Data science is an integral part of modern analytics. It is all about transforming massive, raw data into actionable insights.  Across many industries, data science not only drives innovation but also encourages strategic decision-making.

If you are interested in data science and want to know more about this field, you are at the right blog. Here is a detailed explanation of what career paths it offers. Keep reading to gain valuable insights into available options, so that you can make an informed decision based on your specific needs and preferences.

1.     Data Analyst

If you choose to become a data analyst, you will be responsible for collecting, processing, and analyzing data. You will create reports and dashboards that offer valuable information to the business leaders. However, it is not as easy as it sounds.

To become a data analyst, you must have a strong understanding of statistics, data analysis tools, and communication skills to present your findings. After acquiring strong foundational skills and in-depth knowledge, you can further apply for roles such as Senior Data Analyst and Data Analytics Manager.

2.     Data Scientist

If you want to pursue your career as a data scientist, build a strong foundation in statistics, mathematics, programming, and machine learning techniques. Once you master all these skills, you can further advance your career as a Lead Data Scientist or a Chief Data Scientist, or consider other data science roles at major global companies such as Intuit.

The main focus of data scientists is to extract meaningful patterns from complex datasets. These experts use statistical analysis and machine learning techniques to solve various business problems and create innovative products.

3.     Data Engineer

Data engineers build and maintain the data infrastructure and pipelines. This further helps data scientists to collect, store, and process data efficiently.

Becoming a data engineer requires expertise in SQL (Structured Query Language) and big data technologies, such as Spark and Hadoop. Additionally, you must have a good understanding of working with different cloud computing platforms. It will help you secure roles like Data Architect and Data Manager.

4.     Machine Learning Engineer

Machine learning engineers are in high demand. These experts develop, deploy, and maintain scalable AI models in real-world environments. Many businesses are looking for these systems to automate their repetitive tasks, saving a lot of money and resources.

If you love working with machines and AI models, machine learning engineering is the perfect career for you. But to get into this field, you need proficiency in deep learning frameworks and programming languages like Python.

In the machine learning field, you get many options to advance your career. You can become a Lead Machine Learning Engineer and AI/ML Manager.

5.     AI Research Scientist

An AI research scientist is an expert who pushes the boundaries of artificial intelligence. These professionals conduct research to discover and develop new data approaches and adaptive systems. If you want this role, you must have strong research skills and advanced knowledge of deep learning.

A passionate AI research scientist can lead to roles in academia and advanced research positions in various tech companies.