Data Science or Machine Learning Which Career Path Should You Choose?

Introduction

Data Science and Machine Learning are rapidly becoming the need of the hour. These two fields are fast growing and have huge opportunities. Their impact is being felt across industries and even in our daily lives. Curious how? 

The answer is companies rely on data generated by our every activity to make strategic decisions. These decisions, in turn, shape our virtual and non-virtual activities, which I’ll describe later. 

This article will help you choose whether you should choose data science or machine learning. By the end of this article, you should be able to differentiate between the two roles. Furthermore, you’ll learn the importance of real data science projects and case studies for gaining a competitive advantage in your career. 

Understanding Data Science and Machine Learning

Data Science is a vast and interconnected domain. It uses statistics, programming and machine learning to extract insights from raw data. Here you might be thinking if data science and machine learning are entirely from each other or not. Yes, these are different in their focus areas, but they are hugely interconnected. 

Data science uses machine learning and other techniques like data analysis and visualisation to solve complex issues and help decision-making. It deals with end-to-end data handling processes like collecting, cleaning, analysing, and presenting insights extracted from data. 

Machine learning is a branch of artificial intelligence. It allows systems to learn from data and improve performance without explicit programming. 

Machine Learning and Data Science complement each other. These two fields are like two sides of the same coin. Data Science focuses on getting insights and patterns from raw data. Machine Learning uses these insights to create predictive models and automate decision-making. 

Real-World Applications

Data science and machine learning, when combined, have many innovative applications. After analysing your search and watch history, it recommends your choices on shopping and OTT platforms. Banks use ML algorithms to look out for any unusual transactions and prevent financial fraud. Companies use these two technologies to improve marketing strategies and operations. 

Key Skills Required

Now, you will learn what you need to know and have knowledge about for making a strong career in Data Science and Machine learning. Remember that you’ll need to learn the fundamentals of both these fields. When combined with machine learning skills, data science will give a positive and strong competitive edge. 

Data Science Skills

Data science professionals should know programming languages like SQL and SAS. SQL is important for solving database queries. On the other hand, SAS helps with statistical modelling. You should be proficient in these languages to effectively analyse data in case studies and work on real-world data science projects. 

Secondly, focus on learning data wrangling, cleaning and visualisation techniques. You will need these skills to clean, transform and organise data into usable formats. Furthermore, learn how to use visualisation libraries like Matplotlib and Seaborn to clearly present data insights. 

You should also be proficient in data visualisation tools like Tableau and PowerBI which create interactive dashboards. These dashboards help in transforming complex datasets. Thus helping teams to interpret results and make decisions. 

Machine Learning Skills

The first and most important skill in machine learning is programming. You must be proficient in programming languages. Python is the most popular programming language. It is relatively easy to learn and more flexible than other languages, and it also has many libraries. 

You can also learn R programming languages if you are more inclined to do statistical analysis. Both of these languages are open-source, so you can easily access them. 

Another important skill to have is understanding algorithms, statistics, and Probability. You should have a thorough understanding of algorithms like linear regression, decision trees, and neural networks. You should also grasp statistics and probability so that you can analyse data, improve models and provide accurate predictions. 

Additionally, you should be familiar with ML libraries and frameworks to optimise model training and deployment processes in real-world data science projects and case studies. Three key frameworks are TensorFlow, PyTorch and Scikit-learn. TensorFlow and PyTorch are important for deep learning, whereas Scikit-learn will help you with traditional ML models. 

Work on these skills to work on impactful data science case studies and contribute to real-world projects to drive innovation. 

Career Roles and Salary

Now, coming to the career aspect. As stated before, data science and machine learning offer huge opportunities. Each role will bring unique responsibilities and expectations. I have highlighted some of these roles along with their salary in India to help you better choose the right career path. 

Data Science Career Roles

Data Analyst is the most popular and beginner-level role. You will interpret and visualise data to make strategic decisions in this role. The salary ranges from ₹1.8 Lakh to ₹13 Lakhs for less than 1 year of experience to 6 years of experience.

You can also choose to be a Business Intelligence Analyst and use tools like Tableau and PowerBI. You’ll create visualisation and dashboards to track KPIs and transform raw data. The salary ranges from ₹3.1 Lakhs to ₹18 Lakhs for 1 year of experience to 8 years of experience.

Additionally, you can be a Generalist Data Scientist who handles end-to-end data processes. You will explore various data science case studies and work on real world projects to create strategic business outcomes. The average salary can range from ₹3.8 Lakhs up to ₹28.0 Lakhs per year.

Machine Learning Career Roles

First is the Machine Learning Engineer role, which creates models that can learn and adapt without human intervention. They work closely with data scientists. As per AmbitionBox, salary ranges between ₹3 Lakhs to ₹24 Lakhs for 1 year of experience to 6 years of experience.

Another role is that of an AI Specialist. The focus area of this role is to create intelligent machines and systems which can mimic human intelligence. You will work with deep learning models and natural language processing tools. Salary ranges between ₹8 Lakhs to ₹45 Lakhs for 2 years of experience to 14 years of experience.

You can also be a Data Scientist with specialisation in Machine Learning. In this role, you will develop predictive models and optimize decision-making activities. You will also often collaborate on real world data science projects, including machine learning techniques. Salary ranges between ₹4.0 Lakhs to ₹55.0 Lakhs per year.

Which Career Path is Right for You?

In the end, I’ll come to the most crucial part. In this section, I’ll tell you how to choose between Data Science and Machine learning. You may feel confused, especially when both domains offer lucrative career options. But don’t worry; I’ll help you clear out your confusion in three simple steps, which I’ve outlined below. 

Assess Your Skills

First and foremost, start by analysing your skills. Identify the skills you enjoy working at and excel in. For example, ask yourself these questions: 

• Do you have a strong foundation in mathematics, algorithms and programming? If yes, then you should consider choosing Machine Learning. It focuses mainly on making predictive models and optimising algorithms. Would you like to do this work? If yes, then go for this field. 
• Are you someone who would enjoy working with huge datasets, cleaning data, and extracting insights from data? If yes, then data science could be your path. Are you someone who would like to work on real world data science projects and have data analysis, visualisation and storytelling skills? If yes, then choose this domain. 

Remember that both domains require you to have technical skills like Python and R. However, machine learning especially leans towards developing AI models, whereas data science focuses on understanding business problems with the help of data. 

Identify Your Interests

Secondly, identify what are your interests. Ask yourself what excites you more. Would you like to work on real-world problem-solving or enjoy making technical innovations? For example, answer these questions: 

• Would you enjoy seeing how companies leverage data insights for decision-making? If yes, then data science might be ideal for you. For instance, you might like exploring data science case studies and learning how analytics improve market strategies. 
• Would you like to create automated systems and develop AI applications? Would you enjoy experimenting with deep learning models? If yes, you should go for machine learning specialisation as it offers a hands-on technical focus area.

Align with Your Career Goals

Lastly, it is important to align with your career goals. Whatever field of specialisation you choose, always remember that it should match your long-term career goals. Ask yourself these three questions: 

• Do you want to develop AI models or solve business problems with data? 
• Are you excited about working on real-world data science projects or making machine learning innovations? 
• Where do you see yourself in future? Do you see yourself working on automation-driven projects or helping make strategic decisions through data storytelling? 

I hope answering these three questions will clarify your confusion and help you choose a career that aligns with your goals. Whatever you choose, always be consistent in your learning. Explore various platforms and data science books.

Pickl.AI is one of the best data science learning platforms, providing free data science courses for beginners and professionals. The institution believes in flexible learning and mixing theory with practice. Therefore, you will discover various data science case studies and real-world projects and learn with practising data scientists to gain an edge. 

Conclusion

Data science and machine learning are like the two sides of the same coin. These fields are highly interconnected and often overlap with each other. So, those who wish to gain a strong competitive edge in this domain should choose data science with a specialisation in machine learning. 

By working in these domains, you will explore many data science case studies and work on real world data science projects to get technical knowledge. 

Several platforms additionally allow you to work on real world data science projects with free data science certification. Ultimately, whatever field you choose, ensure it aligns with your long-term career aspirations. All the best, and happy learning to you!