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Difference between Data Science, Big Data & Data Analytics
Data Science , Big Data & Data Analytics , are buzzwords at the moment, they are all about data analysis, but each one with its particularity, in this post we will differentiate a little each of the 3 below.
Data
The data they are everywhere. In fact, the amount of digital data that exists is growing rapidly – in fact, over 2.7 zettabytes of data exist in the digital universe today, and this is expected to grow to 180 zettabytes by 2025.
All of this data – from your photos to your stock market finances – has begun to be analyzed for insights that can help organizations improve their business. That’s why, every day, more organizations are looking for professionals capable of working with data.
It is quite easy to become a data scientist . Once you have an affinity for data analysis the right way, it’s just a matter of practicing your newfound skills enough to become proficient.
In this article, we are going to discuss what is Data Science – Data Science ; Big data; and Data Analytics – Data Analytics , recommended skills for each and potential salaries.
Data Science | Data scientist
What is data scientist? What do data scientists do? Data scientists combine statistics, math, programming, problem solving to capture data in ingenious ways, with the ability to look at data differently to find patterns, along with the activities of cleaning, preparing and organizing the data. This data can be Structured and Unstructured.
Simply put, Data Science is a field that encompasses anything related to cleaning, preparing, and analyzing data. It is an umbrella term for the techniques used to extract data and gain insights from information (datasets).
Data science – Knowledge needed
- In -depth knowledge of SAS and/or R. For Data Science, R is generally preferred.
- Python Coding: Python is the most common coding language that is used in data science along with Java, Perl, C/C++.
- Hadoop Platform: While not always a requirement, it is important to know that the Hadoop platform is preferred for the field. Experience in Hive or Pig is a big plus.
- Database/SQL Coding: While NoSQL and Hadoop are the main focus for data scientists, preferred candidates can write and run complex SQL queries.
- Working with unstructured data: It is extremely important for a Data Scientist to be able to work with unstructured data, whether from social media, video feeds, audio or other sources.
The average salary for a Data Scientist is $1,17,212 . Salary can range from $90,000 to $2,50,000.
BIG DATA
What is a Big Data Analyst ? According to Gartner , Big Data can be defined as “Large volume of data, generated at high speed and variety, that require innovative and cost-effective ways to process, organize and store them, in order to allow better understanding for decision making and process automation.” Through this process, the role of the Big Data Analyst is to obtain insights that help organizations make better business decisions.
Simply put, Big Data is a keyword used to describe immense volumes of data, both unstructured and structured, that flood organizations of all sizes on a day-to-day basis. In other words, Big Data refers to gigantic volumes of data that cannot be effectively processed with traditional software/technologies. Big Data processing starts with the raw data that is not aggregated or organized – and most of the time, it is impossible to store in the memory of a single computer.
BIG DATA – Necessary Knowledge
For those looking to work with Big Data, you will need:
- Analytical skills: The ability to gain insights from the massive amounts of data you will get. With analytical problem-solving skills, you will be able to determine what data is relevant to solving a problem.
- Creativity: You must have the ability to create new methods for gathering, interpreting and analyzing a data strategy.
- Math and Statistical Skills: Well, old-fashioned “number crunching” is absolutely necessary.
- Computing: Computers are the key to the work behind every data strategy. Programmers will have a constant need to create algorithms to turn data into insights.
- Business Competencies: Big Data professionals must have an understanding of the business objectives that are in place, along with the underlying processes that drive business growth and profit.
The average salary for a Big Data professional can range from $1,04,463 to $2,20,000.
Data Analyst | Data Analytics
What is Data Analytics?
Data Analytics is the science of examining raw data to find patterns and draw conclusions from that information, applying an algorithmic or mechanical process to obtain insights. According to Forbes, the big data analytics market will soon surpass $200 billion .
A data analyst’s job lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows; for example, running a series of datasets to look for significant correlations with each other. Data Analytics is used across many industries to enable organizations to make better decisions as well as verify and refute existing theories or models.
DATA ANALYST – necessary knowledge
To exercise the role of Data Analyst, they normally require the following:
- Programming skills: Knowing the R and Python programming languages – extremely important for any data analyst.
- Statistical and math skills: Descriptive and inferential statistics and experimental designs are also indispensable for data analysts.
- Machine Learning – Machine Learning .
- Data Skills: Ability to map raw data and convert it into another format that allows for more convenient consumption of the data.
- Communication and data visualization skills.
The average salary for a Data Analyst is $15,000/month.
Specializations/Courses
Below are links to courses on the topics we cover here in this article.
