Data science and machine learning are interconnected; machine learning is in fact a part of data science. Terms commonly used in the modern tech world like artificial intelligence, big data, deep learning, data science, and machine learning, are sometimes assumed to be similar, and the misconceptions around these interrelated technologies are valid. Over the years, the popularity of these technologies has risen greatly, so it’s important to understand how these terms are different from each other.

In this article, we’ll learn more about data science and machine learning, including their differences, job prospects, and the skills required to become a professional in either discipline.

What Is Data Science?

Data science is the processing, extraction, and analysis of unstructured data generated by a business in order to help develop insights to inform decision-making in businesses of all sizes. If you’re shopping online on Amazon or Walmart, for example, a data scientist could uncover a pattern in your choices using big data, which would help them to better understand overall customer behavior. This helps companies to develop recommendation engines, tagged with the lines like “you may also like” or “since you were viewing this product, you may also be interested in”. This is only possible when the company has enough data to apply algorithms to analyze and extract information.

In simpler terms, data science is a blend of technology, decision-making, and management. It strives to obtain accurate data from sets of unstructured data. Data scientists prepare and analyze data by incorporating skills from computer science, statistics, and database management.

Techniques and technologies used in data science include:

Clustering

Dimensional reduction

Machine learning

Programming languages like Python and R

Frameworks like TensorFlow and Pytorch

Web interfaces like Jupyter Notebook

Data visualization tools like Tableau

Software frameworks like Apache Hadoop

What Is Machine Learning?

Machine learning is an element of data science and the study of algorithms. It is seen as an indispensable part of data science. Machine learning allows computers to learn from data so that they can carry out certain tasks. It is used to process data sets autonomously without human interference. Based on the algorithms, it works on the data extracted from different sources with the help of data science.

Data science has enabled the generation of data in large amounts, which means it has now become difficult for data scientists to manage it manually. This is where machine learning comes in. Machine learning makes it easier for data scientists to manage the data without any external advice or input. This is achieved through techniques like:

Genetic algorithms

Federated learning

Bayesian Networks

Regression analysis

Artificial neural networks

Decision trees

Robot learning

The approach to machine learning is divided into supervised learning, unsupervised learning, and reinforcement learning. Then there’s top modeling and meta-learning. However, as of 2020, deep learning is the learning technique that is mainly used in machine learning.


What’s the Difference between Data Science and Machine Learning

Below is a brief guide to the differences between data science and machine learning.

Data Science: Broad term for multiple disciplines like data analysis, artificial intelligence, and machine learning.  

Machine Learning: A part of data science concerned with the study of algorithms.

Data Science: Involves structuring big data and advising business decision-makers on design changes.

Machine Learning: Involves learning patterns from historical data that has already been extracted by a data scientist.

Data Science: Extracts useful data and creates insights from both unstructured and structured data.

Machine Learning: A skilled data scientist is needed to extract and analyze the data.

Data Science: The data cannot be extracted using a mechanical process.

Machine Learning: Uses techniques like supervised clustering to extract data.

Data Science: Focuses on the broader process and the whole data processing methodology.

Machine Learning: Focuses on algorithms and statistics.


Skills Needed for Data Science and Machine Learning

Below is a brief guide to the key skills required in data science and machine learning.

Data Science

Statistics

Data visualization and data cleansing

Unstructured data management techniques and methods

Programming languages such as R, Python, and Java

Data mining and data cleansing

Understand SQL databases

Big data tools like Pig

Machine Learning

Computer science fundamentals

Data evaluation and modeling

Understanding of algorithms

Natural language processing (NLP)

Statistical modeling

Data architecture plan

Text representation methods

How to Become a Data Scientist

The best way to begin a career in data science is by improving your skills in fields like math, statistics, engineering, computer science, and programming languages such as Python, R, and Java. To start a career in data science, you need to be across the following programs and skills:

•Hadoop: This helps with handling big data, and usually, the big data is split into blocks and distributed using the MapReduce programming model.

•Apache Spark: It provides task dispatching and basic input functionalities to perform analytics.

•Data Visualization: It is the visual description of the given data. For instance, maps, flowcharts, and graphs.

Data Science Careers and Salaries

Listed below is a selection of job titles within the data science field, along with the typical annual salary each role attracts.

Data Scientist: Data scientists are capable of handling large amounts of data to extract and analyze structured data, which helps in advising on the business models of organizations of all sizes. The average annual salary of a data scientist is $139,840.

Application Architect: An application architect keeps track of the behavior of applications used in organizations. The average annual salary of an applications architect is $113,757.

Enterprise Architect: An enterprise architect applies architecture principles and practices to guide organizations through the business, information, process, and technology changes necessary to execute their strategies. The average annual salary of an enterprise architect is $110,663.

Statistician: Statisticians analyze and collect data to track patterns and trends between users and stakeholders. The average annual salary of a statistician is $76,884

Data Analyst: Data analysts help make sense of large data sets in order to help inform business decision-making processes. The average annual salary of a data analyst is $62,453.


How to Become a Machine Learning Expert

If you’re interested in becoming a machine learning expert, you should brush up on the following skills and knowledge:

•Math and logic

•Computer fundamentals to help collaborate with data scientists and develop data sets

•Data modeling and evaluation skills

•Knowledge of probability and statistics

•Communication skills

Machine Learning Careers and Salaries

Machine Learning Engineer: These are programmers who develop systems and machines that can learn and apply learned knowledge based on user behavior. Machine learning experts essentially deal with artificial intelligence. Machine learning engineering jobs grew by 344% between 2015 to 2018. The average annual salary of a machine learning expert is $146,085.

Natural Learning Process Scientist: Natural language processors (NLPs) enable machines to talk to humans and understand their queries. Siri, Cortana, and Google Assistant are a few examples of NLPs. An NLP scientist needs to be fluent and grammatically correct so that the machine acquires the same skills. The average annual salary of an NLP Scientist is $107,000.

Business Intelligence Developer: A business intelligence developer uses machine learning to collect and produce required data for an organization. The average annual salary of a business intelligence developer is $101,728.

Human-Centered Machine Learning Designer: This role is related to machine learning algorithms that are focused on humans and their behavior. An example of this is the suggestion that pops up after you finish watching a series or film on Netflix. It reads the pattern and suggests a similar series or film. The average annual salary of a Human-Centered Machine Learning Designer is $116,668.


Next Steps

If you’re motivated to switch careers, we’re here to help with all the tools, skills, and knowledge you’ll need to become a professional data scientist. Our full-time Data Science course offers an accelerated online program of classes, mentorship, and professional guidance designed to get you a career in data science, fast. If you need to keep working while you learn, try our part-time Data Science bootcamp.

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