Data Science and Data Analytics have become industry buzzwords as a result of the rise of Big Data. Big Data refers to the tremendous data expansion in very big amounts that the entire world now contributes to through texts, emails, different tweets by the user, different user searches on search engines, and also the data generated from IoT and connected devices. Data gets generated each and every day which is so vast and complex that traditional data processing and analysis systems cannot handle it.
We frequently use Data Science and Data Analytics interchangeably since Big Data, Data Science, and Data Analytics are still developing technologies. The fact that both data scientists and data analysts work with big data is the main cause of the misconception. The argument between data science and data analytics is fueled by the substantial differences between data analysts and data scientists.
The majority of us frequently mix up data analytics and data science and use them interchangeably. Professionals in the IT industry are also uncertain about how to utilize these two phrases. Here, you will learn more about both in this 'Data Science vs. Data Analytics' blog so that you can easily tell them apart.
What is Data Analytics?
Data analytics is used to analyze the raw data and draw conclusions. It is useful in a variety of industries since it enables the organization to make decisions based on the findings from the data. In essence, data analytics aids in the conversion of a sizable amount of numbers in the form of data into conclusions that are further useful in making judgments.
The primary focus of data analytics is the execution and processing of statistical analyses on accessible data. It primarily focuses on organizing and processing data to produce insights for resolving business problems. Simply said, it aids in finding solutions to problems that we are aware we cannot resolve on our own. Most often, it helps to produce results that can quickly aid in business improvement. To get business insights, data analytics uses mechanical or computational procedures. It is used by many businesses to improve and inform business decisions while challenging or validating the theories and models that are currently in use.
Process of Data Analytics
Data Analytics involves different tools and different techniques by which users can easily analyze huge amounts of data, which is not possible for humans to handle. Also, there is a particular process that includes the following steps:
- Identifying the criteria and data grouping
- Collecting data from a variety of offline and internet data sources
- Spreadsheet data organization for analysis
- Elimination of repetitious, inconsistent, and inconsistent data
- Before beginning the data analysis process, clean up the data by fixing mistakes.
What is Data Science?
Data science is a discipline that deals with the extraction of useful information and insights from both organized and unstructured data using a variety of algorithms, procedures, and scientific approaches. One of the most in-demand professions at the moment is one that is related to big data. Data science is the application of arithmetic, computation, statistics, programming, and other related techniques to a vast body of data that is presented in a variety of formats in order to derive relevant insights.
The breadth of data science is wider than that of data analytics. In other words, you may consider Data Analytics to be a step in the Data Science process. It can be viewed as one of the crucial stages in the long run of the data science life cycle. Data Science is the combination of all the steps that come before and after the Data Analytics process.
Some Key Factors required to learn data science
Professionals in the field of data science need to be fluent in programming languages like SQL and Python, as well as be able to use their statistical and domain expertise to draw conclusions from business data that will significantly improve the company. These experts also use organized, semi-structured, and unstructured data to apply different Machine Learning algorithms.
Different components of Data Science
By incorporating the processes of data preparation, cleansing, and analysis, data science aids in the handling of Big Data. These procedures use sentiment analysis, predictive analytics, and a number of machine learning principles to extract important information from the collected data.
The following are the three key elements of data science:
- Statistics: It is primarily concerned with the collection, arrangement, analysis, and presentation of data using mathematical techniques.
- Data visualization: By graphically presenting the results of data science in the form of charts, tables, diagrams, and graphs, other employees inside the company are able to understand the knowledge acquired. Additionally, data visualization facilitates quicker decision-making by emphasizing crucial facts.
- Machine learning: The most important aspect of data science is machine learning, which enables the deployment of self-learning algorithms and the most accurate prediction of typical human behavior in specific contexts.
Data Science vs. Data Analytics
Below is a table of some major differences between Data Science and Data Analytics
Data Science vs. Data Analytics- Different Skill sets of Data Scientist
The different skill sets data scientist has:
- Superior proficiency in statistics and predictive analytics
- Programming in Object-Oriented Languages Expert
- Know how to use programs like Hadoop, Spark, MySQL, etc.
- Other necessary abilities include critical thinking, data modeling, and analytical thinking
- The different skill sets data analysts have:
- Basic understanding of statistics and mathematics
- Programming language expertise in R, Python, etc.
- Work with programs like SAS, Microsoft Excel, and Power BI, among others.
- Analytical thinking, data visualization, decision-making, and other abilities are also crucial.
Data Science vs. Data Analytics- Responsibilities of Data Scientist
Some Responsibilities that Data scientists have:
- Collecting data and locating reliable data sources.
- The structured and unstructured data sets need to be pre-processed by a Data scientist.
- Examining vast and complex data to find trends and hidden patterns.
- Developing and utilizing various machine-learning techniques and predictive models.
- Combining models with ensemble modeling.
- Excellent Data Visualization should be used to convey data and insights.
- Creating strategies and providing answers to difficult business problems.
- Work in coordination and partnership with other groups, such as the product development and engineering teams.