Data has become the most important business factor today. As a result, different technologies, methods, and systems have been invented to process, transform, analyze and store data in this data-driven world. Many IT professionals around the world would agree that we live in the age of Big data. Data Science vs Big Data and Data Analytics are the three terms often referred to in literature while discussing the potential benefits of data-driven decision-making. What’s important is that these latest trends create new job opportunities and increase demand for people with the right data skills.
However, there is still a lot of confusion around the key areas of big data, data science, and data analytics. In this post, we will describe these concepts to better understand each technology and how it relates to each other.
What is Big Data?
Big data simply refers to extremely large data sets. This size, combined with the complexity and evolving nature of these datasets, has allowed them to exceed the capabilities of traditional data management tools. In this way, data warehouses and data lakes have emerged as the first solutions for dealing with big data and far outperform traditional databases.
Some datasets that we can truly consider big data are:
- Stock Market Data
- Sports Events and Games
- Social Networks
- Scientific and Research Data
Benefits of Big Data:
Big data is huge and far exceeds the capabilities of normal data storage and processing methods. The volume of data determines whether it can be classified as big data.
Accuracy & Variability:
Due to the enormous complexity of big data, there will inevitably be some inconsistencies in the data sets. Therefore, you need to consider variability to properly manage and process big data.
Large data sets are not limited to a single data type, but consist of different types of data. Big data consists of different kinds of data, from tabular databases to images and audio, regardless of the data structure.
The speed at which data is generated. In big data, new data is constantly generated and often added to data sets. This is very common when it comes to ever-evolving data, such as social media, IoT devices, and monitoring services.
The value of big data assets The validity of big data analysis results can be subjective and evaluated against unique business objectives.
Types of Big Data:
- Structured data
- UnStructured data
- Semi-Structured data
- Structured Data: Any dataset that follows a particular structure can be called structured data. These structured data sets are relatively easy to process compared to other types of data because users can accurately identify the structure of the data. A good example of structured data is a distributed RDBMS that contains data in organized table structures.
- Unstructured Data: This type of data consists of data that does not follow a schema or a preset structure. It’s the most common type of data when it comes to big data; things like text, images, video, and audio are included in this type.
- Semi-Structured Data: This type of data does not fit a particular structure, but it still maintains an observable structure, such as grouping or organized hierarchy. Examples of semi-structured data include markup languages (XML), web pages, email, etc.
Essential Skills to Become a Big Data Specialist:
- Analytical Skills: These skills are essential to understanding data and determining what data is relevant to reporting and finding solutions.
- Computing: Computers are the backbone of any data strategy. Programmers must constantly develop algorithms to process data and convert it into knowledge.
- Business Capabilities: Big data professionals must understand existing business goals and underlying processes that drive business growth and profits.
- Creativity: You must develop new ways to collect, interpret, and analyze a data strategy. Mathematical and
- Statistical Skills: Good and outdated “numerical processing” is also necessary, whether in data analysis, data science, or big data.
What is Data Science?
When it comes to structured and unstructured data, data science is an area that encompasses everything that has to do with cleaning, preparing, and analyzing data.
Thus, data science combines statistics, mathematics, programming, problem-solving, ingenious data collection, the ability to see things differently, and the activity of cleaning, preparing, and aligning data. In addition, this general term encompasses several techniques used to extract knowledge and information from data.
- Maths & Statistics
- Advanced Analytics
- Deep Learning
- ML & Al
- Scientific Method
In data analysis, the primary focus is to gain meaningful insights from the underlying data. However, the scope of data science far exceeds that purpose: data science will address everything from analyzing complex data to creating new algorithms and analysis tools for data processing and cleaning to creating powerful and useful visualizations.
Data Science Tools and Technologies:
Data Science includes programming languages such as R, Python, Julia, which can create new algorithms, machine learning models, and AI processes for big data platforms such as Apache Spark and Apache Hadoop. In addition, can also consider data processing and cleaning tools such as Winpure, data scale, and data visualization such as Microsoft Power Platform, Google Data Studio, Tableau, and visualization frameworks such as matplotlib and ploty data science tools. Because data science covers everything related to data, any instrument or technology used in data analysis and can use big data in some way in the data science process.
Essential Skills to Become a Data Scientist:
- Python Coding: Python is the most widely used programming language in data science, Java, Perl, and C/C++.
- Education: 88% have a master’s degree, and 46% have doctoral degrees. Deep knowledge in SAS or R. R. is generally preferred for data science.
- Working with Unstructured Data: It’s essential for a data scientist to work with unstructured data, whether social media, video, or audio channels.
- Hadoop Platform: While not always necessary, knowing the Hadoop platform is still preferred for the field. It’s also beneficial to have some experience with Hive or Pig.
- SQL Coding/Database: Although NoSQL and Hadoop have become an essential part of data science, it is still preferable if you can write and execute complex queries in SQL.
What is Data Analytics?
When analyzing raw data to find trends and answer questions, the definition of data analysis covers its broad scope of application. However, it includes many techniques with many different objectives. The data analysis process has several components that can support a variety of initiatives. By combining these elements, a successful data analytics originality provides a clear image of where you are, where you’ve been, and where you need to go.
Types of Data Analytics:
There are four types of Data Analytics:
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
- Descriptive Analytics helps answer questions about what happened.
- Diagnostic Analytics helps answer questions about why things happened.
- Predictive Analytics helps answer questions about what will happen in the future.
- Prescriptive Analytics helps answer questions about what should be done.
Data Analytics Tools and Technologies:
There are commercial and open source products for data analysis. They range from simple analysis tools, such as Microsoft Excel’s Analysis ToolPak, which comes with Microsoft Office, to the SAP BusinessObjects suite and open source tools like Apache Spark.
When you look at cloud providers, Azure is known as the best platform for data analytics requirements. Its Azure Synapse Analytics suite, Apache Spark-based Databricks, HDInsights, machine learning, and more provide a complete toolset for all your needs. AWS and GCP also offer Amazon QuickSight, Amazon Kinesis, and GCP Stream Analytics to meet your analytics needs.
In addition, specialized BI tools provide powerful analysis capabilities with relatively simple configurations. Examples include Microsoft PowerBI, SAS Business Intelligence, and Periscope Data. Furthermore, even programming languages like Python can use to create custom analysis scripts and visualizations for more specific and advanced analysis needs. Finally, machine learning algorithms such as TensorFlow and scikit-learn can consider part of the data analysis toolbox; they are popular tools for the analysis process.
What is the Function of Data Analysis?
Data Analysts exist at the interface of information technology, statistics, and business. They combine these areas with helping companies and organizations succeed. The primary goal of a data analyst is to increase efficiency and improve performance by discovering data patterns.
Essential Skills to Become a Data Analyst:
- Programming Skills: Knowledge of programming languages such as R and Python is essential for all data analysts.
- Data Dispute Skills: The ability to map raw data and convert it to another format that allows more convenient use of data communication and visualization capabilities
- Data intuition: A professional must think like a data analyst.
- Statistical and Mathematical Skills: Descriptive and final statistics and experimental designs are necessary skills for data scientists.
- Machine Learning Capabilities