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Looking to accelerate your career growth and increase your income?
Looking to accelerate your career growth and increase your income?
Data Science vs Data Analytics? Which career track is right for you?
This is the age of data! There are infinite amounts of data on the internet which users generate every day. Imagine if somehow this raw data can be processed and analyzed to extract valuable information like what the users want to see and make implementation accordingly, this could result in a state of the art digital product that addresses problems effectively. Sounds good right? All of this is possible by the use of data science and data analytics techniques.
Take Facebook for example, if you see a particular type of content, your feed is populated by similar type of videos and photos. This is effective use of user data and feeding them valuable information back, like Facebook many other companies want to implement this model for their users.
In this guide we have explained the essential difference between data science and data analytics, including job roles, salary range and career path.
What is Data Analytics?
As explained in the example above, we can see that there is huge untapped potential in raw digital data that if collected and analyzed efficiently can yield enormous business benefits. This process of extracting information from data sets to provide insights about the business needs and steps to be taken is call Data Analytics.
Data Analyzers use an array of tools and protocols to analyze the enormous data sets without involving any manual interaction with the data. To break it down Data Analytics consists of the following basic steps:
- Selecting a targeted problem that businesses have, find data points that concern the problem at hand like age, location, gender etc.
- Extract data from the selected data set through multiple online platform and resources
- Refine the acquired data set for analysis. The best industry practice for this organizing data sets is by using spreadsheets or frameworks like Hadoop and Spark
- Get rid of duplicated or faulty data sets before initiating analysis, removing all errors from the set make it ready for analysis
In data analytics, the analyst already has the required information at hand for example an organizational problem that has a confined data set with declared data points ready to be put into predictive analysis.
Data analytics is growing exponentially in terms of its importance in the data industry. Almost all major organizational sectors like real estate, IT firms or even hospitals are in demand for skilled Data Analysts. You can your journey as a Data Analyst by enrolling in our Data Analytics Bootcamp.
What about Data Science?
Data science is a wider array in terms of scope as compared to Data Analytics. We can say that Data Analytics is a branch existing within the Data Science domain and is part of one of the many Data Science life cycles. Data Analytics only concerns the process of Analyzing data sets but Data Science not only includes the analysis but also the before and after processes as well.
Data Scientist along with having great commands over analytics tools like Hadoop and Spark are well skilled with programming languages such as SQL, R and Python, this combination gives Data Scientists an edge over Data Analyst. Data Science experts also have the knowledge to implement Machine Learning algorithms on complex structures, enabling systems to make smart decisions without any human interference.
Breaking it down, Data Science has the following main components:
- Statistical Analysis: Statistical analysis involves creation, collection and extrapolation of data sets through mathematical protocols.
- Data Visualization: Data visualization is an essential part of Data Science, after extrapolating complex data sets and extracting valuable information it know has to be presented for the general public to view as well. This is done in forms of charts, graphs and others figures. Visualization data smartly leads to better and quick decision making in organizations.
- Machine Learning and AI: Artificial Intelligence is not science fiction anymore but it is officially here, machines now have the capacity to make smart decisions on their own. Data Scientist program these capabilities into machines making them smart enough to predict human behavior
A skilled data scientist is an expert in identifying business blockers and problems from multiple sources. He/she analyzing that problem and comes up with counter measures and implementation techniques to remove these blockers.
Head to Head Comparison
By now you must a pretty clear picture of what Data Science and Data Analytics fundamentally refer to. To make this picture even clearer here is a head to head comparison to both data career paths for you:
- Data Science covers a broader domain including web expertise, machine learning, predictive analysis and computer science.
- Data Scientists are considered to have a macro scope
- Data Science related jobs are one of the highest paid jobs in the world right now
- Having knowledge of various data and statistical analysis models is mandatory
- The input Data Science professionals operates are raw and unprocessed. They have the skills to organize it themselves
- The main aim of a data science professional is to identify current and upcoming problems and suggest solutions
- Data scientists need to have a basic knowledge of SEO and basic machine learning models
Enroll in Texas A&M Data Science Bootcamp to launch your career as a Data Scientist.
- Data Analytics is a key component of Data science framework, used in analyzing data sets and extracting information from it
- Data Analysts are considered to have a micro scope
- Though data analyst are well paid but the compensation is lesser is comparison to Data Scientists
- Knowledge of database languages like SQL is mandatory. Also experience with tools like Spark and Hadoop is important
- Data Analyst operate on an already organized data set containing data points and other identifiers available
- Data Analyst are already aware of the problem, there main goal is to come up with action items and step to address these problems
- Data analyst have solid command over analytics tools and frameworks
Enroll in Texas A&M Data Analytics Bootcamp to kickstart your career as a Data Analyst.
To sum it up, Data Science is vast and has a broader scope in the market, Data Analytics on the other hand can act has good start towards your data expert career. There is no right or wrong when choosing a career path, you just have look at the facts see what suits you and make a decision accordingly.