Data Science Vs Big Data vs Data Analytics
Advancement in technology has made it possible for us to collect and store large data. Hadoop and other frameworks have solved the problem of data storage. The problem is how to process this big data. Data processing involves Data Science, Big Data and, Data Analytics. This article looks at the similarities and differences between these Computer Science terms.
Table of Content
– Introduction to Data Science, Big Data and Data Analytics
– The Role of Data Scientists, Big Data Professionals and Data Analysts
– The skills needed to be a Data Scientist, Big Data Professional and Data Analysts
– Salary Prospect
– Use Cases
Introduction to Data Science, Big Data and Data Analytics
Let’s begin by defining these terms.
What Is Data Science?
Data science is the use of statistical tools, mathematical concepts, programming languages and Machine Learning algorithms to uncover hidden patterns from raw data. It involves designing and constructing new models to solve problems using different predictive models, algorithms, prototypes, and custom analysis.
What is Big Data?
Big Data refers to massive amounts of large data which is often unstructured. It is data that can be analyzed for useful insights that can lead to better business decision making.
What is Data Analytics?
Data Analytics involves examining raw data to draw conclusions about that information. It is concerned with discovering meaningful information from data to help in decision-making. Data Analytics involves extracting, storing, cleansing, transforming and, modeling data.
The Roles of Data Scientists, Big Data Professionals and Data Analysts
What do Data Scientists Do?
Data scientists analyze data to draw insights from the data. They use different Machine Learning algorithms to predict the occurrence of certain events in the future. The Data Science process involves finding hidden patterns, unknown correlations, trends, and other useful insights from data.
What do Big Data Professionals do?
The work of Big Data professionals involves dealing with large amounts of unstructured data gathered from different sources coming in at a high speed. Big Data professionals define the structure of data and how to handle it using technologies like Hadoop, Kafka and Spark.
What Do Data Analysts Do?
Data analysts translate numeric figures into words. Every business collects massive amounts of data. The job of a data analyst is to make use of this data so as the business can make better business decisions.
Skills needed to be a Data Scientists, BIG data Professional and Data Analysts
Before becoming a professional, you must have certain skills in your field of specialty. Let’s look at the skills required for you to be any of these professionals.
Skills-set required to be a Data Scientist
– Statistical and Analytical Skills
– Data Mining Skills
– Co –relation skills
– Knowledge of Machine Learning Algorithms
– Know ledge of Deep Learning techniques and Neural Networks
– In-depth Knowledge of Programming
– SQL and Database Management skills
– SAS/R Coding
Skill-set Required to be a Big Data Professional
– Knowledge in the use of technologies like Hadoop and Spark
– Knowledge on how to handle unstructured data
– Basic Knowledge of Programming languages
– SQL and Database coding skills
– Familiarity with MATLAB
– Creative
– Posses Business management skills
– Data Visualization skills
Skill-set Required to be a Data Analyst
– Data Warehousing
– Knowledge in technologies like Hadoop
– Google Analytics and Adobe Skills
– Knowledge in various Programming Languages
– Statistical and scripting skilss
– Data Reporting skills
– Visualization skills
– Knowledge in Database and SQL Coding
– Knowledge in Spread-sheet Management
Salary Prospect
Data Scientists earn an average of $125,000 per year. On the other hand Big Data Professionals earn an average of $97,000 annually while Data Analysts an average of $66,000 per year.
Use Cases
Here is an example of how Data Science, Big Data Professional and Data Analysts work together.
Let’s use Netflix as an example.
Netflix generates massive amounts of data from their clients. This data is unstructured and in the form of audio, text or video files. It is complicated to analyze and process such big and unstructured data using the traditional methods. Data Science could make processing such data for insights easier.
The Role of Data Science in Optimizing Netflix Streaming Experience
- Understanding user behavior: This is how users interact with the service. Data Scientists use data from how the user interacts with Netflix to analyze user behavior. They look at metrics that are likely to have an impact on the user behavior.
- Improving the Streaming Experience: Data Science improves user experience depending on the server that a user is using. People with a higher bandwidth receive different download suggestions compared to people with a lower bandwidth or those using devices on a cellular network
- Optimizing the decisions: Through analyzing customer behavior, Data Science helps Netflix to make optimized decisions.
- Improving Content Quality: Another way Data Science, improves Netflix user’s experience is through offering improved quality. This is made possible by Data Science. Netflix checks the quality of its videos before going live. It makes use of Natural Language Processing and Machine Translation to analyze these videos.
I hope you now have a better understanding of these concepts. Keep it here for more articles on Data Science.