BLOGS

TechIndus 3 August 2020

Data Scientist

Data Scientist – the key influencer in the Company

Data science is an inter-disciplinary field of Study which  uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structured  and unstructured data. Data science is inter related  to big data ,data mining and machine learning.

What is a Data Scientist? What Do They Do?

The field of  Data science works on unifying  statistics, conduct data analysis through machine learning technologies and  domain expertise and their connected techniques and tools  comprehend  and analyze real time issues by incorporating available data. The Data Science process includes incorporating the techniques of  maths, stats, computer science, and subject matter experts /Domain experts  and Data science analysts. In a larger context Data science is the  fourth steam of science (empirical, theoretical/study based, computational and subsequently leading to data-driven). It is further established that science is becoming highly dynamic due to impact of information technology  and the confluence of data into it.

Key Expertise of  Data Scientist

How can I become a data scientist?

It is seen that  data scientists do often come from many different backgrounds including educational, domain expertise which ideally it is anticipated to be strong domain expert, or in an ideal case be experts in the following four fundamental areas.  

• Business/Domain Knowlede
• Mathematics (includes statistics and probability)
• Computer science (e.g., software/data architecture and engineering)
• Communication (both written and verbal)

Apart from the above -there are many more additional  skills and domain expertise that are highly desirable primary the above four are mandatory skill sets required for a Data Scientist.

In the rest of the article we will be referring them as data scientist pillars.

In reality, most data scientists  are often strong in one or two of these fundamentals , but not equally good  in all four. If you do happen to come across  a data scientist who is truly an expert in all, then you’ve found a unicorn.

Based on these fundamental, a data scientist is a person who should be able to make best use of  existing data, and create new Data Structure as per project demand in order to mine comprehensive  information, perfect  and goto market insights. These outcome can be used to influence new business decisions and changes envisioned to achieve business goals.

This is done by utilizing exceptional  business domain expertise, effective communication and outcome interpretation, by utilizing all statistical models, coding languages, software platforms  and data infrastructure.

Data Science Goals And Deliverables

In order to have a deep insight on the importance of these fundementals, the Data scientist must  understand the vision,objectives  and final deliverables and also the data science process itself.

Data science goals and deliverables. A brief  list of generic data science deliverables:

• Prediction (for example predict a value based on inputs)
• Classification (for example spam or not spam)
• Recommendations (for example Amazon and Netflix recommendations)
• Pattern detection and grouping (e.g., classification without known classes)
• Anomaly detection (for example fra for example ud detection)
• Recognition (for example image, text, audio, video, facial)
• Actionable insights (for example dashboards, reports, visualizations,)
• Automated processes and decision-making (for example credit card approval)
• Scoring and ranking (for example FICO score)
• Segmentation (for example demographic-based marketing)
• Optimization (for example risk management)
• Forecasts ( for example sales and revenue)

Each of these are  intended to address a focused goal and solve a specific problem. The question here is which goal, and whose goal is it?

For example, a data scientist may anticipate that his goal is to create a super performing prediction platform/engine. While on the other had the  objective of the companies would be to  use prediction engine to increase revenue.

It  may initially appear as not an issue at initial glance, but  in reality the situation demand the expertise of first fundamental expert (i.e.business domain expertise) which is critical at this time. Often it is seen that Senior  management have business-centric educational backgrounds, such as an MBA and they care the Bottom lines to be improved through use of Data Science.

While numerous Executives are outstandingly keen people, they may not be knowledgeable on all the instruments, methods, and calculations accessible to an Data Scientist researcher (e.g., factual examination, AI, man-made reasoning, etc). Given this, they will be unable to mention to an information researcher what they might want as a last deliverable, or propose the Data sources, highlights (factors), and way to arrive.

Regardless of whether an official can verify that a particular Prediction Engine would help increment income, they may not understand that there are presumably numerous different ways that the organization’s Data can be utilized to build revenue sources also.

It can subsequently not be underscored enough that the perfect Data Scientist  has a genuinely thorough comprehension about how organizations work when all is said is done, and how an organization’s information can be utilized to accomplish high level business objectives.

With noteworthy business area skill, an Data Scientist ought to have the option to normally find and propose new data  activities to enable the business to accomplish its objectives and expand their KPIs.

What skills are needed to be a data scientist?

Data Scientist fundamentals , Skills, And Education In-Depth

As discussed earlier about the data scientist course the domain expertise  and communication skill forms of major part in establishing clear goals / results / outcome anticipated from Data Scientist. Secondly the aforesaid skills are equivally important to present the outcome in the best possible manner to  stakeholders for taking appropriate decisions.

Hence we see that good soft skills, primarily written and verbal communication skills including presentation skills matters a lot in the Data Science Industry . In the current scenario globally where presentation matters the most it is expected that the data scientist showcases best of his  ability to deliver the results in an easy to understand ,compelling, and insightful way, while using appropriate language and Technology jargon for  audience. In addition, results should always be focused on the core business objectives (project objectives and deliverables )

For all of the other phases listed, data scientists must draw upon strong computer programming skills, as well as knowledge about statistics, probabilities, and mathematics in order to understand the data, choose the correct solution approach, implement the solution, and improve on it as well.

The Data Scientist’s Toolbox

Overview of some of the tools used by data scientist

As computer coding is the primary component in Data Science , data scientists must be proficient enough with coding languages including  Java, Python,  R, Julia, Scala, SQL, etc. A data scientist with python skill has will be a good begining. Salary for Data Scientist depends on having more programming skills along with domain knowledge.

while it’s not necessary to be an expert programmer in all of the above, but R, Python, and SQL are definitely key to the onward growth of the Data Scientist, including others software skills such as Scala for big data which are widely becoming prominent these days.

The statistical expertise comes from  mathematics, algorithms, Maths modelling. For data visualization, data scientists usually use packages and libraries wherever needed including the popular ones such as  Matplotlib, D3, Shiny,ggplot2,Scikit-learn, e1071, Pandas, Numpy, TensorFlow etc. For reporting, the data scientists normally  use notebooks and frameworks such as Jupyter, iPython, Knitr, and R markdown. These are extremely  powerful softwares and data are  delivered along with key results. Further tools associated with big data are also used largely  including Hadoop, Spark, Hive, Pig, Drill, Presto, Mahout etc.

On the database side the  data scientists should have substantial knowledge of  top RDBMS, NoSQL, and NewSQL database management systems including MySQL, PostgreSQL, Redshift, MongoDB, Redis, Hadoop, and HBase.

Summary

data scientists plays a pivitol role today(extremely important and high-demand role). They showcase significant impact on a business’ ability to achieve its goals, whether they are financial, operational, strategic,etc

Corporations extract tons of data, and often it’s neglected or underutilized. This data, through meaningful Data mining tools and technologies can provide  actionable insights, which can be used to make critical business decisions and drive significant business change.