This Data Science vs Data Analytics blog covers the below-mentioned topics:
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What is Data Analytics?
Data Analytics mainly deals with performing and processing statistical analysis on available data. It mainly concentrates on processing and organizing data to gain insights for solving business issues. In simple terms, it helps in solving issues that we know we cannot find answers to on our own. Mostly, it assists in producing outcomes that can help in business improvement immediately.
Analytics consists of mechanical or algorithmic processes to gain business
insights. Numerous organizations use it to make improved and well-informed
business decisions while disproving or verifying the existing models and
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Data Analytics Process
process of Data Analytics involves the use of a number of tools as well as
techniques that help analyze huge volumes of data, which is not humanly
possible. The process involves the following steps:
- Determining the requirements and grouping of data
- Gathering information from numerous online and offline data sources
- Organizing data in spreadsheets for analysis
- Removal of inconsistent, incomplete, and repetitive data
- Cleaning the data by correcting errors before the process of data analysis
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What is Data Science?
When compared to Data Analytics, the scope of Data Science is broader. In other words, you may also think of Data Analytics as a process that is contained in Data Science. It can be considered as one of the integral phases of the complete and complex life cycle of Data Science. All the phases that occur before and after the Data Analytics process constitute Data Science.
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Prerequisites to Learn Data Science
having knowledge of SQL, Python, and other such programming languages,
professionals in the field of Data Science must have the ability to combine
their statistical and domain knowledge to derive insights from the business
data for improving the business drastically. Moreover, these professionals
apply various Machine Learning algorithms in structured, semi-structured, and
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Data Science and its Components
Science helps in tackling Big Data by involving the processes of data
preparation, cleansing, and analysis. These processes apply several Machine
Learning concepts, sentiment analysis, and predictive analytics for extracting
significant information from the gathered data.
three main components of Data Science are as follows:
- Statistics: It mainly focuses on collecting, organizing, analyzing, and presenting data using mathematical methodologies.
- Data Visualization: The Data Science outcomes are visually displayed in the form of charts, tables, diagrams, and graphs that allow the other employees in the organization to comprehend the information gained. Besides, data visualization also allows you to make faster decisions by highlighting important information.
- Machine Learning: It is the most significant component of Data Science that allows you to use self-learning algorithms and also predict the natural human behavior in certain situations in the most accurate manner.
Difference Between Data Science and Data Analytics
Now that you have got a better and clearer understanding of both Data Science and Data Analytics concepts, and gain insight into the main topic of this blog, Data Analytics versus Data Science.
Data Science is an essential part of Data
Science that revolves around organizing, processing, and analyzing business
information to solve business issues.
Data Science is a multidisciplinary field
that involves expertise in statistical research, mathematics, Machine
Learning, Data Analytics, and computer science.
scope is micro. It is limited to analytical methods and techniques using
scope is macro. It includes Machine Learning, Artificial Intelligence, and
Data Analytics leads to lucrative jobs, the Data Analytics professionals are
less-paid when compared to Data Scientists.
Science offers some of the highest-paying jobs in the field of IT.
Analysts must have a good understanding of SQL and other databases and should
have good programming skills in Spark/Hadoop, R/Python, etc.
Data Scientists should have a good understanding of concepts such as
data modeling, Machine Learning, advanced statistics, etc.
should have experience in working with BI tools, along with basic knowledge
They should have a basic understanding of languages such as Python/R,
SQL, and SAS.
data received is generally structured on which Data Analysts apply numerous
data visualization techniques and design principles.
Data Scientists use raw and unstructured data to further clean and
organize so that it can be sent for analytics.
Data Analytics, issues are already known to the analysts, so they use
analytics to come up with the most relevant solutions for the issues.
Science digs out new and undiscovered business issues that can further be
converted into innovative use cases and business stories.
Analytics is dominantly used in the industries of travel and tourism,
finance, healthcare, gaming, and more.
Science is used in the areas of Internet research, speech recognition, image
recognition, recommender systems, digital marketing, and more.
Learn about the difference between Data Science, Data Analytics and Big Data in our comparison blog on Data Science vs Data Analytics vs Big Data.
Data Science vs Data Analytics – Responsibilities
The responsibilities of a Data Scientist include:
- Performing Data Collection and identifying authentic Data Sources
- Pre-processing the structured and unstructured data sets
- Identifying trends and hidden patterns by analyzing large and complex data.
- Creating and working with predictive models and various machine-learning algorithms
- Performing ensemble modeling to combine models
- Perform exceptional Data Visualization to present information and findings
- Formulate Strategies and offer solutions to complex business challenges.
- Coordinate and collaborate with various teams including the engineering and product development teams.
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The responsibilities of a Data Analyst include:
- Use Statistical techniques and tools to analyze data and generate reports.
- Working with databases and having knowledge of Database management tools.
- Collecting data from various authentic primary and secondary sources.
- Managing and maintaining Data systems.
- Analyzing complex datasets and identifying, analyzing, and interpreting trends or hidden patterns in them.
- Performing Data Cleaning and Filtration
- Identifying management needs and problems.
- Creating solutions and strategies for improvement of processes.
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Data Science vs Data Analytics – Skills
Skills of a Data Scientist are:
- Advanced level knowledge in Statistics and Predictive Analytics
- Skilled in Object-oriented programming languages
- Works on tools such as Hadoop, Spark, MySQL etc.
- Other essential skills include Analytical Thinking, Critical Thinking, Data Modeling
Skills of a Data Analyst are:
- Elementary level knowledge of Mathematics and Statistics
- Skilled in Programming Languages like R, Python etc.
- Work on tools such as SAS, Microsoft Excel, and Power BI etc.
- Other essential skills include Analytical Thinking, Data Visualization, Decision Making etc.
In this Data Science vs Data Analytics blog, let us now discuss the salary earned by these professionals.
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Data Science vs Data Analytics Salary
According to Glassdoor, the average income of a Data Scientist in the United States is about US$113k per annum while the same of a Data Analyst is US$62k per annum.
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In this ‘Data Science vs Data Analytics’ blog, you learned about what Data Science and Data Analytics are and also the differences between the two. You also learned about the must-have skills required for professionals in this field.
As you can see, Data Science and Data Analytics tread on similar lines. Whether you are looking at the scope or salary, both Data Science and Data Analytics are great options. To conclude, the choice between the two largely depends on the individual’s interests and career goals.
To become a professional and be proficient in the necessary skills for each of these domains, you can choose online training courses. By signing up for such courses, you will gain hands-on experience through solving exercises and executing various real-time projects.
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