Data Scientist Career Path: Step by Step Roadmap in 2024

Data Science is the field of study combining programming skills, mathematics and statistics, and domain expertise to extract meaningful data insights. It deals with extensive volumes of data and leverages modern tools and techniques to find patterns, derive meaningful information, and make better business decisions. 

In Data Science, Machine Learning algorithms are applied to text, numbers, video, audio, etc., to generate Artificial Intelligence (AI) systems to perform work that otherwise require human intelligence. 

According to the US Bureau of Labor Statistics, there will be an expected increase in jobs in data science by 35% by 2032. Data Science is one of the most desired fields, and many professionals want to enter the field. The demand for Data Science is not limited to a single reason; some are mentioned here: 

• Companies generate huge amounts of data daily. They need professionals who know what to do with this data. Here, Data Scientists come into play!
• There are several alternate career paths for Data Scientists, i.e., Data Architect, Data Analyst, Data Engineer, Business Analyst, Data and Analytics Manager, etc.
• The pay for Data Science professionals is high, and salaries are competitive.

A Data Scientist uses statistical and analytical methods to draw valuable insights from data. Data Scientists perform the following tasks:

• Collect, clean, and process data from various sources.
• Apply statistical modeling techniques and machine learning algorithms to develop predictive models and data-driven solutions.
• Work with stakeholders to understand their needs and communicate results effectively.
• Design experiments and continuously explore rising technologies to stay at the forefront of the field.

The Data Scientist roadmap includes essential prerequisites and skills required to become a successful Data Scientist. This roadmap will be suitable for anyone aspiring to start a career in data science. To become a Data Scientist, follow the below-listed guide and prerequisites, which are crucial.

Build a Strong Foundation

Getting a Bachelor in relevant fields such as computer science, data science, or statistics will ensure that you understand the fundamentals of Data Science. In addition, you must know linear algebra, calculus, probability theory, and statistics. These concepts form the basis for many data science techniques and algorithms.

Empower yourself with Data Science Skills

Data Science is a technical field requiring various skills to enter. Some of the technical data science skills are mentioned below:

• Data Structures and Algorithms
• Programming Languages
• Data Analysis
• Machine Learning
• Statistical Analysis
• Data Visualization
• Neural Networks

This field is not limited to just technical skills. Data Science jobs include experts collaborating with engineers, analysts, developers, and others. Soft skills will help them in such collaborations, and some of them are mentioned below:

• Communication skills
• Critical thinking
• Business Intelligence
• Analytical Mindset
• Problem-Solving skills

Earn Certifications

Enrolling in relevant certifications can be a great way to enhance your credentials and demonstrate your expertise. Here are some certifications that can help you establish a career in data science:

• Azure Data Scientist Certification: Designing and Implementing a Data Science solution on Azure by Quickstart: This Data Scientist certification focuses on data science in the Microsoft Azure environment. Microsoft Azure is used to build, train and deploy machine-learning solutions. This course validates your skills in working with Azure technologies and machine learning.
• IBM Data Science Professional Certificate by Coursera: This Data Science professional course provides an understanding of data science tools and techniques using IBM technologies such as Watson Studio and Jupyter Notebooks.
• Google Cloud Certified Professional Data Engineer by Whizlabs: This Data Engineer certification focuses on data engineering and covers data processing, storage, and analysis using Google Cloud Platform (GCP) technologies.
• SAS Visual Business Analytics Professional Certificate by Coursera: This Business Analytics Professional certification is offered by SAS, a popular analytics software provider. It validates your skills in data manipulation, predictive modeling, machine learning, and data visualization using SAS tools.

Get Hands-on Data Science Projects

Undertake projects that demonstrate your skills and understanding of data science. Work on real-world datasets or participate in Kaggle competitions to solve data-related problems. This will showcase your ability to apply your knowledge and provide proof of your skills.

Gain Practical Experience

Seek internships or part-time positions that allow you to work on data science projects. Practical experience will enhance your skills and provide valuable industry exposure and networking opportunities.

Stay Updated and Network

Data science is a rapidly evolving field. Read the latest trends, research, and technologies through blogs, forums, conferences, and online courses. Build a professional network by joining data science online communities. Networking may help you discover job opportunities and connect with experienced professionals.

Apply for Data Science Roles

Once you have built a strong foundation, gained practical experience, and developed a portfolio of projects, start applying for Data Science roles. Make a compelling resume highlighting your skills and projects, and tailor your applications to each specific role. Prepare for interviews by practicing common data science interview questions and showcasing your problem-solving abilities.

The Data Scientist hierarchy varies depending on the size of the organization or the type of industry. But, as a Data Scientist, you can work in entry-level, mid-level and senior-level positions.

Entry-Level Data Scientists

While working as an entry-level Data Scientist, you will work on tasks assigned to you by Senior Data Scientists or other leads of the projects. In this position, you will learn, grow and explore areas of interest. 

Entry-Level Data Scientist Skills

The entry-level data scientist skills include:

• Python
• R
• SAS
• SQL

Mid-level Data Scientists

While the roles and responsibilities of a mid-level Data Scientist will include the same/similar responsibilities to that of an entry-level Data Scientist, you will also have more ownership of your work. But you will have a good understanding of how businesses use data.

Mid-Level Data Scientist Skills

The mid-level Data Scientist skills include:

• Python
• R
• SAS
• SQL
• Coding
• Leadership

Senior-Level Data Scientist

As a senior-level Data Scientist, you take high-level ownership and lead projects. You will work with your data scientists team and other professionals like data analysts and engineers.

Senior-Level Data Scientist Skills

The senior-level data scientist skills include:

• Machine Learning
• Big Data technologies
• Data visualization
• Project management and leadership
• Collaboration
• Problem-solving

The average annual salary for Data Scientists in the U.S. is $82,898, and in India, it is ₹6,901,259. However, there are multiple job roles in Data Science with competitive salaries, and each is an important part of this field. Here is a list of Data Science roles:
 

Artificial Intelligence Specialist 

An Artificial Intelligence(AI) Specialist is a professional who focuses on the development, implementation and optimization of emerging technologies and solutions of artificial intelligence. They must have deep expertise and understanding of machine learning, data analysis, algorithms, etc. 

Required Skills: The skills required to become a Artificial Intelligence Specialist include:

• Programming and software development
• Mathematics and statistics
• Data handling and analysis
• Machine learning and deep learning

Educational Qualifications

• A bachelor’s degree in subjects like Computer Science, Statistics, Computer Engineering, Data Science, or a related field.
   

Data Engineer 

Data Engineers are responsible for producing data from various sources like social media and making it beneficial by building and managing systems that generate this data. They transform and clean data to be scrutinized by data scientists and analysts. Data Engineers use software engineering principles to algorithms to automate the data-flow process. 

Required Skills: The skills required to become a Data Engineer include the following:

• Programming languages
• Data Warehousing
• Data Analysis
• SQL and NoSQL
• Data Visualization

Educational Qualifications

• A bachelor’s degree in information technology, computer science, computer engineering, or a related field.
   

Data Architect 

A Data Architect is responsible for describing the policies, models, methods and technologies for collecting, organizing, storing and accessing a company’s information. They focus on high-level business intelligence relationships and data policies. Data Architects must create blueprints for data management systems, designing plans to integrate and maintain all data sources.

Required Skills: The skills required to become a Data Architect include the following:

• Programming languages
• Machine learning
• Applied Maths and Statistics
• SQL and NoSQL
• Data Visualization

Educational Qualifications

• A bachelor’s degree in information technology, computer science, or a related field.

Organizations are recognizing data science due to its data-driven decision-making. Here are some key points highlighting the positive industry outlook for data scientists:

• The demand for data scientists continues to increase as companies seek to extract insights from their vast data sets.
   
• Organizations of all sizes and across industries are embracing data science and analytics.
   
• Data scientists enjoy career opportunities in various sectors. They can work in industries such as technology, finance, healthcare, e-commerce, consulting, research, and government.
   
• Data scientists continue to enjoy attractive compensation packages due to the high demand and specialized skill set required for the role.

Data Science is growing rapidly and making its way into our everyday lives. There is a lot of data to sort and manage; without Data Science professionals, it is not easy. There is a rising demand for Data Science professionals. 

To stay afloat in the Data Science industry, you must polish your skills and fulfill all the prerequisites. Remember, learning is a continuous process. The more you learn, the better you perform, and the courses at Careervira will help you. We have some beginner-level courses for to start a Data Science career right after 12th, and some of the advanced Data Science courses to choose from.