Machine Learning vs Data Science – Comparison

By TechFunnel Contributors - Published on May 14, 2020
Data Analytics vs Machine Learning

Do you know that over 2.5 quintillion bytes of data is created on a daily basis? According to IBM, it has been forecasted that the number of jobs for every data expert in the United States will increase by 364,000 openings to 2,720,000 by 2020.

In addition, it has also been predicted that by 2020, an estimate of 1.7 MB of data will be generated every second for every human on the planet. Imagine how much data this would be at the end of the year. How much more by the end of the decade? It is therefore obvious that we can not effectively handle data without data science and machine learning.

The burning question therefore is that: how do we intend to process this amount of large data? Now, this is where data science vs machine learning comes into the bigger picture. It should interest you to know that machines have the capacity to learn on their own.

Yes, this is very much possible and in fact realistic in this rapidly developing technological age. Just like humans, machines can be structured and designed to learn more from a good amount of data. Machine learning becomes highly important so that machines are enabled to learn from experience automatically. This is done without the machines needing to be explicitly programmed.

What is Data Science

In simple definition, data science involves analyzing results gotten from data. It explores data in its simplest and most basic form. This is done in order to understand the complex patterns, trends inferences, and behaviors of data logs.

Data science helps an organization to unravel necessary insights needed in the decision making processes of the business. It involves the extraction of useful information from data. In order to do this, data science optimizes a number of other methods from different fields.

(Also Read: What is Data Science? Everything You Need to Know)

What is Machine Learning

The concept of machine learning involves teaching machines how to learn on their own without the need for any human intervention or aid. It feeds data to the machine systems.

Here is how machine learning works: it starts by reading and studying the given data sample. This is done in order to discover necessary and beneficial insights and patterns. These patterns are therefore used to develop a model that will accurately predict the outcome of future contingencies.

It then evaluates the performance of the model by utilizing the given data sample. This process continues until the machine can auto-learn and link the input to the accurate output. All these processes occur in the absence of a human intervention.

Differences Between Data Science and Machine Learning

  1. Scope

    Data Science: The scope of data science centers on the creation of insights gotten from data that deals with all real world complexities. It entails the understanding of data requirements, as well as the process of extracting data, among other tasks.

    Machine Learning: on the other hand, machine learning deals with the accurate classification or prediction of the outcome for new data sets. It entails studying the patterns of historical data through the use of mathematical models.

    The scope of machine learning only comes to play in the data modeling phase of data science. In essence, it can not effectively exist outside of data science.

  1. Data

    Data Science: in terms of data, data science is a concept that is used in the analysis of big data. Data science in this regard comprises of data cleansing, data preparation, and data analysis. It generates majority of its input data in the form of human consumable data. This form of data is designed to be read and evaluated by humans. It usually takes the structure of tabular data or images.

    In addition, the data that is processed in data science does not necessarily have to evolve from a machine or as a result of a mechanical process. It helps in retrieving, collecting, ingesting and transforming large amounts of data that are collectively called big data.

    It is the function of data science to bring structure to big data. It studies big data in order to find compelling patterns. This enables data science to advise business executives to implement effective changes that would revolutionize a business or organization.

    Machine Learning: it is necessary to mention that unlike data science, data is not the main focus for machine learning. Instead, learning is the major focus for machine learning. This is where another major divergence occurs between machine learning vs data science.

    In machine learning, the input data will be generated and processed specifically for algorithm usage. Examples of these data designs under machine learning includes word embedding, feature scaling, adding polynomial features etc.

  1. System Complexity

    Data Science: the system complexity in data science involves the components that would be engaged in the management of unstructured raw data coming. It involves numerous moving components that are normally scheduled by a synchronization system that harmonizes free jobs.

    The operation of data science can also be carried out with manual methods. However, this would not be as efficient as that of machine algorithms.

    Machine Learning: in almost every situation, the most predominant system complexity that is associated with machine learning is the algorithms and mathematical concepts upon which the field is built upon.

    Furthermore, the ensemble models usually have several machine learning models. Each of these models will have significant effect upon the final outcome. The operation of machine learning utilizes numerous techniques such as regression and supervised clustering.

    The system complexity of machine learning involves different types of machine learning algorithms. Some of the most popular ones include matrix factorization, collaborative filtering, clustering, content-based recommendations, and many more.

  1. Necessary Knowledge base and Skill Set

    Data Science: it is pertinent for a data scientist to possess a significant knowledge about domain expertise. He or she would also be required to possess ETL(1) and data profiling skills. A remarkable knowledge about SQL(2) is also needed, as well as expertise with NoSQL systems. B

    Basically, it is necessary for a data scientist to understand and be able to exhibit standard reporting and visualization techniques. Typically, a prospective in the field of data science must work towards possessing significant skills in analytics, programming and domain knowledge.

    Having a very successful career as a data scientist requires the following skills:

    • A strong knowledge of Scala, SAS, Python, R.
    • Ability to evaluate numerous analytical functions
    • The ability to forecast future outcomes based on patterns of past data sets.
    • A reasonable knowledge about machine learning
    • Ability to work with unstructured data. These data may be gotten from several sources such as social media, video etc.
    • A good experience in SQL database coding is also an advantage in becoming highly sought after in the world of data science. In fact, data analytics and machine learning count as one of the numerous methods and processes that are employed in the activities of data science.

    Machine Learning: the primary requirement for an expert in machine learning is a strong background in math understanding. It is equally necessary to have strong knowledge in Python/R programming. A machine learning expert should be able to carry out data wrangling with SQL.

    Model-specific visualization is also a basic requirement for machine learning. Below is a highlight of the basic career skills that would help a prospect advance significantly in the domain of machine learning:

    • An in depth knowledge of how to program
    • Knowledge of probability and statistics
    • Skills on data evaluation and data modeling
    • Expert knowledge in computer fundamentals
    • An understanding of coding in programming languages such as Java, Lisp, R, Python etc.
  1. Hardware Specification:

    Data Science: the hardware specifications here should be horizontally scalable systems. This is because data science involves the handling of big data. Furthermore, the hardware in data science would have to be of high RAM and SSDs. This is to ensure overcoming I/O bottleneck.

    Machine Learning: the hardware specifications for machine learning consists of GPUs. This is necessary in order to carry out intensive vector operations. In addition, the world of machine learning is evolving to use more powerful versions like TPUs.

  1. Components

    Data Science: it is widely known that data science encompasses the whole data network. The components of data science includes:

    • Collecting and profiling of data – ETL (Extract Transform Load) pipelines and profiling jobs
    • Distributed computing and processing of scalable data.
    • Automated intelligence for online recommendations and fraud detection.
    • Exploration and visualization of data for the best intuition of data.
    • Predefined dashboards and BI
    • Data security, data backup, data recovery and data engineering to make sure all forms of data can be accessed.
    • Activation in production mode
    • Automated decisions to run business logic through any machine learning algorithms.

    Machine Learning: the typical components of machine learning are:

    • Understanding the problem to find an efficient solution for the problem.
    • Data exploration – through data visualization in order to get an intuition of features to be used in machine learning model.
    • Data preparation – this component of machine learning involves evaluating a number of possible solutions to data issues to make sure that sure values of all features are in the same range.
    • Data Modeling and Training – this component involves selection of data on the basis of problem type and type of feature set
  1. Performance measure

    Data science: based on this factor, data science’s performance measures are not standardized. This is because the performance measure changes from case to case. Usually, it will be a denotation of concurrency limits in data access, interactive visualization capability, data quality, data timeliness, querying capability etc.

    Machine learning: on the other hand, the performance measures in machine learning models are always transparent. This is because each algorithm will possess a measure to denote how effective or ineffective the model describes the sample data that has been provided. For instance, Root Mean Square Error (RME) is employed in Linear Regression as a denotation of an error in the model.

  1. Development Methodology

    Data science: in terms of methodology development, data science projects are similar to engineering projects with well-defined landmarks.

    Machine Learning: however, the methodology development of machine learning is more aligned to resemble research formats. This is because the first stage is more of a hypothesis formulation, which is followed by attempts to prove the hypothesis with the available data.

  1. Visualization

    Data science: typically, the visualization of data science refers to data directly using any common graphical representations such as pie charts and bar charts amongst others.

    Machine learning: here, visualizations are used to represent a mathematical model of sample data. For instance, it could involve the visualization of confusing matrix of a multiclass classification. This by implication would assist in the quick identification of untrue positives and negatives.

  1. Languages

    Data science: typically, the data science world uses common computing languages such as SQL, and SQL-like languages such as Spark SQL, HiveQL etc. In addition, data science also uses common data  processing scripting languages like Perl, Awk, Sed and many more. Furthermore, another category of popularly used languages in data science is framework-specific and well-supported languages such as Java for Hadoop, and Scale for Spark, amongst others.

    Machine learning: on the other side of the coin, the machine learning world mainly uses Python and R as its major computing languages. In contemporary times, Python is being widely accepted as modern deep learning experts are mainly resorting to Python. It is also necessary to mention that SQL is equally necessary in machine learning processes, most especially in the data exploration phase.


In conclusion, machine learning enhances the processes of data science. This is done by providing a set of algorithms that is useful for data modeling, data exploration and decision making etc. Data science does its part by combining a set of machine learning algorithms in order to make accurate predictions of future outcomes of decisions.

In as much as we have discussed the differences between data science and machine learning, it is necessary to expound that both fields are intertwined, and they aid each other in their various functions.

The world of data storage is fast progressing and you can not afford to be left behind. Get on the data science vs machine learning train today and optimize these fields to improve your business decisions.

TechFunnel Contributors | is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicated to sharing unbiased information, research, and expert commentary that helps executives and professionals stay on top of the rapidly evolving marketplace, leverage technology for productivity, and add value to their knowledge base.

TechFunnel Contributors | is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicate...

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