In this tutorial we will discuss about the skills and steps for becoming Data Scientist. More important thing is we will discuss in this tutorial is whether Scientist should be added or not. If yes why? We will go step by step in making of Data Scientist.
- Difference between Data Analyst and Data Scientist.
- Overview of Data scientist Job.
- Data scientist job offering companies.
Data scientist looks cool when someone says that he is scientist in Data. There are two type of data scientist.
- Person works on raw data to get information using different mathematical(Statistical) modelling. They are called Mathematician as Data Scientist.
- Person who build models using programming skills(Python, R, SAS, etc). They are called Software programming analyst as Data scientist.
Now come to the point why they referred as Scientists which is not correct and
we should not call them as Scientists. They should not be called as Scientist, who does not have PhD degree in the field of Mathematics or relevant.
So here we are clear that we should not call the as
Data Scientist until and unless he does not have any PhD degree and published paper in that particular field.
We will use Data Scientist term for this tutorial
Difference between Data Analyst and Data Scientist:
There’s nothing much different in Data Scientist and Data Analyst both needs almost same skill set. One major difference is I am going to point out if you don’t like my difference please mention it in comments section.
Data scientists are rare unicorn and they masters whole range of skill set. The unicorns who handle raw data and make them presentable to Data Analysts using different techniques like Python, R and so on…
- Data scientists Role is Cleans the Data. Data may Unstructured. Unstructured means Data can be in any format.
- Skills and talents for Data Scientist are Machine learning, Statistics, Modelling and techniques to visualizing data.
- While on other hand Data Analysts are those who analyse and visualize the data using stats and math. And make it presentable.
- Data Analysts role is Process clean data which is provided by Data scientist using statistical data analysis methods.
- Skills and talents for Data Analysts are Excel spreadsheet, Database system(e.g. SQL MySQL), visulaization tools (e.g. Tableau, clickview, etc), and Stats.
Overview of Data Scientist Job:
"A data scientist is better in statistics than Programmers and better Programmer than Statistician"
Decade ago they were no where on the radar, but suddenly popularity arises of Data Scientist in those company’s who only deals with data and make something presentable from raw data. They are data wizard wrangler, they are the unattachable part of businesses who now think about data. They are the key people who boost the revenue of company with their modelling techniques.
The data scientist role has been described as “part analyst, part artist .“A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.”
Whereas a traditional data analyst may look only at data from a single source – a CRM system, for example – a data scientist will most likely explore and examine data from multiple disparate sources. The data scientist will sift through all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem. A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data
There are lot of skills to learn to become data scientist and you should be excel in those skills.
You should have PhD degree.
Lets list out the skills:
- Machine Learning
Mathematics subject in which you should be excel are below:
- Linear Algebra
- Multivariate calculus
Statistics modelling you can learn from here there is whole series of tutorials on statistical modelling.
Time Series tutorials series fully described from scratch check it out here
There are two most important library for data scientist enthusiast, he/she should know Pandas and Numpy.
I am not giving explanation or any source for these libraries.
Few Machine learning topics which i have covered in here are:
- Different types of Regression modelling.
- Different types of Clustering.
- Different types of Analytics.
- Bayes’ Theorem
- Random Forest
There are several responsibilties of data scientist which i am going to present in list view for better understanding:-
- Collecting large amounts of unruly data and transforming it into a more usable format.
- Solving business-related problems using data-driven techniques.
- Working with a variety of programming languages, including SAS, R and Python.
- Having a solid grasp of statistics, including statistical tests and distributions.
- Staying on top of analytical techniques such as machine learning, deep learning and text analytics.
- Communicating and collaborating with both IT and business.
- Looking for order and patterns in data, as well as spotting trends that can help a business’s bottom line.
According to Harvard Business Review (October 2012 edition), job of a data scientist is the sexiest job of 21st century.
While the need for analytics experts is clear, the shortage is shocking!
According to the McKinsey Global Institute (In a May 2011 report): “By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” Imagine what would be the number across the globe…
Worldwide business for Business analytics, Business intelligence totaled at $14.4 billion in 2013, an overall 8 % increase from since 2012.
Data scientist job offering companies:
There are 1000+ companies (Startup and well established) which are hiring data science enthusiast please visit to check all the companies who are offering Data Science Job
If you have any queries please shoot me a mail @ email@example.com or mention in comment section.
Thank you for visiting.