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Data Science vs Machine Learning - What you need to know?

Data Science is an evolutionary extension of the statistics capable of dealing with a huge amount of the data by using robust computer science technologies and machine learning is the major area under Data Science, but they are not the one. Data science includes a vast range of data management technologies too like SQL, Python, R, Hadoop, and Spark programming languages.

On the other hand, machine learning is a field of study that gives computers the ability to learn without programming explicitly. So, machine learning can be seen as the process where the computer works accurately as it learns or drives information from the data it is given. For example, when a person writes a lot of text messages on a phone, it understands the common vocabulary and predicts words faster and more accurately.

Data Science vs Machine Learning – Head to Head Comparisons

Let’s have a look at the below five comparisons between both the technologies – Data Science and Machine learning.

Data Science vs Machine Learning – Head to Head Comparisons

1). Scope

Data science creates insights from the data dealing with real-world complexities. It includes tasks data extraction, requirement gathering, etc. Machine Learning helps to classify or predict outcomes by analyzing historical data through learning patterns using mathematical models or techniques.

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2). Input Data

Read: An Introduction to the Recurrent Neural Networks

Here, input data is the human consumable data which is to be read or analyzed by humans in tabular data formats. In machine learning, input data is transformed specifically for the algorithms used. A few popular examples include Features scaling, adding polynomial features, word embedding, etc.

3). System Complexity

Data Science has components to analyze unstructured data collected from multiple sources. In Data Science, multiple moving components are scheduled by an orchestration layer to synchronize the independent jobs.

In Machine Learning (ML), it is tough understanding complexity of algorithms and mathematical concepts behind ML algorithms. A system program can have multiple ML models and each of them contributes significantly to the final outcome.

4). Required Skills set

To become an expert in Data science field, the person should have the domain experience, knowledge of ETL tools, strong idea of SQL basics, reporting, visualization etc. At the same time, Machine learning domain demands the knowledge of mathematical concepts, Python/R programming, Data wrangling and more.

5). Hardware Specifications

Data science offers horizontally scalable systems to handle a massive amount of the data and high RAM or SSDs to overcome the I/O bottlenecks. For machine learning, GPUs are used on priority for intensive vector operations. There are more powerful versions like TPUs on the way.

Read: Data Science Certifications - Types, Details (Preparation Tips)

Data Science Components vs Machine Learning Components

Data science system covers the entire data lifecycle and typically covers the following components:

  • Data collection and Data profiling
  • ETL (Extraction, Transformation, and Loading) process for data scheduling.
  • Distributing computing for data distribution and processing horizontally.
  • Online data science models to automate intelligence and fraud detection.
  • Data visualization to explore the data in depth and get an in-depth intuition of data.
  • Dashboards and BI for the high-level stakeholders
  • Data engineering to ensure the data accessibility, data backups, data security, and the disaster recovery etc.
  • Data migration and deployment with industry compliance standards.
  • Running business logic on the top of data to automate decisions.

The machine learning process starts with data existence and typically covers the following components:

  • Understand the problem and find the most suitable solutions to solve the problem.
  • Explore data using one or more iterations where data visualization plays a key role in data exploration.
  • Prepare data to deliver the final output with maximum accuracy. Data preparation helps to deal with issues like what to do with the missing data, data scaling features and more.
  • Select the ML model based on the problem and type of feature set.

Data Science vs Machine Learning – What are the key differences?

Here are a few key differences between both technologies Data science and the Machine learning for your reference.

Performance Measure: Data science has not standard performance measures and it changes from the case by case. Performance measures in data science will be an indication of the Data Timeliness, Data Querying, Data quality, Data accessibility, Data visualization capabilities etc.

data science Quiz

In Machine Learning, performance measures are standardized with the help of algorithms. Each model describes how well or bad the system behaves for the given data. For example, Root Mean Square (RME) is an indication of the error in the model.

Visualization: Visualization is general in Data science to represent the data directly using graphs, bars, pie chart etc. In ML, visualization represents a mathematical model of the training data.

Development Methodology: Data science projects are usually aligned as engineering projects with clearly defined milestones. But ML projects are more of research that starts with a hypothesis and trying to get it proved with the available data.

Read: Job Description & All Key Responsibilities of a Data Scientist

Languages: SQL, Hive, and Spark are the most commonly used languages in the Data science for data processing and writing more powerful scripts by using scripting languages along.

At the same time, Python and R programming languages are the popular options in machine learning. Python is gaining more momentum these days when compared to other similar programming languages.

Machine Learning engineer vs Data science engineer – What they do?

There is a common confusion about how the two roles, machine learning engineer and the data science engineer are different. Here we will discuss in brief what they do and how are they different from each other.

What does a Machine Learning Engineer Do?

  • An ML engineer leverages big data tools and different programming frameworks to make sure that data is refined well collected from multiple sources and ready to scale as needed.
  • Machine Learning Engineer feed data into models defined by data scientists. They help to design the theoretical ML model and scale them in the future to store the real-time data.
  • ML engineers build programs that control robots or computers. The algorithms developed by ML engineers enable machines to identify patterns and learn commands itself.

What does a Data Scientist do?

  • To solve the complex problems, businesses turn to data scientists to gather, process, and analyze a huge amount of data and derive meaningful insights from it.
  • Data scientists explore data through all possible aspects and develop programs using different programming languages like Java or more.
  • Data scientists are using online experiments with other methods to help businesses achieve sustainable growth. He helps businesses to develop personalized data products and make informed business decisions.
  • Data scientists majorly focus on the statistical approach and research required to determine which machine learning approach, algorithm, and the prototype will work well.

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Both professions are relatively new, and it depends on the Company how it defines the roles and responsibilities of a data scientist a machine learning engineer. According to the experts, ML engineers are able to write production-level code more proficiently and data scientists help to predict what probably go into that build. So, data scientists are little more ad hoc to drive an informed business decision while ML experts are more focused to the production-level code.

If you are looking to prepare for a data scientist role or want to become a machine learning engineer, then prepare yourself with extensive training online for these courses and learn all the practical aspects of latest IT technologies to excel in the competitive fields.

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