This article represents information related different classes of IT & Non-IT professionals who could take on different data science free courses (as mentioned) and get on to the path of becoming a data scientist. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos.

Following are the different classifications of IT/Non-IT professional which has been addressed later in this article:

- Software Development Stakeholders working on Non-analytics projects
- Datawarehouse/BI Developers
- Big Data Developers
- Statisticians
- Senior Management Executive
- Non-Software Professionals

###### Could I become a Data Scientist?

Anyone matching following criteria could become a data scientist.

- One is
**decent with Mathematics & Statistics concepts**. If not, one should not hesitate to get started with learning basic Maths & statistics concepts. - One should also be
**good at, at least, one programming language**.

Following is the list of different stakeholders with relevant course information that they could use to get started on the journey of becoming a data scientist:

- Software Development Stakeholders working on Non-analytics projects
**Developers of any sort**could become a ‘Data Scientist’. The software developers, in general, could get an entry as a data specialist in data analytics projects. In order to become a data specialist, he/she could get started with machine learning and probability & statistics course at Coursera.org. There are several popular courses on machine learning on Coursera.org including Machine Learning by Andrew NG that could help a developer do a**deep dive in the waters of machine learning**. In addition, there are courses on statistics such as following which could help developers get a good understanding on statistical concepts:- Mathematical Biostatistics Boot Camp 1 by John Hopkins University
- Statistics: Making Sense of Data by University of Toronto
- Statistical Inference by John Hopkins University.

**Project/Delivery Managers**who are looking forward to gain high-level understanding in machine learning techniques to lead analytics projects involving a team of analysts. There would surely be need for managers with knowledge of data analytics and this is where these managers would be hired. They may not be required to get into depth of machine learning. It may be a good idea to get started with Introduction to Data Science by Bill Howe from University of Washington.**Business Analysts (BA)**looking to gain understanding on Data analytics and related Machine Learning (ML) techniques. It would help BAs to work comfortably on Data analytics projects due to familiarity with data analytics/ML terminologies. BAs could get a high level understanding of data science by checking out course such as Introduction to Data Science course on Coursera.org.**Information/Data Architects**dealing with data modeling/database design could also get started with data science and become experts in the area of Predictive Analytics. They could get started with data science by checking out course such as Introduction to Data Science course on Coursera.org.

- Datawarehouse/BI Developers
- BI professionals could learn Data science/machine learning techniques to get involved with predictive analytics. As a matter of fact, DW/BI developers are already versed with descriptive analytics techniques and aware of basics statistics concepts. Thus, it could get comparatively easier for them to understand different predictive analytics techniques.

Following are some of the useful courses for Datawarehouse/BI Developers:

- Introduction to Data Science by Bill Howe from University of Washington
- Mathematical Biostatistics Boot Camp 1 by John Hopkins University
- Machine Learning by Andrew NG from Stanford University. This course could be useful if one wants to go into greater details of machine learning techniques.

- Big Data Developers
- Hadoop developers looking to learn machine learning techniques
- Python/R professionals wanting to understand the applications of Big Data and related technologies

Following are some of the useful courses for Big Data developers:

- Introduction to Data Science by Bill Howe from University of Washington
- Mathematical Biostatistics Boot Camp 1 by John Hopkins University
- Machine Learning by Andrew NG from Stanford University. This course could be useful if one wants to go into greater details of machine learning techniques.

- Statisticians
- Statisticians wanting to understand and implement the statistics and machine learning techniques and related Big data technologies
- SAS/SPSS Professionals wanting to have a deeper understanding in Data Analytics

Following are some of the useful courses for Statisticians:

- Introduction to Data Science by Bill Howe from University of Washington
- Mathematical Biostatistics Boot Camp 1 by John Hopkins University
- Statistics: Making Sense of Data by University of Toronto
- Statistical Inference by John Hopkins University

- Senior Management/Executive Professionals
- Senior management stakeholders wanting to gain a high-level understanding on data analytics. It would help senior management stakeholders to understand and appreciate data analytics projects and invest appropriately. They could simply touch-base with this course, Introduction to Data Science by Bill Howe from University of Washington, and learn enough to be informed about analytics.

- Non-Software Professionals
- Analysts wanting to understand Data Science methodologies
- Journalists wanting to understand statistics and machine learning techniques to provide data related insights into their articles
- Bloggers wanting to gain a very high level understanding of statistics concepts and related ML techniques at a very high level

Following are some of the useful courses for Non-Software Professionals:

- Introduction to Data Science by Bill Howe from University of Washington
- Mathematical Biostatistics Boot Camp 1 by John Hopkins University. This would help them understand key statistics concepts in order to perform data analysis on different news-related stories and present detailed analysis.

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