Data Anaylst Vs Data Scientist

By Author: Thomas
Total Articles: 3
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A Data Scientist is somebody who, based on historical trends, can foresee what will happen in the future, while a Data Analyst is somebody who simply analyses significant data insights.

The duties of a Data Scientist include estimating the uncertain, while the tasks of a Data Analyst include aiming at the known from fresh perspectives.

It is presumed that a Data Scientist will produce their own concerns while a Data Analyst will seek solutions to a given collection of data questions.

A Data Analyst tackles business challenges, whereas a Data Scientist not only tackles business challenges but also catches up with those issues which once addressed would have the most value creation.

But this isn’t it. This was a basic description of what each of the job profiles entails. However, there is more to it and we would like you to have access to the in-depth information about it while we carry the Data Analyst VS Data Scientist comparison forward.

A Day in the Life of a Data Analyst

A lot of organizations can gain an advantage from getting a Data Analyst on board, it is beneficial in ...
... a lot of sectors—from healthcare industries to the IT sector to fast food places. For companies who would like to learn more about the requirements of customers or end-users, the information that data analysts bring to an organization can prove to be important.

Irrespective of the sector wherein they operate, Data Analysts may expect to spend their time building data collection systems and assembling their results into reports that can enable their businesses to grow.

Data Analysts rummage through data to produce reports and visualizations to discover what insights that exists somewhere in the data. Following are some of the major roles and responsibilities that are expected out of a Data Analyst:

Data gathering
Cleaning data
Processing data
Producing reports
Locating trends & patterns
Collaborating with stakeholders
Setting up infrastructure
A Day in the Life of a Data Scientist

The everyday activities of a data scientist mostly revolve around data, just like its name suggests. Data scientists invest a great deal of their time collecting information, analyzing data, defining data, all for several different purposes and in many varied contexts. Data-related activities that could be handled by a data scientist include:

Collecting and merging data
Analyzing data
Identifying patterns or trends
Make use of several tools, including R, Tableau, Python, Matlab, Hive, etc.
Building and testing new algorithms
Coming up with data solutions
Creating predictive models
Developing data visualizations
Writing up results
Getting together proofs of concepts
The skillset of Data Analysts and Data Scientists kind of overlap with each other, but there is a substantial distinction between the two too. Both the job profiles want you to be good at mathematics, have extensive knowledge of algorithms, great communication skills, and an understanding of software development.

The Advent of Data Analysis & Data Science

The existence of such vast quantities of data has been seen by companies as a source of competitive advantage. It is apparent that businesses who could easily use this data can make stronger business conclusions and respond responsibly, directly placing them ahead of competitors who did not have these insights.

Data Analyst Vs Data Scientist

So how does the job of a data analyst is different from that of a data scientist?

Typically, it is assumed that a data scientist will identify the problems that will benefit an organization and then continue to address them, while the company representative will provide a data analyst with problems to find a solution with that guidance.

It is presumed that both profiles will compose queries, coordinate with technical staff to obtain the correct information, perform data munging (getting data into the correct format, simple to analyze/interpret), and extract relevant data. In certain instances, it is not anticipated that a data analyst can create statistical models or be experienced in ML and advanced programming. Instead, a data analyst usually works with other BI tools/packages or on simplified structured SQL or similar databases.

Requirements For A Data Analyst

An ideal Data Analyst candidate is expected to have an undergraduate STEM (science, technology, engineering, or math) degree. It is considered to be necessary but is not required these days. However, the candidate is required to have extensive knowledge of mathematics, science, programming, databases, and predictive modeling.

Skills of a Data Analyst:

Degree in math, analytics, science, or statistics.
Hands-on experience with languages such as R, Python, Java, SQL.
An effective blend of analytical abilities, cognitive enthusiasm, and expertise in reporting.
A decent knowledge of data mining methods and innovative technologies including MapReduce, Spark, ML, Deep Learning, artificial neural networks, etc.
Good understanding of agile development methodology.
Exceptional written and verbal communication skills.
Experience in working with Microsoft Excel and Office.
Requirements For A Data Scientist

Data scientists differ from data analysts in many ways such that they are required to have more technical and mathematical knowledge. With data analysts, it becomes quite mandatory to hold a degree in Computer science. A data scientist must also be comfortable communicating their results to the organization’s stakeholders in addition to having a good understanding of data. It is not a simple job to identify someone qualified in mathematics and coding who is also skilled in communicating.

Skills of a Data Scientist:

Master’s degree or Ph.D. in statistics, maths, or computer science.
Knowledge of languages like R, Python, SQL, etc.
Knowledge of statistical and data mining methods, which includes linear model/regression, random forest, boosting, trees, text mining, and more.
Knowledge of methods such as clustering, decision tree learning, and artificial neural networks in ML.
Knowledge of advanced statistical methods and principles, including regression, distribution properties, and statistical tests.
4-5 years of experience in data set manipulation and statistical model development.
Web-service experience: Redshift, S3, Spark, DigitalOcean, etc.
Knowledge of distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.