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Data Degree vs. Data Bootcamp: Which Is Best for You? (2022)


Table of Contents

Analyzing raw facts qualitatively and statistically to obtain information is known as data analytics. If you have recently decided to choose a career in data science then you have made a good decision. This has become the highest growing sector, which will also pay you pretty well. Though the terms ‘Data Analytics and ‘Data Science’ are used conversely as they are different from each other. The performance of any business can be optimized by analyzing its behavioral pattern through Data Analysis. Like any other Degree, Data Analytics Degree is also a formal education degree. You can pursue this degree from any University or business school.

Both Degree and Bootcamp are reciprocal. This is because bootcamps will help people to gain knowledge about some specific information when they move towards data science. Master’s degrees already helped professionals to advance in their careers. Bootcamps have admission standards to set for the students who want to take admission in the field of data science. Computer science degree vs. Bootcamp comparison can be better understood in the below comparison.

Bootcamp pros and cons:

Pros of Bootcamp

  • Prepare you for a master’s degree: In Bootcamp, you can learn basic skills which will guide you to obtain a master’s degree. 
  • Cheaper: Bootcamps are usually less expensive as compared to master’s degrees.
  • Short: The duration of bootcamps is for a very short time like a few weeks or months. 

Cons of Bootcamp

  • Less structured: many boot camps provide tutoring, but you need a lot of self-instructions.
  • Challenging: Bootcamps are usually time-consuming, like a master’s degree.
  • Geared to beginners: As bootcamps target people who are looking for a career change, they offer a basic program.


Required Skills That You Need To Land A Job In Data Analytics?

Many companies have realized the importance of data-driven traffic and increasing profit. Data Analytics is one of the in-demand skills in today’s generation. The majority of organizations look forward to hiring those candidates who are giving the best of these skills. Both soft skills and technical skills govern the process of Data Analysis, and they combine to give accurate results.


Some of the technical skills required for Data Analysis are:


SQL stands for ‘Structured Query Language’ which was designed in 1970. It is used to update or retrieve the data in the database. SQL is used by data analysts to perform various tasks like reading, manipulating (add or deleting), and analyzing data. One of the essential parts of Data Science is Relational Database. SQL is used as the robust API for relational databases. We need to remove data from the Data Analysis database, where SQL comes into the picture. Expanded data systems like Hadoop use SQL to manage relational databases and to process their structured data. Programming languages like SQL, Python, and Oracle need a solid base of experience.


Machine Learning is a crucial component of data analytics. A branch of Artificial Intelligence says that machines/systems can learn from data. They can make decisions by identifying patterns that require minimal human interaction, Here the models are automatically produced to analyze data. It analyzes more complex, more significant data and gives faster, accurate results.


R is one of the highly spreading languages of data analytics. The structure and syntax of R are designed to support data analytics and statistical computation. R is required to manage, store, and analyze data, and also handle the large and complex data. The below-mentioned features of R make it essential for data analytics:

  • Multiple Calculations: R is a vector language, and it can do numerous tasks at one time. 
  • Run code without a computer: You don’t need compilers to run code as it is an interpreted language. This feature makes coding more accessible.
  • Statistical Analysis: This R language is used in various other places—for example, in Genetics, Biology, Medicines, and Financial studies.
  • Data Science Support: R provides support functions like graphs, charts, data structure, and statistical analysis.


  • Microsoft Excel

It is the most affordable and reliable tool for data analytics. Organizing raw data in the spreadsheet makes it readable, which is easy to extract and take action. Large and carved-up data can be easily visualized in Microsoft Excel, It doesn’t require any other software. Including field customization of complex data You can also use its functions for calculations. Though it cannot come up with full-scale data analysis, it can present data in easy to read format and accurate estimates.


  • Probability and Statistics

Data is a set of qualitative and quantitative details collected from different sources. The estimates and probability theory are very important for making predictions. Probability and Statistics are the basics of Data Analysis, and statistical methods are primarily helpful in future analysis.


  • Data Visualization

Presenting data in graphical and pictorial format is known as data visualization. Its elements are graphs, maps, infographics, dashboards, and charts. Tools of data visualization provide easy access to understanding patterns and trends.


  • Statistical Programming

There are various reasons statistical programming language is vital for data scientists. The programming languages allow rearranging and analysis of unstructured data.


Some Of The Soft Skills Required For Data Analysis Are:


  • Critical Thinking

Understanding the problem thoroughly by analyzing the facts is Critical Thinking. Analysis and thinking are done by asking questions, taking feedback, collecting the data, and developing possible solutions. The questioning ability should be developed strongly. Multiple exponents use several ways of interviewing. It is done because users are unable to express their concerns accurately. Steps involved in critical thinking are:

  1. Identifying an issue
  2. Identify the cause of the problem.
  3. Collect information about the issue by research
  4. Organize data in a structured manner
  5. Execute the solution
  6. Analyze which solution was accurate or didn’t work
  7. Find different ways to modify the solution.


  • Problem-solving skills

Problem-solving skills will help to investigate the information which should be integrated into the platform. Also, what transformations are to be made and how to use data.


  • Verbal and Written communication skills

Good verbal and written communication expertise is essential. It helps in delivering and making people understand the information quickly. Communication skills are helpful during a job interview to present your technical skills effectively and accurately.


  • Mathematical ability (understanding numerics)

One of the critical skills for a data scientist is mathematical ability. A solid mathematical competency is essential for aspiring candidates. Statistics, linear algebra, and advanced calculus are helpful subjects.


What Are The Advantages Of A Data Analytics Degree?

Data is collected at every point to analyze the patterns. Here are some advantages of a Data Analytics Degree:


The flexibility of working in various sectors

One of the advantages of Data Analytics is that it is not restricted to working in one particular sector. All industries are benefited from Analytics. Some industries where data analytics skill is in high demand are logistics, finance, healthcare, education, retail, and human resource. For instance, if an organization launches an online campaign, a data analyst can be invaluable for evaluating data.


Job opportunities are high, and it is an in-demand skill.

One of the reasons for the high salary is demand. The demand for Data Analytics professionals is too high. Organizations rely on data, but the people who can do this job correctly and efficiently are limited. Even at the entry-level, they demand huge salaries and perks. India is considered the second most prominent hub for job careers in data science after the United States of America.


A lot of organizations have Big Data as their priority.

Data Analytics is considered one of the critical factors of growth by organizations. The organization’s policies and marketing strategies are highly impacted by the immense volume of data and insights to increase performance and make more profit. Organizations collect data from their customers.


No prior knowledge for certification

Learning and doing a certification course in Data Analytics doesn’t require any prior knowledge. Almost anyone can get themselves certified in it. You need to invest quality time and effort to learn the skill. When you are ready for the certification, find yourself a course. An online program from a reputed source will be best. Ask for recommendations and go through the reviews. Try to collect information about the institution’s background and teachers. Understand the pattern and flow of the course. 


Five Main Differences Between Data Science And Data Degree


Data Analyst

A data Analyst typically identifies the trend by collecting data to make strategies for the business leaders. It is more focused on solving problems and answering questions by statistical analysis. To make queries in a Relational Database, data analysts use tools like SQL. A data analyst puts the data in a readable format, cleans and discards the unusable data, and figures out ways to deal with missing data. Data Analyst does routine data analysis and prepares reports regularly. Data forecasting is done using Microsoft Excel, and intelligence software creates dashboards. A data analyst may figure out the reason for sales drop or develop dashboards.


Data Science

Data scientists typically design data modeling processes, create algorithms and predictive models. Data scientists spend significant time designing tools, system automation, and frameworks. The main focus of data scientists is to solve complex problems. They do it by developing new methods and tools to extract information. Data scientists have statistical and mathematical knowledge. They also have hacking skills to solve problems in ingenious ways. The way data is analyzed, manipulated, and stored is done by data scientists. APIs and ETL pipelines are used for data mining. Languages like R or Python are used for cleaning the data. Data scientists are more concerned about the consequences of modeling techniques.


What Is The Process Of Being A Data Scientist?


  • Develop the Data Skills

You can become a data scientist without having prior work experience in data. You can do it by developing a background in a data science career. You have to build a strong base in this field to reach the high-level designation like a data scientist. There are some common and primary areas where you can improve your knowledge. These areas are statistics, mathematics, programming, engineering, etc. While working with raw data fundamentals of Excel, SQL, Python, and R will be helpful.


  • Learn Data Science Basics

You can opt for a course in data science or Bootcamp. It will help you to acquire fundamental data science knowledge. Learn essentials to collect, store, analyze, visualize and present data in every tool. Include advanced applications like PowerBI and Tableau.


  • Learn Important Languages For Data Science

Data scientists rely on different specialized tools for cleaning, analyzing, and modeling data. Some programming languages are compulsory, and you should be familiar with them. These languages are R or Hive Python and Query Language, for example, SQL. One of the most critical tools for working with R on a server is Rstudio Server. Data Science highly uses Machine learning. This artificial machine learning tool helps machines learn from themselves. It is helpful in places where you have to identify images or extract trends from social media platforms.


  • Develop Practical Skills By Working On Data Science Projects

After learning data skills and data science tools, you can begin using your acquired knowledge. While practicing, try various levels like beginning with the research. You can research the market sector, collect and define data, clean, and test, i.e., also remove useless data. Finally, you can apply your algorithms to analyze and model the data. 


  • Develop Visualizations And Practice Them

You can use programs like PowerBI, Plotly, Infogram, Tableau, Bokeh for practicing visualizations from scratch. Excel remains very useful. It helps in calculations and creating a visualization. You have to present your project to the audience. For that, you need proper communication skills.


  • Build A Portfolio That Showcases Your Data Science Skills

Once you have done research training and practicing skills on the project, it’s time to describe the skills in a portfolio that will help you land your dream job. The portfolio will support you in your job search. Display your work on Github as it will show your process and results. 

  • Raise your profile/ portfolio

A self-executed project is the best way to impress hiring managers. To present your profile more attractively, clearly explain your process. Highlight your creativity and technical skills. 


  • Apply for jobs relevant to Data Science

There are different subfields of data science like Machine Learning, Data Engineering, Data Analysis. Search in the market for which company or organization is working on these skills that you are specialized in. Confirm before you start working with the organization. 


The Final Thought

While comparing data science Bootcamp vs. masters, both have their advantages and disadvantages. The best degree for a data analyst is a Master’s degree, and with more specialization, you get better pay. For an entry-level executive, a degree will help to get into this specific field. As a data analyst using flexible tools will help better analyze results. Highlight your work and creativity by publishing it on Github. Here we have mentioned some of the meaningful information by which you can choose the correct course for your career. 


Also, read some interesting information about, What Kind Of Questions Can I Expect In A Coding Bootcamp

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