Introduction to Data Science

May, 03-2019

BISP

Introduction to Data Science

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data science is the same concept as data mining and big data: "use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems". At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value:

Data Warehouse

Discovery of data insight
Development of Data product

the final product for us will be the "BUSINESS VALUE".
Diving in at a granular level to mine and understand complex behaviors, trends, and inferences. It's about surfacing hidden insight that can help enable companies to make smarter business decisions.

In reality, Data Science is evolving so fast and has already shown such an enormous range of possibilities that a wider definition is essential to understanding it. It's quite easy to see and feel its impact. Data science, when applied to different fields can lead to incredible new insights. And the folks that are using it are already reaping the benefits…The 'Data Science platform' is now an official thing – but what is it, or what is one?

A complete definition of Data Science?
A solid data science platform includes big data integration, data wrangling tools, data discovery and exploration, machine learning algorithms, experimentation tools, team collaboration tools and automated tools to deploy, test and monitor trained models in production.

To be a Data Scientist?
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. Be Data scientists need to spend 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 code. After the data comes into shape data into shape, a crucial part is exploratory data analysis, which combines visualization and data sense. Then after find patterns, build models, and algorithms—some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product.

Data scientists require knowledge of math or statistics...

A natural curiosity is also important, as is creative and critical thinking.
What can you do with all the data?
What undiscovered opportunities lie hidden within?

Technical or Expertise Skill-set: Data Scientist

Python Coding: Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++. ...
Hadoop Platform
SQL Database/Coding
Apache Spark
Machine Learning and AI
Data Visualization
Unstructured data

S.No	Expertise Streams	Data Analyst	M.L. Engineer	Data Engineer	Data Scientist
1	Programming tools	V. Important	V. Important	V. Important	V. Important
2	Data Visualization and communication	V. Important	Little Imp.	Little Imp.	V. Important
3	Data Intuition	Little Imp.	V. Important	Little Imp.	V. Important
4	Statistics	Little Imp.	V. Important	Little Imp.	V. Important
5	Data Wrangling	Not Important	Not Important	V. Important	V. Important
6	Machine Learning	Not Important	V. Important	Not Important	V. Important
7	Software Engineering	Not Important	Little Imp.	V. Important	Little Imp.
8	Multivariable Calculus and linear Algebra	Not Important	V. Important	Not Important	Little Imp.

Why to learn Data Science?
Generally, employers looking for candidates to fill data scientist positions that have knowledge capabilities into:

Experience with programming languages like Python, Perl, C/C++, and Java
Proficiency in big data software platforms like Hadoop
In-depth knowledge of at least a few analytical tools like SAS and R

Why training is important for Data Science Technolgy?
A misconception out there that you need a sciences or math Ph.D. to become a legitimate DATA SCIENTIST. The point that data science is multidisciplinary. A Highly-focused study in academia is certainly helpful but doesn't guarantee that graduates have the full set of experiences and abilities to succeed. Our data Science training courses are a mixture of study materials, real-world scenarios, use cases, and hands-on experience and we will be providing a real-time project to our candidates or professionals so that you can easily prepare himself for the role of a data scientist. We have recommended our experienced experts and real-time professionals for training and guidance out there, as well as, share real-time experience with candidates.

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