Different Career Paths in Data Science
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The document provides an overview of different career paths in data science, including data scientist, data engineer, and data analyst roles. It summarizes the typical job duties, skills required, tools used, and average salaries for each role. Additionally, it notes the large and growing demand for data science professionals, with over 215,000 open jobs in the US as of January 2017 and top hiring locations of San Francisco, New York, and Seattle.
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Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
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Data Scientist vs Data Analyst vs Data Engineer - Role & Responsibility, Skil...
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
Data Culture Keynote and Exec Track Birm Dec 8th
Business today is starting to understand the value of data, and some organisations are outperforming their competition by putting data at the heart of their thinking. Leveraging data to change business models, understand their customers and employees better and deliver new revenue streams is the driving force in this new data centric era.
Jon Woodward - MSFT
Dave Coplin - MSFT
Mike Bugembe - JustGiving
Gary Richardson - KPMG
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This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. 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.
The 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
5. Experienced professionals who would like to harness data science in their fields
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Data Visualization
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Data Preparation
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Model Evaluation and Deployment
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Different Career Paths in Data Science
- 1. Data Science Career Paths Overview & Comparison
- 2. The world produces 2,500,000,000, 000,000,000+ bytes of data every day, that’s equal to 10 million Blu-Ray disks stacked the height of 4 Eiffel Towers. Source: http://www.vcloudnews.com/every-day-big-data- statistics-2-5-quintillion-bytes-of-data-created-daily/
- 3. A Data Scientist solves problems with large amounts of data. Example: Analyze tweets to figure out whether or not a tweet sent to a company is positive or negative.
- 4. The US alone will face a shortage of 150,000+ data analysts and an additional 1.5 million data-savvy managers
- 5. What makes Springboard unique?3 common roles in Data Science
- 7. Data Scientist Also called Statisticians, Data Managers
- 8. A Data Scientist will be able to take data science projects from end to end. They can help store large amounts of data, create predictive modeling processes and tell stories about the findings Data Scientist
- 9. Average salary $USD 115,000 Data Scientist Skills needed Mathematics, Programming, Communication Tools used SQL, Python, R
- 10. Data Engineer Also called Data Architects
- 11. Data Engineers are versatile generalists who create data pipelines to help process large amounts of data. They typically focus on coding, cleaning up data sets, and implementing requests that come from Data Scientists. Data Engineer
- 12. Average salary $USD 100,000 Data Engineer Skills needed Programming, Mathematics, Big Data Tools used Hadoop, NoSQL, Python
- 13. Data Analyst Also called Business Analysts
- 14. A Data Analyst helps people from across the company understand data insights they’ve unearthed. Data Analyst
- 15. Average salary $USD 65,000 Data Analyst Skills needed Statistics, Communication, Business Knowledge Tools used Excel, Tableau, SQL
- 16. How do the roles connect? Inspired by Drew Conway
- 18. ~215,000 Open job positions Data from Indeed.com, January 2017
- 19. Top 3 hiring locations in the U.S. San Francisco New York Seattle
- 20. What makes Springboard unique?Data Science Profiles
- 21. DJ Patil Data Scientist As the former Chief Data Scientist of the United States Office of Science and Technology Policy, DJ is the perfect prototype of the Data Scientist.
- 22. Doug Cutting, Data Engineer The creator of Hadoop and a member of Apache’s Board of Directors is a model of the data engineer who builds tools to process big data.
- 23. Roger Huang, Data Analyst The humble author of this piece, while nowhere near as talented as the two individuals referenced above, did serve a brief stint as a data analyst for a pharmaceutical company.
- 24. Data science is a new and exciting field filled with lucrative career paths. Curious to learn more? Check out Data Science Career Track!
- 25. Ready for the next step? Read more at www.springboard.com/blog/data-science-career-paths- different-roles-industry