Data Science is among the most fascinating industrial roles out there today. As one of the most promising and in-demand career paths, this field continues to evolve with skilled professionals as Data Scientists.
Today’s Data Scientists are past the traditional skills of analyzing large amounts of data, data mining and flawless programming skills. They are uncovering useful intelligence for their organizations, businesses and enterprises the world over.
What is Data Science?
Data Scientists identify relevant questions, collect data from multiple different data sources, organize collected information, find faults in it, translate faults into solutions, and communicate their findings in a way that positively affects business decisions.
Examples
Data Scientists are vastly employed in multiple roles, depending on the industry, the business and its goals. For e.g., Robert Chang, initially a Data Scientist at Twitter, now works for Airbnb. Both companies operate in industrial extremities and cater to different needs of a consumer.
Say suppose, you as a Data Scientist may work with Booking.com or Etsy (American e-commerce website focused on handmade or vintage items & craft) in future and handle massive online experimental frameworks for products that differ immensely.
LinkedIn listed ‘Data Scientist’ as one of the most promising jobs post 2018 and as the most in-demand by companies. Data Scientists are required in almost all industries, which is resulting in data scientists being increasingly valuable to companies across industries, big and small.
At ease? Not yet?
Know these Interesting facts about Data Science
As revealed by Jonathan Nolis, a Data Scientist in the US who collaborates with Fortune 500 companies, the ability to make good PowerPoint slides is more important for a Data Scientist than the ability to use the most sophisticated deep learning models. He made a case for the former since communicating results remains a critical part of data work.
What exactly is the secret sauce of a Data Scientist?
The skills Data Scientists need are focused around a strong quantitative background in statistics and linear algebra as well as programming knowledge but they are changing (and the most important one isn’t deep learning!). The fundamental skills of Data Scientists which are widely known to be so necessary today are likely to change on a relatively short timescale.
The abilities to build and use deep-learning infrastructures are not the key skills for data scientists today. Instead they the abilities to learn on the fly and to communicate well are of prime importance in order to answer business questions and explain complex results to a nontechnical audience.
New techniques enter and exit
But critical thinking & quantitative, domain-specific skills will always remain a requirement. As an aspiring Data Scientist, you should focus less on techniques and more on questions.
As we’re seeing rapid developments in increasing automation of a lot of data-science drudgery, these days even a great deal of machine learning and deep learning is being automated.
80% of a Data Scientist’s valuable time today is spent on simply discovering, cleaning-up, and arranging data, leaving only 20% to actually perform analysis.
Nearly all Data Scientists you will find today make their daily bread and butter through (in order):
- Data collection
- Data cleaning
- Building dashboards and reports
- Data visualization
- Statistical inference
- Communicating results to key stakeholders
- Convincing decision makers of their results
Data Science: 4 Fundamental Areas you must be good At
Data scientists often come from many different educational and work experience backgrounds. However, most Data Scientists should be experts in four fundamental areas or ideally be strong in them.
Most Data Scientists are strong in these 4 fundamental areas (in no particular order of priority or importance):
- Mathematics (including statistics & probability)
- Communication (verbal and written)
- Business/Domain that they are operating in
- Computer science (fundamental programming knowledge to start with)
Quick fact:
As IBM declares, the demand for data scientists is forecasted to see a spike by 28% post 2020.
Data Science Courses
To become a Data Scientist, you can study for an undergraduate degree in the following fields or in any other related fields:
- Computer Science & Engineering or any other field in Engineering
- Data Science & Engineering
- Information Science & Engineering
- Information Technology
- Computer Applications
- Computer Science
- Data Analytics
- Data Sciences
- Mathematics and Computer Science
- Mathematics and Statistics
- Statistics and Computer Science
- Applied Mathematics
- Computational Mathematics
- Financial Mathematics
- Mathematics
- Applied Statistics
- Industrial Statistics
- Mathematical Statistics
- Statistics
After your graduation you may opt to do a postgraduate degree or diploma in any one of the following fields or in any related field:
- Data Analytics
- Data Sciences
- Data Science & Engineering
- Business Analytics and Business Intelligence
- Computer Science
- Computer Science & Engineering
- Information Science & Engineering
- Applied Mathematics
- Applied Statistics
- Financial Mathematics
- Mathematics
- Mathematical Statistics
- Statistics
It could be a good idea to obtain a certificate in SAS Analytics software / SAP HANA / IBM Cognis / IBM SPSS / Similar Business Analytics and Business Intelligence tools along with a certificate in Hadoop / Spark or similar big data analytics tools. Also you may obtain a certificate in Python/ R/ SQL/ UML/ Microsoft Visio/ similar tools.
Data Scientists utilize key technical tools and skills, including:
| R | Apache Spark | Apache Pig |
| Apache Hadoop | NoSQL databases | Cloud computing |
| Python | Tableau | iPython notebooks |
| MapReduce | D3 | GitHub |
After formal schooling and college, remember to go for specialization courses in any of these tools to widen your horizons.
Not to forget, new techniques enter and exit.
Whether you are working or not, make sure that you keep learning about the new toys in the block to have an edge over others!
Data Science Courses Online
Demand for data-driven decision makers is continuously growing at an alarming rate. According to our analysts, the ultimate new-age skills that nearly all companies desire most prominently include statistics, math, data management, data visualization, statistical programming, and machine learning.
Whether you’re looking at advanced learning to add to your foundation or even if you’re just beginning to dip your toes in this field, we’ve got you covered with these most popular new courses and specializations from world-class companies and universities!
Free Top 10 Data Science Courses Online for Beginners
#1 UDACITY FREE INTRODUCTION TO DATA SCIENCE COURSE
You can access the course here.
#2 ALISON FREE FUNDAMENTALS OF DATA SCIENCE COURSE
You can access the course here.
#3 SKILLATHON FREE DATA SCIENCE COURSE
You can access the course here.
#4 FREE COURSE BY WORLDQUANT UNIVERSITY
You can access the course here.
#5 DATA SCIENCE ETHICS BY COURSERA
You can access the course here.
#6 FREE ONLINE DATA ANALYTICS COURSE ON IBM
You can access the course here.
#7 KDNUGGETS DATA SCIENCE COURSE
You can access the course here.
#8 FREE COURSE ON DATA CAMP
You can access the course here.
#9 FREE COURSE ON KONFINITY
You can access the course here.
#10 DATA QUEST FREE COURSE
You can access the course here.
Way to go! Here is a list of 53 great specializations and introductory data science courses online for you
Data Science Courses Online : Fundamentals
- The Data Scientist’s Toolbox by Johns Hopkins University via Coursera
- Duke University’s Linear Regression and Modeling on Coursera
- Applied Data Science with Python Specialization by the University of Michigan on Coursera
- IBM’s Databases and SQL for Data Science on Coursera
- Harvard University’s Data Science: Linear Regression on EdX
- IBM Data Science Professional Certificate by IBM on Coursera
- Data Analytics in Health – From Basics to Business by KU Leuven University via edX
- Getting and Cleaning Data by Johns Hopkins University via Coursera
- Process Mining: Data science in Action by Eindhoven University of Technology via Coursera
- University of California, Davis (UCDavis) SQL for Data Science on Coursera
Data Science Courses Online: Python for Data Science
- Computer Science & Programming Using Python by MITx on EdX
- Statistics With Python Specialization by the University of Michigan on Coursera
- Data Science Ethics by the University of Michigan on Coursera
- Rice University’s An Introduction to Interactive Programming in Python (Coursera)
- Python Data Structures by the University of Michigan on Coursera
- Python for Data Science by the University of California, San Diego on EdX
- Programming for Everybody (Getting Started with Python) by the University of Michigan on Coursera
- Python and Statistics for Financial Analysis by the Hong Kong University of Science and Technology on Coursera
- Complete Data Science Training with Python for Data Analysis by the University of California, San Diego on EdX
- Problem Solving, Python Programming, and Video Games by the University of Alberta on Coursera
- University of Michigan’s Using Databases with Python on Coursera
Data Science Courses Online: R for Data Science
- Data Science: R Basics by Harvard University on EdX
- Microsoft’s Introduction to R for Data Science on EdX
- Data Science: R Basics by Harvard University on EdX
- Data Analysis with R by Facebook on Udacity
- Essential Math for Machine Learning: R Edition by Microsoft on edx
- R for Data Analysis on Alison
- Statistics and R by Harvard University on EdX
Data Science Courses Online: Machine Learning & AI for Data Science
- Stanford University’s Machine Learning on Coursera
- The University of Washington’s Machine Learning Specialization (Coursera)
- Columbia University’s Machine Learning for Data Science and Analytics on EdX
- Artificial Intelligence for Robotics by Georgia Tech Masters on Udacity
- AI For Everyone by IBM on Coursera
- The University of New South Wales’ Designing the Future of Work on Coursera
- Georgia Tech’s Machine Learning on Udacity
- Intro to Artificial Intelligence by Georgia Tech Masters on Udacity
Data Science Courses Online: Big Data for Data Science
- Ethics in AI and Big Data by the Linux Foundation on EdX
- Duke University’s Managing Big Data with MySQL on Coursera
- Security and Privacy for Big Data – Part 2 by EIT Digital on Coursera
- Big Data Fundamentals by the University of Adelaide on EdX
- Big data and Language 1 by Korea Advanced Institute of Science and Technology (KAIST) on Coursera
- Big data and Language 2 by Korea Advanced Institute of Science and Technology (KAIST) on Coursera
- Big Data, Genes, and Medicine by the State University of New York on Coursera
- Big Data Modeling and Management Systems by the University of California San Diego on Coursera
- Big Data and Education by the University of Pennsylvania on EdX
Data Science Courses Online: Deep Learning for Data Science
- Introduction to Deep Learning by National Research University Higher School of Economics on Coursera
- Deep Learning for Business by Yonsei University on Coursera
- Deep Learning with Tensorflow by IBM on EdX
- Deep Neural Networks with PyTorch by IBM on Coursera
- Computational Neuroscience by the University of Washington on Coursera
- Deep Learning in Computer Vision by National Research University Higher School of Economics on Coursera
- Deep Learning with Python and PyTorch by IBM on EdX
- Applied AI with Deep Learning by IBM on Coursera
If you are ready to take yourself to the next level, check out 6 more specializations and intermediate courses by top stakeholders in this industry:
1. Machine Learning Using SAS Viya by SAS
2. Tensorflow in Practice Specialization by deeplearning.ai
3. Python Data Products for Predictive Analytics Specialization by University of California, San Diego
4. Clinical Data Science Specialization by University of Colorado
5. Applied AI: Artificial Intelligence with IBM Watson Specialization by IBM
6. Statistics with SAS by SAS
Data Science: List of 6 Tips to Stay Ahead With an Edge
Learn new things and embrace curiosity. No matter if you’re just getting started or well on your way in your data science career, here are some tips:
- Keep in mind that you will not necessarily need a traditional degree to work in the AI or any other tech field.
Online courses are far more accessible and available now as well as loaded will all the ammunition you need. You can very well get industry-relevant training and experience online!
- Instead of just collecting certificates, try and understand what you’re learning.
You have to put your knowledge into practice. Certificates will always be there to celebrate big successes!
- Pull from social media!
Follow leaders, experts, and influencers in the field. Share your certificates and achievements with the network you’ve built. Start crafting a strong portfolio.
- Communicate & Interact with real people
Join communities like deeplearning.ai community, Github, Kaggle etc. to participate in micro-competitions and to engage with other learners as well as learn from their experiences.
- Time management is essential to success
Build a schedule and allot time for making progress in your courses.
- Read constantly. Blogs and articles are great! Stay up-to-date in the field.
Data Science Jobs
You may be hired as Data Scientist / Data Science Consultant / Research Analyst (Data Science) / Management Trainee in different consulting firms as well as MNCs.
You may also get a job in the role of a Data Analyst and then work your way up into Data Science roles.
Examples of organizations that you can look forward to for full-time opportunities:
- Leading consulting firms are Accenture, Bain & Company, Boston Consulting Group (BCG), Deloitte, Ernst & Young, KPMG, McKinsey, PwC, etc.
- Online Retail Organizations like Amazon, Flipkart, Etsy etc.
- Banks like Axis Bank, HDFC, Citi Bank, etc. and Fin Tech Companies such as insurers, consultancies, financial institutions, investment banking companies or others like Kasisto, Tesorio, Splunk, YotaScale Inc, Zestfinance, Scienaptic Systems, Underwrite.Ai, Kensho etc.
- Social Media like Facebook, Twitter, etc.
- Internet and IT giants such as IBM, Google, Microsoft, Facebook, Tencent, Twitter, etc.
- Space research and administration organisations such as NASA, ISRO, etc.
- Technology / research divisions of Goldman-Sachs, JP Morgan Chase.
- Travel, Hospitality and Tourism industry facing companies such as Booking.com, GoIbibo, Airbnb and lots more
Almost all industries and companies, big or small, are investing in digitalization and automation so as a Data Scientist of the future you shall not have to hunt long but remember to keep pace with concurrent changes in the field. This field is laced with tremendous competition which is quickly building up too.
(Source: CrowdFlower 2017)
Data Science Jobs: Examples of industries using Data Science
- Communications & Media Analytics
- High Technology Analytics like Sports Management Analytics
- Energy & Resources Analytics
- Financial Services like Banking & Wealth Management Analytics and Insurance Analytics
- Healthcare Analytics and Life Sciences Analytics
- Manufacturing Analytics
- In the Public Sector for Associations & Non-profits Analytics, Education Analytics, Government Analytics
- Consumer Goods Analytics
- Retail & Wholesale Analytics
- Services Analytics including Marketing, Engineering, Construction industries across their workforce, knowledge base, projects, utilization, and costs.
- Travel & Transportation
- Travel & Transportation Analytics
Data Science Jobs: Company Departments that use Data Science
In a company/ enterprise, whichever industry that it is operating in, various verticals of the business can be benefitted if they leverage the offerings of Data Science. These verticals include:
- Finance
- Human Resources
- IT Management
- Marketing
- Sales
- Supply Chain
- Support and Services
Data Science Jobs: Popular Technology Used
The mentioned departments use nearly all or most of the following technologies to transform data into business intelligence metrics. Most data science jobs will require you to be conversant with at least few of these.
| AWS | Alation |
| Alteryx | Amazon Redshift |
| AtScale | Cloudera |
| DataRobot | Hadoop |
| Excel and R | Jethro |
| Hortonworks | Microsoft Azure |
| Informatica | SAP |
| MapR | Salesforce |
| SQL Server | Splunk |
| Snowflake | Trifacta |
| Vertica | Teradata |
Data Scientist Salary: How Much Will You Earn?
The figures given as Data Scientist salary are purely indicative and have been curated to bear utmost resemblance to existing industry trends.
- At the entry-level (0 – 2 years), as a Data Scientist, you will be earning anything between Rs. 25,000 to 2,00,000 per month.
- At the junior level after 2-6 years of experience, you will be earning anywhere between Rs 50,000 to 3,00,000 per month.
- At the middle level of 6-12 years of work experience, your earning will rise to Rs 1,00,000-4,00,000 per month.
- At the senior level after 12 years of work experience, your earning will be between Rs. 1,50,000 to 6,00,000 per month.
Data Science: Useful Links
- What is Data Science? – UC Berkeley explains
- Projects – Stanford Data Science Initiative (Stanford University)
- Data Science Stories – Hackernoon
- What do Data Scientists Do? – Harvard Business Review
- Tableau Research Papers – Interesting Innovations (includes PDF)
Must Read
- 51 Top Universities in India
- Careers in Technology
- A career in Statistics
- All you need to know about B Sc Computer Science
- Scope of Mathematics
- The bachelor of engineering degrees
- Computer Science vs. Information Technology
- About Computer Applications Masters (MCA)
- Choosing an alternative career plan
List of 10 Data Science Blogs You Must Follow
- SmartData Collective; Run By: Social Media Today
- Data Science Central; Run By: Vincent Granville
- No Free Hunch; Run By: Kaggle
- What’s The Big Data; Run By: Gil Press
- Simply Statistics; Run By: Jeff Leek, Roger Peng, and Rafa Irizarry
- insideBIGDATA; Run By: Rich Brueckner
- Data Science 101; Run By: Ryan Swanstrom
- Datafloq; Run By: Mark Van Rijmenam
- Data Science Report; Run By: Starbridge Partners
- Dataconomy; Run By: Dataconomy Media
Data Science: Conclusions
Are you being bugged by the constant nagging question, ‘what is data science’? Well, we hope we’ve served you most of the answer. Thinking about wandering into the vast expanse of Data Science? It is undoubtedly one of the most sought-after job roles in the industry today. Whether you’ve already planned a career in it or just starting to dip your toes in the thoughts of it, allow us to make it a bit easier for you. Talk to an expert today to figure out more of what you must know before you begin your journey.
A specialist in actionable intelligence and knowledge delivery through research and analyses centric literary compositions, with demonstrated skills in scholarly & cross-disciplinary writing / editing and data interpretation/representation with a high degree of proficiency surrounding technical and scientific disciplines. A post graduate in Biophysics and graduate in Chemistry, she has interned with the prestigious Saha Institute of Nuclear Physics. Sreenanda comes with years of proven experience in secondary research and academic literature development.