Data Science vs Machine Learning

What is Data Science?

Data Science is the field of study to process the data residing in the organization’s repositories by applying various scientific methods.

It is a discipline that brings together statistics, data analysis, Machine Learning, Computer Science, and their related methods to process the data and understand the underlying patterns in it.

It includes collection, cleaning, and preparation of the data and identifying the patterns to generate insights that can help organizations become data-driven in decision-making for the growth and success of the company.

Data Scientists are responsible for implementing various data science techniques for an organization. They collect and process structured and unstructured data from a business point of view and apply various methods such as statistics, machine learning, etc., for insights generation.

Skills Required to Become a Data Scientist

If you are looking to pursue a career in Data Science, below are the skills you will need to be proficient in regardless of your role -

• Strong programming knowledge of Python, R, Scala, etc.
• Experience in SQL database coding
• Knowledge of various data wrangling techniques
• Sound understanding of various Machine Learning algorithms
• Deep knowledge of Mathematics and Statistics concepts
• Ability to process structured and unstructured data
• Data mining, cleaning, and visualization skills
• Knowledge of Big Data processing frameworks such as Apache Spark, Hadoop, etc.
• Business acumen/Domain expertise
• Strong communication skills

Limitations of Data Science

One of the biggest challenges in a Data Scientist’s life is finding the right data for business problems. The issues with data can be classified as either quantity or quality. Applying data science techniques to insufficient, messy, and noisy data can lead to arbitrary or misleading results.

What is Machine Learning?

Machine learning is a field in Computer Science that enables systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves. Movies recommendations by Netflix and Facebook/Instagram feeds are some of the examples that are powered by Machine Learning techniques.

Machine Learning Engineers focus on implementing various tools and techniques to automate the predictive models. A Machine Learning Engineer typically works as part of a larger data science team and will communicate with data scientists, administrators, data analysts, data engineers, and data architects.

Skills Required to Become a Machine Learning Engineer

Below skills are required to pursue a successful career in the Machine Learning field -

• In-depth knowledge of computer fundamentals and programming knowledge of Python, R, Scala, etc.
• Sound understanding of various Machine Learning algorithms
• Advanced knowledge of Mathematics and Statistics concepts
• Knowledge of data modeling and evaluation

Inherent Limitations of Machine Learning

Though Machine Learning algorithms let computers learn the underlying patterns in the data with minimal interventions, it still requires engineers to optimize and tune the algorithms each time to work on new business problems.

Aside, there are many problems that can’t be solved by applying Machine Learning. Also, these algorithms might add complexity to a business process if the problem can be solved using traditional statistical methods.

Where is Machine Learning Used in Data Science?

We can understand the use of Machine Learning in Data Science by understanding its lifecycle. The Data Science lifecycle consists of 6 different steps, as shown in the below diagram.

• Business Requirements - This step includes an understanding of the business problems to which we want to apply data science tools and techniques. For e.g., building a recommender system to improve customer experience and engagement, predicting customer churn, etc.
• Data Acquisition - This step includes getting access to the right set of data for the given business problems. For e.g., getting access to items purchased by customers for building the product recommender system.
• Data Processing - In this step, raw data is transformed into a suitable format that can be processed and explored.
• Data Exploration- In this step, various statistical and visualization methods are applied to explore the patterns and trends in the data.
• Modeling- This is the step where Machine Learning algorithms are used to model the input data and learn the underlying patterns in it. This entire process includes cleaning & preparation of the data and training, testing & evaluating the Machine Learning model.
• Deployment - Once the Machine Learning model is trained, it is deployed in the business process to predict the outcomes.

Difference Between Data Science and Machine Learning

To understand the difference between Data Science and Machine Learning, we need to refer to the Venn diagram shown below. Data Science can be considered as a combination of Computer Science, Mathematics, and Stats along with domain expertise, while Machine Learning mainly focuses on Computer Science and Applied Mathematics fundamentals.

So the main difference between these two techniques is understanding the business domain. If you wish to become a Data Scientist, then you need to acquire domain expertise to process the data in such a way that it can help companies grow and become profitable. If you can’t understand businesses and their problems, then you can’t use data science techniques in the best way for the organizations.

Data Science VS Machine Learning