Data Science is the sexiest job in the 21st Century
- What is Data Science?
- In the times of big data, A questions as simple as , “What is Data
Science?” can result in many answers.
-Data Science as someone who finds solution to problem by analyzing big or small data using
appropriate tools and the tells stories to communicate her findings to relevant stockholders.
-Data Science is the sexiest job in the 21st Century. Data science is the study of data.
-Data science is a field about processes and systems, to extract data from various forms,
whether it is an unstructured or structured form.
Data science is relevant today because we have tons of data available. We used to worry about
lack of data, now we have a data deluge. In the past, we didn't have algorithms, now we have
algorithms. In the past, the software was expensive, now it's open source and free. In the
past, we couldn't store large amounts of data now, for a fraction of the cost, we can have
gazillions of data sets for very low costs, so the tools to work with data, the very
availability of data, and the ability to store and analyse data, it's all cheap, it's all
available, it's all ubiquitous, it's here. There's never been a better time to be a data
Data Science is more than just building predictive models - it is also about explaining the
models and using them to help people to understand data and make decisions. Data
visualization is an integral part of presenting data in a convincing way.
- What exactly does a data scientist do?
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 being human.
She spends a lot of time in the process of collecting, cleaning, and munging data, because
data is never clean.
Data Scientist role is needed when a company’s data volume and velocity exceeds a certain
level that requires more robust skills for sorting through a rolling sea of unstructured data
(big data) to identify questions and pull out critical information. Data scientist addresses
business problems. It also gives an accurate prediction of the value of business once solved.
Data cleansing and processing, predictive modeling, machine learning, identifying questions,
running queries, applying statistical analysis, correlating disparate data, storytelling and
- What is Data Analyst?
- Most of the people thinks that both Data Scientist and Data Analyst
are same but there is a slight difference between Data Scientist and Data Analyst if you will
see in a concentrated way.
Data Analyst works to interpret data to get actionable insights for the company. Data Analyst
only address business problems. Data Analyst only solves the questions given by business.
Data collection and processing, programming, machine learning, data munging, data
visualization, applying statistical analysis.
- What skills are needed to be a data scientist?
3. Machine learning, deep learning, AI
5. Data architecture
6. Risk analysis, process improvement, systems engineering
7. Problem solving and good business intuition
8. Critical thinking
- What is the process of becoming a data scientist?
1. Get good at stats, math and machine learning.
2. Learn to code.
3. Understand databases.
4. Master data munging, visualization and reporting.
5. Level up with Big Data.
6. Experience, practice and meet fellow data scientists.
- How should companies get started in Data Science?
- So the first thing a company has to do is to start recording
information, start capturing data. Data about costs and then differentiated by labour costs
and material costs. The cost to, how much it costs to sell one product and the total cost.
And then you look at the revenue. Where is your revenue coming from? Is 80% of your revenue
coming from 20% of your customers? Or is it the other way around? So first thing first, start
capturing data. Once you have data, then you can apply algorithms and analytics to it.
So the first thing to do would be to capture data. If you're not capturing it, start
capturing it. If you're capturing it, archive it. Do not overwrite on your old data thinking
you don't need it anymore. Data never gets old, data is always relevant. Even if it's a
hundred years old, 200 years old, it is relevant to you and your firm and your success. So
keep data, capture it, archive it. Make sure nothing goes to waste. Make sure there's a
consistency so someone 20 years later trying to understand that data should be able to do so.
So have proper documentation. Do it now, put the best practices for data archiving in place
the moment you start a business. And if you're already in business and you haven't done it,
do it now.
- Start measuring things.Too many companies haven't measured things properly for a decade and
then they decide they want data science. Data science inside a company is only going to be as
valuable as the data collected. Garbage in, garbage out is a rule in any sort of analysis.
- If something is not measured, it's very difficult to improve it or to change it. So the
very first step is measurement. If companies have existing data, then they should start
looking at it and cleaning it. If they don't have existing data, then they need to start
- Companies should remember that it's key to have a team, so it's not one data scientist but
a team of them, that each of them have strengths in different areas of data science.
- Applications of Data Science
- I think one of the good new applications of data science is in the
medical field. Like in drug delivery or cancer treatment.
Google Search is an application of data science.
Augmented reality is new implementation of data science.