#web developer vs data scientist salary

[bestcollegedelhi]

BCA vs. B.Tech in Computer Science: Which One Should You Choose?

Deciding between BCA (Bachelor of Computer Applications) and B.Tech in Computer Science after 12th can be tough. Both courses open doors to the tech industry, but they have different approaches, durations, and career paths. If you're confused about which one is right for you, this blog will break it down in simple terms—no technical jargon, just clear comparisons to help you make the best choice.

What is BCA?

BCA is a 3-year undergraduate degree focused on computer applications, software development, and programming. It’s more practical and less theory-heavy compared to engineering.

Key Features of BCA:

Duration: 3 years

Eligibility: 12th pass (any stream, but Maths is helpful)

Focus: Coding, software development, databases, web technologies

Admission: Mostly based on 12th marks (no entrance exam in most colleges)

Who Should Choose BCA?

If you want a shorter, more affordable degree

If you prefer hands-on coding over heavy engineering concepts

If you didn’t take Science (PCM) in 12th but still want to enter IT

If you want to start working sooner (since it’s a 3-year course)

What is B.Tech in Computer Science?

B.Tech (CS) is a 4-year engineering degree that covers computer science in depth—including programming, algorithms, hardware, networking, and advanced topics like AI and machine learning.

Key Features of B.Tech (CS):

Duration: 4 years

Eligibility: 12th with Physics, Chemistry, and Maths + Entrance exams (JEE, state CETs)

Focus: Engineering concepts, problem-solving, research, and innovation

Admission: Highly competitive (requires entrance exam preparation)

Who Should Choose B.Tech (CS)?

If you want an engineering degree with strong technical depth

If you’re okay with a longer, more challenging course

If you aim for higher-paying jobs in top tech companies

If you plan to study abroad or pursue research (engineering degrees have global recognition)

BCA vs. B.Tech (CS): Key Differences

1. Course Duration & Depth

BCA: 3 years, more focused on applied programming and software skills.

B.Tech (CS): 4 years, covers engineering principles, advanced computing, and research.

2. Admission Process

BCA: Simple admission based on 12th marks (some colleges may have entrance tests).

B.Tech (CS): Requires clearing tough entrance exams like JEE Main, JEE Advanced, or state CETs.

3. Syllabus & Subjects

BCA: More about coding (Java, Python, web development), databases, and IT applications.

B.Tech (CS): Includes advanced maths, algorithms, data structures, AI, and computer architecture.

4. Career Opportunities

BCA Graduates: Usually start as software developers, web designers, or IT support specialists. Salaries range from ₹3-6 LPA for freshers.

B.Tech (CS) Graduates: Get roles like software engineers, data scientists, or AI specialists with starting salaries between ₹5-12 LPA (higher in top companies).

5. Higher Studies & Growth

After BCA: You can do MCA (Master of Computer Applications) or switch to an MBA. Certifications (like AWS, Java, or cybersecurity) help in career growth.

After B.Tech (CS): Options include M.Tech, MBA, MS abroad, or research. Engineering degrees are preferred for higher studies in tech fields.

Which One is Better for You?

Choose BCA If:

You want a quick entry into the IT industry (3-year course).

You prefer coding and software development over complex engineering theories.

You didn’t take Science (PCM) in 12th or don’t want to prepare for engineering entrances.

You want a more affordable degree (BCA fees are usually lower than B.Tech).

Choose B.Tech (CS) If:

You want an engineering degree with deeper technical knowledge.

You’re ready for a 4-year challenging course with maths and algorithms.

You aim for higher-paying jobs in top tech firms (Google, Microsoft, etc.).

You plan to study abroad or work globally (B.Tech has better international recognition).

Final Thoughts: No Right or Wrong Choice

Both BCA and B.Tech (CS) can lead to great careers in IT. The best choice depends on your interests, academic background, and career goals.

If you want to start working early and love coding → BCA is a great choice.

If you want an engineering degree with more opportunities → B.Tech (CS) is better.

Remember, your skills and experience matter more than just the degree. Many BCA graduates become successful developers, and many B.Tech graduates move into management. What matters is how you use your education to build your career.

[tccicomputercoaching]

Web Development vs. Software Development: Key Differences

Web Development vs Software Development Key Differences is an important topic for anyone considering a career in the tech industry. Web development focuses on designing, constructing, and maintaining websites and web applications, while software development involves creating applications that run on various platforms such as computers, mobile devices, and embedded systems. Understanding the key differences between these two fields can help you choose the right career path.

What Is Web Development?

Definition and Overview

The web development process is responsible for designing, constructing, and maintaining websites and web applications. This process can include tasks ranging from laying out the design of a website to writing the back-end code that gets it to really work.

Types of Web Development

Frontend Development: Deals with the visible part of the website as well as user experience through languages like HTML, CSS, JavaScript.

Backend Development: Handles the server, the database, the server-side scripting, and the application logic via languages like PHP, Python, Node.js.

Full Stack Development: Combines the method of frontend and backend development all together.

What Is Software Development?

Definition and Overview

Software development is referred to as the process involving the conception, specification, design, programming, testing, and maintenance of software applications, which can operate on various platforms such as computers, handheld devices, and embedded systems.

Types of Software Development

Desktop Software Development: These applications link with the operating system of computers, which can be either Windows or macOS or Linux.

In Mobile App Development, software for mobile devices for Android and iOS platforms.

Embedded Systems Development: Involves the software running an application for specific hardware such as smart TVs, IoT devices, and automotive systems.

Skills Required for Web Development

Programming Languages: HTML, CSS, JavaScript, PHP

Frameworks: React.js, Angular, Vue.js

Databases: MySQL, MongoDB

Versioning: Git and GitHub

Skills Required for Software Development

Programming Languages: Java, Python, C++

Frameworks: .NET, Spring Boot, Electron

Databases: PostgreSQL, SQLite

Development Tools: Visual Studio, IntelliJ IDEA

Career Opportunities in Web Development

Frontend Developer

Backend Developer

Full Stack Developer

Web Designer

E-commerce Developer

Salary Trends

Entry-Level: $50,000 – $70,000 per year

Mid-Level: $70,000 – $100,000 per year

Senior-Level: $100,000+ per year

Career Opportunities in Software Development

Software Engineer-Mobile application developer

Mobile App Developer

Game Developer

Data Scientist

AI/ML Engineer

Salary Trends

Entry-Level: $60,000–$80,000 a year

Mid-Level: $80,000–$120,000 a year

Senior Level: $120,000 and above a year

Which Career Path is for You?

Web development suits you if you like designing and programming for the web.

Software development is for you if you enjoy doing something across different platforms on complex applications.

Both fields are very rewarding, but generally speaking, software development is better paid and somewhat more complicated.

Conclusion

Both web development and software development are rewarding career paths with great opportunities. If you are looking for structured training in either field, TCCI-Tririd Computer Coaching Institute provides expert guidance and practical courses to help you master the necessary skills. Whether you aspire to become a web developer or software engineer, investing in the right education will set you up for success.

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[learnmorewithus]

BCA vs. B.Tech in Computer Science: Which Degree is Right for You?

Choosing between a Bachelor of Computer Applications (BCA) and a Bachelor of Technology in Computer Science (B.Tech CSE) can be a significant decision for students interested in technology and computing. Each program offers unique benefits and career paths, so understanding the differences is essential for aligning the choice with your career goals.

Duration and Structure of the Program

BCA is typically a 3-year undergraduate program focused on core aspects of computer applications, software, and programming fundamentals. It covers topics like database management, web development, and programming languages, providing a more application-oriented curriculum.

B.Tech CSE is a 4-year engineering degree that delves deeper into both hardware and software aspects. This course includes advanced subjects like data structures, algorithms, operating systems, and hardware fundamentals, offering a more comprehensive view of computer science.

Depth of Technical Knowledge

BCA emphasizes practical, software-centric skills. Students learn programming, software development, and web technologies, which prepare them for software development, web development, and IT roles. However, it may lack the in-depth engineering concepts found in B.Tech.

B.Tech CSE goes deeper into computer science theory, covering both software and hardware elements. Graduates gain an in-depth understanding of algorithms, computer architecture, and system design, which can be beneficial for advanced technical roles, such as software engineering or network engineering.

Career Opportunities

BCA graduates are well-suited for roles in software development, app development, system administration, and web design. Many BCA graduates work in IT services, support, or as software developers. It’s also a good stepping stone for students who plan to specialize with certifications or pursue an MCA (Master of Computer Applications).

B.Tech CSE graduates can pursue a broader range of technical roles, such as software engineering, data science, cybersecurity, and artificial intelligence. The engineering aspect of B.Tech opens doors to more technically demanding roles, often with more responsibilities and, potentially, higher salaries.

Higher Studies and Specializations

BCA graduates can pursue an MCA to gain advanced knowledge in applications and become competitive with B.Tech graduates. The BCA-MCA path can provide a similar technical depth as B.Tech.

B.Tech graduates often pursue M.Tech or specialize further in fields like data science, machine learning, or cybersecurity. They may also have an advantage when applying for technical research roles or Ph.D. programs.

Which One is Right for You?

BCA might be the better option if you prefer a shorter, more practical program focused on application-level skills and plan to enter the IT workforce quickly or pursue a specialized Master’s degree.

B.Tech CSE is ideal for those who want a comprehensive, research-oriented understanding of computer science, covering both software and hardware. If you’re interested in a career as a software engineer, data scientist, or want to explore technical roles in-depth, B.Tech may be the more suitable choice.

In summary, both BCA and B.Tech in Computer Science can lead to rewarding careers in technology. The decision ultimately depends on your career goals, preferred learning style, and the level of technical depth you wish to achieve in your studies.

[deltainfoteklive]

Data Science vs Full Stack Developer

In today's technology-driven world, both data science and full-stack development have emerged as prominent and rewarding career paths. Data science involves extracting insights from vast data sets, while full-stack development deals with creating end-to-end web applications. Each field offers unique challenges, and choosing the right one depends on your interests, strengths, and career goals. This article will provide a thorough comparison between data science and full-stack development, helping you understand the key differences and make an informed decision about which path to pursue. Data Science vs Full Stack Developer: Which one to choose and Why? Let's dive deep into the comparison between data science and full stack development, exploring various aspects of each field and what makes them distinct from each other. Data Science: The Path of Insights What is Data Science? Data science is an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. It involves techniques from statistics, computer science, and domain knowledge to analyze data, identify patterns, and make data-driven decisions. Why Choose Data Science? Data science offers a plethora of opportunities in diverse industries like healthcare, finance, marketing, and more. With a growing demand for data-driven decision-making, data scientists are in high demand, and the job market is ripe with exciting roles. The ability to uncover hidden trends, predict outcomes, and drive strategic initiatives makes data science an intellectually stimulating and rewarding career choice. Full Stack Developer: The Architect of Web Applications What is Full Stack Development? Full stack development involves the complete process of designing, building, and managing web applications. Full stack developers are well-versed in both front-end and back-end technologies, enabling them to create seamless, user-friendly, and functional web experiences. Why Choose Full Stack Development? As a full stack developer, you have the opportunity to work on every aspect of web development. From designing user interfaces to managing databases, you gain a comprehensive understanding of the entire web application process. The demand for full stack developers is on the rise as businesses seek versatile professionals who can handle various aspects of development efficiently. Data Science and Full Stack Development: A Comparative Analysis Salary and Job Prospects: Both data science and full stack development offer lucrative career paths. Data scientists often earn competitive salaries due to their expertise in handling complex data sets and providing valuable insights. On the other hand, full stack developers' salaries are equally attractive, given their versatility and ability to work on the entire web development process. The choice between the two ultimately depends on individual preferences and interests. Skill Set and Learning Curve: Data science demands a strong foundation in mathematics, statistics, and programming. Proficiency in programming languages like Python and R is crucial, along with a deep understanding of machine learning algorithms. On the contrary, full stack development requires expertise in front-end technologies like HTML, CSS, and JavaScript, as well as back-end technologies like Node.js, Java, or Ruby on Rails. The learning curve in both fields is steep, but with dedication and practice, mastery can be achieved. Career Growth Opportunities: In the data science domain, career growth can lead to roles like data engineer, machine learning engineer, or data science manager. With experience and expertise, data scientists can become pivotal decision-makers within organizations. In full stack development, career growth may involve becoming a technical lead, software architect, or even starting your own web development venture. Both paths offer promising and satisfying career progression. Work-Life Balance: Achieving a balance between work and personal life is vital for career satisfaction. Data science and full stack development roles can sometimes involve long hours, especially when dealing with complex projects or tight deadlines. However, many organizations today promote a healthy work-life balance, and it's crucial to choose a workplace that aligns with your priorities. Job Stability and Future Demand: Both data science and full stack development show no signs of slowing down in terms of demand. As businesses increasingly rely on data-driven decisions and online presence, the need for experts in these fields will only grow. Job stability in both areas is promising, offering a sense of security and stability for aspiring professionals. FAQs FAQ 1: What are the prerequisites for becoming a data scientist? To become a data scientist, a strong foundation in mathematics, statistics, and programming is essential. Familiarity with programming languages like Python and R, along with knowledge of machine learning algorithms, will set you on the right path. FAQ 2: Can I transition from full stack development to data science? Certainly! Many skills acquired in full stack development, such as programming and problem-solving, can be valuable assets in data science. With additional learning and a focus on data analysis techniques, transitioning to data science is feasible. FAQ 3: Which field offers better long-term growth opportunities? Both data science and full stack development offer excellent long-term growth prospects. However, choosing the field that aligns with your interests and passions will lead to a more fulfilling and successful career in the long run. FAQ 4: Are there any specific industries where one field dominates? Data science finds extensive applications in industries like healthcare, finance, marketing, and e-commerce, where data-driven decisions are crucial. On the other hand, full stack development plays a vital role in shaping web applications across all industries. FAQ 5: Can I pursue both data science and full stack development simultaneously? While it's possible to gain knowledge in both fields, becoming an expert in both simultaneously might be challenging. It's advisable to focus on one field primarily and gain proficiency before delving into the other. FAQ 6: How do I decide which path to choose? Deciding between data science and full stack development boils down to understanding your interests, strengths, and long-term career goals. Consider the skills you enjoy using, the problems you want to solve, and the industries that align with your passion. Conclusion: In conclusion, both data science and full stack development offer exciting and rewarding career opportunities. Data science involves diving deep into data sets to extract insights, while full stack development focuses on creating web applications from start to finish. Each path has its own set of challenges and opportunities. Ultimately, the decision to choose one over the other should be based on your interests, strengths, and passion for the respective field. Whichever path you choose, dedication, continuous learning, and a passion for technology will pave the way for a successful and fulfilling career. Read the full article

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Looking to hire a #BestWebDevelopmentCompanyInUSA for your web applications, connect with Narola Infotech where you can fulfill all your requirements from their resources. #DataScienceApplications are helping retailers conduct market basket analysis, develop recommendation engines and be more efficient. Web development is going to experience a huge transformation due to the emergence of data science. Read this blog to know more.

[graduatecareercounselling]

What’s Next After a Degree in BCA? Explore the Future Scope and Career Options

Bachelor’s in computer application (BCA) is usually preferred by students who learned the subjects in the science stream or by those students who come from commerce or humanities backgrounds and end up discovering an interest in computer science. Deciding on BCA Vs. B.Tech CS Vs. B.Sc Computer Science or on getting a degree in BCA after class 12 can open up a plethora of opportunities. A degree in BCA allows you to work in technical fields like software development, android development, web development, etc.

However, just a BCA degree is not enough to land your dream job. In this blog, we will be discovering the skills to be developed or courses to be pursued along with or after BCA to make you a worthy candidate for high-paying jobs.

What’s Next After a Degree in BCA?

Career options that BCA graduates can opt for to scale up their careers.

1. Master’s in Business Administration

The most versatile career option after getting a degree in BCA is completing an MBA. A master’s of business administration course can help a BCA graduate get a high-paying job offer after graduation. With an MBA degree, a BCA graduate can acquire management positions in his or her dream companies. It can help him or her create a strong professional network in the field or he or she can start his or her own business.

It helps students grow professionally in the field of computer science and technology. The combination of BCA and MBA helps you land a managerial or governance executive role in the IT field, which is a high-profile job for BCA graduates.

BCA graduates with MBA get the opportunity to learn various skill sets such as:

Digital thinking

Strategic thinking

Leadership qualities

Business strategy

Network development

If a person is willing to pursue an MBA after BCA, he/she needs to appear for the combined admission test. The combined admission test or CAT is a national-level entrance exam for getting into MBA colleges across India.

After completing an MBA after BCA, one can secure job roles such as those of:

Officials of IT governance

Officials of IT financial management

Chief technical officer or chief information officer

IT directors and managers

IT service management experts

2. Data Scientist

The role of data scientists requires knowledge in the field of computer science. To get into the field of data science, one requires to get an additional certification along with their BCA degree. This certificate can be in the fields of:

Programming

Productive analysis

Data visualization

Machine learning

Data mining

Natural language processing

A data scientist is required to do the work of analyzing data using various tools and software programs. The average salary of a data scientist starts with Rs 7  LPA and may rise as per the specialization and skill set of a person.

3. A Future in Web 3.0

Like data science, the boom of Web 3.0 along with the rise of the blockchain industry offers myriad career opportunities. The blockchain industry can be explored by BCA graduates as blockchain developers or managers. People are rapidly discovering their career options in this field. It offers some of the best tech jobs for newbies as well as experienced professionals.

Top tech companies are constantly in search of personnel with skills in blockchain. Jobs can be found in these domains:

Blockchain technology

Composer solidarity

Java

Smart contracts

The average salary of a blockchain developer starts from Rs 6.5  LPA. It is for sure one of the highest-paying jobs today and is constantly growing each day.

4. Master’s in Computer Application

The most obvious career path after getting a degree in BCA is pursuing a master’s in computer applications (MCA). A master’s in computer applications is one of the most sought-after courses after BCA to become IT professionals. MCA degree and the skills taught help you land a role in software development. It makes you versatile and conversant with the demands of the IT industry. MCA can make you eligible for working in top multinational companies as,

Software engineer

Software consultant

Testing engineer

System analyst

Data administrator

Programmer

5. Master’s in Computer Management

A master’s in computer management after BCA gives you a skill set and inculcates computer knowledge with managerial training. Along with logical, critical thinking, and problem-solving skills, students are also taught skills in marketing and management. They can get jobs as:

Computer system analysts

Computer information system management expert

Information security analyst

Database analyst administrator

Government Job Opportunities After BCA

After a graduation degree in BCA in India, a student gets several opportunities to get into first-class government services.

1. Union Public Service Commission Jobs

UPSC is one of the most sought-after careers across the country. The spectrum of responsibilities and power that a young person can get after clearing this exam are unsurmountable. A person who wishes to serve the nation through government services can appear for UPSC civil as well as defense services exams.

2. Bank Exams

For candidates who pass BCA, the Government of India offers several opportunities to appear for multiple banking examinations like:

3. RBI Assistant

IBPS clerk and IBPS officer are two career options that open up a plethora of jobs for BCA graduates.

4. Indian Railways

The Railways recruitment board offers career options for BCA graduates in technical and non-technical roles.

Freelancing Jobs

Freelancers are self-employed persons who work as a one-man army. The freelancing industry is experiencing a boom across developing countries. A freelancer earns between Rs 8 lakh and Rs 20 lakh per annum.

As a freelancer, a BCA student can learn skills like:

User interface development

Android development

Website design

The career options listed above give a holistic view for students who want to pursue a degree in BCA. Choose the best option.

Conclusion

Selecting your dream career from the above-mentioned courses can become a tedious task. We at Mindler provide graduate career counselling to help you select the best field for your future. We offer several courses that can give a new direction to your life. Start with us the journey that can change the course of your life forever. Visit our website here.

[skillslash01]

Data science vs. Computer science - Differences to Know in 2022

Data science vs computer science: Modern life is impacted by computer technology and big data in various ways, including how we purchase, navigate cities and interact with others on social media. These digitally-driven technologies were created, made user-friendly, and have the potential to help businesses improve their goods and services thanks in large part to the work of data scientists and computer scientists. Despite this, the specific responsibilities of experts in data science and computer science are very different in terms of the products they produce and the skill sets they need.

Through today's blog, we will understand data science vs computer science better. So, let's start with their meanings.

What is Computer Science?

The study of computers and computing concepts can be summed up as computer science. Along with their uses in technology and science, computer design and architecture are included.

There are many different areas of research in computer science to pursue. Computer science works with both software and hardware as well as additional components like the web and networking.

In contrast to the software portion, which deals with programming principles and languages, the hardware portion studies computer design and operational procedures. Operating Systems and compilers are additional components of computer science.

A computer can be programmed to perform any task you specify thanks to science. This field teaches how to use computers to create, not simply to consume, and it places a strong emphasis on problem-solving and innovation across industries.

What is Data Science?

ML, algorithms, data interpretation, computer applications, mathematics, and statistics are all combined in the field of data science to tackle challenging problems by drawing conclusions from unstructured data.

According to a 2013 study by Sciencedaily.com, 90% of all data in existence today was produced in the two years prior. Consider that. We gathered nine times as much data in only two years than all previous human civilizations together had done in thousands of years.

A calculation indicated that there will have been a staggering 45 zettabytes of data by the year 2020. We require data science to transform all of this information into something we can use and to put it to use in practical situations.

Data Science vs Computer Science: Key Differences

Using technologies like data visualization, data mining, and statistical analysis for prediction, data scientists seek out meaning in vast swaths of data. They create the foundational systems required for testing, machine learning-based decision-making, analytics, and the amplification of final data products.

The study of computing architecture and design is referred to as computer science, on the other hand. Utilizing resources like programming languages and artificial intelligence, computer scientists create computer hardware, software, and networking systems. They develop fresh computer methodologies, either from scratch or by creatively repurposing those that already exist.

It's important to note that computer scientists and data scientists can both play cross-functional roles. To learn more about corporate objectives, data scientists typically collaborate closely with top executives. With this knowledge, they may plan how to use corporate data to enhance their offerings. Software design and automation are often the areas that computer scientists, who often possess degrees in computer engineering, concentrate on.

Our contemporary world depends on both professions. The software that powers the navigational computer and mobile applications we use, like Google Maps, may be developed by a computer scientist. The data produced by such apps may be analyzed by a data scientist. The usefulness of the app can be enhanced, for instance, by keeping track of users' most preferred driving routes.

Data Science vs Computer Science: Difference in Salaries

Both data scientists and computer scientists make a high average annual salary due to the rising demand for professionals who can manage the growing amounts of data generated by organizations and program in-depth. Data scientists in India receive an annual salary of INR 1,100,000. It is expected that a computer scientist will make INR 1,846,542 annually in compensation.

Technical proficiency in data collection and analysis, as well as outstanding management and communication abilities, are requirements for a data scientist. The easiest route to becoming a data scientist is through a certification course with a placement guarantee and project certification.

A good academic background and proficiency in statistics, mathematics, and programming are requirements for aspiring data scientists. Additionally, in the fields of engineering, statistics, or computers, they could seek a Ph.D. The professional aspirations of data scientists are catered to by some educational institutions through specific fast-track programs.

The task of creating new technologies falls to computer scientists, many of whom choose to focus on one area of research in particular. In addition to developing and streamlining algorithms, computer scientists also evaluate novel systems and ideas. Students who aspire to become successful computer scientists should seek a certification course with a placement guarantee. Such certification should cover important topics like Programming, Mathematics, algorithms, OS, data structure, and software security.

Final Words

We now reach the concluding parts of today's blog, "Data Science vs Computer Science". We first went through a brief introduction to both these domains. Next, we understood the key differences between data science vs computer science, and finally, we viewed what average salaries professionals in these domains fetch.

If you're an aspirant and wish to make a career in any of these domains, look no further because Skillslash is there to guide you. Apart from having the recognition of the best data science institute in Bangalore, Skilllslash has also built a top-notch online presence. The computer science and Data Science Course In Bangalore with placement guarantee will ensure you never search for a course again in your life. Skillslash also provide Full Stack Developer Course in Bangalore with 100% Job. To know more or get started, Contact the support team. Good luck.

[evoldir]

Fwd: Job: CornerstoneGenomics.GenomeScientist

Begin forwarded message: > From: evoldir@evol.biology.mcmaster.ca > Subject: Job: CornerstoneGenomics.GenomeScientist > Date: 25 July 2022 at 05:18:48 BST > To: rdmpage@gmail.com > > > Job:CornerstoneGenomics.GenomicsScientist > > It's our mission at Cornerstone Genomics Inc. to create digital tools > that dramatically accelerate research in human and wildlife biology. Our > serverless web application, CodeXome�, provides scientists with the > means to precisely and rapidly assess the impact of gene variation > on biological function or disease. As part of our Genomics Research > and Development team, you will play a critical role in building the > most comprehensive comparative genomics application on the market. We > strive to provide users with tools powerful enough to meet the moment > of this era of big data, while presenting these findings in an equally > elegant and clear form. Your contributions and innovations will directly > benefit our clients in academic, government, and corporate institutions, > accelerating their speed to discovery, and providing clarity in their > genomic research efforts. > > A central focus of this position is to utilize long-read sequence data to > model gene evolution. The first major priorities include evaluating and > optimizing existing de novo genome assembly algorithms using PacBio HiFi > reads, then designing strategies to align genes among taxa. As a > comparative genomics platform, these assemblies serve as the foundation for > multiple avenues of further research including, but not limited to: (1) > identifying orthologous sequences; (2) modeling ancestral haplotypes; (3) > clarifying patterns among or between clades of taxa (ex. Core vs. accessory > genes, Methylation signatures, Structural variation). Another core > component of our platform is providing users with functional information > thus, this position will also entail utilizing data to ascribe and quantify > features like selection and conservation across genomes. > > We are seeking someone who wants to use more than just their technical > skill set: we are a group of problem solvers drawing upon a diverse set of > professional backgrounds to lay the foundations of our start-up. We are a > collection of fearless teammates - people willing to fail forward each day, > and put aside any feelings of imposter syndrome. We seek communicators who > find creative ways to share their domain knowledge with the rest of the > team and our customers. > > We encourage applications from all authorized to work in the USA, and some > eligible candidates may also qualify for the IPERF National Science > Foundation program (optional). > > Applications accepted until August 20, 2022. > Salary negotiable starting at $78,000 > Full health care provided > We are located in Frederick, Maryland or Cape Cod, Massachusetts but remote > work options within USA are possible. > > Website: > Cornerstonegenomics.com > > Jill Pecon-Slattery, Ph.D. > CEO and Founder > Cornerstone Genomics Inc > > Questions? > Please contact jill@cornerstonegenomics.com > We will answer you within 24 hours. > > How To Apply: > Please send to jill@cornerstonegenomics.com > 1) Cover Letter detailing how you meet the job requirements and shared > interests > 2) CV > 3) Names and contact information of 2 references > > > > *Jill Pecon-Slattery, Ph.D.* > CEO and Founder > Cornerstone Genomics

[webdeveloperprakerja]

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Data Science vs Full Stack Developer

In today's technology-driven world, both data science and full-stack development have emerged as prominent and rewarding career paths. Data science involves extracting insights from vast data sets, while full-stack development deals with creating end-to-end web applications. Each field offers unique challenges, and choosing the right one depends on your interests, strengths, and career goals. This article will provide a thorough comparison between data science and full-stack development, helping you understand the key differences and make an informed decision about which path to pursue. Data Science vs Full Stack Developer: Which one to choose and Why? Let's dive deep into the comparison between data science and full stack development, exploring various aspects of each field and what makes them distinct from each other. Data Science: The Path of Insights What is Data Science? Data science is an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. It involves techniques from statistics, computer science, and domain knowledge to analyze data, identify patterns, and make data-driven decisions. Why Choose Data Science? Data science offers a plethora of opportunities in diverse industries like healthcare, finance, marketing, and more. With a growing demand for data-driven decision-making, data scientists are in high demand, and the job market is ripe with exciting roles. The ability to uncover hidden trends, predict outcomes, and drive strategic initiatives makes data science an intellectually stimulating and rewarding career choice. Full Stack Developer: The Architect of Web Applications What is Full Stack Development? Full stack development involves the complete process of designing, building, and managing web applications. Full stack developers are well-versed in both front-end and back-end technologies, enabling them to create seamless, user-friendly, and functional web experiences. Why Choose Full Stack Development? As a full stack developer, you have the opportunity to work on every aspect of web development. From designing user interfaces to managing databases, you gain a comprehensive understanding of the entire web application process. The demand for full stack developers is on the rise as businesses seek versatile professionals who can handle various aspects of development efficiently. Data Science and Full Stack Development: A Comparative Analysis Salary and Job Prospects: Both data science and full stack development offer lucrative career paths. Data scientists often earn competitive salaries due to their expertise in handling complex data sets and providing valuable insights. On the other hand, full stack developers' salaries are equally attractive, given their versatility and ability to work on the entire web development process. The choice between the two ultimately depends on individual preferences and interests. Skill Set and Learning Curve: Data science demands a strong foundation in mathematics, statistics, and programming. Proficiency in programming languages like Python and R is crucial, along with a deep understanding of machine learning algorithms. On the contrary, full stack development requires expertise in front-end technologies like HTML, CSS, and JavaScript, as well as back-end technologies like Node.js, Java, or Ruby on Rails. The learning curve in both fields is steep, but with dedication and practice, mastery can be achieved. Career Growth Opportunities: In the data science domain, career growth can lead to roles like data engineer, machine learning engineer, or data science manager. With experience and expertise, data scientists can become pivotal decision-makers within organizations. In full stack development, career growth may involve becoming a technical lead, software architect, or even starting your own web development venture. Both paths offer promising and satisfying career progression. Work-Life Balance: Achieving a balance between work and personal life is vital for career satisfaction. Data science and full stack development roles can sometimes involve long hours, especially when dealing with complex projects or tight deadlines. However, many organizations today promote a healthy work-life balance, and it's crucial to choose a workplace that aligns with your priorities. Job Stability and Future Demand: Both data science and full stack development show no signs of slowing down in terms of demand. As businesses increasingly rely on data-driven decisions and online presence, the need for experts in these fields will only grow. Job stability in both areas is promising, offering a sense of security and stability for aspiring professionals. FAQs FAQ 1: What are the prerequisites for becoming a data scientist? To become a data scientist, a strong foundation in mathematics, statistics, and programming is essential. Familiarity with programming languages like Python and R, along with knowledge of machine learning algorithms, will set you on the right path. FAQ 2: Can I transition from full stack development to data science? Certainly! Many skills acquired in full stack development, such as programming and problem-solving, can be valuable assets in data science. With additional learning and a focus on data analysis techniques, transitioning to data science is feasible. FAQ 3: Which field offers better long-term growth opportunities? Both data science and full stack development offer excellent long-term growth prospects. However, choosing the field that aligns with your interests and passions will lead to a more fulfilling and successful career in the long run. FAQ 4: Are there any specific industries where one field dominates? Data science finds extensive applications in industries like healthcare, finance, marketing, and e-commerce, where data-driven decisions are crucial. On the other hand, full stack development plays a vital role in shaping web applications across all industries. FAQ 5: Can I pursue both data science and full stack development simultaneously? While it's possible to gain knowledge in both fields, becoming an expert in both simultaneously might be challenging. It's advisable to focus on one field primarily and gain proficiency before delving into the other. FAQ 6: How do I decide which path to choose? Deciding between data science and full stack development boils down to understanding your interests, strengths, and long-term career goals. Consider the skills you enjoy using, the problems you want to solve, and the industries that align with your passion. Conclusion: In conclusion, both data science and full stack development offer exciting and rewarding career opportunities. Data science involves diving deep into data sets to extract insights, while full stack development focuses on creating web applications from start to finish. Each path has its own set of challenges and opportunities. Ultimately, the decision to choose one over the other should be based on your interests, strengths, and passion for the respective field. Whichever path you choose, dedication, continuous learning, and a passion for technology will pave the way for a successful and fulfilling career. Read the full article

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Fwd: Job: CornerstoneGenomics.GenomeScientist

Begin forwarded message: > From: evoldir@evol.biology.mcmaster.ca > Subject: Job: CornerstoneGenomics.GenomeScientist > Date: 25 July 2022 at 05:18:48 BST > To: rdmpage@gmail.com > > > Job:CornerstoneGenomics.GenomicsScientist > > It's our mission at Cornerstone Genomics Inc. to create digital tools > that dramatically accelerate research in human and wildlife biology. Our > serverless web application, CodeXome�, provides scientists with the > means to precisely and rapidly assess the impact of gene variation > on biological function or disease. As part of our Genomics Research > and Development team, you will play a critical role in building the > most comprehensive comparative genomics application on the market. We > strive to provide users with tools powerful enough to meet the moment > of this era of big data, while presenting these findings in an equally > elegant and clear form. Your contributions and innovations will directly > benefit our clients in academic, government, and corporate institutions, > accelerating their speed to discovery, and providing clarity in their > genomic research efforts. > > A central focus of this position is to utilize long-read sequence data to > model gene evolution. The first major priorities include evaluating and > optimizing existing de novo genome assembly algorithms using PacBio HiFi > reads, then designing strategies to align genes among taxa. As a > comparative genomics platform, these assemblies serve as the foundation for > multiple avenues of further research including, but not limited to: (1) > identifying orthologous sequences; (2) modeling ancestral haplotypes; (3) > clarifying patterns among or between clades of taxa (ex. Core vs. accessory > genes, Methylation signatures, Structural variation). Another core > component of our platform is providing users with functional information > thus, this position will also entail utilizing data to ascribe and quantify > features like selection and conservation across genomes. > > We are seeking someone who wants to use more than just their technical > skill set: we are a group of problem solvers drawing upon a diverse set of > professional backgrounds to lay the foundations of our start-up. We are a > collection of fearless teammates - people willing to fail forward each day, > and put aside any feelings of imposter syndrome. We seek communicators who > find creative ways to share their domain knowledge with the rest of the > team and our customers. > > We encourage applications from all authorized to work in the USA, and some > eligible candidates may also qualify for the IPERF National Science > Foundation program (optional). > > Applications accepted until August 20, 2022. > Salary negotiable starting at $78,000 > Full health care provided > We are located in Frederick, Maryland or Cape Cod, Massachusetts but remote > work options within USA are possible. > > Website: > Cornerstonegenomics.com > > Jill Pecon-Slattery, Ph.D. > CEO and Founder > Cornerstone Genomics Inc > > Questions? > Please contact jill@cornerstonegenomics.com > We will answer you within 24 hours. > > How To Apply: > Please send to jill@cornerstonegenomics.com > 1) Cover Letter detailing how you meet the job requirements and shared > interests > 2) CV > 3) Names and contact information of 2 references > > > > *Jill Pecon-Slattery, Ph.D.* > CEO and Founder > Cornerstone Genomics

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Data Analytics Vs  Data Science

According to PayScale, data scientists in IT made a median annual salary of $91,260 as of 2018. Day to day he cleans, rationalizes, and fashions vast portions of knowledge to derive understanding and tease stories from it. When he’s not wrestling with numbers, Russ takes to the hills mountaineering and backpacking California’s many trails.

R has a big neighborhood of programmers that use and assist it online, so there isn't any need to develop every little thing alone. The ability to glean actionable insights from complex knowledge—which would require the automation of context-particular important pondering—is even further away. Additionally, information scientists with deep business experience and notable business acumen will proceed to see excessive demand for his or her skills. The ability to identify all delicate knowledge and secure it additionally demonstrates two essential advantages of knowledge science—value propositions the sector offers. First, the enterprise has the capacity to make good use of big information. Second, it has the will and skill to protect personal safety despite the continuing challenges of a dynamic security panorama. Ethical finest practices for the sphere of knowledge science additionally include figuring out and scrubbing delicate knowledge.

This is not simply to guard users; it protects companies, who can undergo severe popularity injury and buyer loss when they fail to protect sensitive knowledge. Both information science and business analytics concentrate on solving enterprise issues, and each involve collecting knowledge, modeling it, and then gleaning insights from the information. The major difference is that business analytics is restricted to business-related problems such as revenue and costs. Big information is often unstructured, so deep learning is a crucial subset of knowledge science research. Deep studying makes use of hierarchical synthetic neural networks to engage in the machine learning process. These artificial neural networks are like advanced webs of neuron nodes, very similar to the human mind.

If the info doesn’t capture the precise cause of variation, data science will fail. Machine learning, however, refers to a bunch of techniques used by information scientists that permit computers to study from knowledge. These techniques produce results that carry out well without programming express rules. Data scientists are required to have a mix of math, statistics, and laptop science, in addition to a curiosity in—and information-of—the enterprise world.

Although traditional knowledge evaluation applications strategy data in a linear trend, the deep learning system's hierarchy of operations enables a nonlinear method to issues. However, different techniques that require human enter are additionally a part of data science as we understand it at present.

While the algorithms are already mature, we are still in the early phases of discovering fruitful ways to use them. First, master the techniques and algorithms of “classical” machine learning, which function building blocks for advanced matters. While it’s nice to implement a number only for studying purposes, the truth is that algorithms have gotten commodities. Thanks to mature machine learning libraries and cloud-based mostly options, most practitioners truly by no means code algorithms from scratch. As more and more industries begin to gather information on clients and products, the necessity for knowledge scientists will continue to develop.

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