According to Forbes, we produce 2.5 quintillion bytes (2.5 billion gigabytes) of data every day at our current pace and the main reason is the Internet of Things.
Here is what all happens in 1 minute in our world -
- Google conducts 3,877,140 searches.
- Amazon makes $258,751.00 in sales
- Tinder users match 6,940 times
- Uber takes 1,389 rides
- Spotify streams over 750,000 songs
- 12,986,111 texts sent
- Twitter users send 473,4000 tweets
- YouTube users watch 4,333,560 videos
- Snapchat users share 2,083,333 snaps
and chats
So much data in TBs has been produced until you read this line.
Data is very important and crucial. We care about the data stored even in our small mobiles. Now the question is:
- How the companies analyse this large amount of data?
- Who make value out of this data?
- How they process it- for recommendations and automation?
- Who provides this valuable and useful data to Machine Learning programmers to write algorithms to be able to effectively use them?
- Who cleans and validates data to ensure accuracy and uniformity?
- Who identifies these patterns and trends in data, interprets the data to discover new industry opportunities?
The answer to all the above questions is "THE DATA SCIENTIST".
Who are Data Scientists?
According to Cathy O'Neil and Rachel Schutt, the author of Doing Data Science: "More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as a human being. He/she spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills—skills that are also necessary for understanding biases in the data, and for debugging logging output from the code."
In general word, we can say Data Scientist is the one who studies & analyses data broadly and integrates deeply with tools, to give it a proper shape, find patterns and trends in it, create algorithms and give some meaningful and valuable result from it.
Data Scientists are highly educated. According to industry resource KDnuggets, 88% of data scientists have at least a master's degree and 46% have PhDs-and while there are notable exceptions, a very robust educational background is usually required to develop the depth of knowledge necessary to be a data scientist.
How to become a Data Scientist?
- You should love data, I mean real data, big data, an unstructured data and lots of data.
- Next up in the list - Mathematics and Statistics must be your good friends, followed by Computer Science and Engineering.
- You should have a good command on Python (not the snake, a language) and R language.
- Love to work on Data Structures and Algorithms.
- You should have an Intellectual Curiosity and Business Acumen, they are necessary to solve the business obstacles which are critical, and be able to identify new ways in which the business should be leveraging its data.
- Hadoop, Hive, Pig or Spark are also very strong selling points and are heavily preferred in many cases. According to CrowdFlower, on 3490 LinkedIn data science jobs, Apache Hadoop is ranked as the second-most valuable skill for a data scientist with a 49% rating.
- Machine Learning and AI- these skills will help you to solve different data science problems that are based on predictions of major organizational outcomes.
- Data Visualization- as I mentioned about data generation above, that data needs to be turned into such format that is easy to perceive. As a data scientist, you must be able to visualize data with the aid of data visualization tools such as ggplot, d3.js, Matplotlib and Tableau. They give you the ability to work on data directly and quickly.
How to start with Data Science?
There are many resources on the internet to start your journey of Data Science, but the journey should be in the precise and directed path because to be a data scientist you should have correct resources ( which are useful in one way or another ), directed paths and some exciting projects which you could showcase to the world.
Coding Blocks provides a vast and excellent resource for Data Science called Data Science Master Course including Python, Numpy, Matrices, Algebra, Data Structures, OOPS, Modules, File Handling, Data Acquisition, Data Visualization, Data Analysis using Pandas, Probability Distributing and Statistics, K-Nearest Algorithms, Multivariate and Logistics Regression, Feature Engineering and the list goes on. The course also includes 15+ projects like Titanic Survivor Prediction, Odd One Out, Emoji Prediction, Dominant Color Extraction, Face Recognition etc, 8 Data Science Challenges like Movie Rating Prediction, Chemicals Segregation, Hard-work Pays Off etc and Multiple Webinars.
The instructor Prateek Narang is an ace programmer. Currently doing his Masters in Machine Learning from IIT Delhi, he has worked with SanDisk and HackerEarth in the past. He has also won prestigious hackathons including Google’s Code For India and Smart City Hackathon. A Computer Science Graduate from DTU, he is highly popular among students for his teaching methods.
Why Coding Blocks?
- All the mentors are well-experienced industry experts.
- Got live Webinars on topics.
- All topics are covered with real-life examples. The course covers a wide range of topics. Course's content is totally hands-on.
- Coding Blocks has Teaching Assistants who are always there to helps you in Classroom and online as well, so in case of any doubt, they're available for you.
- They have their own online coding platform Hacker Blocks where you can practise as much you can, and participate in the various contests and show your skills. You will learn lots of things.
- Available in both online and offline(4 centers across India) mode.
- You will get 10 months of access for completing the online course.
- You can take a free trial of any online course.
Scope of Data Scientist
Since Data Scientists play with data, this makes this job the hottest trending and the most exciting job in the world. The demand has increased by 70% and is increasing day-by-day as new data is being produced in enormous amounts, and as data increases, demand increases. According to Team Lease staffing Agency, by 2020, India will confront a demand-supply gap of 200,000+ data scientists. The NASSCOM (The National Association of Software and Services Company) has proposed educational programs to incorporate big data and data science in engineering schools.
MNCs like Google, Amazon, Microsoft, Oracle, Uber, Ola, Accenture, Air France, Airbnb, IKEA, Infosys, TCS, Fiat, BMW, PepsiCo, HCL Technologies, HP, Heineken, Robert Bosch, AT&T, Axis Bank, Huawei, Honda, Maruti, Hero, Royal Dutch Group, Swiss Bank, IBM etc need data scientists. They are also needed in Federal Corporation and Government Sectors or agencies like FBI, CIA, RAW, Ministries of different countries, United Nations, UNESCO, Red Cross etc.
According to Glassdoor the average salary of a Data Scientist is $113,436 and in India the average base pay of Data Scientist is ₹947,698.
Conclusion
Data Science is definitely new buzz in the Tech industries, it is growing very rapidly and one may say it is the backbone of companies which are based on customers and their data. To ensure this backbone works properly, companies are hiring data scientist and paying higher salaries because the aim of data scientist is provide better results in a more efficient ways.
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