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Data Scientist vs Data Analyst vs Data Engineer - Role & Responsibility, Skills, Salary |Simplilearn

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Data Scientist vs Data Analyst vs Data Engineer - Role & Responsibility, Skills, Salary |Simplilearn

In this presentation, we will decode the basic differences between data scientist, data analyst and data engineer, based on the roles and responsibilities, skill sets required, salary and the companies hiring them. Although all these three professions belong to the Data Science industry and deal with data, there are some differences that separate them. Every person who is aspiring to be a data professional needs to understand these three career options to select the right one for themselves. Now, let us get started and demystify the difference between these three professions.

We will distinguish these three professions using the parameters mentioned below:
1. Job description
2. Skillset
3. Salary
4. Roles and responsibilities
5. Companies hiring

This Master’s Program provides training in the skills required to become a certified data scientist. You’ll learn the most in-demand technologies such as Data Science on R, SAS, Python, Big Data on Hadoop and implement concepts such as data exploration, regression models, hypothesis testing, Hadoop, and Spark.

Why be a Data Scientist?
Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data scientist you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.

Simplilearn's Data Scientist Master’s Program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures, tools and analytics, and many more. The courseware also covers a capstone project which encompasses all the key aspects from data extraction, cleaning, visualisation to model building and tuning. These skills will help you prepare for the role of a Data Scientist.

Who should take this course?
The data science role requires the perfect amalgam of experience, data science knowledge, and using the correct tools and technologies. It is a good career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind are most suited to pursue the Data Scientist Master’s Program, including:
IT professionals
Analytics Managers
Business Analysts
Banking and Finance professionals
Marketing Managers
Supply Chain Network Managers
Those new to the data analytics domain
Students in UG/ PG Analytics Programs

Learn more at https://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training

In this presentation, we will decode the basic differences between data scientist, data analyst and data engineer, based on the roles and responsibilities, skill sets required, salary and the companies hiring them. Although all these three professions belong to the Data Science industry and deal with data, there are some differences that separate them. Every person who is aspiring to be a data professional needs to understand these three career options to select the right one for themselves. Now, let us get started and demystify the difference between these three professions.

We will distinguish these three professions using the parameters mentioned below:
1. Job description
2. Skillset
3. Salary
4. Roles and responsibilities
5. Companies hiring

This Master’s Program provides training in the skills required to become a certified data scientist. You’ll learn the most in-demand technologies such as Data Science on R, SAS, Python, Big Data on Hadoop and implement concepts such as data exploration, regression models, hypothesis testing, Hadoop, and Spark.

Why be a Data Scientist?
Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data scientist you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.

Simplilearn's Data Scientist Master’s Program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures, tools and analytics, and many more. The courseware also covers a capstone project which encompasses all the key aspects from data extraction, cleaning, visualisation to model building and tuning. These skills will help you prepare for the role of a Data Scientist.

Who should take this course?
The data science role requires the perfect amalgam of experience, data science knowledge, and using the correct tools and technologies. It is a good career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind are most suited to pursue the Data Scientist Master’s Program, including:
IT professionals
Analytics Managers
Business Analysts
Banking and Finance professionals
Marketing Managers
Supply Chain Network Managers
Those new to the data analytics domain
Students in UG/ PG Analytics Programs

Learn more at https://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training

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Data Scientist vs Data Analyst vs Data Engineer - Role & Responsibility, Skills, Salary |Simplilearn

  1. 1. Cover slide? I have asked Siddam again. Will update it once I get it.
  2. 2. What’s in it for you? Job Description Skillset Salary Roles and Responsibilities Companies Hiring
  3. 3.  A data scientist is one who uses advanced level of data techniques to derive to business conclusions  He/she is the senior most in the team and have an in-depth knowledge of statistics, data handling and machine learning  They take the inputs from Data Engineers and Analysts and formulate actionable insights for the business Data Scientist
  4. 4.  A data scientist is one who uses advanced level of data techniques to derive to business conclusions  He/she is the senior most in the team and have an in-depth knowledge of statistics, data handling and machine learning  They take the inputs from Data Engineers and Analysts and formulate actionable insights for the business Data Scientist  A data analyst is an entry-level member into the data analytics team  He/she needs to be good in technical skills and know the basics of data handling, modeling and reporting  They can move into the roles of Data Engineer and Data Scientist with more experience Data Analyst
  5. 5.  A data scientist is one who uses advanced level of data techniques to derive to business conclusions  He/she is the senior most in the team and have an in-depth knowledge of statistics, data handling and machine learning  They take the inputs from Data Engineers and Analysts and formulate actionable insights for the business Data Scientist  A data analyst is an entry-level member into the data analytics team  He/she needs to be good in technical skills and know the basics of data handling, modeling and reporting  They can move into the roles of Data Engineer and Data Scientist with more experience Data Analyst Data Engineer  A data engineer is an intermediary between the data analyst and the data scientist  He/she needs to have expertise in developing, constructing and maintaining architectures  Generally, they work on big data and submit their reports to the data scientist to analyze
  6. 6. Skillset Programming Languages Python, R, SQL, SAS, Java Frameworks Pig, Spark, Hadoop Technologies Machine Learning, Deep Learning Data Scientist
  7. 7. Skillset Programming Languages Python, R, SQL, SAS, Java Frameworks Pig, Spark, Hadoop Technologies Machine Learning, Deep Learning Data Scientist Programming Languages Python, R, SQL, SAS, JavaScript Tools SAS Miner, Microsoft Excel, SSAS, SPSS Data Analyst
  8. 8. Skillset Programming Languages Python, R, SQL, SAS, Java Frameworks Pig, Spark, Hadoop Technologies Machine Learning, Deep Learning Data Scientist Programming Languages Python, R, SQL, SAS, JavaScript Tools SAS Miner, Microsoft Excel, SSAS, SPSS Data Analyst Programming Languages Python, R, SQL, SAS, Java Frameworks Hadoop, MapReduce, Hive, Pig, Apache Spark, Data Streaming, NoSQL Data Engineer
  9. 9. Data Scientist $ 137,000 Salary Source: Glassdoor
  10. 10. Data Scientist Data Analyst $ 137,000 $ 67,000 Salary Source: Glassdoor
  11. 11. Data Scientist Data Analyst Data Engineer $ 137,000 $ 67,000 $ 116,000 Salary Source: Glassdoor
  12. 12. Roles and Responsibilities • Mine and clean data and process unstructured data • Designing models to work on big data • Infer and interpret the analysis on big data • Lead the entire data team to achieve their goals • Deliver conclusions that have a direct business impact Data Scientist
  13. 13. Roles and Responsibilities • Mine and clean data and process unstructured data • Designing models to work on big data • Infer and interpret the analysis on big data • Lead the entire data team to achieve their goals • Deliver conclusions that have a direct business impact • Gather information from a database through querying • Process data and provide summary reports • Use basic algorithms in their work • Have core skills in statistics, data munging, data visualization and exploratory data analysis Data Scientist Data Analyst
  14. 14. Roles and Responsibilities • Mine and clean data and process unstructured data • Designing models to work on big data • Infer and interpret the analysis on big data • Lead the entire data team to achieve their goals • Deliver conclusions that have a direct business impact • Mine through data for insights • Convert erroneous data into a usable form for further analysis • Write queries on data • Maintain the design and architecture of data • Create large data warehouses using ETL Data Scientist Data Analyst Data Engineer • Gather information from a database through querying • Process data and provide summary reports • Use basic algorithms in their work • Have core skills in statistics, data munging, data visualization and exploratory data analysis
  15. 15. Companies Hiring Data Scientist
  16. 16. Companies Hiring Data Scientist Data Analyst
  17. 17. Companies Hiring Data Scientist Data Analyst Data Engineer

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