

Become a Data Scientist in 8 Steps:

STEP 1: Develop the Right Data Skills

If you have no past experience with data, you can still become a Data Scientist, but you will need to establish the requisite foundation to pursue a data science career. Data Scientist is a high-level position, and you’ll want to establish a broad base of knowledge in an associated discipline before you get there. Mathematics, engineering, statistics, data analysis, programming, or information technology are all options; some Data Scientists have even worked in finance and baseball scouting.

STEP 2: Data Science Fundamentals

A data science school or boot camp can help you learn or improve your data science skills. Expect to learn fundamentals like how to collect and store data, analyze and model data, and visualize and present data using every tool in the data science arsenal, including specialist apps like Tableau and PowerBI etc.

You should be able to utilize Python and R to develop models that assess behavior and forecast unknowns, as well as repackage data into user-friendly forms, by the end of your course.

STEP 3: Key Programming Languages for Data Science

Data scientists use a variety of specialized tools and algorithms designed for data cleansing, analysis, and modeling. A statistical programming language like Python, R, or Hive, as well as question dialects like SQL, are expected of Data Scientists notwithstanding broadly useful Excel. RStudio Server, which provides a development environment for working with R on a server, is one of the most crucial tools for Data Science Jobs. 

STEP 4: Develop your practical data skills by working on data science projects.

Once you’ve learned the fundamentals of the programming languages and digital tools utilized by Data Scientists, you can go on to the next step. You can begin putting them to use right away, sharpening and expanding your abilities. Take on tasks that require a diverse set of abilities, such as managing and querying databases using Excel and SQL, analyzing data with statistical methods with Python and R, building models that evaluate behavior and give new insights, and utilizing the statistical analysis to anticipate unknowns.

STEP 5: Create visual representations and practise presenting them

Practice creating your own visualizations from start using programs like Tableau, PowerBI, Bokeh, Plotly, or Infogram, and find the best method to let the data speak for itself. Even though the underlying assumption of spreadsheets is simple – creating calculations or graphs by connecting the data in their columns – Excel remains immensely helpful after more than 30 years and is nearly inevitable in the field of data science.

STEP 6: Create a portfolio to demonstrate your data science abilities.

After you’ve completed your preliminary research, received training, and put your new talents to the test by completing a diverse set of tasks, the next step is to demonstrate your abilities by creating the polished portfolio that will land you your ideal job. Indeed, your portfolio could be the most crucial factor in your job search. For example, BrainStation’s Data Science Bootcamp is designed to provide students with a project-based learning experience that allows them to develop an impressive portfolio of completed real-world projects. It is one of the most effective strategies to distinguish yourself in the job market.

STEP 7: Raise Your Profile

A well-executed project that you complete independently might be an excellent method to exhibit your expertise and impress potential employers. Choose something you’re passionate about, pose a question about it, and try to answer it with statistics. As previously said, you should consider putting your work on GitHub.

Document your journey and provide your findings with a comprehensive description of your process, showing your technical abilities and inventiveness. 

STEP 8: Apply for Data Science Jobs that Interest You

Within the discipline of data science, there are numerous roles. People often specialize in numerous subfields after learning fundamental skills, such as Data Engineers, Data Analysts, or Machine Learning Engineers, among many others. You can also refer to Data Science Interview Questions and Answers. Find out what a company’s priorities are and what projects they’re working on, and make sure it fits your strengths, ambitions, and future plans. Also, look beyond Silicon Valley: locations like Boston, Chicago, and New York are experiencing a technical talent shortage, so there are plenty of chances!

Conclusion

Keep in mind that Data Scientists have typically been highly educated; according to the most recent statistics, 88 percent have a Master’s degree and 46 percent have a Ph.D. A Bachelor’s degree in computer science, mathematics, or statistics is one of the most popular educational paths to a profession in data science.