May, 03-2019
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Introduction to Data Science

"DATA SCIENCE"?

In reality, Data Science is evolving so fast and has already shown such enormous range of possibility 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 a Data scientist need to 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 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 PhD. 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 is a mixture of study materials, real-world scenario , 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 data scientist. We have recommended our experienced experts and real time professionals for training and guidance out there, as well as they shares real time experience with candidates.

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