Data Scientist is the sexiest job of the 21st century. Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020.
To start a career as Data Scientist while you are pursuing your scholars, you must know have deep knowledge as well as practical approach of following topics.
- R Programming/Python Programming
- Good knowledge of algorithms like Linear Regression, Logistic regression etc. of data mining.
If you know tools like Map Reduce, Rapid Miner, Tableau etc. getting internship is easier.So in order to pursue a career as data scientist, you need to:
- First acquire the relevant skills in the technologies mentioned above so that you can understand the basic role and job that is involved.
- Secondly you need to practice your skills and showcase your talents by doing relevant projects in Data Science.
- You need to find some internship in the companies hiring data scientist.
- Finally appearing for the interviews which may help you get hired as Data Scientist.
You may acquire skills from any of the platform or resource mentioned below
- Training centers: A lot of training and coaching centers are there which offers courses in these technologies.
- Edureka, Udemy, Simplilearn: There are a lot of Online platforms which provide training & certifications for Data Science.
- edWisor.com is one such platform which not only gets you skilled in technologies also helps candidates get paid internships at product-based companies. So give it a try.
Empirically,almost every IT company offers data science internship. My suggestion is to make a profile on Internshala, Let’s intern, Greymeter, Angellist ,LinkedIn, Glassdoor, naukari or Monster to search for “Data Science Internships” and set the search limit to just Bay Area.
Here I am naming a few companies, Facebook, Square, Uber, eBay, Google, Twitter, Salesforce, Apple, LinkedIn, AirBnB, Yahoo, Pinterest, Palantir, Yelp, Snapchat, Instagram, Quora all hire data science interns.
What about some Advice? Guidance ? Stories ? in Data Science , to know more about this field,
We are providing top 10 influencers who are making impact in Big Data, Data Science, and Machine Learning. Follow them and stay up-to-date with the latest news in Big Data, Data Science, Analytics, Machine Learning and AI. This list refers basically KDnugget reference list for Data Science’s Experts.
A best-selling author, strategy consultant and CEO. He’s published numerous reports and books on Big Data and has advised several well-recognized firms. He has figured among the top five LinkedIn business influencers.
Principal and Founder of Bersin by Deloitte, a leading firm in strategic and research consulting for talent management, leadership development, recruiting and training.
Former US Chief Data Scientist under President Barack Obama, responsible for creating and establishing major data-driven initiatives in healthcare, criminal justice and national security among others. He was also Head of Data Products and Chief Scientist at LinkedIn.
CEO and Founder of x.ai, an AI personal assistant to schedule your meetings.
For the last 18 years, Carla has worked with Fortune 100 and 500 companies and is highly experienced tackling complicated databases and deciphering complex business needs to provide insight into key performance metrics.
Recognized leader and author of several books and articles; senior adviser to Deloitte Analytics; Distinguished Professor of Information Technology and Management at Babson College and MIT Fellow at the Center for Digital Business.
Data Science, Business Intelligence and Data Mining expert; President of KDnuggets, voted Best Twitter and Top Influencer in Big Data and Data Science.
8.Ronald van Loon
Recognized in the field of digital transformation by publications and organizations as Onalytica, Dataconomy, and Klout. In addition to these recognitions, Ronald is also an author for a number of leading big data websites, including The Guardian, The Datafloq, and Data Science Central.
Kirk is a major and well-recognized Big Data and Data Science advisor, TedX speaker, consultant, researcher, blogger, Data Literacy advocate.
Vin has 8 years of experience using modern data science/machine learning tools and methodologies in both startups and Fortune 10 companies.
List of E-mail Newsletters to Subscribe to get Data Science latest news
Follow, learn and make your own story. Here are some resources which answer your questions like,
What is trending?
What are new tools to learn?
How to get job in data science?
Where to apply?
And many more questions are there to be answered.
The solution is to turn to email newsletters, which can help you keep a handle on the latest news, tools and tutorials.Here are my picks for the best data science newsletters.
Data Elixir is a good all-rounder, with a mix of news, opinion and tutorials. It also maintains a data science jobs board and includes the latest job listings in each week’s email.
Data Science Roundup
Data Science Roundup rather than providing an extensive list of articles, DS Roundup usually includes just four or five great reads.
KDnuggets news is purely a list of links without any summaries, but even still it’s one of the most comprehensive roundups of what has happened in the week.
The Analytics Dispatch
The Analytics Dispatch, the articles tend to cover the more analysis topics.
Dataskeptic is provides podcast and tutorials on topics in statistics, machine learning, big data, artificial intelligence, and data science.
Data Science Weekly
DSW also conduct some fantastic Data Science Interviews and maintain a really thorough list of Data Science Resources including books, meetups, datasets, blogs and data scientists on twitter.
Here is suggestion for research topics in Data Science
- Developing a unifying theory of data mining
- Scaling up for high dimensional data and high speed data streams
- Mining sequence data and time series data
- Mining complex knowledge from complex data
- Data mining in a network setting
- Distributed data mining and mining multi-agent data
- Data mining for biological and environmental problem
- Data Mining process-related problems
- Security, privacy and data integrity
- Dealing with non-static, unbalanced and cost-sensitive data
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