In the field of Data, you often hear recurring terms like machine learning, data mining, data science, etc. For somebody new in the field, those terms can be quite confusing. That makes it of utmost importance to understand the meaning of those basic terms in the world of Data. In this article, we will explore the difference between Data Science, Data Analytics, Machine Learning, and Big Data
Data Science is defined as the study of data. It is a discipline in which the Data Scientist uses a series of tools to collect, analyze and interpret a huge amount of unstructured or structured data, in order to find useful information. Those tools are related to the combined use of programming, algorithms, machine learning, and statistics.
The typical tasks of a data scientist include but are not limited to exploring and analyzing data coming in different formats and types. For example, extracting data from videos and from text surveys to derive information from it. They may include creating new statistical/machine learning models or use existing ones to derive information. For instance, running a RandomForest model to predict the future price of houses. Another common task is doing predictive analysis by using previous data patterns to predict future ones.
If you plan to become a Data Scientist, you will need to have a wide range of skills mainly related to coding, statistics, and mathematics. You will need to become familiar with some programming languages, mainly Python and R. Although, you might need to learn other ones such as Java, MatLab, SAS, etc). Additionally, you should also be comfortable using statistical and mathematical concepts (Mean, Median, rank, Bayes, etc) and apply them to data. Any good data scientist should also be able to visualize and present the data in an understandable manner especially to someone with little knowledge about the technical aspect of it. You should as well be familiar with database systems such as Hadoop, Hive, MySQL. Finally, you should have extended knowledge about machine learning.
If you got all of the above, well you have the potential of making over 100k a year. Data Scientists are needed pretty much everywhere and in most if not all industries.
Data Analytics & Data Analysis
When we talk about Data Analytics, getting insights is the key. The basic idea is to run a dataset through an algorithm or a series of algorithms in order to derive insights from it. For instance, we can explore the correlation between the amount of rain in a place and the quality of the wine. Or you may want to check the relation between the amount of fertilizer used and the yields. Another example could be the relation between the quality of a product and the number of sales. Those relations come in various forms and in every single industry.
As a data analyst, your task will be to gather, clean, and understand datasets. Once the data preparation is done, you will transform the data to get some insights from it. Finally, you will need to design compelling reports using different reporting tools. In other words, it means that you will need to summarize your data and make sure that the quality of the data is maintained. Sometimes, you may need to use your database querying skills and programming skills to get insights from your data.
The skills of a data analyst are quite similar to the one of a data scientist. The particularity resides in the fact that you will need a little bit more focus on data visualization. Like a Data Scientist, you will need to be very comfortable in using many programming languages and possess a very good understanding of the various statistical and mathematical concepts. In addition to that, you should have an advanced understanding of data storing and data warehousing. You should as well be able to query any type of database.
Big Data refers to extremely voluminous data (both structured or unstructured) that require additional complex steps in order to process it and find useful information. Nowadays, we create about 2.5 quintillion bytes of data every single day. And that number keeps on increasing. As a result, big data specialists need to know how to collect, store, share and query that huge pile of data.
You will need to be able to understand the data and identify what is relevant. A core task would be to find an optimized way to collect, process, and share the data. You should additionally be able to complete complex tasks in the least amount of time. Understanding the various business goals so that your tasks contribute to the growth of the business entity is a must.
Like the data scientist or data analyst, to become a Big Data specialist you will need to have some programming skills in addition to having a good statistical and mathematical base
Machine Learning Specialist
Machine learning is the process of creating and using algorithms to create programs that are designed to run on their own. Once the program design, there should not need any human interaction necessary to improve it. Indeed, machine learning programs have the ability to learn by themselves based on the data being provided.
Machine learning can be revolutionary for businesses that are looking to automate their data analytic processes. The real-like application includes recommender systems, ad placement, spam filters, stock trading, fraud detection, and many more.
As a machine learning specialist, you will need advanced programming skills along with mathematics and statistics. You will need to be familiar with various learning algorithms and techniques, such as deep learning, neural network, and all the like. Those are sometimes used along with autonomous systems programmed to run independently.
To conclude, I will leave you with this great illustration that summarizes what it takes to be a perfect data scientist.