Is Data Science and Data Analytics the same? Here are some key differences

The buzzword next generation or “NextGen” has brought a huge change and has a disruptive approach and a new meaning to human life. Does this term ring a bell in your mind? If not, here are a few things you might have heard of to jog your memory; Nextgen Cars, Nextgen Films, Nextgen Edu, Nextgen Healthcare.

Nextgen Technology introduced us to countless things like the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Robotics, Data Science, Business Analytics, Data Analytics, and so on.

With the impact of the COVID-19 pandemic, the economy did slow down. However, it witnessed the inherent need for NextGen technologies and how they are going to integbe ral for a stable economy in the future.

A couple of years ago, these terms seemed like something we would only see in a futuristic movie but with time, there is a growing realization of the importance of NextGen technologies. Naturally, when it comes to choosing a career path, everyone wants to ride this wave and choose a career that has an extremely bright future.

A common question that arises when it comes to Nextgen careers is, ‘Why should I pursue a career that is important in the future if I could choose a career that is important now?.’ To be completely honest, if all of us keep on following our current herd mentality and keep on completing the degree that everyone is doing, all of us are going to regret this 30 years later, when we’ll be able to do very little to change our fate.

This is exactly why Dheya believes that we as a whole should weigh out our pros and cons before choosing a career path. We bring expertise in the following criteria to help you choose the right career path:

a) evaluate your personality strengths and passion areas

b) assisting you in choosing the matching career

c) guide you to plan the appropriate career path

Now that we’ve talked about NextGen Careers and the potential they hold in the future, let’s look at two fields that are very important for every business right now and will be even more important in the future:

• Data Analysis 

• Data Science

People often use these terms interchangeably; as if they play they play the same role. Although they mainly surround data, there are a few fundamental differences in these two fields and how they affect businesses.

The intention of this article is to help you understand what  these fields are and which career might be a better fit for you:

The traditional definition of Data Analysis is “a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making” but in simpler words, data analysis is the process of presenting data in an understandable manner.

A Data Analyst is a person who analyses or examines data to derive meaning or information to help make critical decisions.

• The responsibilities of a data analyst are:-
• Collects the data
• Cleanses the data and presents it in a suitable structure
• Analyses or examines the data
• Discards irrelevant data
• Collects additional or missing data
• Analyses or manipulates the data
• Processing of Data, Statical modeling
• Derives meaning, information, or trends from data
• Provides information to the leadership to make a business decision

When identification, measurement, and analysis come into the picture, statistics play a key role. Data analysts use statistical rules and principles to solve problems. Although, today’s problems related to business data are very complex and hence it requires modern tools and techniques such as Advanced MS Excel, SQL, R programming, and SAS to solve them.

Analysts present data in a visual medium such as graphs and charts to help the organization understand data relevant to the performance of their company. This is why some of the graphic/visualization tools like Power BI, Tableau are used.

If one had to summarize what a data analyst is, one could say a data analyst brings meaning to raw data.

The definition of Data Science is “A field that employs scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data and to apply knowledge and actionable insights across a wide range of application domains”. In simpler words, Data science is a field in which various methods are used to collect data.

Data Scientist: A data scientist is someone who writes code, and combines it with statistics to find new insights from data.

A data scientist: –

• Designs data models, systems, and infrastructures
• Defines data collection processes
• Defines data sampling models
• Define data storage processes
• Establishes algorithms
• Develops evaluation and predictive models
• Designs & develops tools and automation
• Use of tools/ programming languages like R, Python, SPSS, machine learning

Data scientists will study the business’ needs and problem complexity, and then develop and design tools to extract data. A data scientist needs to possess mathematical and statistical knowledge. Their main job is collecting data relevant to the requirements stated to them.

With the advancement of the internet and advanced computer processing, there is an enormous amount of data generated that needs advanced processing techniques. This is addressed under the label “Big Data”, and forms a key part of a Data scientist’s life.

Data scientists design ways to collect and analyze data that the Data Analyst can subsequently present.

For both positions, the basic education path is similar. The basic education required is 10+2 with the science or commerce background ; with subjects like mathematics, statistics, or computer science.

After passing the class 10th exam in India, choose science or commerce with mathematics, statistics, or computer science subject(s).

• 12th Science with Mathematics, and Physics/Chemistry/Computer Science, followed by a degree in either, 

– Bachelor of Engineering / Technology (4 years)

– Bachelor of Computer Science (3 to 4 years)

– Bachelor with Mathematics or statistics (3 years)

– Bachelor of Business Analytics (3 to 4 years)

OR

• 12th Commerce with Mathematics/Statistics/Economics; followed by a degree in either

-Bachelor with Mathematics or statistics (3 years)

–Bachelor of Business Analytics (3 to 4 years)

Note: During the bachelor’s education students are suggested to focus on learning Analytics within a college or through certification courses.

• After completing graduation, take a job for 2 to 3 years as a data analyst to gain practical experience.
  
• After gaining practical experience, get the master degree with a specialization in Data Analytics (M. Tech or MS)
• Take up a job and then pursue Ph.D. in due course.
• Periodically, keep doing certifications in advanced Data analytics courses to keep up with the industry.

• 12th Science with Mathematics, and Physics / Chemistry / Computer Science; then a degree in either,

– Bachelor of Computer Engineering / Technology, Software or Information technology (4 years)

– Bachelor of Computer Science (3 to 4 years)

– Bachelor of Technology – Data Science & Machine Learning (3 to 4 years)

• After graduation, work for 2 to 3 years as a data scientist to gain practical experience.
• After gaining practical experience, get a master’s degree in Data Science (M Tech or MS).
• Take up a job and then pursue Ph.D. in due course.
• Periodically keep doing certifications in advanced data science courses techniques to keep up with the market.

Once you complete your graduation in either data analysis or data science, it is best advised to complete a master’s degree to help your career growth skyrocket. This is also supported by the statistical data, that shows people with higher education grow faster in their career path. In a similar flow, a Ph.D. also would be the icing on the cake.

While choosing a master’s degree, there are 2 possibilities,

• Get on with your master’s degree immediately after finishing graduation: This is a preference by some candidates to have continuity in their education. As most of these candidates are of the mindset that they don’t want to pursue further education once they’ve started their life as a working professional

OR

• Work for 2 or 3 years and then go for masters. This will help the student get real-time experience in the subject area. This will also help the candidate study market needs as well as experience a work environment. This will help the student to fine-tune their expertise subject(s) during their masters. This is the recommended approach as there is a period of practical experience between two degrees and helps the candidate start their master’s degree with more knowledge and a fresh mindset.

The best way to make the most out of your graduation experience is by applying for internships. This will help you gain a new perspective on the subjects you are learning and help you learn how to apply the principles you learn in college in a real-life situation. Another advantage in the period of your graduation is going to help you understand if you want to work in the field you have picked for the rest of your life.

Based on the experience and learning, you can always make a correction. The correction could be moving from Data Scientist to Data Analyst or moving to other roles in a similar path with additional learning (like Artificial Intelligence, etc.).

A person who likes numbers, mathematics, calculations, logic, programming, and problem-solving approaches should definitely consider either a Data Analyst or Data Scientist as a profession. Although if you want to choose between these two, here is a simple differentiation:

• If one is interested in mathematics, statistics, modeling, or visualization, then a Data Analyst would be a close match
• If one is good at mathematics, statistics, programming, machine learning, logic, and algorithms, then a Data Scientist is better suited.

Both the roles are Industry agnostic and have a big-time demand. These are futuristic roles.

All types of industries and organizations are facing:

– the challenge of managing huge data
– deriving meaning from the data, and
– making faster and logical decisions. 

Managing Data is a huge role in every sector. A few fields that need Data Analysts and Data Scientists are Government, Public Sector, Large and Small Corporations, Defense, Crime investigation, United Nations, World Health Organization, and so on.

Data scientists and data analysts are complementary to each other as they assist organizations in making the best decision suited for a specific problem that it is facing.

The example of the types of Data that tend to be issues are:

• Banking: Transaction volumes, Fraud prediction
• Government: Various citizens program planning (short term and long term) linked to the population and demographic distribution.

Consumer product companies (CPG) or Car manufacturing companies can take the help of Data Analysts to develop dashboards that can help them track critical parameters like production data, Revenue numbers, customer complaints, etc.

Such Consumer product companies (CPG) or Car manufacturing companies can take the help of Data Scientists to develop data models or predictive models to see the likely rise or fall in car sale volumes or demand for a particular CPG product in a specific demographic.

There are several types of data analytics that help solve a separate part of the problem. They are as follows

• Descriptive Analytics: 
  The key role of descriptive analytics is to address that a problem has come up. It answers the question “What happened?” in form of a visual presentation of the data that appears to have caused a problem.
• Diagnostic Analytics
  A diagnostic analyst answers the question, “Why did it happen?” These data analysts do this by using certain methods such as data mining, correlations, and drill-down.
• Predictive Analytics
  As the name suggests, the role of predictive analytics in a business is to predict future trends relevant to the organization and what can be done in order to take complete advantage of them
• Prescriptive Analytics
  Prescriptive analytics plays two huge roles in a business. The first is to answer the question, “What can we do?” as soon as a problem arises to help the organization overcome a particular situation. The second is to show the company how it can reach a particular goal. In simpler words, this type of analytics answers the question, “What can we do to make this happen?”

• Cognitive Analytics
  This is by far one of the most essential fields when it comes to data analytics. Cognitive analytics comes into play when unstructured data needs to collect and structured for it to be used by other types of analysts

Following are a few career paths a data scientist can follow

• Data engineer
• Mathematician / Statistician
• Machine learning scientist
• Programming scientist
• Data Modelling scientist

There are a few things you need to take notice of before you apply for your first job in data science and data analytics. These are criteria that you need to keep in mind irrespective of how good your scores are:

1. Interview Questions

– List the similarities and differences between data analyst and data scientist roles

– Explain the process and tools used 

– What are current industry trends and data types that are relevant to the company

– What are the types of challenges you might face while working?

– Tell me about the programming languages that are used in Data Science?

– Why do you want to take up this role?

2. Essential Skills

These are a few skills you need to sharpen regularly in order to have a successful career in Data Science

• Data Analyst
  Good knowledge of mathematics and static
  Organize, Analyze and manage data to derive the meaning
  Creative thinking
  Problem-solving skills
  High Patience
  Visualization skills
  Communication and Teaming skills
• Data Scientist
  Good knowledge of mathematics and static
  Creative thinking
  Problem-solving skills
  Higher Patience
  Visualization skills
  In-depth Knowledge of programming languages
  Data Management Skills

This article intends to give you an overview of the similarities and differences between the roles of Data Analyst and Data Scientist, as well as the career approach. We hope this helps you to understand some of the details about both these roles. I would encourage you to research more before you decide to pursue any of these roles.

The key to succeeding in these fields is,

– Know yourself (capabilities and strengths)
– Have commitment & dedication
– Have a lot of patience to work with huge data sets.

Dheya is always working to keep aspiring students up to date with current and futuristic and trending roles, as well as futuristic career options. Dheya with its proven tools and processes will help you to understand your strengths and abilities and support you in choosing the best-suited career paths.

Needless to say, our highly experienced and certified career mentors will guide you every step of the way. 

We wish you all the Best for all your future endeavors!

– Hemant V Bapat
Dheya Career Mentor