How to start a career in Data Science - The ultimate Guide | 1search.co

July 30, 2021

Today Data Science is brimming with opportunities for young college graduates and even working professionals to explore. Data is being generated every second by millions of people around the globe.

We have generated and stored more data in the last 2 years than we did in the last 50. eBay generated 50TB of data on a daily basis. Similarly, big companies and governments collect huge amounts of data every day which is produced via all kinds of devices and apps which know no end. This data, also called big data, is very valuable as important insights can be generated from it to help them take big decisions regarding policies, production, operations, and others.

Data scientists play a huge role in extracting, maintaining, storing, and analyzing this data to help the government and companies to produce meaningful inferences out of it. They also use programming languages to make software and dashboard to store this data. Most of the data contain valuable information and also personal details of the citizens which shouldn’t fall in the wrong hands. It’s the data scientist’s job to protect them from cyber threats.

What are the career opportunities in Data Science?

A Data Scientist is definitely a lucrative and attractive Job profile however there are many more job roles that are equally valuable, interesting, and comes with a huge salary package. These job roles are interconnected yet very different from one another and worth pursuing. Have a look:

Data Analyst

A data analyst uses statistics to interpret data and analyze results to turn it into valuable information for the organization. They collect and process massive amounts of data by creating algorithms from time to time. SQL, R, SAS, Python are some of the languages that they use to analyze the data and thus a solid understanding of the same is a must if you plan on pursuing the Data Analyst Job Role. Along With it, a great analytical and problem-solving skill set is also a must-have.

Salary- $60k - $70k per annum

Data Engineer

A data engineer builds massive big data ecosystems for businesses. These ecosystems must be scalable and stable so that data scientists can run their algorithms without any issues. They also upgrade the ecosystem from time to time. To become a data engineer one must be well versed in R, NoSQL, Hive, C++, Java and more.

Salary- $86k - $105k

Data Architect

Database management is a huge task for all organizations that deal with big data especially e-commerce companies. A Data Architect builds the blueprints for data management so that the data is well managed as well as protected. They also create tools for data engineers to help them build big data ecosystems. If Data Architect is your career goal, you must start learning Hive, Pig, Spark, and also how to deal with data warehouses.

Salary- $85k - $107k

Machine Learning Engineer

A machine learning engineer builds data pipelines by implementing machine learning algorithms like clustering, classification and performs A/B testing to test these algorithms. Solid knowledge of Python, Java, and other languages is a must if you want to pursue this career.

Salary- $111k - $137k

Data Scientist

Every database has a trend that can give valuable insights to businesses and help them make important decisions. These trends are identified by Data Scientists. They analyze and process raw data to solve business problems for organizations. Data scientists can predict the future using these trends, how cool is that! Data scientist is a highly lucrative job role and to become one you must be an expert in R, MatLab, SQL, Python, to name a few.

Salary- $95k - $195k

There are other Job profiles that come in the purview of Data Science like Business Intelligence Manager, Data Administrator, Business Analyst, etc.

What are the qualifications that you need?

Education

To pursue a career in data science it’s great if you have a bachelor’s in data science or any other computer-related field so that you can build up your knowledge and skillset from the start. The more languages and tools you know, the better it is for you. However, if you have a bachelor's in any other field, you will have to work harder for sure. Take courses online or offline and attend data science boot camps to gain a solid understanding of all the tools and techniques that you will need in your respective job role. Most data scientists have a postgraduate degree as well. It’s pretty common these days and most professionals get one through an online course. Although there are no shortcuts there. To become an expert in your field, it’s imperative that you master all the tools and languages.

Certifications

When it comes to Data Science and Machine Learning, there is something new coming up every day. That’s where certifications come in handy. A four-year bachelor's degree can only teach you the basics but you have to specialize in a field or industry if you want to become an expert. Whether it is Big Data or AI or something else. Take up as many certification courses as you need to master Data Science. There are several platforms that can help you get paid or even free courses in Data Science. Take advantage of those.

Choosing a Language to master

There is n number of programming languages out there. The most popular being Python, Java, R, SQL, and others. You never know which new language can take over and make your favorite one obsolete. However, it is very important for a Data Scientist to master at least one language. Many data scientists master High-Level Programming language and so should you. Irrespective of which language you choose to master out of the 250 programming languages out there, you must have an in-depth knowledge of these most popular and widely used ones: Python, R, SQL, and Java.

Stay Up to date and Keep Learning!

You can never stop learning in data science if you want to be on top of your game. There are new languages, tools and software discovered all the time and one must be up to date. Follow websites and newsletters which post updates and new content on data science to know what’s the latest. Go and research more about these new technologies and join courses and certificates to gain more knowledge in them. Thus, keep learning!