Data Science and Machine Learning are two of the most trendy words in the technology ecosystem. You have friends or colleagues who work as data scientists or as machine learning engineers. You see a lot of available jobs with this kind of names and you see companies publishing ads about their products or services saying all things like “our data scientists/our machine learning engineers did this and that and we’re so proud”.
Do you feel like people are using these words without a clear understanding about what each means? Then let’s try a no nonsense, clear explanation on what is the difference between data science and machine learning.
Interested in more stories like this? Follow me on Twitter at @b_dmarius and I'll post there every new article.
- What is Data Science
- What is Machine Learning
- What is the difference between Data Science and Machine Learning
- Skills required for becoming a Data Scientist
- Skills required for becoming a Machine Learning engineer
What is Data Science
Data Science referes to a collection of processes and techniques which allow people to capture and collect data, transform it, analyze the data and then draw powerful and meaningful conclusions and business or scientific conclusions from it.
Before you judge me, I too don't like fancy definitions, so let me introduce you:
The layman's checklist to what is data science
- Find out what data an application, a process, a service or generally speaking an entity needs.
- Figure out how to efficiently capture that data
- Analyze why some data is useful and if an entity needs to capture more or less(seriously) data
- Data exploration
- Data analytics
- Data modelling
- Data mining - getting information out of data
- Report the data
- Visualize the data
Data science helps us understand our data. Where is the data coming from, how is it generated and how can we improve our data capture methods. Most of the times, we will be tempted to get more data than we actually need, because it makes us feel more confident on our decisions.
But in these times when data privacy has become such an important matter, businesses and legal entities need to learn how to be more efficient about the data they use in such way that they use strictly what they need and this data is carefully and securely stored and handled.
Data is meaningless without the information which is extracted from it, therefore the gist of data science is getting information out of the data. And by information, I mean meaningful information. Information which is needed to guide businesses, governments and any kind of legal entity while taking decisions.
What is Machine Learning
Machine Learning is a subfield of the much bigger Artificial Intelligence field which is focused on writing algorithms that can find patterns in the data and then act and take decisions according to those patterns without being specifically programmed to do so.
Does this definition help you in any way? It does not help me either.
The layman's checklist to what is machine learning
- Define a problem you would like to solve using machine learning
- Gather and prepare data that illustrates the problem you want to solve
- Build a mathematical or statistic model to try and find patterns in the data
- Use findings and test the patterns found on another set of data, ideally never seen by the model
- After you've tested your patterns and you're confident they model the real world closely enough, use these patterns to automate decisions and actions into your application
Machine Learning is no magic(although sometimes its results might make it look like it). It is, indeed, as simple as that. You find a pattern in data and extrapolate for that, hoping the pattern will keep applying in the future.
The difference between Data Science and Machine Learning
The difference between Data Science and Machine Learning stands in the day-to-day activities that a data scientists and a machine learning engineer might have while doing their work. The two fields are of course very correlated, which each domain borrowing results from the other.
Some people say that machine learning fits into the broader category of data science, while other people say it the other way around.
My 2 cents on this is that these two disciplines are complementary to each other, while also being overlapped up until one point.
My opining is that a machine learning engineer should be more inclined to programming, while also having a pretty good knowledge foundation of data science and data analytics. At the same time, a data scientist should be more focused on data analytics while also maintaining a solid foundation of programming.
Skills needed for Data Science
With Data Science being such a broad domain, a good data scientists should have pretty decent skills in all areas of analytics, but should have very good knowledge of the following:
- Maths and statistics
- Database manipulation(SQL, NoSQL)
- At least one scripting language for data processing, like R or Python
- Solid analytical skills
Skills needed for Machine Learning
A Machine Learning enginner should have a pretty strong game in:
- Programming - here Python seems like the most obvious choice, but good knowledge of various other programming languages and technologies is also kind of neccessary.
- Maths and statistics
- Analytical skills
- Data science techniques and algorithms
In this article we've discussed a little bit about the key differences between data science and machine learning. We saw some fundamental characteristics of both fields, we've done a comparison between machine learning and data science and then we've listed some of the most important skills data scientists and machine learning engineers should have.
Thank you so much for reading this! Interested in more stories like this? Follow me on Twitter at @b_dmarius and I'll post there every new article.