When Data Science Met Machine Learning

If you hadn't been living on a remote island with no power and internet connection for recent years, you've likely found out about data science and machine learning. It's difficult to miss these hot topics. Each time we talk about chatbots, self-driving vehicles, predictive analysis, AlphaGo, different machine learning techniques is used to implement these technologies. While evangelists and stories are plenteous, machine learning hasn't become the main concern for business yet. If we look at public perception, machine learning algorithms are near science fiction and opting for ML as a business process is as yet a high obstruction.

Subsequently, this blog is planned for responding to handy inquiries as opposed to setting the vision and evangelizing the pattern. The discussion is about an umbrella term data science and machine learning, how they both communicate, and what the changes are brought about by the merger of these two fields.

Here, we’d like to discuss artificial intelligence as well. This term is interchangeably used with ML, so let’s have a look at how these terms are related.

The Connection Between Data Science, Artificial Intelligence And Machine Learning

Artificial Intelligence, much like data science is an extensive domain of applications, frameworks, and more that target reproducing human intelligence through machines. Artificial Intelligence speaks to an action planned feedback of observation.

Observation > Planning > Action > Feedback of Observation

Data Science utilizes various parts of this loop or pattern to take care of specific issues. For example, in the initial step, for example, Observation, data researchers attempt to distinguish designs with the assistance of the data. Thus, in the subsequent stage, for example, planning, there are two perspectives:

1. Discovering every single imaginable solution
2. Finding the best solution among all solutions

Data science makes a framework which interrelates both the previously mentioned perspectives and assists organizations with pushing ahead.

In spite of the fact that it's conceivable to clarify machine learning by accepting it as an independent subject, it can best be comprehended with regards to its condition, i.e., the environment or the framework it is used in.

Basically, machine learning is the connection that associates Data Science and AI. That is on the grounds that it's the way toward learning from data after some time. In this way, AI is the tool that enables data science to get results and the answers for specific issues. Notwithstanding, machine learning is the thing that helps in accomplishing that objective. A genuine case of this is Google's Search Engine.

Google's web index is a result of data science. It utilizes predictive analysis, a system used by AI, to convey savvy results to the clients. For example, if the individual types “data science training online” in Google's web search tool, then AI gathers this data through machine learning.

So, when the individual types this keyword, AI kicks in, and with predictive analysis finishes the sentence as data science training online near me which is the most common suffix to the user’s query.

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Data Science and Machine Learning process 

Moving forward, how about we split the entire DS/ML task to steps and investigate where the Data Science part completes and Machine Learning tasks. 

The Data Science procedure can be marginally unique relying upon the objectives of the project and the methodologies used, yet it generally reenacts the following: 

Finding and deciding the objective 

It is essential to comprehend the business issue first. The data scientist ought to extract the proper inquiries, comprehend, and characterize the objectives of the issue to be unraveled. This step is not as easy as it seems because there’s so much more to it that it takes a whole lot of business mindset plus time.

Data collection and storage

The data scientist deals with gathering and scratching data from a few sources, for example, SAP servers, API databases, and online storage. Some of the time all data is now gathered in a convenient data warehouse, yet once in a while, you have to put forth an attempt to get the data. 

Data handling and cleaning 

Notwithstanding the machine learning algorithms, one would not have the option to take in anything from data that contains an excessive amount of noise or is excessively conflicting with the truth: garbage in, garbage out. To make the entire task fruitful we have to clean the procured data. 

After data collection, the data is processed. This stage incorporates data cleaning and data transformation. Data cleaning is the most tedious procedure, as it includes handling numerous perplexing situations. For instance: 

• clashing data types 
• incorrect attributes
• missing values or attributes 
• duplicate values 

Data Analysis 

At this point, understanding what should really be possible with the data is significant for both business and the Data Scientist, here comes the research analysis part. With Exploratory Data Analysis (EDA), the data scientist distinguishes and refines the selection of factors to be used for the subsequent stages. 

Data modeling 

We now proceed towards the core stage of this process, that is, data modeling. The Data Scientist chooses at least one potential model and calculations and chooses the measurement of the model's exhibition. At this point, statistical and machine learning strategies are applied to the data to decide the model that best meets the business necessities. He prepares models from accessible data and tests them to choose the best model. 

Interpretation of results

Once the analysis is performed on your data set, you can interpret the results based on your objectives. If you’ve achieved the expected results, the analysis was successful otherwise you have to look for the loopholes in your approach and re-follow these steps to achieve better results.

Communicate the results

The trickiest part isn't yet done after interpretation. You have to meet with clients and partners again to convey business results. This step might seem really simple and easy, but in actuality, it is not. Communicating the results can be tricky when you’re presenting your analysis to the non-technical stakeholders and teammates. For smoothly winding up this process, data visualization comes to your rescue. You can use different data visualization techniques like charts, pie charts, graphs and etc. to communicate the results.

At this stage the task may end — maybe the business has arrived at its objective, or POC has not demonstrated an arrival on speculation for the business, and further work isn't required. 

Conclusion

Like every single other field, the data science goes towards the full stacks. Such individuals who gather in some fundamental field for themselves and expertise to make all other parts (data analysis, statistical modeling, or data visualization). So data science meets with machine learning to bring more positive changes in the work of data management.

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