Welcome to the world of data science.
Data science has been a trending field of study in recent times.
This is because of the amount of data that we create constantly and the computing power
that is available with advancements in technology.
But.
What is data science.
Think about what happens when you book a ride on Uber.
You open the Uber app on your phone and tell the app where you want to go.
Uber tries to find the nearest cab.
Since then the directions to come pick you up and take you to your destination.
That was simple.
But in the background the seemingly simple task is carried out by collecting mountains
of data from various sources.
Like the phones, the map and historic trends of traffic and demand for rides.
With this data modern day computers are programmed to calculate the nearest driver to you.
The best route to your location and destination.
The time it will take and what you should pay.
In other words this is made possible with data science.
Data science has countless other applications as well. It is at the intersection of Statistics,
data analysis and machine learning.
It is a combination of scientific methods, models and algorithms working together to
extract actionable business insights from data.
The U.S. faces a shortage of 140000 to 190000 people with deep
analytical skills.
And one point five million managers who can analyze big data to make effective decisions.
The average salary of a data scientist is around $118000
So.
Still interested in data science as a profession.
Continue on to learn more about who can become a data scientist.
Why data scientists matter.
What is the data science lifecycle.
How big data is driving the Data Science Revolution.
The career prospects for Data Science.
Data is the oil of our generation.
Data science is becoming indispensible in today's digitally driven world.
Helping businesses understand consumer behavior, fine tune its messaging and capture new market
share.
To become a data scientist.
You don't need to have a technical background to be a data scientist.
What you do need is in-depth knowledge and mathematics, analytical reasoning the ability
to work with large amounts of data.
It would also help to have a strong intellectual quest.
Knowledge of data engineering, visualization ability and excellent business acumen.
If you do come from a non-technical background you will likely use R.
If you are from a technical background then you could use python and r.
It is all about understanding the possibilities and asking the right questions all in the
search for the best answers.
Every company is flooded with data.
And they have more data than they know what to do with.
So regardless of the industry vertical, data science is likely to play a key role in your
organization's future success.
Data scientists help find new ways of reducing costs, entering new markets and customer demographics.
And launching new products or services.
Data science also has found social and medical applications such as child welfare and predictive
diagnosis as well.
So.
What does the typical data science lifecycle look like.
The data discovery step includes the search for different sources of relevant data.
Structured or unstructured data.
Then you make a decision to include specific data sets into your analysis.
The data preparation includes converting data from different sources into a common format.
You will standardize the data.
Look for anomalies and make it more appropriate to work with.
The data science models are built using statistics, logistic and linear regression.
Differential and integral calculus among other mathematical techniques.
You could use tools like R, Python, SRS, SQL, Tableau and so on.
Getting things in action phase includes checking the data models for its effectiveness and
ability to deliver the results.
You will have to verify the model works if not you have to rework on your model.
A data scientist needs to liaison with the various teams and be able to seamlessly communicate
his findings to key stakeholders and decision makers in the organization.
Another critical element of data science are algorithms which are a process of set of rules
to solve a certain problem.
Some of the important data science algorithms include regression, classification and clustering
techniques.
Decision Trees and random forest
Machine learning techniques like supervised, unsupervised and reinforcement learning.
In addition to these there are many algorithms that organizations develop to serve their
unique needs.
Big data is driven by the data science revolution.
Big data is the engine propelling the rise of data science.
Hadoop is a popular big data framework used by most organizations.
Hadoop works in a distributed manner wherein both the processing and storing of data is
distributed on commodity hardware.
Hadoop is easily scalable, highly economical, fault tolerant and secure.
Hadoop consists of Hadoop distributed file system or HDFS for storing data and uses
map reduce for processing data.
Another emerging framework is Apache Spark, which is touted to be up to 100 times faster
than map reduce.
Spark.
Stores the data and the RAM so iterative processing is fast and efficient.
It also deploys direct a cyclic graph or D.A.G for processing of data.
There is a huge demand and supply mismatch when it comes to data scientists.
Due to this, salaries of data scientists are among the best in the industry.
Top companies like Amazon, Google, Facebook, Microsoft in the tech space to others like
Exxon Mobile.
Visa, Boeing, General Electric and Bank of America are actively hiring data scientists.
Now that you have learned about data science, why data science is indispensable.
the data science lifecycle, how it relates to big data.
It is time you start your journey in this promising domain and see your career soar.
Intellipaat offers data science training course designed with the help of leading industry
professionals to help you learn the key skills of data acquisition, exploration, wrangling
and data visualization.
You will also master statistical analysis, predictive modeling, machine learning.
And a whole host of skills necessary for a data scientist.
Thank you.
For inquiries.
Feel free to contact us.