How to Set the Path for a Successful Career in Data Science?

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Data scientists are the backbone of every strong organization. It is up to these individuals to crunch numbers, do the math, and provide precise, actionable data that the company can use in their favor. Moreover, data scientists are some of the most highly paid individuals from the computer science field. The average data scientist makes one of the highest salaries as compared to other technical areas. Given the sheer scope and demand for this job, it’s pretty obvious why a Computer Science major would be interested in working towards a career as a data scientist. Here’s everything you need to know about being a data scientist and planning out your learning to become one.

What Does a Data Scientist Do?

A data scientist is a perfect blend between a mathematician and a computer scientist. A data scientist works with large volumes of complex data to develop and design machine learning algorithms to glean meaningful business insights from that data. They is quite different from other jobs that basic database jobs because it requires a person to act proactively. Data scientists help a company understand user behavior and preferences and see what changes they need to make in the future. This job lets you play around with artificial intelligence and complex machine learning algorithms to make sense of large volumes of data without meaning otherwise.

Data Science Job Roles

Apart from becoming a data scientist, there are two other prominent job roles you can apply for in the data science domain- data analyst and data scientist. The first is the role of a data analyst. This is the simpler of the two positions and involves more common day-to-day tasks like gathering data, creating models, analyzing trends, and making suggestions based on the findings. 

The second role is that of a Business Intelligence Analyst. The skills required to become a BI analyst are a bit more diverse. They need expertise in business, economics, management, and other related fields. The job still includes understanding the information provided by useless numbers. However, it requires the data scientist to focus a bit more on the business end of things. 

How to Become a Data Scientist?

The journey to becoming a data scientist is pretty simple, at least on paper. If you’re an aspiring computer science student that wants to follow this line of work someday, you need to follow three steps.

Get a Bachelor’s Degree

A bachelor’s degree is necessary when it comes to becoming a data scientist. Almost every job in this field requires you to have a bachelor’s degree. It’s the first stepping stone for a successful career as a data scientist. Most universities have machine learning and AI courses as an optional part of the curriculum. If you find yourself given the option to decide between machine learning, databases, networking, etc., make sure you choose the subjects related to machine learning and AI.

Get a Master’s Degree or a Ph.D.

The second step for the process is a master’s degree or a Ph.D. These two degrees give you the advanced knowledge required to become a data scientist, which is relatively significant, especially if you want to learn about the research part of the job and offer something to the world of machine learning. A master’s degree and a Ph.D. are optional when it comes to becoming a data scientist. However, they do improve your opportunities and open up a lot more doors in reputable companies. A Bachelor’s is enough in most cases, but a master’s and Ph.D. are great for a data scientist.

Create a Decent Portfolio of Projects

A project portfolio will make or break your entire career when it comes to the data science field. You can pick any interesting data scientist project idea – be it in retail, finance, media, or in the world of cryptocurrencies and stock trading. These industries are all about crunching the number, understanding charts, and predicting future prices. Data scientists globally are creating and selling algorithms that help traders understand where the prices will go and decide whether a coin is a profitable investment. This is one of the most trending data science project ideas to practice and add to your portfolio.

Companies aren’t interested in learning how much time you’ve spent on Google Collab or how proficient you are at Python; they want to see how you’ve utilized that knowledge to create practical projects. There are three ways to work on data science projects before getting your big break in the industry.

–      Internships

Internships are one of the best ways to get hands-on experience with your field during your undergrad years. Look for companies around town that might be hiring data science students for some projects. It would be best to find such companies during summer break since many organizations open positions to hire data science interns. They only last for 4-6 weeks. However, the experience you’ll get from working there will be invaluable. 

The best part about internships is that you’re getting access to quality data sets. You can learn and create projects from random CSV files you find on Kaggle. However, those data sets cannot match the practicality of data you’ll get in these internships.

–      Freelancing 

Freelancing is another great way people are building their portfolios. There are tons of projects out there on websites like Fiverr, Upwork, etc. The best part is that you’re getting paid a lot for building a data science project portfolio as well. It’s a win-win situation, and there’s no better scenario for a data science student.

Having prepared a decent portfolio of data science projects, its time to prepare nail your data science interview with these 100+ Data Science Interview Questions and Answers.

Wrapping Up

The job of a data scientist is one of the most sought after in the entire computer science industry. It’s one of the highest-paying jobs as well. If you’re looking to set the path for a successful career in this field, we’ve mentioned the steps. All that’s left is for you to follow them!

James Kirk

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