Hi guys this is Rob from simply learn and today we're going to look at three very important data related roles in the field of data science and then we're going to pit them against each other so welcome to data scientist versus data analyst versus data engineer now let's have a look at what's in store for you firstly we will talk about the job .

Descriptions the skill sets required for each role the salary roles and responsibilities and the companies hiring for these positions so now let's have a look at each of these roles in detail first off let's have a look at data scientist now a data scientist is able to create machine learning based tools or processes within the company .

Now they use advanced data techniques such as clustering division trees neural networks and so on so that they can derive business conclusions they are the senior most member in the team which involves a data engineer as well as a data analyst now they need to have in-depth knowledge of statistics data handling and machine learning they also .

Take inputs from data engineers as well as analysts so that they can formulate actionable insights for the business now data scientists also needs to have the same skills as a data analyst and an engineer but needs to have a lot more in-depth knowledge and expertise with these skills next up we have data analyst now data analyst is someone who .

Is able to translate numeric data into a form that everyone in the organization can understand now this is an entry-level position in the data analytics team he or she needs to have technical skills in programming languages such as Python and have knowledge of tools like Excel and understand the basics of data handling .

Modeling and reporting now in due time they can move up the ranks by taking up roles of data engineer and data scientist with some experience that they can accumulate over the years and finally we have DITA engineer our data engineer is someone who's involved with pairing data who's involved with preparing data for analytical or .

Operational purposes now they are the intermediary between the data analyst and the data scientist he or she needs to have a lot of experience when it comes to developing constructing and maintaining architectures now they do generally work on big data and submit their reports to the data scientist so that they can be analyzed now let's have .

A look at the skill sets required for each of these roles first off we have data scientist now since this role is a little more coding oriented need to know a great deal when it comes to programming languages programming languages such as Python our SQL SAS Java and so on now you also need to be well-versed with frameworks in relations .

To big data such as big spoken do peaking of a do if you want to learn more about how it works I suggest you click on the top right corner and watch our video on what to do coming back data scientists also need to be well versed with machine learning deep learning and other similar technologies next up we have data .

Analyst now this role is much less technical as compared to a data scientist as well as a data engineer considering how its entry-level here knowing programming languages is a great bonus so an idea about programming languages such as Python or SQL javascript SSAS and so on is a great benefit at the same time you do need to .

Be well-versed with tools such as SAS miner Microsoft Excel s SAS SPSS and so on and finally we have data engineer now being a data engineer requires you to be well versed with a bunch of programming languages as well as frameworks now you need to know about programming languages such as Python our SQL SAS Java and so on while having expertise in frameworks .

Such as hadoop mapreduce hive big apache spark data streaming no SQL and so on now let's talk about money or the salary each of these roles get firstly we have the data scientists who answer whopping 137 thousand US dollars per annum then we have the data analyst 267 thousand dollars per annum which is a pretty high salary when you consider that it's only .

An intern at the job and a data engineer which is in the median with a hundred and sixteen thousand US dollars per annum now let's talk about roles and responsibilities firstly we have the data scientist now the data scientist gets to work with a lot of unstructured data so they need to mine and clean the data so that it's usable they need to be .

Able to design machine learning models to work on the Big Data they need to infer and interpret the analysis on big data to be able to lead an entire team to achieve the goals of the organization and deliver conclusions that have a direct business impact now let's have a look at the roles and responsibilities of a data analyst they need to use .

Queries to gather information from a database they need to process the data and provide summary reports they need to use basic algorithms for their work such as linear regression logistic rushon and so on and have core skills in statistics data munging data visualization and exploratory data analysis and finally we have data .

Engineer now they need to mine through the data so that they can gain insights from it they need to convert erroneous data into a useable form so that they can be further analyzed they need to write queries on data they need to maintain the design as well as architecture of the data and create large data warehouses using ETL or .

Extract transform node now let's have a look at some of the companies hiring for this role firstly for data scientists you have Citibank Facebook Schneider Intel Amazon and so on for data unless you have enforces rockin these are capital one Walmart and so on and for data engineer you have google cisco flow kosgeb descend apple .

Spotify and much much more so if all this is inspired you to get started with data science I suggest you take simply learn certification now I'm choosing this certification because it acts as great entry point for starting your career as a data analyst or a data engineer now with this certification it goes through all the important concepts .

When it comes to data science and has 68 hours of in-depth learning for e.l.f industry based projects interactive learning with Jupiter notebooks and dedicated mentoring sessions from our faculty of industry experts now some of the concepts are you'll be going through statistical analysis and business applications Python environment setup .

Mathematical computing with Python which is numpy SCI pi data manipulation with pandas machine learning with scikit-learn and so much more so then you can start up on your step to becoming a data and list a data engineer and then eventually a data scientist now if you're already working as a data analyst or a data engineer you can .

Become a certified data scientist which simply learns data scientist master's program this goes through all the important concepts that you need to know so that you can become a successful as well as certified data scientist and with that we've reached the end of this video I hope this has inspired you to get certified and get ahead in your .

Career thank you for watching and stay tuned for more from simpler hi there if you like this video subscribe to the simply learn YouTube channel and click here to watch similar videos to nerd up and get certified click here