As the world becomes increasingly data-driven, the demand for professionals who can analyze and interpret data has skyrocketed. Data Science and Machine Learning are two of the hottest fields in technology today, but what’s the difference between the two?

As a coder, it’s important to understand the distinction between these two fields and the skills required to succeed in them.

In this article, we’ll explain the differences between these in-demand fields, the importance of each field, the role of coding in both, the skills required, and how to get started.

Data Science and Machine Learning

Data Science and Machine Learning are two related fields that deal with the processing, analysis, and interpretation of data.

Data Science is the practice of extracting insights and information from data, while Machine Learning is a subset of Data Science that focuses on building algorithms that can learn from data and make predictions or decisions based on that data.

ML is a multidisciplinary field that draws on techniques from statistics, mathematics, computer science, and domain-specific fields such as biology, finance, or marketing.

Data Scientists are responsible for collecting, cleaning, and organizing data, designing experiments, and building models to extract insights and make predictions or recommendations based on that data.

Machine Learning, on the other hand, is a subfield of Artificial Intelligence (AI) that focuses on building algorithms that can learn from data and make predictions or decisions.

ML algorithms can be supervised, unsupervised, or semi-supervised, depending on the amount and type of data available. Supervised learning algorithms learn from labeled data, while unsupervised learning algorithms learn from unlabeled data.

Semi-supervised learning algorithms combine both labeled and unlabeled data to make predictions.

Before we move into the next section, ensure you have a good grip on data science essentials like Python, MongoDB, Pandas, NumPy, Tableau & PowerBI Data Methods. If you are looking for a detailed course on Data Science, you can join GUVI’s Data Science Career Program with Placement Assistance. You’ll also learn about the trending tools and technologies and work on some real-time projects.  

Additionally, if you want to explore Python through a self-paced course, try GUVI’s Python self-paced course.

Understanding the differences

The main difference between Data Science and Machine Learning is the focus of each field.

Another way to think about the difference between the two is that Data Science is a broader field that includes Machine Learning as a subfield.

Machine Learning is one of the many techniques that Data Scientists use to extract insights from data.

However, it is a powerful technique that has many applications beyond Data Science, such as natural language processing, computer vision, and robotics.

The role of coding and how it differs

Coding is an essential skill for anyone interested in pursuing a career in either of these fields. Data Scientists and ML engineers use coding to collect, clean, and organize data, build models, and interpret results. Let us differentiate based on 3 main factors:

1) Coding Practices

When it comes to coding practices in data science and machine learning, there are notable differences that should not be overlooked. Although both fields are interrelated, developers need to understand the purpose and required expertise before starting the coding process.

Machine learning developers usually work with languages such as C++ and Python, which they learn and understand thoroughly to build and test their models. Python is the most common choice for ML.

Conversely, data scientists use low-level and high-level languages to code systematic thinking to fulfill the purpose of data analysis. High-level languages require more significant expertise but can get the job done more quickly.

Therefore, most data scientists tend to use high-level assembly language to perform their functions. Some examples of these languages will be discussed below.

2) Techniques and end-results

Machine learning and data science, while sharing similarities, serve different purposes and require unique coding techniques.

Data scientists analyze datasets to prove hypotheses and communicate their findings through reports or visuals, to form theories or tell stories based on data. Hence, they use techniques like Regression, Classification, Linear regression, Anomaly detection, Decision tree, and much more. 

In contrast, machine learning developers create algorithms and software that enable computers to learn independently, recognize patterns, and solve problems without supervision. This results in models and algorithms that can be applied to accelerate decision-making processes in various fields.

3) The different skillset

In data science, there are certain skills that experts should have under their belt. These include data mining, data cleaning, and data visualization.

On the other hand, if you’re a machine learning coder, you need to have a thorough understanding of applied mathematics and data modeling.

However, it’s important to note that the world of machine learning is expansive, and depending on the type of model you’re creating, you may need additional skills. For instance, if you’re working on natural language processing, you’ll need to have a deep understanding of grammar and syntax for both humans and computers.

Top 6 must-have skills

To succeed in Data Science or Machine Learning, you need a combination of technical and soft skills. Technical skills include programming, statistics, and machine learning algorithms. Soft skills include communication, collaboration, and problem-solving.

Technical skills:

• Programming: proficiency in at least one programming language, such as Python or R.
• Statistics: knowledge of statistical methods and techniques, such as hypothesis testing, regression analysis, and Bayesian inference.
• Machine Learning: knowledge of ML algorithms, such as decision trees, random forests, and deep learning.

Soft skills:

• Communication: the ability to communicate complex technical concepts to non-technical stakeholders.
• Collaboration: the ability to work effectively in a team environment, with people from diverse backgrounds and skill sets.
• Problem-solving: the ability to identify and solve complex problems using data and analytical techniques.

Popular programming languages – Python, R, and Java

1. Python is the most popular programming language for both these fields, thanks to its simplicity, flexibility, and rich ecosystem of libraries and tools. Python is easy to learn and has a large community of developers who contribute to open-source libraries and tools, such as numpy, pandas, and sci-kit-learn. Python also has a growing number of libraries for deep learning, such as TensorFlow and PyTorch.
2. R is another popular language for Data Science, especially in academia and research. R has a large number of libraries for statistical analysis and visualization, such as ggplot2 and dplyr. R also has a strong community of developers who contribute to open-source packages and tools.
3. Java is also used in some Machine Learning applications, particularly in the development of large-scale distributed systems. Java is a popular language for building enterprise applications, and its scalability and performance make it well-suited for handling large volumes of data.

Top tools and libraries

Top tools include Jupyter Notebooks, which provides an interactive environment for working with data and building models, and pandas, a library for data manipulation and analysis in Python.

Other popular libraries include TensorFlow, PyTorch, and sci-kit-learn, which provide tools for building and training machine learning models.

In addition to these libraries, there are many other tools and cloud platforms available for Data Science and Machine Learning. Some of them are:

• Google Cloud Platform: a cloud-based platform that provides tools for data processing, storage, and analysis, as well as Machine Learning services.
• Amazon Web Services: a cloud-based platform that provides a wide range of services, including data processing, and storage.
• Microsoft Azure: a cloud-based platform that provides services for data processing, storage, and tools for building as well as deploying Machine Learning models.

Career opportunities

As the demand for data-driven insights continues to grow, so do career opportunities. And these jobs come with some of the most lucrative salary packages in the tech industry:

• Data Scientists: Earning around Rs. 26L per annum the highest data science salary figure in India, they are responsible for collecting, cleaning, and analyzing data, and building models to extract insights and make predictions.
• Machine Learning Engineers: With the highest figures indicating a salary of Rs. 21L per annum for experienced professionals, these guys are responsible for building and deploying Machine Learning models as well as integrating them into existing systems.
• Data Analysts: responsible for analyzing and interpreting data, and communicating insights to stakeholders. They make around Rs. 12L per annum which is said to be the highest and increasing every year.
• Business Intelligence Analysts: responsible for analyzing and interpreting business data, and making recommendations to improve performance. They bag swanky packages ranging up to Rs. 16.5L per annum which increases with the amount of experience gathered.

Kickstart your Data Science journey by enrolling in GUVI’s Data Science Career Program where you will master technologies like MongoDB, Tableau, PowerBI, Pandas, etc., and build interesting real-life projects.

Alternatively, if you would like to explore Python through a Self-paced course, try GUVI’s Python Self-Paced certification course.

The Importance of Data Science and Machine Learning in Today’s World

The amount of data being generated is growing exponentially, and companies that can make sense of this data are gaining a competitive advantage.

Both of them are being used in a wide range of industries, including healthcare, finance, marketing, and e-commerce, to name just a few.

In healthcare, ML algorithms are being used to analyze medical images, diagnose diseases, and develop personalized treatment plans and in finance, these algorithms are being used to detect fraud, predict market trends, and develop investment strategies.

In marketing, Data Science techniques are being used to identify customer segments, personalize marketing campaigns, and measure the effectiveness of advertising.