This presentation about "Data Science Engineer Career, Salary, and Resume" will help you understand who is a Data Science Engineer, the salary of a Data Science Engineer, Data Science Engineer Skillset and Data Science Engineer Resume. Data science is a systematic way to analyze a massive amount of data and extract information from them. Data Science can answer a lot of questions, as well. Data Science is mainly required for better decision making, predictive analysis, and pattern recognition. Below are topics that we will be discussing in this presentation: 1. Introduction to Data Science 2. Who is a Data Science Engineer 3. Data Science Engineer Skillset 4. Data Science Engineer job roles 5. Data Science Engineer salary trends 6. Data Science Engineer Resume Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. The 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, 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. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in analytics and data science Learn more at https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training

2. What’s in it for you?
1. Introduction to Data Science

3. What’s in it for you?
1. Introduction to Data Science
2. Who is a Data Science Engineer

4. What’s in it for you?
1. Introduction to Data Science
2. Who is a Data Science Engineer
3. Data Science Engineer skillset

5. What’s in it for you?
1. Introduction to Data Science
2. Who is a Data Science Engineer
3. Data Science Engineer skillset
4. Data Science Engineer job roles

6. What’s in it for you?
1. Introduction to Data Science
2. Who is a Data Science Engineer
3. Data Science Engineer skillset
4. Data Science Engineer job roles
5. Data Science Engineer salary trends

7. What’s in it for you?
1. Introduction to Data Science
2. Who is a Data Science Engineer
3. Data Science Engineer skillset
4. Data Science Engineer job roles
5. Data Science Engineer salary trends
6. Data Science Engineer resume

8. What does a Machine
Learning Engineer do?
Introduction to Data
Science

9. Introduction to Data Science
i
Data science is a systematic way to analyze a massive amount of data and extract information from them

10. Introduction to Data Science
Better Decision Making
Either A or B?
Predictive Analysis
What will happen next?
Pattern Recognition
Is there any important hidden
information in the pattern?
Data Science is mainly needed for:

11. Which route will help me reach
faster?
2
Introduction to Data Science
Data Science can answer a lot of questions as well!
How many viewers like the same
movies?
1
Will this AC work for 3 years or fail
earlier? Yes or No?
3
Who will win the World Cup?
4

12. What does a Machine
Learning Engineer do?
Who is a Data Science
Engineer?

13. Who is a Data Science Engineer?
A Data Science Engineer is
someone who has

14. Who is a Data Science Engineer?
Programming experience in
Python and R (expert level
knowledge). Ability to write
proficient codes
1

15. Who is a Data Science Engineer?
Strong SQL and big data
experience. Strong coding
skills with hands-on big data
experience
2

16. Who is a Data Science Engineer?
Ability to visualize models and
troubleshoot code of the
models
3

17. Who is a Data Science Engineer?
Ability to visualize models and
troubleshoot code of the
models
3

18. Who is a Data Science Engineer?
Versatile problem solver
equipped with strong
analytical and quantitative
skills
4

19. Who is a Data Science Engineer?
A self-starter with a strong
sense of personal
responsibility and a technical
orientation
5

20. Who is a Data Science Engineer?
Strong product intuition, data
analysis skills, and business
presentation skills
6

21. Who is a Data Science Engineer?
Great teammate with excellent
interpersonal skills
7

22. What does a Machine
Learning Engineer do?
Data Science Skillset

23. Big Data
Data Science Engineer Skillset
Database
Knowledge
Statistics
Programming
Tools
Data Wrangling
Data
Visualization
Machine
Learning

24. Database Knowledge
Tools required
SQL (Structured Query Language) is an essential language for extracting a
large amount of data from data sets. Knowledge of SQL is mandatory for Data
Science Engineers
Database
Knowledge

25. Statistics
Statistics
Statistics is a subset of mathematics that deals with collecting, analyzing, and
interpreting data. Therefore, data scientists need to know statistics
Statistics Probability

26. Programming
Tools
Programming Tools
Master any one of the specified programming languages. Programming Tools
such as R, Python, SAS are essential to perform analytics in data
 Python is an open-source
general purpose
programming language
 Python libraries like
NumPy and SciPy are
used in Data Science
 SAS can mine, alter,
manage, and retrieve
data from a variety of
sources
 Can perform statistical
analysis on the data
 R is a free software
environment for statistical
computing and graphics
 Supports most Machine
Learning algorithms for
Data Analytics like
regression, association,
clustering, etc.

27. Data Wrangling
Data Wrangling
Data Wrangling is the process of transforming raw data into an appropriate
format to make it useful for analytics
Data Wrangling involves:
Cleaning raw data
Structuring raw
data
Enriching raw data

28. Machine
Learning
Machine Learning
Knowledge of Machine learning techniques such as supervised machine
learning, decision trees, linear regression, KNN, etc. is useful for few job roles
KNN Linear Regression Decision Tree

29. Data
Visualization
Data Visualization
Data visualization is the study and creation of a visual representation of data.
Data visualization uses algoritms, statistical graphics, plots, information
graphics and other tools to communicate information clearly and effectively

30. Big Data
Big Data
Big Data is a massive amount of data which cannot be stored and processed
using traditional methods
Big Data has various benefits like –
• Access to social data can enable organizations to tune their business
strategies
• Big data can improve customer experience

31. Non Technical Skills
Companies look for
someone who can
clearly and fluently
translate technical
findings to a non-
technical team
3
A Data Science
Engineer needs to work
with everyone in the
organization, including
customers
4
Updating knowledge by
reading contents and
relevant books on
trends in data science
1
Intellectual
Curiosity Understanding how the
problem solved can
impact the business
2
Business Acumen
Communication
Skills
Team Work

32. Non Technical Skills
Companies look for
someone who can
clearly and fluently
translate technical
findings to a non-
technical team
3
A Data Science
Engineer needs to work
with everyone in the
organization, including
customers
4
Updating knowledge by
reading contents and
relevant books on
trends in data science
1
Intellectual
Curiosity Understanding how the
problem solved can
impact the business
2
Business Acumen
Communication
Skills
Team Work

33. Non Technical Skills
Updating knowledge by
reading contents and
relevant books on
trends in data science
1
A Data Science
Engineer needs to work
with everyone in the
organization, including
customers
4
Intellectual
Curiosity Understanding how the
problem solved can
impact the business
2
Business Acumen
Companies look for
someone who can
clearly and fluently
translate technical
findings to a non-
technical team
3
Communication
Skills
Team Work

34. Non Technical Skills
Updating knowledge by
reading contents and
relevant books on
trends in data science
1
Companies look for
someone who can
clearly and fluently
translate technical
findings to a non-
technical team
3
Intellectual
Curiosity Understanding how the
problem solved can
impact the business
2
Business Acumen
Communication
Skills A Data Science
Engineer needs to work
with everyone in the
organization, including
customers
4
Team Work

35. What does a Machine
Learning Engineer do?
Data Science Job Roles

36. 1. Data Scientist
Languages
R, SAS, Python, MATLAB,
SQL, Hive, Pig, Spark
Companies hiring Data
Scientists
Perform predictive analysis and
identify trend and patterns that can
help in better decision making
Role
Understanding challenges of a
system and offer best solutions

37. 2. Data Analyst
Languages
R, Python, JavaScript, HTML,
C/C++,SQL
Role
Responsible for a variety of tasks
such as visualization, optimization,
and processing large amount of
data
Companies hiring Data
Analysts
Performs queries on database from
time to time. Create and modify
algorithms which can be used to
reduce information from large
databases

38. 3. Data Architect
Languages
SQL, XML, Hive, Pig, Spark
Ensuring that data Engineers have
best tools and systems to work with
Role
Creates blueprints for data
management with the best security
measures
Companies hiring Data
Architects

39. 4. Data Engineer
Languages
SQL, R, MATLAB, SAS,SPSS,
Python, Java, Ruby, C++, Perl,
Hive, Pig
Updates the existing systems with
better version of the current
technologies to improve the
efficiency of the databases
Role
Develops, constructs, tests and
maintains architectures (such as
databases and long-scale
processing systems)
Companies hiring Data
Engineers

40. 5. Statistician
Languages
SQL, R, MATLAB, SAS, SPSS,
Stata, Python, Perl, Hive, Pig,
Spark
Creates new methodologies for
engineers to apply
Role
Extract and offer valuable reports
from the data clusters through
statistical theories and data
organization
Companies hiring Data
Engineers

41. 6. Database Administrator
Languages
Java, SQL, Ruby on Rails,
XML, C#, Python
Role
Ensures that all the databases are
available to all relevant users, and
is performing correctly and is being
kept safe
Some of the tasks involved are -
Monitoring, operating and
maintaining databases; Installation,
configuration, defining schemas,
training users, etc.
Companies hiring Data
Engineers

42. 7. Data and Analytics Manager
Languages
SQL, R, SAS, Java, Python,
MATLAB
Role
Oversees the data science
operations and assigns the duties
to the team according to skills and
expertise
Improves business processes as an
intermediary between business and
IT
Companies hiring Data
Engineers

43. 8. Business Analytics
Languages
SQL
Role
Act as a link between the data
engineers and the management
executives.
Possess specialized knowledge of
their business domain, and apply
that knowledge and analysis
specifically to the operation of the
business
Companies hiring Data
Engineers

44. What does a Machine
Learning Engineer do?
Data Science Engineer
Salary Trends

45. Data Science Salary Trends
In US, the average salary is
$117,345/year
Source - Glassdoor

46. Data Science Salary Trends
In India, the average salary is
₹9,50,000/year
Source - Glassdoor

47. Data Science Salary Trends
National average salary for different job roles in data science
Source - Glassdoor

48. Data Science Salary Trends
Growth in Data Science job listings
Source - Glassdoor

49. What does a Machine
Learning Engineer do?
Data Science Engineer
Resume

50. Note- please show the resume