7 answers
1
Question
7 answers
Updated
Kartik’s Answer
Hi Kiran, if you are interested in learning Data Science and building a career in it, I would suggest that you break it down into multiple small steps, and try to concentrate on one step at a time -
- Data gathering (eg - using APIs of various data sources)
- Data processing and storing into structured format (eg - Python scripting)
- Querying the structured data to get meaningful information (eg - SQL or noSQL)
- Visualise the data in the form of graphs, charts etc. (eg - Tableau, Splunk)
For each of these steps, there are different technologies that you will need to learn, which are mentioned in the brackets. And these are just some of the examples, many more things are there in each of these spaces.
Updated
Tushar’s Answer
There are quite a few free online resources which can be referred to, here are few suggestions:
Coursera – Data Science Specialization
Coursera provides one of the longest-established online data science educations, through John Hopkins University. It isn’t completely free – if you can afford it, you are expected to pay a course and certification fee – but this is waived for students who don’t have the financial resources available.
Comprised of 10 courses, the specialization covers statistical programming in R, cluster analysis, natural language processing and practical applications of machine learning. To complete the program, students create a data product which can be used to solve a real-world problem.
Coursera – Data-Driven Decision Making
Also from Coursera, this course is provided by PwC so unsurprisingly focuses more on business applications than theory. It covers the spectrum of tools and techniques which are being adopted by businesses today to tackle data challenges, and the different roles that data specialists can fill in modern organizations. Students are also tutored on selecting the best tools and frameworks for solving problems with data. The four-week course concludes with a task involving deploying a data solution in a simulated business environment,
EdX – Data Science Essentials
This course is provided by Microsoft and forms part of their Professional Program Certificate in Data Science, although it can also be taken as a stand-alone course through EdX. Students are expected to have an “introductory” knowledge of R or Python – the two most popular languages for data science programming at the moment. Subjects covered include probability and statistics, data exploration, visualization, and an introduction to machine learning, using the Microsoft Azure framework. Although all of the course material is free, students can pay ($90 in this case) for an official certificate on completion.
Udacity – Intro to Machine Learning
Machine learning is undoubtedly one of the hot topics in data science right now, and this course aims to give a full overview, from theory to practical application. As well as an introduction to selecting data sources and choosing which algorithms best fit a particular problem the course also forms a part of Udacity’s paid-for “nanodegree” in data analysis.
IBM – Data Science Fundamentals
IBM provides a number of free online courses through its portal formerly known as Big Data University and now rebranded as Cognitive Class. This program covers data science 101, methodology, hands-on applications, programming in R and open source tools. Collectively they should take around 20 hours to complete although those with prior experience of computer science will probably progress more quickly, whereas complete beginners may take a little bit longer.
California Institute of Technology – Learning from Data
This course focuses on machine learning and is delivered as a series of video lectures along with homework assignments and a final exam. As well as an overview of how computers “learn”, it goes into depth with the mathematics (students are expected to have a working knowledge of matrices and calculus, so this one isn’t for complete maths newbies).
Dataquest – Become a Data Scientist
Dataquest is an independent online training provider rather than being affiliated with a university like most of the others here. It offers free access to much of its course materials although you can also pay for premium services which include tutored projects. It offers three paths – data analyst, data scientist and data engineer, and with endorsements from Uber, Amazon and Spotify it looks like a good way to get a feel for whether or not you will enjoy studying data science, without spending money.
KDNuggets – Data Mining Course
KDNuggets is a well-known business and data science website and it has compiled its own free data mining syllabus. There are modules on machine learning, statistical concepts such as decision trees, regression, clustering and classification (see my data science glossary for an introduction to these terms) as well as an introduction to practical implementations of the technology.
The Open Source Data Science Masters
Rather than being offered by an organization or institution, this course is comprised of a collection of open-source materials and resources, available freely online. Subjects covered include natural language processing of the Twitter API using Python, Hadoop MapReduce, SQL and noSQL databases and data visualization. It also includes a grounding in the algebra and statistics needed to understand the fundamentals of data science. Of course there is no certification but the program can be completed at your own speed and works great as a gateway to the wealth of information on data science available online.
Coursera – Data Science Specialization
Coursera provides one of the longest-established online data science educations, through John Hopkins University. It isn’t completely free – if you can afford it, you are expected to pay a course and certification fee – but this is waived for students who don’t have the financial resources available.
Comprised of 10 courses, the specialization covers statistical programming in R, cluster analysis, natural language processing and practical applications of machine learning. To complete the program, students create a data product which can be used to solve a real-world problem.
Coursera – Data-Driven Decision Making
Also from Coursera, this course is provided by PwC so unsurprisingly focuses more on business applications than theory. It covers the spectrum of tools and techniques which are being adopted by businesses today to tackle data challenges, and the different roles that data specialists can fill in modern organizations. Students are also tutored on selecting the best tools and frameworks for solving problems with data. The four-week course concludes with a task involving deploying a data solution in a simulated business environment,
EdX – Data Science Essentials
This course is provided by Microsoft and forms part of their Professional Program Certificate in Data Science, although it can also be taken as a stand-alone course through EdX. Students are expected to have an “introductory” knowledge of R or Python – the two most popular languages for data science programming at the moment. Subjects covered include probability and statistics, data exploration, visualization, and an introduction to machine learning, using the Microsoft Azure framework. Although all of the course material is free, students can pay ($90 in this case) for an official certificate on completion.
Udacity – Intro to Machine Learning
Machine learning is undoubtedly one of the hot topics in data science right now, and this course aims to give a full overview, from theory to practical application. As well as an introduction to selecting data sources and choosing which algorithms best fit a particular problem the course also forms a part of Udacity’s paid-for “nanodegree” in data analysis.
IBM – Data Science Fundamentals
IBM provides a number of free online courses through its portal formerly known as Big Data University and now rebranded as Cognitive Class. This program covers data science 101, methodology, hands-on applications, programming in R and open source tools. Collectively they should take around 20 hours to complete although those with prior experience of computer science will probably progress more quickly, whereas complete beginners may take a little bit longer.
California Institute of Technology – Learning from Data
This course focuses on machine learning and is delivered as a series of video lectures along with homework assignments and a final exam. As well as an overview of how computers “learn”, it goes into depth with the mathematics (students are expected to have a working knowledge of matrices and calculus, so this one isn’t for complete maths newbies).
Dataquest – Become a Data Scientist
Dataquest is an independent online training provider rather than being affiliated with a university like most of the others here. It offers free access to much of its course materials although you can also pay for premium services which include tutored projects. It offers three paths – data analyst, data scientist and data engineer, and with endorsements from Uber, Amazon and Spotify it looks like a good way to get a feel for whether or not you will enjoy studying data science, without spending money.
KDNuggets – Data Mining Course
KDNuggets is a well-known business and data science website and it has compiled its own free data mining syllabus. There are modules on machine learning, statistical concepts such as decision trees, regression, clustering and classification (see my data science glossary for an introduction to these terms) as well as an introduction to practical implementations of the technology.
The Open Source Data Science Masters
Rather than being offered by an organization or institution, this course is comprised of a collection of open-source materials and resources, available freely online. Subjects covered include natural language processing of the Twitter API using Python, Hadoop MapReduce, SQL and noSQL databases and data visualization. It also includes a grounding in the algebra and statistics needed to understand the fundamentals of data science. Of course there is no certification but the program can be completed at your own speed and works great as a gateway to the wealth of information on data science available online.
Updated
Gitika’s Answer
Hi Kiran, I believe you want to understand how to learn Data Science or make a career in Data Science. If my understanding is correct, I have few suggestions for you. Data Science is a hot topic and a great career choice now a days. Lot of reputed college for e.g. ISB are offering full time and part time courses in Data Science and Data Analytics. You can also register in virtal training courses offered by many training vendors to learn about Data Science and make a career out of it. Hope it is useful for you. Please let me know if you are looking for anything specific regarding Data Science.
Updated
Natasha’s Answer
In addition to the suggestions mentioned above, I recommend looking at the resources listed here: https://medium.com/data-science-at-microsoft/data-science-learning-resources-193ccf6fafb
These include a variety of online courses, textbooks, blogs, and even podcasts!
These include a variety of online courses, textbooks, blogs, and even podcasts!
Updated
Sreeram’s Answer
Apart from all the great answers, i want to mention Kaggle (https://www.kaggle.com/). Sign up and start practicing! There is only one way you can really learn data science - and that is by doing. You can also work on developing an impressive portfolio on kaggle and use it to get an internship.
Start here - https://www.kaggle.com/c/titanic
Start here - https://www.kaggle.com/c/titanic
Updated
Ujjaini’s Answer
Hi Kiran,
You can get into Data Science a number of ways. If you have a Graduate degree in Economics, or another quantitative field, such as Statistics you will be able to apply for an entry level job in Data Science or Analytics right after you graduate. Data Science companies visit college and university campuses and sometimes hire for both internship positions as well as full time positions. You can try for an internship role after your undergraduate degree if you want a feel for the industry.
If you are still in college, encourage you to take courses like Statistics, Machine Learning, Econometrics, SAS and Python if available.
You could also have an Engineering background with coding experience, that way you can make a lateral entry into Data Science leveraging your knowledge of coding.
Best of luck!
Best of luck!
You can get into Data Science a number of ways. If you have a Graduate degree in Economics, or another quantitative field, such as Statistics you will be able to apply for an entry level job in Data Science or Analytics right after you graduate. Data Science companies visit college and university campuses and sometimes hire for both internship positions as well as full time positions. You can try for an internship role after your undergraduate degree if you want a feel for the industry.
If you are still in college, encourage you to take courses like Statistics, Machine Learning, Econometrics, SAS and Python if available.
You could also have an Engineering background with coding experience, that way you can make a lateral entry into Data Science leveraging your knowledge of coding.
Best of luck!
Best of luck!
Updated
Firoz’s Answer
Areas of understanding or skills that can be very helpful include:
-- Data Visualization
-- Tableau
-- SAS
-- SQL
-- Foundational understanding of data modeling
-- Understanding of how data flows from systems to landing areas to data use layers
-- Hadoop
-- Impala
-- Python
-- Data Visualization
-- Tableau
-- SAS
-- SQL
-- Foundational understanding of data modeling
-- Understanding of how data flows from systems to landing areas to data use layers
-- Hadoop
-- Impala
-- Python