Introduction

There has been a lot of debate, confusion, and chaos around the topic of Data Science vs Machine Learning. Data Science and Machine Learning fields are expanding exponentially, and organizations are on a lookout for experts in these fields. They can seamlessly maneuver massive sets of data and provide actionable business insights. A recent research study conducted by IBM suggested that in 2020-2021, the jobs market for U.S. data experts will rise from 350,000 to a whopping 2,700,000. This is a major occurrence towards Data Science and Machine Learning fields.

Many people today have a lot of questions regarding both the domains, like, ‘What is the difference between Data Science and Machine Learning?’ By the end of this article, you’ll have a thorough understanding of Data Science Machine Learning.

Let’s jump right into the hot topic Data Science vs. Machine Learning.

1) What is Data Science?

People in the data industry have tried their hand at defining Data Science for many decades. However, the best possible answer to this question is via the model created by Hugh Conway in 2010. This model considers three aspects, such as subject expertise & statistics, hacking skills, and math. A combination of these three elements forms the foundation of Data Science.

Is Data Science and Machine Learning the same? No. Data Science is a term employed to deal with big data, which involves cleaning, storing, and evaluating the data. Data scientists collect data from various sources and use sentiment analysis, predictive analytics, and Machine Learning to derive actionable insights from the data sets obtained. Data scientists understand the business implications of data and can provide accurate insights and predictions that can be employed to drive vital strategies. But then again, how is Data Science different from Machine Learning? Let’s understand what Machine Learning is, and it shall bring some clarity.

2) What is Machine Learning?

Machine Learning is the practice of employing complex algorithms to collect data, analyze it, and then make predictions for a particular topic. Conventional software used for Machine Learning consists of predictive analysis and statistical analysis, which are used to identify trends and capture information based on extracted data. In simple terms, Machine Learning is a subset of Data Science.

Facebook carries out a well-known instance of the application of Machine Learning. The Machine Learning algorithms of Facebook collate behavioral information of all its users. The algorithm recommends articles based on users’ data (past behavior on the platform and connected applications). Facebook’s algorithm also suggests notification to users based on its findings. Similarly, Amazon and Netflix also employ the Machine Learning algorithm to recommend products or movies to users based on their past behavior.

Machine Learning applications are picking up pace across industries. Employing Machine Learning to cut costs and make business decisions is the foremost driver of its exponential growth. However, using these techniques to target specific groups of people for political, economic, and social propaganda raises ethical concerns as well. Since algorithms are formulated based on data generated by humans, they are, at times, perceived to garner social biases into their findings.

3) Data Science vs Machine Learning

The understanding of Data Science vs Machine Learning is crucial for all aspiring Data Science professionals.

Our Data Science and Machine Learning the same? Machine Learning comes under the Data Science field, as Data Science is a broad term for multiple disciplines. Machine Learning employs techniques, including supervised clustering and regression. Data Science might not be a mechanical process. Data Science is an umbrella term that focuses on the whole methodology of data processing, including algorithms and statistics, as sub-parts of it.

Data Science includes the transformation, ingestion, collection, and retrieval of large data sets, which is also known as big data. Data Science assists in the structuring of big data, identifying trends, and guiding decision-makers to formulate effective strategies.

4) What are the similarities between Data Science and Machine Learning?

Although a majority of the people keep asking for the differentiating factors, there are a few similarities between both domains. Knowing these similarities will elevate your understanding of Data Science and Machine Learning. Also, combining the appropriate skill sets in these two fields will help you secure a compelling future in your career.

Perhaps the most similar aspect between Machine Learning and Data Science is that both fields share certain essential skills for their functionality. Both the fields perform similar engineering functions such as a Machine Learning Engineer employing SQL to insert predictions or suggestions into a model, or a Data Scientist employing SQL to query a database. Both Data Science and Machine Learning require an in-depth understanding of Python (or R), GitHub, code sharing, and version control.

5) What is the difference between Data Science and Machine Learning?

The differences between these two fields are the ones that fuel the debate of Data Science vs Machine Learning. There are a few key features of both these fields, that make them different from each other. These are as follows:

  • Data Science
    • Presents and notifies findings
    • Analyzes results
    • Classification and regression
    • Supervised and unsupervised algorithms
    • Focuses on algorithms and statistics
  • Machine Learning
    • Incorporation of models into a UI/ warehouse/ table
    • Scheduling
    • Scaling
    • Automation
    • Focuses on programming and software engineering

6) What are the skills required to become a Data Scientist?

Anyone looking to have a strong career in the Data Science field should have complete knowledge of 3 specific departments, such as domain knowledge, programming, and analytics. Here is a list of skills that will help you ascend in your Data Science career.

  • Use big data tools such as Pig, Hive, Hadoop
  • Understand SQL databases
  • Programming languages like Python and R
  • Be well-versed with unstructured data management
  • Data visualization
  • Data cleaning and data mining
  • Statistics

7) What are the skills required to become a Machine Learning Expert?

This field takes a different approach to statistics. Here is a list of essential skills that can help you jump-start your career in the Machine Learning field:

  • Text representation skills
  • Data architecture design
  • Natural language processing
  • Be well-versed with the concepts and applications of algorithms
  • Data modeling and evaluation
  • Statistical modeling
  • Fundamentals of computer science

Conclusion

Different job descriptions, companies, and people will indeed have a novel take on these two fields, as Data Science Machine Learning is significantly separate fields. There are some skills common in these two fields. However, a Data Scientist usually focuses on the interpretation of outcomes, model building, and statistics. A Machine Learning expert takes the model from Data Science, scales and deploys it into production. The debates and discussions on the topic of Data Science vs Machine Learning will continue to trouble people.

However, we hope this article has brought about some clarity for you about the topic at hand. If you’re interested in making a career in either of the domains, you can check out our 6-month-long online Full Stack Data Science program, an industry-recommended and validated course, aligned to the SSC NASSCOM curriculum that will help you in getting better job opportunities.

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