Career in Decision Science: Ultimate Guide [2021]

For decades now, data has been sprinting its way up towards the leading edge of businesses. With the availability of sophisticated storage systems, it is now possible to store huge amounts of data generated by sales, customer interactions, and digital experiences. Mechanisms for easy integration of disparate systems, have ensured that enormous volumes of data are continually flooding the business world.

Data technologies have managed to tame this data into actionable insights. However, data is going to continue to spawn in the future. If exploited efficiently, massive repositories of data can provide a huge opportunity for businesses. This is where decision science comes in.

Decision science requires the ability to embrace data and utilize it in a manner that can help stakeholders arrive at critical business decisions. Utilizing data effectively to make informed business decisions translates to making intelligent inferences from data, creating effective stories, identifying relevant challenges, and then applying this knowledge correctly to the right set of problems in the business world.

A career in decision science involves creating solutions based on sound fundamentals of probability, forecasting, experimentation, and computational expertise.

Skills Required by Decision Scientists

Decision scientists require a combination of certain indispensable skills, which they use in collaboration to extract value from data and solve problems. These skills include the ability to use advanced knowledge of mathematics to understand patterns and trends. Then, statistical science helps to perform technical analysis of data based on patterns and trends.

Machine learning helps to assess possibilities galore and make predictions without human intervention. Further, decision scientists require business acumen, so they can perform an astute analysis of critical challenges and make important decisions to tackle them.

They play a very important role in using the data extracted from small pockets that exist in silos and assembling the chunks together, using the knowledge of business dynamics coupled with intuition and far-sightedness to create the big picture. In short, decision scientists are artists who combine the various sciences of maths, technology, and business for doing their job.

Read: Data Science vs Decision Science

Role of Decision Scientist in Various Industries

The expertise of decision scientists is useful in many industries. These include driving sales in the retail industry, providing value to the banking industry, transforming the aviation industry, contributing to the healthcare industry, transportation, communication, education, and so many more. The kind of work that one can expect to do is described using the examples of retail and banking sectors.

1. Retail

As a decision scientist in the retail industry, you will see yourself using data for developing pricing strategies, discount techniques, and sales tactics. You will determine the right price points that would be favourable to a retail store and will contribute to the increase in revenue.

Similar contributions can be made in the areas of vendor management, inventory management, and store planning to arrive at important decisions such as the kinds of products that should be displayed together depending on the behaviour of consumers and their triggers for impulse purchases. Not only this, but you will also see yourself using trends in social media to analyze the interests of your customers and conducting marketing campaigns to drive sales.

2. Banking

In the banking industry, decision scientists use information about customer data and customer trends to categorize their clients. Based on customer lifetime value and other relevant factors, you will use predictive analysis to personalize services and products for diverse groups of your clients. This will also involve strategizing personalized marketing initiatives and relevant interactions to acquire as well as retain customers. Nevertheless, it will involve contribution towards designing products that can help achieve business growth.

Similarly, decision scientists play a major role in the areas of fraud prevention, and risk mitigation resulting in banks saving a huge amount of money. Huge customer data can be leveraged to understand, for example, the probability of fraud even before it is committed or chances of default on payments right at the stage of handing loans out.

Based on how customers in a specific group use technology, decision scientists ensure investments to optimize the development and use of appropriate technological systems to derive value. Demographics play an important role in this area. For example, in a specific region where there is a large population of young people who use their mobile phones for payments, a sound business strategy may require spending on the development and maintenance of a mobile application for the bank.

However, for a different group of customers, who do not have expertise in technology, are probably retired and have time may prefer personal interaction. Serving this group may mean the deployment of relationship management personnel to create satisfied customers and win brand loyalty. This may further extend to creating specialized products for a group that is highly likely to invest in specific financial products, thus promoting cross-selling.

Must Read: Data Science Career Path

Career Options in Decision Science

You may be familiar with technology or may have had a start in a field that is entirely different, yet you can work your way in the field of decision science. Here are the various career options that you can explore:

**1. **Business Analyst

Business analysts conduct the initial research to understand a customer’s business. They form crucial links in the chain of exploring requirements and pain areas of a customer’s business. They perform an end-to-end analysis of business processes and workflows. Then, they convert this information in a language that is easily understandable by technical users and software architects who further create designs and develop software to meet the customers’ expectations.

Skills Required by Business Analysts

Business analysts must have excellent communication skills. As a business analyst, you will need to build a fantastic relationship with customers. You will need to ask a lot of relevant questions to be able to find out what exactly a customer needs. Next, you will set up numerous meetings with your technical team to ensure what they build concurs with what the customer has in mind. You will also need to understand how to document business flows and building prototypes in the form of sketches and wireframes.

Salary Earned by Business Analysts in India

According to PayScale, the average salary earned by business analysts in India is ₹607,209.

**2. **Software Developer

Software development requires creating software to enable business processes and workflows.

The discipline requires understanding requirements, creating high-level and detailed designs for a project, creating software, and finally testing the code to meet specific business requirements of a customer. Software engineering requires the use of programming languages and various tools, for example, Git, GitHub, IntelliJ, etc. As a software engineer, you will also need a thorough understanding of software architecture and knowledge of backend and front-end development techniques.

Skills Required by Software Developers

Software developers require expertise in programming languages, experience with a database, mathematical prowess, problem-solving skills, attention to detail, ability to work in a team and to collaborate.

Salaries Earned by Software Developers in India

According to Glassdoor, the average salary developed by software engineers in India is ₹607,209.

Must Read: Data Science vs Decision Science: Which One You Should Choose?

3. Data Scientist

Data scientists automate manual processes. They identify the correct sets of data for analysis and use statistical methods of data analysis for visualizing, classifying, and segregating data. A data scientist wears multiple hats for their work, including that of a software engineer, data analyst, business analyst, etc.

Skills Required by Data Scientists

Data scientists should know how to work with Statistics. They should know at least one programming language, for example, R or Python. They should be able to perform data extraction, transformation, and loading, preferably using ETL tools. Data scientists should be able to work with data wrangling and data exploration, data visualization, and machine learning.

Salaries Earned by Data Scientists in India

According to PayScale, the average salary developed by software engineers in India is ₹812,528

4. Data Analyst

Data analysts gather information from various sources and query data to obtain information. They make joins, combine data, and present it as requested for various data requests.

Skills Required by Data Analysts

Data analysts should be able to work with database tables to collate data and create reports. You should be able to use tools like SQL and PostgreSQL and dashboard tools such as Tableau, PowerBI, and so on.

Salaries Earned by Data Analysts

According to PayScale, the average salary developed by software engineers in India is ₹419,465

#data science #career in data science #decision science

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How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

Career in Decision Science: Ultimate Guide [2021]

For decades now, data has been sprinting its way up towards the leading edge of businesses. With the availability of sophisticated storage systems, it is now possible to store huge amounts of data generated by sales, customer interactions, and digital experiences. Mechanisms for easy integration of disparate systems, have ensured that enormous volumes of data are continually flooding the business world.

Data technologies have managed to tame this data into actionable insights. However, data is going to continue to spawn in the future. If exploited efficiently, massive repositories of data can provide a huge opportunity for businesses. This is where decision science comes in.

Decision science requires the ability to embrace data and utilize it in a manner that can help stakeholders arrive at critical business decisions. Utilizing data effectively to make informed business decisions translates to making intelligent inferences from data, creating effective stories, identifying relevant challenges, and then applying this knowledge correctly to the right set of problems in the business world.

A career in decision science involves creating solutions based on sound fundamentals of probability, forecasting, experimentation, and computational expertise.

Skills Required by Decision Scientists

Decision scientists require a combination of certain indispensable skills, which they use in collaboration to extract value from data and solve problems. These skills include the ability to use advanced knowledge of mathematics to understand patterns and trends. Then, statistical science helps to perform technical analysis of data based on patterns and trends.

Machine learning helps to assess possibilities galore and make predictions without human intervention. Further, decision scientists require business acumen, so they can perform an astute analysis of critical challenges and make important decisions to tackle them.

They play a very important role in using the data extracted from small pockets that exist in silos and assembling the chunks together, using the knowledge of business dynamics coupled with intuition and far-sightedness to create the big picture. In short, decision scientists are artists who combine the various sciences of maths, technology, and business for doing their job.

Read: Data Science vs Decision Science

Role of Decision Scientist in Various Industries

The expertise of decision scientists is useful in many industries. These include driving sales in the retail industry, providing value to the banking industry, transforming the aviation industry, contributing to the healthcare industry, transportation, communication, education, and so many more. The kind of work that one can expect to do is described using the examples of retail and banking sectors.

1. Retail

As a decision scientist in the retail industry, you will see yourself using data for developing pricing strategies, discount techniques, and sales tactics. You will determine the right price points that would be favourable to a retail store and will contribute to the increase in revenue.

Similar contributions can be made in the areas of vendor management, inventory management, and store planning to arrive at important decisions such as the kinds of products that should be displayed together depending on the behaviour of consumers and their triggers for impulse purchases. Not only this, but you will also see yourself using trends in social media to analyze the interests of your customers and conducting marketing campaigns to drive sales.

2. Banking

In the banking industry, decision scientists use information about customer data and customer trends to categorize their clients. Based on customer lifetime value and other relevant factors, you will use predictive analysis to personalize services and products for diverse groups of your clients. This will also involve strategizing personalized marketing initiatives and relevant interactions to acquire as well as retain customers. Nevertheless, it will involve contribution towards designing products that can help achieve business growth.

Similarly, decision scientists play a major role in the areas of fraud prevention, and risk mitigation resulting in banks saving a huge amount of money. Huge customer data can be leveraged to understand, for example, the probability of fraud even before it is committed or chances of default on payments right at the stage of handing loans out.

Based on how customers in a specific group use technology, decision scientists ensure investments to optimize the development and use of appropriate technological systems to derive value. Demographics play an important role in this area. For example, in a specific region where there is a large population of young people who use their mobile phones for payments, a sound business strategy may require spending on the development and maintenance of a mobile application for the bank.

However, for a different group of customers, who do not have expertise in technology, are probably retired and have time may prefer personal interaction. Serving this group may mean the deployment of relationship management personnel to create satisfied customers and win brand loyalty. This may further extend to creating specialized products for a group that is highly likely to invest in specific financial products, thus promoting cross-selling.

Must Read: Data Science Career Path

Career Options in Decision Science

You may be familiar with technology or may have had a start in a field that is entirely different, yet you can work your way in the field of decision science. Here are the various career options that you can explore:

**1. **Business Analyst

Business analysts conduct the initial research to understand a customer’s business. They form crucial links in the chain of exploring requirements and pain areas of a customer’s business. They perform an end-to-end analysis of business processes and workflows. Then, they convert this information in a language that is easily understandable by technical users and software architects who further create designs and develop software to meet the customers’ expectations.

Skills Required by Business Analysts

Business analysts must have excellent communication skills. As a business analyst, you will need to build a fantastic relationship with customers. You will need to ask a lot of relevant questions to be able to find out what exactly a customer needs. Next, you will set up numerous meetings with your technical team to ensure what they build concurs with what the customer has in mind. You will also need to understand how to document business flows and building prototypes in the form of sketches and wireframes.

Salary Earned by Business Analysts in India

According to PayScale, the average salary earned by business analysts in India is ₹607,209.

**2. **Software Developer

Software development requires creating software to enable business processes and workflows.

The discipline requires understanding requirements, creating high-level and detailed designs for a project, creating software, and finally testing the code to meet specific business requirements of a customer. Software engineering requires the use of programming languages and various tools, for example, Git, GitHub, IntelliJ, etc. As a software engineer, you will also need a thorough understanding of software architecture and knowledge of backend and front-end development techniques.

Skills Required by Software Developers

Software developers require expertise in programming languages, experience with a database, mathematical prowess, problem-solving skills, attention to detail, ability to work in a team and to collaborate.

Salaries Earned by Software Developers in India

According to Glassdoor, the average salary developed by software engineers in India is ₹607,209.

Must Read: Data Science vs Decision Science: Which One You Should Choose?

3. Data Scientist

Data scientists automate manual processes. They identify the correct sets of data for analysis and use statistical methods of data analysis for visualizing, classifying, and segregating data. A data scientist wears multiple hats for their work, including that of a software engineer, data analyst, business analyst, etc.

Skills Required by Data Scientists

Data scientists should know how to work with Statistics. They should know at least one programming language, for example, R or Python. They should be able to perform data extraction, transformation, and loading, preferably using ETL tools. Data scientists should be able to work with data wrangling and data exploration, data visualization, and machine learning.

Salaries Earned by Data Scientists in India

According to PayScale, the average salary developed by software engineers in India is ₹812,528

4. Data Analyst

Data analysts gather information from various sources and query data to obtain information. They make joins, combine data, and present it as requested for various data requests.

Skills Required by Data Analysts

Data analysts should be able to work with database tables to collate data and create reports. You should be able to use tools like SQL and PostgreSQL and dashboard tools such as Tableau, PowerBI, and so on.

Salaries Earned by Data Analysts

According to PayScale, the average salary developed by software engineers in India is ₹419,465

#data science #career in data science #decision science

Help Your Data Science Career By Publishing Your Work!

This guide aims to cover everything that a data science learner may need to write and publish articles on the internet. It covers why you should write, writing advice for new writers, and a list of places that invite contributions from new writers.

Let’s get to it!

Why you should write:

Writing isn’t just for “writers”. The art of writing well is for everyone to learn - programmers, marketers, managers and leaders, alike. And yes, data scientists and analysts too!

You should write articles because when you do:

You learn:

Writing teaches you the art of writing. It’s kind of circular but it’s true.

Make no mistake, the art of writing isn’t about grammar (although, that’s important) and flowery language (definitely not important). It’s about conveying your thoughts with clarity in simple language.

And learning this art is important even if you absolutely know that you don’t want to write blogs/articles for a living. It’s important because all the jobs have some form of writing involved - messages, emails, memos and the whole spectrum. So basically, writing is a medium for almost any job you can have.

Apart from that, when you write you learn the things that you thought you knew but didn’t really know. So, writing is an opportunity to learn better.

#data science career tips #guide #guides #publishing work #writing guide

50 Data Science Jobs That Opened Just Last Week

Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, we list down 50 latest job openings in data science that opened just last week.

(The jobs are sorted according to the years of experience r

1| Data Scientist at IBM

**Location: **Bangalore

Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

2| Associate Data Scientist at PayPal

**Location: **Chennai

Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.

Apply here.

3| Data Scientist at Citrix

Location: Bangalore

Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.

Apply here.

4| Data Scientist at PayPal

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

5| Data Science at Accenture

**Location: **Bibinagar, Telangana

Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.

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#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india

Things Not To Put On Your Data Science Resume

They say, the first impression is the last, and thus resumes are the first impression of any professionals including data scientists while searching for a job.

Landing on a data science job is tiring but if successful, can be extremely rewarding as it is the highest paid profession of the current era. With such a great offer, the competition also increases massively with the highest proportion of 33.7% open jobs in data analytics. For getting a potential call for an interview resume is of course crucial. And that’s the reason, having a perfect, updated and relevant resume is critical for landing on a data science job amid this crisis.

While we have spoken extensively about what a data science resume should look like, here, we will discuss some things that one can do away with while writing their resume for a data scientist job.

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Also Read: Can A Chartered Accountant Become Data Scientist?

Do Not Put A Vague Summary/ Objective

The first thing that data scientists shouldn’t put is a vague and irrelevant summary or objective in their resume. Creating a summary or an objective which is more relevant to the job will provide an extra edge and prevent recruiters from distracting from your profile. As a matter of fact, the whole resume should be completely specific to the requirement of the job; whether it be summary, skills or certifications.

One can start their resume with their specific information as to whether they are junior data science or a senior or whether just a graduate. And then continuing the same, the data scientists can state their objectives and what value they can bring to the business. In short, the more one customises their summary and objective according to role expected, the more chance they have to land on a data science selection pile of recruiters.

Also Read: Tips To Create A Compelling Cover Letter To Land A Data Science Job

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#careers #data science career #data science resume #data science resume tips #data science