It is predicted that data science, big data, and machine learning will hold the top spot for hot professions of the future. Data has now become an integral tool for decision making. All human activities generate huge volumes of data every day. And with this, acquiring data science skills now holds an even important position in decision making and studying behavioral impacts in various day to day applications
As a result, the need for data sciences and analytics is constantly growing and evolving. This calls for learning new and innovative ways to deal with the huge volumes of data that keep streaming into an ecosystem.
A lot of organizations are incorporating data analytics into their regular operations. They are creating a strong muscle in the field of data sciences and are hence, setting benchmarks for the rest of the market. To help you and your peers get started on a learning track for data science skills, we have compiled a list of the resources you can use.
How can one gain Data Science skills?
While the role of a data scientist is lucrative and interesting, it needs some basic skills and the urge to learn and develop within you. While there are several academic programs that provide formal training on several data science skills, there are various open-source learning resources to gain knowledge and experience in the field.
Some of the resources to get learn good Data Science skills from are:
A. Open-Source Data Science blogs:
- R bloggers
R-Bloggers is a blog and content aggregator that contains articles on various statistical algorithms, data manipulations, and visualization techniques. Moreover, the blog site hosts various tutorials on how to execute different algorithms on R, one of the most popular programming languages used in Data Sciences. It was started by Tal Galili and has now become a reliable resource for both advanced and beginner-level data scientists. You will find articles and blogs on multiple R systems, commands, libraries and packages on the site.
Would you also not like content on how to implement data science algorithms from scratch? R-Bloggers can help you with various snippets of R-codes and clarifications on widely faced problems in R programming. Since it is an open-source forum, people can contribute to it in all forms.
- Towards Data Science
Towards Data Science has become a large community of 70,000 unique daily visitors on an average as of July 2018. It is yet another online blog and content forum that hosts articles on machine learning, data science, visualization tools, and programming techniques. The content is hosted on an online publishing platform, Medium and covers even the most niche technologies and nascent use-cases of data science. A highly able team checks and validates the quality, authenticity, and readability of the content put up on the forum.
You can also apply to be a contributor on Towards Data Science by sending the team a copy of the CV and the link to a sample write-up on Medium. This blog site can help people stay on top of all developments in the Data Science industry. They can also find help on how to execute certain algorithms and solve some real-world data science problems.
- Machine Learning (Theory)
Machine Learning is a blog site started and managed by John Langford, the Director of Learning at Microsoft Research. This collaborative machine learning blog covers a wide range of topics like mathematics, information theory, predictive analytics and statistics among many others. Furthermore, Langford uses this forum to share his knowledge, expertise and personal insights on learning theory, neuroscience, economics and other unconventional areas of application of data science. The forum covers conferences and related events, thus facilitating accelerated learning within the community.
- Miscellaneous Data Science Blogs
You can also explore several other blogging platforms and sites that cover various aspects of data science and analytics. Edwin Chen, a data scientist with experience at Dropbox, Microsoft, and Clarium Capital Management writes his blog to convey his thoughts on the subject.
We feel FastML, Data Mining Blog and Statistical Modelling, Causal Inference, and Social Science are some popular blogs for data science and analytical learning. Datahut also has a blog site that hosts various informative articles about the application of data science in industries like retail, manufacturing, finance and even security.
B. MOOCs and Courses on Data Science
Coursera has one of the most exhaustive collections of online courses on any subject. You can start learning the fundamentals of the science and even the more advanced concepts. The MOOC platform has a huge global outreach and courses on industrial applications of data science (Social Media Data Analytics, Materials Data Sciences and Informatics, Data Science in Stratified Healthcare and Precision Medicine). We have also listed a few courses you can get started with:
– Machine Learning (by Stanford University): The course covers concepts like regression, classification and neural networks. Besides this, it also delves deeper into the concepts of data mining. engineering and preprocessing. This course gives a holistic training in data sciences.
– Mathematics for Machine Learning (by Imperial College London): This is a collection of 3 courses that cover mathematical concepts like linear algebra, multivariate analysis, and Principal Component Analysis. Since data science relies heavily on mathematics, it is necessary that individuals learn the fundamentals before implementing the algorithms.
– Data Science Specialization (by Johns Hopkins University): This specialization comprises of 9 courses and a capstone project on various subjects. While it covers basic topics like R programming, it also provides training on Practical Machine Learning. Hence, it is often deemed as a good starting point for data science beginners.
Coursera offers several other courses for studying data science and analytics. You can explore these courses and sign up for them using a few simple steps.
If you are a beginner, Datacamp is a great place to learn to programme in R and Python. With its step-wise approach, you can take one problem at a time and solve/ debug incomplete lines of code. Since the prices of these courses are affordable, people from various age groups and professions can pursue these courses. Datacamp also offers regular discounts on various features and courses.
You can thus, pursue multiple tracks with around 20 courses each to build and strengthen the programming muscle. Some of the popular learning tracks are Data Scientist with Python, Quantitative Analysis with R, Data Manipulation with Python and Data Visualization with R.
- Multiple other learning sites
There are several other websites that offer courses in data science, analytics, programming, and even industrial case studies. Udacity, Udemy, edX and Khan Academy are a few options available currently. Since most of these sites are also available as mobile applications, one can pursue these courses on the go. These courses can help people improve their data science skills easily.
C. Practice sites for Data Science projects
Kaggle is touted as one of the best platforms for developing and testing ones’ data science skills. Most data scientists use Kaggle as a forum for applying machine learning and statistical algorithms to real-world industrial problems.
It is a community-based forum where people contribute snippets of codes and datasets for others to learn from. Furthermore, any individual can think of a project from scratch, create data for the same and host for others to participate. Since there is immediate feedback from the peers on all activities, Kaggle facilitates accelerated learning and growth in the field of data science.
Hackerearth has practice exercise and challenges hosted by a lot of leading companies and industry players. Individuals who want to put their data science skills to use can easily sign up for any of these competitions. While a lot of these competitions are designed for the purpose of hiring individuals, one can also participate in them just for improving the problem-solving skills. With real-world problems and sample datasets, these challenges can help you develop the problem solving, programming and mathematical skills.
- Multiple other practice resources
Although theoretical training is necessary for any subject, practical experience is mandatory for you to acquire and grow the data science skills.
Websites like Dataquest provide a platform for gaining hands-on experience in the field. A user can work on data, write codes and build projects on this forum. Datastock facilitates that by providing cleaned and ready-to-use datasets across verticals like eCommerce, healthcare and travel industry.
Datahut has the capability to scrape usable information from any website and store them in a structured format for convenient usage. An individual can use these datasets to perform analysis and gain insights. Although these services are paid, they can be used to create a good portfolio for a data scientist which may guarantee him a lucrative opportunity in the data science world.
D. Online Data Science Communities
Besides courses, practice sites and reading material, there are a lot of channels for community-based learning. Although data science is a relatively new field, it has grown rapidly. You can learn from people who have now mastered the field. There a lot of forums which can help you connect to a data science community.
Analytics Vidhya is one such ecosystem. People here learn, compete, teach and give feedback to their peers- all in the field of data science and analytics. Stack Overflow is an online develop community where people post questions on various programming languages and algorithms. People in the community then answer these questions the best to their knowledge, debate on the same and provide the best possible learning experience to each other. This is the fastest way to learn and solve some problems in the data science world.
E. Books on Data Science
Are you someone who likes learning from books instead of online resources? Don’t worry! We have you covered too. There are several industry experts and statisticians who have published books in the field. Some of these books focus on a particular programming language like R, Python or SQL. However, several others talk about how to use data and information to derive actionable predictions or insights. Predictive Analytics by Eric Siegel is a good book most data scientists have come across.
Machine Learning Yearning by Andrew Ng is another great recommendation for people who want to work on their data science skills. Individuals can explore various options and decide which book suits them the best. However, there are various open-source resources which can solve the purpose equally well.
While all the above resources can help one to get information on the field, the prerequisite is the intent and the interest. You can use a few or all the resources to learn as much as he/she wants to.
Universities across the globe have also designed certificate courses and formal programs for data science and analytics. While the field is nascent, it is also growing at a rapid rate. It would be best for individuals to join the bandwagon now before it is too late.