Data Science and Machine Learning are the same?

Data Science And Machine Learning Are The Same

The words data science and machine learning are frequently used together, however, you must understand the differences between machine learning and data science if you are planning on building a career in either. While different individuals, companies, and job descriptions have different versions or ideas about what each career is, I feel there is a significant separation between data science vs. machine learning. Both positions do some sort of engineering, whether it is the data scientist queuing the database using SQL, or the machine learning engineer using SQL to put suggestions or predictions back into the newly labeled column/field. Of the different profiles focused on data, the machine learning engineers are crucial to making models developed by the data scientists, from the data prepared by data engineers, and the use cases identified and developed by the product or business managers, come to life.

A data scientist may be focused on the same degrees, statistics, mathematics, or actuarial science, while a machine learning engineer will focus their primary attention on software engineering (and some institutions offer a specialization in machine learning as a certification or a degree). The most relevant skills that data scientists must learn to be effective machine learning engineers are those related to software engineering, including being able to write optimized code, preferably in C++, run vigorous tests, and understand and build and operate existing or custom tools and platforms to reliably deploy and manage models. While data science model-building may take place in sandbox environments such as Kaggle, where models are not built to serve real-world predictions, learning to scale model deployment, monitoring, and associated machine learning engineering tasks is possible only in real-world industrial settings. Data science can also be done using hand-guided techniques, although these are less effective than machine algorithms. Machine learning cannot exist without data science, since data must first be prepared for modeling, training, and testing.

On the one hand, data science is focused on visualizing data and making it better presented, while machine learning is focused more on learning algorithms and training on data and live experiences. Data Science is a wide-ranging research area using algorithms and models of machine learning to analyze and manipulate data.

Data analytics is the process of extracting meaningful insights from data using a variety of analytic techniques and tools. A Data Scientist gathers the raw data from different sources, prepares and pre-processes data, and applies Machine Learning, Predictive Analysis algorithms to derive actionable insights from the collected data. While the data scientist is expected to predict the future from past patterns, the data analyst is expected to extract meaningful insights from the different sources of data. Data analytics deals with finding patterns from past data to forecast future events, whereas artificial intelligence involves analyzing data, making assumptions, and is designed to make predictions that are beyond the capabilities of humans.

Data analytics is concerned with finding patterns within a given piece of data, whereas AI is designed to automate this process, giving machines human-like intelligence. Data analytics is about finding answers to existing questions and gaining insights through exploring new perspectives. To extract valuable insights from data and to tackle difficult analytical problems, data science approaches integrate data analytics, algorithmic development, and various data analytics techniques and technologies. Data Science is a multidisciplinary field, which is focused on extracting insights from a vast dataset — whether it is unstructured or structured.

The major difference between them is that data science, being a broad term, not only focuses on algorithms and statistics, it takes care of the whole methodology behind processing the data. Data Science is a very confusing term, and it is, therefore, essential to understanding how the two are distinct. Data science can be seen as combining several parent disciplines, including data analytics, software development, data science, machine learning, predictive analytics, data mining, and many others. We caught up with Eric Taylor, senior data scientist at CircleUp, for a Fireside Chat on Simplilearn, to learn what makes data science such a fascinating field, and which skills will help professionals get a solid foothold in this rapidly growing domain.

Data scientists would not find it too hard to make the jump to a machine learning career, as they will have worked intimately on Data Science technologies frequently used in machine learning anyway. Whether or not you have coding experience, you can be a good Data Scientist by learning the tools and techniques required for working with data, and by getting a good knowledge of the field. A data scientist needs to possess skills to work with Big Data tools such as Hadoop, Hive, and Pig, statistics, and to code in Python, R, or Scala. Machine learning engineers must know computing basics and statistical modeling, and also possess expertise in natural language processing, data assessment, and modeling, and data architecture design.

I soon came to understand that machine learning engineering is like data science, but different enough that it requires a unique skill set. Learning programming languages such as R, Python, and Java is necessary for understanding and cleaning data to use for building ML algorithms. When compared with the traditional methods for statistical analysis, Machine Learning has evolved to become the best method to extract and work with the largest datasets that are most difficult, thus making data science simpler and less messy. Artificial intelligence integrates big data sets via iterative computing and smart algorithms, helping computers to automatically learn.

Machine Learning, by contrast, is meant to take action on a granular level right away, such as making an instant recommendation for a user looking at a selection of products on your e-commerce store, regular optimizations to keywords that you are using for promotions to create savings, or engaging with a customer who is unsure via a chatbot. A machine learning engineer may occasionally wish to understand how algorithms such as XGBoost or Random Forest function, and they will have to examine model hyperparameters to tune them to perform memory-and-size-constrained studies. Science is all about experiments and knowledge-building, and this requires having a few motivating questions about the world, and hypotheses, which can be brought into data and tested using statistical methods. The better data scientists understand machine learning, the better predictions and assessments they can make, and the more deliberate actions they can make, with no human input.