#sql programming development in us

[ldsengineers]

SQL Programming Development Company in the USA

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[mikotos-nape-nibbler]

if i'm being honest i don't really give a shit about the discourse of whether 09 is good or bad DID rep nor do i give a shit about how his DID manifests. i find that what makes him interesting isn't a label for a set of symptoms but rather his expression of those symptoms cultivated by the world he was raised in and how he responds or continues those behaviours. it's honestly more interesting to analyze the overarching systems in society that assisted in developing Mikoto Kayano into a murderer.

how different would he be if Japanese society were more open about emotions and struggles? how different would he be if he were receiving mental health treatment? would he still become a murderer and, if so, what made those mental health treatments ineffective? if he had been hired at another japanese company that didn't follow black company practices, would he still have become a murderer? if so, why? what were the factors that made an average, young adult become a murderer? so on so forth.

honestly the fetish this fandom has on his dissociated self that gives the illusion of multiplicity — when, in reality, he's a singular whole that is fragmented; cracks lined with dissociative barriers, chiseled by continuous stress — detracts from the complexity of his character and writing, flanderizing him into a prop of DID that the fandom puppets into theatrics of stigmatization that same fans claim to "educate" against when, in truth, spout blasé hearsays encrypted with a DIDcore-lese that does nothing for communicating information about the disorder and, instead, excludes and dissuades the general population that that supposed "education" is directed towards.

"Mikoto's a system from the interactive music project MILGRAM. His alter, 'John', murdered a bunch of people on the train."

so there's a program called "Mikoto" and you named the SQL table "John" with the ALTER command that somehow murdered a bunch of people. did the train running the Mikoto program malfunction because of some zero day error with the John table?

"'He has Dissociative Identity Disorder. John's a protector and Mikoto's the host."

so is this Mikoto guy some vessel or something for some supernatural ouija board summon and the John guy is like the familiar or bodyguard summoned? is their character just that? a job and occupation? that sounds boring. and what do those jobs have to do with a disorder on dissociation? are you talking about something like "occupation disorder" or "stuck-in-their-work-self disorder"? or is this some DnD homebrew class type?

what purpose does inaccessible language have if you're trying to educate the average person who isn't familiar with those online community terms that aren't even universal terms in literature nor research? and how are these terms even relevant to discussing Mikoto's character when the concepts it supposedly encompasses aren't universal nor applicable to all possible subjective presentations that a human brain can develop? and yet the fandom, and much of the online mental disorder community, treats those sociolect terms as an axiom — a universal truth, universal terms — and the lack of adherence to that speech is seen as wrong or sin.

"[insert link to some website claiming to have info on DID] is a good website if you wanna read up on what DID is!!"

and then that supposed resource uses highly specialized, nonuniversal, sociolect terms that is jargon to those who are only familiar with layman terms which makes the "information" — if it even is faithfully derived from research — seem like further jargon rather than a comprehensible source for educating.

applying this to Mikoto's character, those who aren't already acquainted with how he's spoken about likely view discussions or comments on him as indecipherable jargon thus it must mean Mikoto's character is just jargon thus not well-written or boring because according to the bubble of jargon people describe this guy with: Mikoto Kayano = computer program system + dnd classes or some chuunibyou alter ego personality savior complex + party hoster or vessel for some supernatural thing + mechanical switch that states 0 or 1 + going through some mitosis split

how does any of that relate to dissociation? people have made DID symptoms so convoluted, yet structured that convolutedness through terms and expressions that sound like some Gary Sue Ebony Dementia Darkness Raven trope that completely detracts from the fact that it's a dissociation disorder rather than a roleplay character form.

have people even considered the fact that if Mikoto had never been imprisoned, he, nor anyone, would even know he has DID— if he even counts as having DID? if how his brain works regarding how he handles stress doesn't impact his life negatively- even if he has all the symptoms of DID if how his brain works regarding how he handles stress doesn't cause disorder for him, he wouldn't have dissociative identity disorder.

the less people view Mikoto as the "alters guy", the more complex understandings can be gathered and discussed within the fandom. the more people view Mikoto as the "average, normal guy who committed a murder, but why? what caused an average, hardworking guy to commit the most grievous sin of murder?", the better the range of insight and curiosity into what shapes a person and the factors in their world — a reflection of our present reality — at play which interact and weave with one another to shape and respond to its members; the opposite of cutting off the fluidity and interwovenness Mikoto has with people and environments outside of his self that people constrain him to, that prison cell of a single label characteristic: "DID".

conclusion: for the love of torch novelgram, let's talk about Mikoto Kayano like the multifaceted, complex, shaped-by-the-socioeconomic-stratae-of-the-world-he-is-part-of-and-interacts-with well-written character he is.

[nishtha135]

Which coding languages should I learn to boost my IT career opportunities?

A career in IT needs a mix of versatile programming languages. Here are some of the most essential ones:

Python – Easy to learn and widely used for data science, machine learning, web development, and automation.

JavaScript – Key for web development, allowing interactive websites and backend work with frameworks like Node.js.

Java – Known for stability, popular for Android apps, enterprise software, and backend development.

C++ – Great for systems programming, game development, and areas needing high performance.

SQL – Essential for managing and querying databases, crucial for data-driven roles.

C# – Common in enterprise environments and used in game development, especially with Unity.

[allaboutkeyingo]

SQL Server 2022 Edition and License instructions

SQL Server 2022 Editions:

• Enterprise Edition is ideal for applications requiring mission critical in-memory performance, security, and high availability

• Standard Edition delivers fully featured database capabilities for mid-tier applications and data marts

SQL Server 2022 is also available in free Developer and Express editions. Web Edition is offered in the Services Provider License Agreement (SPLA) program only.

And the Online Store Keyingo Provides the SQL Server 2017/2019/2022 Standard Edition.

SQL Server 2022 licensing models 

SQL Server 2022 offers customers a variety of licensing options aligned with how customers typically purchase specific workloads. There are two main licensing models that apply to SQL Server:  PER CORE: Gives customers a more precise measure of computing power and a more consistent licensing metric, regardless of whether solutions are deployed on physical servers on-premises, or in virtual or cloud environments. 

• Core based licensing is appropriate when customers are unable to count users/devices, have Internet/Extranet workloads or systems that integrate with external facing workloads.

• Under the Per Core model, customers license either by physical server (based on the full physical core count) or by virtual machine (based on virtual cores allocated), as further explained below.

SERVER + CAL: Provides the option to license users and/or devices, with low-cost access to incremental SQL Server deployments.   

• Each server running SQL Server software requires a server license.

• Each user and/or device accessing a licensed SQL Server requires a SQL Server CAL that is the same version or newer – for example, to access a SQL Server 2019 Standard Edition server, a user would need a SQL Server 2019 or 2022 CAL.

Each SQL Server CAL allows access to multiple licensed SQL Servers, including Standard Edition and legacy Business Intelligence and Enterprise Edition Servers.SQL Server 2022 Editions availability by licensing model:  

Physical core licensing – Enterprise Edition 

• Customers can deploy an unlimited number of VMs or containers on the server and utilize the full capacity of the licensed hardware, by fully licensing the server (or server farm) with Enterprise Edition core subscription licenses or licenses with SA coverage based on the total number of physical cores on the servers.

• Subscription licenses or SA provide(s) the option to run an unlimited number of virtual machines or containers to handle dynamic workloads and fully utilize the hardware’s computing power.

Virtual core licensing – Standard/Enterprise Edition 

When licensing by virtual core on a virtual OSE with subscription licenses or SA coverage on all virtual cores (including hyperthreaded cores) on the virtual OSE, customers may run any number of containers in that virtual OSE. This benefit applies both to Standard and Enterprise Edition.

Licensing for non-production use 

SQL Server 2022 Developer Edition provides a fully featured version of SQL Server software—including all the features and capabilities of Enterprise Edition—licensed for  development, test and demonstration purposes only.  Customers may install and run the SQL Server Developer Edition software on any number of devices. This is  significant because it allows customers to run the software  on multiple devices (for testing purposes, for example)  without having to license each non-production server  system for SQL Server.  

A production environment is defined as an environment  that is accessed by end-users of an application (such as an  Internet website) and that is used for more than gathering  feedback or acceptance testing of that application.   

SQL Server 2022 Developer Edition is a free product !

[macadamiastudies]

December 31st, 2025

Going to start 2025 programming, so if you need me, I'll be lost in a sea of coffee, bugs, and late-night breakthroughs.

Let’s see where this year takes us. Wishing all of you a beautiful 2025.

Todays been a busy day; life is starting to get back to normal so I'm back to developing a schedule and hopefully sticking to it.

What I did today:

❄ finally finished planning out my portfolio so I can start coding right away in 2025 ❄ lots of scrapbooking ❄ found some projects to do on python ❄ reviewed SQL ❄ started adding to my resume so I can keep it up to date

To-do Jan 1st:

🎆 start programming my portfolio again 🎆 five leet code questions 🎆 check out some free resources people recommended to me 🎆 some GMAT review 🎆 2 hours of Azure studying

Playlist for the day:

[xaltius]

Unlocking the Power of Data: Essential Skills to Become a Data Scientist

In today's data-driven world, the demand for skilled data scientists is skyrocketing. These professionals are the key to transforming raw information into actionable insights, driving innovation and shaping business strategies. But what exactly does it take to become a data scientist? It's a multidisciplinary field, requiring a unique blend of technical prowess and analytical thinking. Let's break down the essential skills you'll need to embark on this exciting career path.

1. Strong Mathematical and Statistical Foundation:

At the heart of data science lies a deep understanding of mathematics and statistics. You'll need to grasp concepts like:

Linear Algebra and Calculus: Essential for understanding machine learning algorithms and optimizing models.

Probability and Statistics: Crucial for data analysis, hypothesis testing, and drawing meaningful conclusions from data.

2. Programming Proficiency (Python and/or R):

Data scientists are fluent in at least one, if not both, of the dominant programming languages in the field:

Python: Known for its readability and extensive libraries like Pandas, NumPy, Scikit-learn, and TensorFlow, making it ideal for data manipulation, analysis, and machine learning.

R: Specifically designed for statistical computing and graphics, R offers a rich ecosystem of packages for statistical modeling and visualization.

3. Data Wrangling and Preprocessing Skills:

Raw data is rarely clean and ready for analysis. A significant portion of a data scientist's time is spent on:

Data Cleaning: Handling missing values, outliers, and inconsistencies.

Data Transformation: Reshaping, merging, and aggregating data.

Feature Engineering: Creating new features from existing data to improve model performance.

4. Expertise in Databases and SQL:

Data often resides in databases. Proficiency in SQL (Structured Query Language) is essential for:

Extracting Data: Querying and retrieving data from various database systems.

Data Manipulation: Filtering, joining, and aggregating data within databases.

5. Machine Learning Mastery:

Machine learning is a core component of data science, enabling you to build models that learn from data and make predictions or classifications. Key areas include:

Supervised Learning: Regression, classification algorithms.

Unsupervised Learning: Clustering, dimensionality reduction.

Model Selection and Evaluation: Choosing the right algorithms and assessing their performance.

6. Data Visualization and Communication Skills:

Being able to effectively communicate your findings is just as important as the analysis itself. You'll need to:

Visualize Data: Create compelling charts and graphs to explore patterns and insights using libraries like Matplotlib, Seaborn (Python), or ggplot2 (R).

Tell Data Stories: Present your findings in a clear and concise manner that resonates with both technical and non-technical audiences.

7. Critical Thinking and Problem-Solving Abilities:

Data scientists are essentially problem solvers. You need to be able to:

Define Business Problems: Translate business challenges into data science questions.

Develop Analytical Frameworks: Structure your approach to solve complex problems.

Interpret Results: Draw meaningful conclusions and translate them into actionable recommendations.

8. Domain Knowledge (Optional but Highly Beneficial):

Having expertise in the specific industry or domain you're working in can give you a significant advantage. It helps you understand the context of the data and formulate more relevant questions.

9. Curiosity and a Growth Mindset:

The field of data science is constantly evolving. A genuine curiosity and a willingness to learn new technologies and techniques are crucial for long-term success.

10. Strong Communication and Collaboration Skills:

Data scientists often work in teams and need to collaborate effectively with engineers, business stakeholders, and other experts.

Kickstart Your Data Science Journey with Xaltius Academy's Data Science and AI Program:

Acquiring these skills can seem like a daunting task, but structured learning programs can provide a clear and effective path. Xaltius Academy's Data Science and AI Program is designed to equip you with the essential knowledge and practical experience to become a successful data scientist.

Key benefits of the program:

Comprehensive Curriculum: Covers all the core skills mentioned above, from foundational mathematics to advanced machine learning techniques.

Hands-on Projects: Provides practical experience working with real-world datasets and building a strong portfolio.

Expert Instructors: Learn from industry professionals with years of experience in data science and AI.

Career Support: Offers guidance and resources to help you launch your data science career.

Becoming a data scientist is a rewarding journey that blends technical expertise with analytical thinking. By focusing on developing these key skills and leveraging resources like Xaltius Academy's program, you can position yourself for a successful and impactful career in this in-demand field. The power of data is waiting to be unlocked – are you ready to take the challenge?

[sindhu14]

What is Python, How to Learn Python?

What is Python?

Python is a high-level, interpreted programming language known for its simplicity and readability. It is widely used in various fields like: ✅ Web Development (Django, Flask) ✅ Data Science & Machine Learning (Pandas, NumPy, TensorFlow) ✅ Automation & Scripting (Web scraping, File automation) ✅ Game Development (Pygame) ✅ Cybersecurity & Ethical Hacking ✅ Embedded Systems & IoT (MicroPython)

Python is beginner-friendly because of its easy-to-read syntax, large community, and vast library support.

How Long Does It Take to Learn Python?

The time required to learn Python depends on your goals and background. Here’s a general breakdown:

1. Basics of Python (1-2 months)

If you spend 1-2 hours daily, you can master:

Variables, Data Types, Operators

Loops & Conditionals

Functions & Modules

Lists, Tuples, Dictionaries

File Handling

Basic Object-Oriented Programming (OOP)

2. Intermediate Level (2-4 months)

Once comfortable with basics, focus on:

Advanced OOP concepts

Exception Handling

Working with APIs & Web Scraping

Database handling (SQL, SQLite)

Python Libraries (Requests, Pandas, NumPy)

Small real-world projects

3. Advanced Python & Specialization (6+ months)

If you want to go pro, specialize in:

Data Science & Machine Learning (Matplotlib, Scikit-Learn, TensorFlow)

Web Development (Django, Flask)

Automation & Scripting

Cybersecurity & Ethical Hacking

Learning Plan Based on Your Goal

📌 Casual Learning – 3-6 months (for automation, scripting, or general knowledge) 📌 Professional Development – 6-12 months (for jobs in software, data science, etc.) 📌 Deep Mastery – 1-2 years (for AI, ML, complex projects, research)

Scope @ NareshIT:

At NareshIT’s Python application Development program you will be able to get the extensive hands-on training in front-end, middleware, and back-end technology.

It skilled you along with phase-end and capstone projects based on real business scenarios.

Here you learn the concepts from leading industry experts with content structured to ensure industrial relevance.

An end-to-end application with exciting features

Earn an industry-recognized course completion certificate.

For more details:

[pancakeke]

does anyone have experience confronting their employer about your responsibilities getting wildly out of sync with your job title? my title is supply chain analyst but more than 50% of my work is not supply chain related at all anymore. I'm being treated mostly as a data analyst. data analyst salaries are 10% higher than supply chain analysts salaries in my area plus there are many more job opportunities for data analysts. I want a title change.

also does anyone have experience writing a resume when your responsibilities are not related to your title? should I just say I'm a data analyst because that's the work I'm doing or is there some special section I can add noting secondary work as an unofficial data analyst. there is other shit I do very often too that is barely related to either role.

anyone who has been following me for a while is gonna think I'm sooo late to this realization but it's getting pretty clear to me that my workplace is not going to stop pushing me into roles that I'm not appropriately compensated for, and because these roles are not official I will never receive any support, even when I explicitly request it. I have already been denied support multiple times.

this isn't just random people asking me to do one-time odd jobs either. our COO, CFO, and multiple directors ask me directly to do shit like investigate errors in our website and its tools to uncover what is causing data to display incorrectly. this is to shift labor off our web dev team and onto me, why us paid way less than a developer. I do NOT know ANY programming, I just know the database. this has happened five or six times now.

I also manage customer issues directly with the customers because our sales team does not appropriately train anyone in their department. I create orders for them too because they can not be trusted to enter them (not because they're untrained, but because they were not entering these intentionally). I provide records and reporting for accounting audits. I build weird calculators and generators in Excel for every department except two (IT and web dev) because those two can't justify devoting time to those projects and integrating them into our site. I create extremely weird queries to trace information that out database does not track appropriately (and this will never be fixed).

there are zero docs for anything I do except the ones I personally wrote, and only very limited notes scattered around. I was never trained and only picked sql and stuff up cause when I was a buyer digging into data helped me solve problems more efficiently. I have been begging web dev to tell me when they update anything because they keep breaking extremely serious tools but they have been ghosting me entirely. literally all they have to do is CC me on update emails but they won't do it.

also there isn't anyone else in the company who is proficient in sql and stuff (outside dev, and they're miles above me) to back me up so if I'm overloaded or need other help I'm shit out of luck. this makes taking PTO a fucking nightmare too because I'm always in the middle of helping someone out of a fucking fire and everyone does that shit where they say I can take PTO any time I want without worrying but then constantly assign me with critical tasks and demand results asap.

It is beyond time for me to be realistic about this job and what I want to do with myself 40 hours a week until I die, so I need to start working toward either making my current situation more tolerable or going elsewhere.

[biopractify]

How to Transition from Biotechnology to Bioinformatics: A Step-by-Step Guide

Biotechnology and bioinformatics are closely linked fields, but shifting from a wet lab environment to a computational approach requires strategic planning. Whether you are a student or a professional looking to make the transition, this guide will provide a step-by-step roadmap to help you navigate the shift from biotechnology to bioinformatics.

Why Transition from Biotechnology to Bioinformatics?

Bioinformatics is revolutionizing life sciences by integrating biological data with computational tools to uncover insights in genomics, proteomics, and drug discovery. The field offers diverse career opportunities in research, pharmaceuticals, healthcare, and AI-driven biological data analysis.

If you are skilled in laboratory techniques but wish to expand your expertise into data-driven biological research, bioinformatics is a rewarding career choice.

Step-by-Step Guide to Transition from Biotechnology to Bioinformatics

Step 1: Understand the Basics of Bioinformatics

Before making the switch, it’s crucial to gain a foundational understanding of bioinformatics. Here are key areas to explore:

Biological Databases – Learn about major databases like GenBank, UniProt, and Ensembl.

Genomics and Proteomics – Understand how computational methods analyze genes and proteins.

Sequence Analysis – Familiarize yourself with tools like BLAST, Clustal Omega, and FASTA.

🔹 Recommended Resources:

Online courses on Coursera, edX, or Khan Academy

Books like Bioinformatics for Dummies or Understanding Bioinformatics

Websites like NCBI, EMBL-EBI, and Expasy

Step 2: Develop Computational and Programming Skills

Bioinformatics heavily relies on coding and data analysis. You should start learning:

Python – Widely used in bioinformatics for data manipulation and analysis.

R – Great for statistical computing and visualization in genomics.

Linux/Unix – Basic command-line skills are essential for working with large datasets.

SQL – Useful for querying biological databases.

🔹 Recommended Online Courses:

Python for Bioinformatics (Udemy, DataCamp)

R for Genomics (HarvardX)

Linux Command Line Basics (Codecademy)

Step 3: Learn Bioinformatics Tools and Software

To become proficient in bioinformatics, you should practice using industry-standard tools:

Bioconductor – R-based tool for genomic data analysis.

Biopython – A powerful Python library for handling biological data.

GROMACS – Molecular dynamics simulation tool.

Rosetta – Protein modeling software.

🔹 How to Learn?

Join open-source projects on GitHub

Take part in hackathons or bioinformatics challenges on Kaggle

Explore free platforms like Galaxy Project for hands-on experience

Step 4: Work on Bioinformatics Projects

Practical experience is key. Start working on small projects such as:

✅ Analyzing gene sequences from NCBI databases ✅ Predicting protein structures using AlphaFold ✅ Visualizing genomic variations using R and Python

You can find datasets on:

NCBI GEO

1000 Genomes Project

TCGA (The Cancer Genome Atlas)

Create a GitHub portfolio to showcase your bioinformatics projects, as employers value practical work over theoretical knowledge.

Step 5: Gain Hands-on Experience with Internships

Many organizations and research institutes offer bioinformatics internships. Check opportunities at:

NCBI, EMBL-EBI, NIH (government research institutes)

Biotech and pharma companies (Roche, Pfizer, Illumina)

Academic research labs (Look for university-funded projects)

💡 Pro Tip: Join online bioinformatics communities like Biostars, Reddit r/bioinformatics, and SEQanswers to network and find opportunities.

Step 6: Earn a Certification or Higher Education

If you want to strengthen your credentials, consider:

🎓 Bioinformatics Certifications:

Coursera – Genomic Data Science (Johns Hopkins University)

edX – Bioinformatics MicroMasters (UMGC)

EMBO – Bioinformatics training courses

🎓 Master’s in Bioinformatics (optional but beneficial)

Top universities include Harvard, Stanford, ETH Zurich, University of Toronto

Step 7: Apply for Bioinformatics Jobs

Once you have gained enough skills and experience, start applying for bioinformatics roles such as:

Bioinformatics Analyst

Computational Biologist

Genomics Data Scientist

Machine Learning Scientist (Biotech)

💡 Where to Find Jobs?

LinkedIn, Indeed, Glassdoor

Biotech job boards (BioSpace, Science Careers)

Company career pages (Illumina, Thermo Fisher)

Final Thoughts

Transitioning from biotechnology to bioinformatics requires effort, but with the right skills and dedication, it is entirely achievable. Start with fundamental knowledge, build computational skills, and work on projects to gain practical experience.

Are you ready to make the switch? 🚀 Start today by exploring free online courses and practicing with real-world datasets!

[web-scraping-tutorial-blog]

Top 5 Programming Languages to Master in 2025

Programming language theory is the subfield of computer science that studies the design, implementation, analysis, characterization, and classification of programming languages.

1. Java

You might ask, “Is Java obsolete?” Of course not.

Why is Java still popular? Java is one of the oldest and most robust programming languages. It is also an object-oriented language mainly used for Android application development. This is one of the main reasons it is still used today. However, with the advent of programming languages ​​like Kotlin (also suitable for Android development), Java is becoming less popular.

2. Swift

3. SQL

4. JavaScript

5. Python

The amazing thing about Python is that it’s a general-purpose programming language used to build a wide range of applications. Furthermore, it is active in artificial intelligence. Self-driving cars, Wal-Mart auto-payment, and many automation and machine learning (ML) apps were developed through Python. This makes this language more important and rapidly popularizes. In addition, Python is easier to learn than all other languages ​​and is easy for beginners. You can also build complex applications relatively easily and quickly. In the United States, the average salary for Python developers is about $ 78,000, while experienced developers can be as high as $ 122,000.

[lunacoding]

SQL GitHub Repositories

I’ve recently been looking up more SQL resources and found some repositories on GitHub that are helpful with learning SQL, so I thought I’d share some here!

Guides:

s-shemee SQL 101: A beginner’s guide to SQL database programming! It offers tutorials, exercises, and resources to help practice SQL

nightFuryman SQL in 30 Days: The fundamentals of SQL with information on how to set up a SQL database from scratch as well as basic SQL commands

Projects:

iweld SQL Dictionary Challenge: A SQL project inspired by a comment on this reddit thread https://www.reddit.com/r/SQL/comments/g4ct1l/what_are_some_good_resources_to_practice_sql/. This project consists of creating a single file with a column of randomly selected words from the dictionary. For this column, you can answer the various questions listed in the repository through SQL queries, or develop your own questions to answer as well.

DevMountain SQL 1 Afternoon: A SQL project where you practice inserting querying data using SQL. This project consists of creating various tables and querying data through this online tool created by DevMountain, found at this link https://postgres.devmountain.com/.

DevMountain SQL 2 Afternoon: The second part of DevMountain’s SQL project. This project involves intermediate queries such as “practice joins, nested queries, updating rows, group by, distinct, and foreign key”.

[teqful]

How-To IT

Topic: Core areas of IT

1. Hardware

• Computers (Desktops, Laptops, Workstations)

• Servers and Data Centers

• Networking Devices (Routers, Switches, Modems)

• Storage Devices (HDDs, SSDs, NAS)

• Peripheral Devices (Printers, Scanners, Monitors)

2. Software

• Operating Systems (Windows, Linux, macOS)

• Application Software (Office Suites, ERP, CRM)

• Development Software (IDEs, Code Libraries, APIs)

• Middleware (Integration Tools)

• Security Software (Antivirus, Firewalls, SIEM)

3. Networking and Telecommunications

• LAN/WAN Infrastructure

• Wireless Networking (Wi-Fi, 5G)

• VPNs (Virtual Private Networks)

• Communication Systems (VoIP, Email Servers)

• Internet Services

4. Data Management

• Databases (SQL, NoSQL)

• Data Warehousing

• Big Data Technologies (Hadoop, Spark)

• Backup and Recovery Systems

• Data Integration Tools

5. Cybersecurity

• Network Security

• Endpoint Protection

• Identity and Access Management (IAM)

• Threat Detection and Incident Response

• Encryption and Data Privacy

6. Software Development

• Front-End Development (UI/UX Design)

• Back-End Development

• DevOps and CI/CD Pipelines

• Mobile App Development

• Cloud-Native Development

7. Cloud Computing

• Infrastructure as a Service (IaaS)

• Platform as a Service (PaaS)

• Software as a Service (SaaS)

• Serverless Computing

• Cloud Storage and Management

8. IT Support and Services

• Help Desk Support

• IT Service Management (ITSM)

• System Administration

• Hardware and Software Troubleshooting

• End-User Training

9. Artificial Intelligence and Machine Learning

• AI Algorithms and Frameworks

• Natural Language Processing (NLP)

• Computer Vision

• Robotics

• Predictive Analytics

10. Business Intelligence and Analytics

• Reporting Tools (Tableau, Power BI)

• Data Visualization

• Business Analytics Platforms

• Predictive Modeling

11. Internet of Things (IoT)

• IoT Devices and Sensors

• IoT Platforms

• Edge Computing

• Smart Systems (Homes, Cities, Vehicles)

12. Enterprise Systems

• Enterprise Resource Planning (ERP)

• Customer Relationship Management (CRM)

• Human Resource Management Systems (HRMS)

• Supply Chain Management Systems

13. IT Governance and Compliance

• ITIL (Information Technology Infrastructure Library)

• COBIT (Control Objectives for Information Technologies)

• ISO/IEC Standards

• Regulatory Compliance (GDPR, HIPAA, SOX)

14. Emerging Technologies

• Blockchain

• Quantum Computing

• Augmented Reality (AR) and Virtual Reality (VR)

• 3D Printing

• Digital Twins

15. IT Project Management

• Agile, Scrum, and Kanban

• Waterfall Methodology

• Resource Allocation

• Risk Management

16. IT Infrastructure

• Data Centers

• Virtualization (VMware, Hyper-V)

• Disaster Recovery Planning

• Load Balancing

17. IT Education and Certifications

• Vendor Certifications (Microsoft, Cisco, AWS)

• Training and Development Programs

• Online Learning Platforms

18. IT Operations and Monitoring

• Performance Monitoring (APM, Network Monitoring)

• IT Asset Management

• Event and Incident Management

19. Software Testing

• Manual Testing: Human testers evaluate software by executing test cases without using automation tools.

• Automated Testing: Use of testing tools (e.g., Selenium, JUnit) to run automated scripts and check software behavior.

• Functional Testing: Validating that the software performs its intended functions.

• Non-Functional Testing: Assessing non-functional aspects such as performance, usability, and security.

• Unit Testing: Testing individual components or units of code for correctness.

• Integration Testing: Ensuring that different modules or systems work together as expected.

• System Testing: Verifying the complete software system’s behavior against requirements.

• Acceptance Testing: Conducting tests to confirm that the software meets business requirements (including UAT - User Acceptance Testing).

• Regression Testing: Ensuring that new changes or features do not negatively affect existing functionalities.

• Performance Testing: Testing software performance under various conditions (load, stress, scalability).

• Security Testing: Identifying vulnerabilities and assessing the software’s ability to protect data.

• Compatibility Testing: Ensuring the software works on different operating systems, browsers, or devices.

• Continuous Testing: Integrating testing into the development lifecycle to provide quick feedback and minimize bugs.

• Test Automation Frameworks: Tools and structures used to automate testing processes (e.g., TestNG, Appium).

19. VoIP (Voice over IP)

VoIP Protocols & Standards

• SIP (Session Initiation Protocol)

• H.323

• RTP (Real-Time Transport Protocol)

• MGCP (Media Gateway Control Protocol)

VoIP Hardware

• IP Phones (Desk Phones, Mobile Clients)

• VoIP Gateways

• Analog Telephone Adapters (ATAs)

• VoIP Servers

• Network Switches/ Routers for VoIP

VoIP Software

• Softphones (e.g., Zoiper, X-Lite)

• PBX (Private Branch Exchange) Systems

• VoIP Management Software

• Call Center Solutions (e.g., Asterisk, 3CX)

VoIP Network Infrastructure

• Quality of Service (QoS) Configuration

• VPNs (Virtual Private Networks) for VoIP

• VoIP Traffic Shaping & Bandwidth Management

• Firewall and Security Configurations for VoIP

• Network Monitoring & Optimization Tools

VoIP Security

• Encryption (SRTP, TLS)

• Authentication and Authorization

• Firewall & Intrusion Detection Systems

• VoIP Fraud DetectionVoIP Providers

• Hosted VoIP Services (e.g., RingCentral, Vonage)

• SIP Trunking Providers

• PBX Hosting & Managed Services

VoIP Quality and Testing

• Call Quality Monitoring

• Latency, Jitter, and Packet Loss Testing

• VoIP Performance Metrics and Reporting Tools

• User Acceptance Testing (UAT) for VoIP Systems

Integration with Other Systems

• CRM Integration (e.g., Salesforce with VoIP)

• Unified Communications (UC) Solutions

• Contact Center Integration

• Email, Chat, and Video Communication Integration

[education43]

What Are the Qualifications for a Data Scientist?

In today's data-driven world, the role of a data scientist has become one of the most coveted career paths. With businesses relying on data for decision-making, understanding customer behavior, and improving products, the demand for skilled professionals who can analyze, interpret, and extract value from data is at an all-time high. If you're wondering what qualifications are needed to become a successful data scientist, how DataCouncil can help you get there, and why a data science course in Pune is a great option, this blog has the answers.

The Key Qualifications for a Data Scientist

To succeed as a data scientist, a mix of technical skills, education, and hands-on experience is essential. Here are the core qualifications required:

1. Educational Background

A strong foundation in mathematics, statistics, or computer science is typically expected. Most data scientists hold at least a bachelor’s degree in one of these fields, with many pursuing higher education such as a master's or a Ph.D. A data science course in Pune with DataCouncil can bridge this gap, offering the academic and practical knowledge required for a strong start in the industry.

2. Proficiency in Programming Languages

Programming is at the heart of data science. You need to be comfortable with languages like Python, R, and SQL, which are widely used for data analysis, machine learning, and database management. A comprehensive data science course in Pune will teach these programming skills from scratch, ensuring you become proficient in coding for data science tasks.

3. Understanding of Machine Learning

Data scientists must have a solid grasp of machine learning techniques and algorithms such as regression, clustering, and decision trees. By enrolling in a DataCouncil course, you'll learn how to implement machine learning models to analyze data and make predictions, an essential qualification for landing a data science job.

4. Data Wrangling Skills

Raw data is often messy and unstructured, and a good data scientist needs to be adept at cleaning and processing data before it can be analyzed. DataCouncil's data science course in Pune includes practical training in tools like Pandas and Numpy for effective data wrangling, helping you develop a strong skill set in this critical area.

5. Statistical Knowledge

Statistical analysis forms the backbone of data science. Knowledge of probability, hypothesis testing, and statistical modeling allows data scientists to draw meaningful insights from data. A structured data science course in Pune offers the theoretical and practical aspects of statistics required to excel.

6. Communication and Data Visualization Skills

Being able to explain your findings in a clear and concise manner is crucial. Data scientists often need to communicate with non-technical stakeholders, making tools like Tableau, Power BI, and Matplotlib essential for creating insightful visualizations. DataCouncil’s data science course in Pune includes modules on data visualization, which can help you present data in a way that’s easy to understand.

7. Domain Knowledge

Apart from technical skills, understanding the industry you work in is a major asset. Whether it’s healthcare, finance, or e-commerce, knowing how data applies within your industry will set you apart from the competition. DataCouncil's data science course in Pune is designed to offer case studies from multiple industries, helping students gain domain-specific insights.

Why Choose DataCouncil for a Data Science Course in Pune?

If you're looking to build a successful career as a data scientist, enrolling in a data science course in Pune with DataCouncil can be your first step toward reaching your goals. Here’s why DataCouncil is the ideal choice:

Comprehensive Curriculum: The course covers everything from the basics of data science to advanced machine learning techniques.

Hands-On Projects: You'll work on real-world projects that mimic the challenges faced by data scientists in various industries.

Experienced Faculty: Learn from industry professionals who have years of experience in data science and analytics.

100% Placement Support: DataCouncil provides job assistance to help you land a data science job in Pune or anywhere else, making it a great investment in your future.

Flexible Learning Options: With both weekday and weekend batches, DataCouncil ensures that you can learn at your own pace without compromising your current commitments.

Conclusion

Becoming a data scientist requires a combination of technical expertise, analytical skills, and industry knowledge. By enrolling in a data science course in Pune with DataCouncil, you can gain all the qualifications you need to thrive in this exciting field. Whether you're a fresher looking to start your career or a professional wanting to upskill, this course will equip you with the knowledge, skills, and practical experience to succeed as a data scientist.

Explore DataCouncil’s offerings today and take the first step toward unlocking a rewarding career in data science! Looking for the best data science course in Pune? DataCouncil offers comprehensive data science classes in Pune, designed to equip you with the skills to excel in this booming field. Our data science course in Pune covers everything from data analysis to machine learning, with competitive data science course fees in Pune. We provide job-oriented programs, making us the best institute for data science in Pune with placement support. Explore online data science training in Pune and take your career to new heights!

[allaroundnerd]

Codetober Day #2

2. What programming languages do you know?

Depends on how we wanna define "know" but here are the ones I at least know the basics of:

-Python - my very first coding class was in this, then I proceeded to not use it again for a long time until recently when I tried my hand at writing a Discord bot for Pathfinder/DnD 3.5 -Perl - Please do not ask me to write anything in this. I did one semester and proceeded to never use it again. -MIPS - same as Perl -C++ - I'm not great with this one but my first college decided this was a great place to go once we learned Python. I hadn't used it in a long time unfortunately, till I tried to pick up Unreal Engine -Java - After leaving college #1 and a bit of community college, I first learned Java. This is what I mainly use in my 9-5 work since all of my corporate positions have required it, along with... -SQL - I picked this up in college as well and while I went a pretty long time after graduating without using it, I DO know how to do some scripting and queries. -Javascript - I didn't use this much initially until my first big boy job changed my role to that of a full-stack developer as opposed to a software developer. -C# - Aside from a single semester in college, I actually didn't use this all that much till my most recent job. They are moving away from it and towards Java, so this works out. I also used this to try to learn programming in Unity. Outside of the professional stuff... -LUA - I used to be a HUGE WoW fan and stopped playing around Legion. Before that, I aspired to write add-ons for the game and found out this was the scripting language for it. -GML - Aka "GameMaker Language," I tried my hand at this engine as well when I was bouncing around trying to see which engine was a good fit.

[aimerscomputer]

Diploma in Computer Application

A Diploma in Computer Application (DCA) is a popular short-term course that offers a solid foundation in computer fundamentals and applications. It's designed to equip individuals with the necessary skills to work in various IT-related roles.

Why Choose a DCA Course?

Quick and Efficient: DCA courses are typically shorter in duration, making it a time-effective way to acquire essential computer skills.

Practical Learning: The curriculum emphasizes hands-on training, allowing students to gain practical experience with software applications and hardware components.

Diverse Career Opportunities: A DCA certification opens doors to a wide range of job roles, including:

Data Entry Operator

Computer Operator

Web Designer

Software Tester

Technical Support Specialist

IT Assistant

Foundation for Further Studies: A DCA can serve as a stepping stone for higher education in computer science, information technology, or related fields.

Core Subjects in a DCA Course

Computer Fundamentals: Basic concepts of computers, hardware components, and software applications.

Operating Systems: Understanding and using various operating systems like Windows, Linux, and macOS.

Microsoft Office Suite: Proficiency in MS Word, Excel, PowerPoint, and Outlook.

Internet and Web Technologies: Basics of the internet, web browsing, email, and web development tools like HTML, CSS, and JavaScript.

Database Management Systems: Introduction to database concepts and SQL.

Programming Languages: Basic programming concepts in languages like C, C++, or Python.

How to Choose a Good DCA Institute

When selecting a DCA institute, consider the following factors:

Experienced Faculty: Ensure that the institute has experienced and knowledgeable faculty members.

Infrastructure: Well-equipped computer labs and other facilities are essential for practical learning.

Placement Assistance: A good institute should offer job placement assistance to help students secure employment.

Course Curriculum: The curriculum should be up-to-date and relevant to industry standards.

Fee Structure: Compare fees and financial aid options offered by different institutes.

By pursuing a Diploma in Computer Application, you can enhance your digital literacy, boost your career prospects, and stay relevant in the ever-evolving technological landscape.

[aionlinemoney]

The Role of Machine Learning Engineer: Combining Technology and Artificial Intelligence

Artificial intelligence has transformed our daily lives in a greater way than we can’t imagine over the past year, Impacting how we work, communicate, and solve problems. Today, Artificial intelligence furiously drives the world in all sectors from daily life to the healthcare industry. In this blog we will learn how machine learning engineer build systems that learn from data and get better over time, playing a huge part in the development of artificial intelligence (AI). Artificial intelligence is an important field, making it more innovative in every industry. In the blog, we will look career in Machine learning in the field of engineering.

What is Machine Learning Engineering?

Machine Learning engineer is a specialist who designs and builds AI models to make complex challenges easy. The role in this field merges data science and software engineering making both fields important in this field. The main role of a Machine learning engineer is to build and design software that can automate AI models. The demand for this field has grown in recent years. As Artificial intelligence is a driving force in our daily needs, it become important to run the AI in a clear and automated way.

A machine learning engineer creates systems that help computers to learn and make decisions, similar to human tasks like recognizing voices, identifying images, or predicting results. Not similar to regular programming, which follows strict rules, machine learning focuses on teaching computers to find patterns in data and improve their predictions over time.

Responsibility of a Machine Learning Engineer:

Collecting and Preparing Data

Machine learning needs a lot of data to work well. These engineers spend a lot of time finding and organizing data. That means looking for useful data sources and fixing any missing information. Good data preparation is essential because it sets the foundation for building successful models.

Building and Training Models

The main task of Machine learning engineer is creating models that learn from data. Using tools like TensorFlow, PyTorch, and many more, they build proper algorithms for specific tasks. Training a model is challenging and requires careful adjustments and monitoring to ensure it’s accurate and useful.

Checking Model Performance

When a model is trained, then it is important to check how well it works. Machine learning engineers use scores like accuracy to see model performance. They usually test the model with separate data to see how it performs in real-world situations and make improvements as needed.

Arranging and Maintaining the Model

After testing, ML engineers put the model into action so it can work with real-time data. They monitor the model to make sure it stays accurate over time, as data can change and affect results. Regular updates help keep the model effective.

Working with Other Teams

ML engineers often work closely with data scientists, software engineers, and experts in the field. This teamwork ensures that the machine learning solution fits the business goals and integrates smoothly with other systems.

Important skill that should have to become Machine Learning Engineer:

Programming Languages

Python and R are popular options in machine learning, also other languages like Java or C++ can also help, especially for projects needing high performance.

Data Handling and Processing

Working with large datasets is necessary in Machine Learning. ML engineers should know how to use SQL and other database tools and be skilled in preparing and cleaning data before using it in models.

Machine Learning Structure

ML engineers need to know structure like TensorFlow, Keras, PyTorch, and sci-kit-learn. Each of these tools has unique strengths for building and training models, so choosing the right one depends on the project.

Mathematics and Statistics

A strong background in math, including calculus, linear algebra, probability, and statistics, helps ML engineers understand how algorithms work and make accurate predictions.

Why to become a Machine Learning engineer?

A career as a machine learning engineer is both challenging and creative, allowing you to work with the latest technology. This field is always changing, with new tools and ideas coming up every year. If you like to enjoy solving complex problems and want to make a real impact, ML engineering offers an exciting path.

Conclusion 

Machine learning engineer plays an important role in AI and data science, turning data into useful insights and creating systems that learn on their own. This career is great for people who love technology, enjoy learning, and want to make a difference in their lives. With many opportunities and uses, Artificial intelligence is a growing field that promises exciting innovations that will shape our future. Artificial Intelligence is changing the world and we should also keep updated our knowledge in this field, Read AI related latest blogs here.