Intro to Data Science
Hello everyone!! my name is Anugrah Nurhamid you can call me uga. This is my first article. I really excited with data science, so I’ll try to join a data science program at one platform in Indonesia. Now, I has finished my boot camp program. So, I wanna share about the data science, what that do, why that do and who that do. Let’s start begin with the introduction about data science.
What is data science ?
“Data science is a concept used to tackle big data and includes data cleansing, preparation, and analysis.” — simplilearn by Srihari Sasikumar.
A data scientist collects a lot of data from various sources. After that, they will do an analysis about the data to get insight and critical information for applies to machine learning, predictive analysis, sentiment analysis and anything they can take from the data. In data science not only about “the science”, but there is still a lot of knowledge that mus be learned including.
Why You Need it?
Before I start to explain about that. I wanna tell about part of the data science is business intelligence , data analyst, data scientist & machine learning engineer. What is the difference in each part of it? and Why company need it?
Business Intelligence
After the data has been gathered & organized by data engineer or database administrator or if the company have a big data, probably will done by big data engineer. Why you need it? “Use data to create reports and dashboards for take insights from a data to gain business the company”. What the technique involved of business intelligence is analyze the data & present about the insights of the data form of reports, dashboards and many more.
Data Analyst
Between data analyst and business intelligence, overall almost have a same things what they can do. But, data Analyst focuses on algorithms to determine relationship between data offering insights. The major difference between Business Intelligence and Data analyst is that Analytics has predictive capabilities whereas BI helps in informed decision-making based on analysis of past data.
Data Scientist
The superiority of the data scientist is “assess potential future scenarios by using advanced statistical method”. Some method that can do at data scientist is Regression, Logistic Regression, Clustering, Times Series and many more. So, data scientist is more than analyze the data & present what insights about the data, but they can do predictive analysis from the data with many method they know.
Machine Learning Engineer
“Utilize artificial intelligence to predict behavior in unprecedented ways”. The knowledge is about causal descriptions, predictions, and inferential derived from structured and unstructured data. Machine learning engineer will develop algorithms to make it easier for machines to see their own programming data, then determine the patterns that are in them, and after that train themselves to understand a command.
The techniques performed by machine learning engineer can also be carried out by a data scientist, among other is.
- Supervised Learning. “In Supervised learning, you train the machine using data which is well ‘labeled.’ It means some data is already tagged with the correct answer.” — 99guru.com
- Unsupervised Learning. “Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the ‘unlabelled’ data.” — 99guru.com
- Reinforcement Learning. “Similar to supervised learning, but instead of minimizing the loss, one maximizes reward.”
Simplicity to access the methods used in data science. You can reach all the method on original documentation seems like scikit learn, xgboost and many more about method & algorithm that you wanna use for analysis, visualize or predictive analysis.
Besides of that, you can reach ease online course about the data science. For example you can get free 2 months in datacamp and another platform about leaning data science is udemy, dqlab (in Indonesian Language), udacity and many more. So, if you interesting about data science you can get information from their website.
That is all from me, forgive my RIP English, LOL. I hope you guys can get something about generally data science. Feel free to ask, you can reach me or correct me if I wrong on the column comment.
Happy learning, mate! Have a nice day! :)
Source :
- Principal, Article Datanest by Nabih Ibrahim
- simplilearn article (About Data Analyst, Data Science, & Machine Learning ) by Srihari Sasikumar
- 99guru article (Supervised vs Unsupervised Learning)
