Data Science Roadmap 2024 - Everything You Should Know!

Data Science Roadmap 2024 - Everything You Should Know!

Summary

This comprehensive blog post will explore the various steps to building a thriving career in data science. From discovering the perfect data science role that aligns with your passion and exploring the diverse responsibilities and salary ranges associated with each role based on experience, various data science skills one should have, how to build a data science portfolio etc., this blog is a final roadmap that has got you covered completely.

As per the current industry job trends, Data Science is a highly employable and appealing profession. 

Data Science Platform Market is expected to grow 15.4% from 2022 to 2029, reaching $ 298.16 billion.
Data science continues to be one of the most promising and in-demand careers. LinkedIn reports that there are over 24,000 data science jobs open in India.

This massive demand for data science introduced a swift surge in the number of Data Science Course providers.

This blog will help you understand who can study data science, their roles and responsibilities, and their average salaries.

Must Read > Why is Data Science Always in Demand?

Eligibility for Data Science

Data Science has been in great demand in the industry. Students have started looking forward to studying data science subjects to cope with the demand.

Industries started upgrading the skills of their teams to remain competitive. Several institutes and course providers picked the industry needs and designed suitable courses in Data Science.

So, who is eligible to study data science?

Anyone willing to learn Data Science can opt to enroll in a data science course, whether a newcomer or a professional. Engineers, Marketing Professionals, Software, and IT professionals can study Data Science. For regular courses in Data Science, basic high school-level subjects are the minimum requirement.

Data science generally combines concepts from statistics, computer science, and mathematics. An additional advantage is having a student with a degree in a STEM field—science, technology, engineering, or mathematics. Otherwise, students from any stream, like business studies or the arts, are also eligible for relevant courses in data science. It does not matter if you have study or career gaps; you only need the right skills for the role.

Data Science Roadmap - Everything you need to know

The following blog will explain different steps for a successful data science career. From finding the proper data science role where your passion fits into the different responsibilities each role carries and their salaries with respect to experience, this blog covers it all.

OdinSchool | Data Science Roadmap

Step 1 - Finding Your Passion

Now that you have decided to embark on a data science career, it becomes crucial to understand your passion and determine which specific data science role aligns with it. The roles mentioned in the following blog typically fall under these three personas. 

First Persona

These data science professionals possess a deep passion for uncovering meaningful patterns within raw data and excel at using that data to drive insights and direction for the business. They are highly skilled at crafting compelling stories with the data, enabling them to effectively communicate their findings and drive impactful business decisions. They are generally called data analysts, storytellers, visualizers, etc.

Second Persona

Efficiency-oriented data scientists desire to optimize code for maximum speed, computational efficiency, and modularization. They deeply understand programming techniques and are skilled at streamlining processes to improve efficiency. They are generally referred to as data scientists or data engineers or software engineers or, research scientists, etc.

Third Persona

The last data scientist group is passionate about building propensity, forecast, optimization, and segmentation models to help drive business decisions. They are generally called machine learning data scientists, data architects, product scientists, etc.

Other than the roles mentioned above, there are many other data science roles. The most common data science roles, with their responsibilities and average salaries, are mentioned below. All three personas are necessary for a company to succeed, so finding which area provides you with the greatest job satisfaction is an important personal decision.

Step 2 - Enroll in a data science program

Enrolling in a Data Science Bootcamp is among the simplest ways to learn data science from the start. OdinSchool has a track record of successfully placing fresh graduates, working professionals, and professionals with lengthy career gaps in prestigious organisations like Prolifics, PepsiCo, Capgemini, Indegene, etc.  

 

Step 3 - Know & Master Skills for a Data Science Career

A successful career in data science demands a blend of soft skills, subject knowledge, and technical skills. Below is a detailed analysis of the critical competencies you should concentrate on,

Technical Skills

  1. Programming Languages: Proficiency in programming languages such as R and Python is essential for data analysis, visualisation, and manipulation. These languages offer the resources to manage massive datasets and carry out intricate computations.

  2. Statistics and Probability: A strong foundation in statistics and probability is essential for understanding data distributions, hypothesis testing, and inferring meaningful insights from data.

  3. Machine Learning and Artificial Intelligence: Developing predictive models, finding patterns in data, and automating data-driven decision-making all require an understanding of machine learning algorithms and methodologies.

  4. Data Wrangling and Cleaning: Data wrangling involves cleaning, transforming, and preparing data for analysis. This includes handling missing values, outliers, and inconsistencies to ensure data quality.

  5. Data Visualization: Effective data visualization is key for communicating insights to stakeholders. Skills in creating charts, graphs, and dashboards are crucial for presenting findings clearly and understandably.

Tip: You don't have to get scared by these technical skills. The easiest way to master these skills is to join a streamlined data science course.

Domain Expertise

  1. Industry Knowledge: A background in a specific industry, such as healthcare, finance, or retail, provides valuable context for data analysis. Understanding the business domain allows you to ask relevant questions and identify actionable insights.

  2. Attention to Details: Extreme attention to detail is necessary to ensure accuracy and integrity, which is possible only when you have good subject knowledge. Data errors might affect decision-making and result in false conclusions.

  3. Problem-Solving Skills: Data professionals often solve complex problems using data. Strong problem-solving skills are essential for framing problems effectively, designing experiments, and interpreting results.

  4. Critical Thinking: Data analysis requires a critical eye to identify biases, anomalies, and patterns in data. Critical thinking skills help you evaluate data objectively and draw sound conclusions.

 

Soft Skills

  1. Communication Skills: Effective communication of findings to non-technical audiences is crucial for data science experts. Written and oral communication proficiency is essential for effectively conveying complex concepts clearly and compactly.

  2. Collaboration and Teamwork: All data science professionals work collaboratively with engineers, analysts, and domain experts. Effective collaboration and teamwork are crucial for sharing knowledge, resolving conflicts, and achieving common goals.

  3. Adaptability and Learning: Data science is an ever-evolving profession; new technologies and methodologies are constantly emerging. To stay current and maintain the relevance of your talents, you must be adaptable and eager to learn.

Tip: An easier way to gain these skills is to enrol in a comprehensive data science course that supports overall development rather than just technical skills.

Step 4 - Engage in real-world projects

Theoretical knowledge in data science is merely the starting point; the real magic happens when theory meets application.

Engaging in projects bridges the gap between theory and application, providing a platform to implement learned concepts effectively. These projects offer a chance to solve authentic business problems, showcasing the practical application of data-driven solutions. 

Moreover, real-world projects keep you updated with industry practices, ensuring your skills align with current trends. And this is a very strong reason why you should join a data science program like OdinSchool, which helps you work with real-time projects.

Download the brochure here to check the different real-world projects.

Step 5 - Create a strong data science portfolio

A data science portfolio is a tangible representation of your skills, experiences, and capabilities. It is a powerful tool for establishing your credibility, standing out in the job market, and showcasing your potential to contribute meaningfully to data science projects and teams.

Here are some steps to follow to create a strong data science portfolio:

  1. Highlight Your Contributions: Specify your role in each project. If it was a team effort, clarify your contributions and responsibilities. Employers want to understand your skills and teamwork capabilities.

  2. Include Code Samples: Share your code snippets or link to your GitHub repository. This allows potential employers to assess your coding style, structure, and understanding of best practices.

  3. Create Visualizations: Integrate visualizations that effectively communicate your findings. This demonstrates your data storytelling skills and makes your portfolio visually appealing.

  4. Explain Data Cleaning and Preprocessing: Clearly explain how you handled data cleaning and preprocessing. This showcases your ability to work with messy data, a common challenge in the field.

  5. Incorporate a Variety of Data Sets: Use diverse data sets to showcase your adaptability. It could be data related to finance, healthcare, e-commerce, or any other exciting field.

  6. Include a README File: For projects on GitHub, include a README file with a brief overview, project structure, dependencies, and instructions for running or reproducing your analysis.

  7. Demonstrate Machine Learning Skills: If applicable, showcase your machine learning expertise. Discuss the models you used, the rationale behind your choices, and the performance metrics.

  8. Keep It Updated: Regularly update your portfolio with new projects and skills. This reflects your commitment to continuous learning and growth in the field.

  9. Include Educational Projects: If you're relatively new to data science, include projects from your coursework or personal studies. Highlight your ability to apply theoretical concepts to practical problems.

  10. Showcase Soft Skills: Mention any soft skills you utilized during the projects, such as communication, problem-solving, and critical thinking.

Remember, your data science portfolio is a dynamic representation of your evolving skills and experiences. Tailor it to reflect your unique strengths and aspirations within the field.

Step 6 - Enhance your online presence

Improving your online presence as a data science aspirant is crucial for networking, learning, and showcasing your skills. 

Here are some strategies to enhance your online presence:

Optimize Your LinkedIn Profile

  • Craft a compelling LinkedIn profile highlighting your skills, experiences, and aspirations.
  • Use a professional photo, write a concise but informative headline, and provide detailed descriptions of your education, projects, and work experience.

Engage in Social Media

  • Actively participate in relevant social media platforms such as Twitter, LinkedIn, and data science forums.
  • Share interesting articles, insights, and your own project achievements.
  • Engage in discussions and follow key influencers in the data science community.

Write Technical Articles

Share your knowledge by writing technical articles on platforms like Medium or towardsdatascience.com. This will establish you as an authority in your field and provide a valuable resource for others in the community.

Build a Strong GitHub Profile

  • Maintain an active GitHub profile with well-documented projects.
  • Include a README file for each project that explains the problem, your approach, and the results. This will demonstrate your coding skills and project management abilities.

Seek and Provide Mentorship

  • Connect with experienced professionals in the data science field for mentorship.
  • Likewise, consider offering mentorship to those who are earlier in their journey. Mentorship relationships can provide valuable insights and expand your network.

data science Interview Preparation Kit

Step 7 - Prepare for a data science interview

In the competitive landscape of data science, excelling in interviews is key to landing your dream role.

Here's a strategic approach to preparing for data science interviews:

  1. Review Core Concepts: Brush up on fundamental data science concepts, algorithms, and statistical methods. Be prepared to discuss them fluently.

  2. Problem-Solving Practice: Solve real and simulated data science problems. Platforms like LeetCode, HackerRank, and Kaggle provide valuable practice.

  3. Case Studies and Projects: Revisit your previous projects and be ready to discuss the challenges, methodologies, and outcomes. Case studies demonstrate your problem-solving prowess.

  4. Behavioural Questions: Prepare for behavioural questions that assess your communication skills, teamwork, and adaptability. Use the STAR (Situation, Task, Action, Result) method for structured responses.

  5. Stay Updated: Follow industry trends, emerging technologies, and recent developments. Interviewers may inquire about your awareness of the evolving data science landscape.


Step 8 - Focus on professional networking

Engaging with other data science professionals is a good way to advance your career.

  • Contribute to open-source data science projects on platforms like GitHub. This showcases your coding skills and demonstrates your commitment to collaborative work within the community.

  • Become a member of online data science communities such as Kaggle, Stack Overflow, and Reddit. Participate in discussions, ask questions, and contribute to the community. This helps you stay updated on industry trends and build connections.

  • Attend Virtual Meetups and Webinars: Attend virtual meetups, webinars, and conferences related to data science. This helps you stay informed about the latest developments and provides opportunities to connect with professionals in the field.

Step 9 - Get a suitable job

Enrolling in a data science course with placement support, such as OdinSchool, proves invaluable. The regular placement drives featuring esteemed IT companies like Pepsico, TCS, Cognizant, CtrlS, and others provide a direct avenue for students to secure promising career opportunities.

This strategic collaboration underscores the significance of joining a program that not only imparts quality education but also actively facilitates connections with top industry players, ensuring a smoother transition from learning to thriving in the dynamic field of data science.

OdinSchool | Data Science Roles, Responsibilities, and Salaries

Data Science Roles, Responsibilities, and Salaries

Data science job salaries are competitive, both in India and worldwide. Data science salaries vary depending on experience, skills, and industry.

Below is data for top data science roles, responsibilities, and average salaries per experience. (Source: Ambitionbox.com, naukri.com, indeed.com) 

Data Scientist

Entry-Level Data Scientist (0-4 years)

  • Collect, clean, and prepare data for analysis
  • Develop and apply machine learning algorithms to extract insights from data
  • Build predictive models to solve business problems
  • Communicate data-driven insights to stakeholders

Salary Range for an entry-level data scientist: ₹8,00,00 p.a

Mid-Level Data Scientist (5-9 years)

  • Develop and implement more sophisticated machine learning models
  • Solve more complex business problems using data science
  • Lead data science projects
  • Mentor junior data scientists

Salary Range for a Mid-Level data scientist: ₹11,87500 p.a

Senior Level Data Scientist (10+ years)

  • Develop and apply cutting-edge machine-learning techniques
  • Solve highly complex business problems using data science
  • Develop and implement data science strategies across the organization
  • Serve as a data science thought leader within the industry

Salary Range for a Senior Level data scientist: ₹27,00,000 p.a

Machine Learning Engineer

Entry-Level Machine Learning Engineer (0-4 years)

  • Design and implement machine learning systems
  • Develop and train machine learning models
  • Deploy and monitor machine learning models in production
  • Collaborate with data scientists to solve business problems using machine learning

Salary Range for an entry-level data Machine Learning Engineer: ₹7,00,000 p.a

Mid-Level Machine Learning Engineer (5-9 years)

  • Design and implement more scalable and performant machine learning systems
  • Develop and train more complex machine learning models
  • Solve more challenging business problems using machine learning
  • Mentor junior machine learning engineers

Salary Range for a Mid-Level data Machine Learning Engineer: 12,95,145 p.a

Senior Level Machine Learning Engineer (10+ years)

  • Design and implement machine learning systems that can handle big data
  • Develop and train state-of-the-art machine learning models
  • Lead machine learning projects
  • Serve as a machine learning expert within the organization

Salary Range for a Senior Level data Machine Learning Engineer: ₹18,00,000 p.a

 

 

Data Engineer

Entry-Level Data Engineer (0-4 years)

  • Design and build data pipelines
  • Collect, store, and manage data
  • Develop and implement data quality procedures
  • Ensure data is available and accessible to data scientists

Salary Range for an entry-level Data Engineer: ₹7,20,000 p.a

Mid-Level Data Engineer (5-9 years)

  • Design and implement more complex data pipelines
  • Develop and implement more sophisticated data quality procedures
  • Collaborate with data scientists and other stakeholders to ensure data is used effectively.

Salary Range for a mid-level Data Engineer: ₹13,80,000 p.a

Senior Level Data Engineer (10+ years)

  • Lead data engineering projects
  • Develop and implement data architecture
  • Oversee the data engineering team
  • Serve as a data engineering expert within the organization

Salary Range for a Senior Level Data Engineer: ₹18,70,000 p.a

Data Analyst

Entry-Level Data Analyst (0-4 years)

  • Collect, organize, and analyze data from various sources
  • Identify trends and patterns in data
  • Develop data visualizations to communicate insights
  • Prepare reports and presentations to stakeholders

Salary Range for an entry-level Data Analyst: ₹5,59,000 p.a

Mid-Level Data Analyst (5-9 years)

  • Develop more advanced data visualizations and dashboards
  • Conduct more in-depth data analysis
  • Build predictive models to forecast future trends
  • Provide more strategic insights to stakeholders

Salary Range for a mid-level Data Analyst: ₹7,00,000 p.a

Senior Level Data Analyst (10+ years)

  • Lead data analytics projects
  • Develop and implement data analytics strategies
  • Manage and mentor data analysts
  • Represent the data analytics team to senior management

Salary Range for a senior-level Data Analyst: ₹11,97,572 p.a

Business Intelligence Analyst

Entry-Level Business Intelligence Analyst (0-4 years)

  • Develop and implement business intelligence solutions
  • Analyze data to identify business opportunities and risks
  • Create dashboards and reports to track key performance indicators (KPIs)
  • Provide insights to help businesses make informed decisions

Salary Range for an entry-level Business Intelligence Analyst: ₹6,54,700 p.a

Mid-Level Business Intelligence Analyst (5-9 years)

  • Develop more sophisticated business intelligence solutions
  • Conduct more complex data analysis
  • Build predictive models to forecast business outcomes
  • Provide more strategic recommendations to senior management

Salary Range for a Mid-Level Business Intelligence Analyst: ₹9,90,000 p.a

Senior Level Business Intelligence Analyst (10+ years)

  • Lead business intelligence initiatives
  • Develop and implement business intelligence strategies across the organization.
  • Manage and mentor business intelligence analysts
  • Serve as a data evangelist within the organization

Salary Range for a senior-level Business Intelligence Analyst: ₹11,60,175 p.a

Data Quality Analyst

Entry-Level Data Quality Analyst (0-4 years)

  • Assess and monitor data quality
  • Identify and resolve data quality issues
  • Implement data quality improvement initiatives
  • Ensure data is accurate, consistent, and complete

Salary Range for an entry-level Data Quality Analyst: ₹7,00,000 p.a

Mid-Level Data Quality Analyst (5-9 years)

  • Develop and implement more sophisticated data quality strategies
  • Conduct more in-depth data quality audits
  • Collaborate with data engineers and data scientists to prevent data quality issues.
  • Provide training and guidance on data quality practices

Salary Range for a Mid-Level Data Quality Analyst: ₹8,80,000 p.a

Senior Level Data Quality Analyst (10+ years)

  • Lead data quality initiatives
  • Develop and implement data quality governance frameworks
  • Serve as a data quality expert within the organization
  • Advocate for data quality across the organization

Salary Range for a Senior Level Data Quality Analyst: ₹10,60,000 p.a

Data Visualization Specialist

Entry-Level Data Visualization Specialist (0-4 years)

  • Design and create data visualizations
  • Communicate data insights effectively using visualizations
  • Collaborate with data scientists to create impactful visualizations
  • Stay up-to-date with the latest data visualization tools and techniques

Salary Range for an entry-level Data Visualization Specialist: ₹5,90,000 p.a

Mid-Level Data Visualization Specialist (5-9 years)

  • Design and create more sophisticated data visualizations
  • Develop interactive data visualizations
  • Use data visualization to tell compelling stories
  • Train and mentor junior data visualization specialists

Salary Range for a Mid-Level Data Visualization Specialist: ₹7,40,000 p.a

Senior Level Data Visualization Specialist (10+ years)

  • Lead data visualization projects
  • Develop data visualization standards and guidelines
  • Serve as a data visualization expert within the organization
  • Evangelize data visualization across the organization

Salary Range for a Senior Level Data Visualization Specialist: ₹9,00,000 p.a

Data Product Manager

Entry-Level Data Product Manager (0-4 years)

  • Develop and manage data products
  • Define and prioritize product requirements
  • Collaborate with engineers and data scientists to deliver products
  • Measure and analyze product usage

Salary Range for an entry-level Data Product Manager: ₹8,20,000 p.a

Mid-Level Data Product Manager (5-9 years)

  • Develop and manage more complex data products
  • Conduct market research to identify product opportunities
  • Develop and implement product roadmaps
  • Manage a team of data product managers

Salary Range for a Mid-Level Data Product Manager: ₹24,50,000 p.a

Senior Level Data Product Manager (10+ years)

  • Lead data product strategy
  • Oversee the development and launch of data products
  • Serve as a data product expert within the organization
  • Advocate for data products across the organization

Salary Range for a Senior Level Data Product Manager: ₹33,70,000 p.a

 

 

Build Hands-on Experience

For those aspiring to enter the world of data science, practical experience is key.

Joining a data science course with hands-on projects is a great way to apply your learning. These projects act like real-world scenarios, letting you use the tools and techniques you've been studying.

It's not just about theory; it's about getting your hands dirty with actual data. This hands-on approach helps you understand concepts better and teaches you how to solve problems – a crucial skill in data science. So, if you're looking to build a career in data science, roll up your sleeves and dive into projects to gain valuable experience.

Final Take

As the demand for data science continues to soar and job trends reflect its exponential growth, it is evident that it holds immense significance in today's professional landscape. This surge has led to the establishment of numerous Data Science course providers, catering to the escalating need for skilled professionals.

The eligibility criteria for entering the field have broadened, allowing individuals from diverse backgrounds to partake in this transformative journey. Aspirants can tailor their roles within Data Science based on their passions, whether uncovering patterns, optimizing efficiency, or building predictive models. 

Ultimately, a career in Data Science holds immense potential for those willing to embark on a journey of continuous learning and adaptability.

So, go and explore data science!

Frequently Asked Questions (FAQs)

What are the qualifications for data science?

A bachelor's degree in any discipline or a STEM degree is an added advantage.

Can I do data science after the 12th?

Yes, you can study data science after the 12th. Students from any educational stream can study a data science course at the graduation or certification level.

Why do I find data science interesting?

I like reading different kinds of patterns. Data science is where massive volumes of structured and unstructured data are studied through modern tools, and AI helps uncover specific patterns and trends.

What is a real-time project in data science?

Data science projects cover a broad spectrum of business and industry and hold immense value. These projects expose individuals to various data sets, mirroring the diversity of data they may encounter in professional scenarios.

How do you get real-life data science projects?

  1. Join a professional data science course, as at the end, you will be given real-life industry problems to work on as your final project.

  2. Participate in various data science hackathons.

  3. Attend different networking events.

What should be in a Data Science portfolio?

Your end-to-end data science skills should be demonstrated in your data science portfolio through a range of projects, with a focus on your strongest area of expertise. 

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