#entry level jobs for data analyst

[flexispheres]

National Plastic Action Partnership Analyst Job, Vientiane, Lao PDR

The United Nations Development Programme (UNDP) is seeking a dedicated National Plastic Action Partnership (NPAP) Analyst in Vientiane, Lao PDR. This exciting opportunity not only positions you at the heart of the fight against plastic pollution but also enables you to work closely with national authorities, international organizations, and influential private-sector partners. If you’re ready to…

[petruchio]

Caroline can I ask what kind of corporate girlboss excel job you have?

I am also in this sector, but I frequently find myself in admin roles (even when the job title doesn’t seem like an admin role?)

The directors and vps always love my high preforming ass, and I know the other admins aren’t dishing out the pivot tables and interactive reports that I am!

I love excel and I’m tired of being a glorified secretary. At this rate my only promotion left will be C-Suite Support (death sentence.)

I just want to be one of those marketing girls walking around the office with vague tasks and Excel / PowerPoint / meeting-based deliverables!

What kind of role do you have? What job titles would you recommend looking for?

yeah totally! i work as an account manager. it’s a lot of emails, client calls, and looking at data. some of the more heavy excel based stuff might be more operations teams — that is what i did at my last job and that can be fun too but also can get tedious since it’s more internally task based and less about interfacing with other people. like my last job was kind of like doing logic puzzles all day in excel and now i do more client facing work which is more like rushing the most boring sorority in the world. (by which i mean it’s mostly just about getting on calls and asking about people’s day and sounding professional so that they want to keep working with you. the rest of it is mostly answering emails and formatting reports in excel.)

anyway the jobs will tend to be called “account manager” or “customer success manager” or “client success manager”, stuff like that. account coordinator is also often the entry level title for the position. there’s different names in different industries though so there’s stuff like client partner or customer retention manager or business development rep (that is usually a bit more sales-y though, and it really depends on your personality if you’re interested in more sales-heavy roles). for more operational stuff you just kind of insert operations into any of those titles or try some analyst roles or something! so like operations manager or coordinator. really depends on your background and what you’re looking to get into but those could be a place to start.

anyway yeah! basically i *am* one of those marketing girlies with deliverables and client calls and spreadsheets who goes to conferences and happy hours and client dinners and stuff. honestly its pretty fun if you like talking to people and there’s definitely a strategic element as well outside of just making clients happy. so it’s a bit of both.

[cyberstudious]

Hey! This is very random, but I saw that you work in cyber security right now. I work in data science, but I'm really interested in cyber security and considering making a switch. I was wondering what kind of cybersecurity work you do, and what has been the most helpful for you to learn what you need for your job!

Hi! Cybersecurity is a really broad field, and you can do a lot of different things depending on what your interests are.

My work is mostly focused around automating things for security, since my background is in programming. Automation is really helpful for speeding up boring, monotonous tasks that need to get done, but don't necessarily need a human involved. A good example is automated phishing analysis, since phishing reports are a big chunk of the cases that security analysts have to deal with, and an analyst usually follows the same few steps at the beginning. Rather than someone having to manually check the reputation of the sender domain, check the reputation of any links, and all of that every single time, we can build tools to automatically scan for things like that and then present the info to the analyst. The whole idea here is to automate the boring data retrieval stuff, since computers are good at that, and give the analyst more time for decision-making and analysis, since humans are good at that.

If you're coming from data science, you might be interested in detection engineering. Cybersecurity is essentially a data problem - we have a ton of logs from a ton of different sources (internal logs, threat intelligence feeds, etc.) - how do we sort through that data to highlight things that we want to pay attention to, and how can we correlate events from different sources? If you're into software development or want to stay more on the data science side, maybe you could also look into roles for software development at companies that have SIEM (Security Information and Event Management) products - these are essentially the big log repositories that organizations rely on for correlation and alerting.

As for starting to learn security, my general go-to recommendation is to start looking through the material for the Security+ certification. For better or worse, certifications are pretty big in security, much more so than other tech fields (to my knowledge). I'm a bit more hesitant to recommend the Security+ now, since CompTIA (the company that offers it) was bought by a private equity company last year. Everyone is kind of expecting the prices to go up and the quality to go down. (The Security+ exam costs $404 USD as of writing this, and I think I took mine for like $135ish with a student discount in 2022). However, the Security+ is still the most well-known and comprehensive entry-level certification that I'm aware of. You can (and should) study for it completely for free - check out Professor Messer's training videos on YouTube. There are also plenty of books out there if that's more of your thing. I'd say to treat the Security+ as a way to get a broad overview of security and figure out what you don't know. (It's certainly not a magic ticket to a job, no matter what those expensive bootcamps will tell you.)

If you aren't familiar with networking, it's worth checking out Professor Messer's Network+ training videos as well. You don't need to know everything on there, but having an understanding of ports, protocols, and network components and design is super useful. I hear a lot that the best security folks are often the ones who come from IT or networking or similar and have a really solid understanding of the fundamentals and then get into security. Don't neglect the basics!

One thing that I'll also add, based on conversations I've had with folks in my network… getting a job in cybersecurity is harder now than it used to be, at least in the US (where I am). There are a ton of very well-qualified people who have been laid off who are now competing with people trying to get into the field in the first place, and with the wrecking ball that Elon is taking to the federal government (and by extension, government contractors) right now… it's hard. There's still a need for skilled folks in cyber, but you're going to run into a lot of those "5 years of experience required for this entry-level job" kind of job postings.

On a slightly happier note, another thing you should do if you want to get into cyber is to stay up to date with what's happening in the industry! I have a masterpost that has a section with some of my favorite news sources. The SANS Stormcast is a good place to start - it's a 5 minute podcast every weekday morning that covers most of the big things. Black Hills Infosec also does a weekly news livestream on YouTube that's similar (but longer and with more banter). Also, a lot of infosec folks hang out on Mastodon & in the wider fediverse. Let me know if you want some recs for folks to follow over there.

The nice thing about cybersecurity (and computer-related fields in general, I find) is that there are a ton of free resources out there to help you learn. Sometimes it's harder to find the higher-quality ones, but let me know if there are any topics you're interested in & I'll see what I can find. I have a few posts in my cybersecurity tag on here that might help.

Thank you for your patience, I know you sent this in over a week ago lol but life has been busy. Feel free to send any follow-up questions if you have any!

[skytechacademy]

Can you explain the differences between A+, Network+, and Security+ certifications from CompTIA? Which certification is considered more valuable and why?

Certainly! CompTIA offers several certifications that are widely recognized in the IT industry. A+, Network+, and Security+ are three of the most popular certifications, each focusing on different areas of IT. Here's a breakdown of each:

A+ Certification:

Focus: This certification is geared towards entry-level IT professionals and covers foundational skills in IT hardware, software, networking, and troubleshooting.

Topics: A+ covers areas such as PC hardware, operating systems (Windows, Linux, macOS), networking, mobile devices, security, and troubleshooting.

Job Roles: A+ certification holders often work in roles such as technical support specialists, help desk technicians, and field service technicians.

Value: A+ is valuable for individuals starting their IT careers as it provides a solid foundation of IT knowledge and skills. It's often a prerequisite for more advanced certifications.

Network+ Certification:

Focus: Network+ focuses specifically on networking concepts and skills required for IT professionals working with networks, both wired and wireless.

Topics: Network+ covers areas such as network technologies, installation and configuration, media and topologies, management, security, and troubleshooting.

Job Roles: Network+ certification holders typically work in roles such as network administrators, network technicians, and systems engineers.

Value: Network+ is valuable for individuals seeking to specialize in networking. It provides a comprehensive understanding of networking fundamentals and is recognized by employers as validation of networking knowledge and skills.

Security+ Certification:

Focus: Security+ is focused on cybersecurity concepts and skills, covering best practices in securing networks, systems, and applications.

Topics: Security+ covers areas such as network security, compliance and operational security, threats and vulnerabilities, application, data, and host security, access control, identity management, and cryptography.

Job Roles: Security+ certification holders often work in roles such as security analysts, security specialists, security administrators, and network security engineers.

Value: Security+ is highly valuable in today's cybersecurity landscape. It demonstrates proficiency in cybersecurity principles and practices and is often required or recommended for cybersecurity-related roles.

In terms of which certification is considered more valuable, it largely depends on your career goals and the specific job role you're targeting. However, comptia Security+ certification is often regarded as more valuable in terms of salary and job prospects due to the increasing demand for cybersecurity professionals and the critical importance of cybersecurity in modern IT environments. That said, all three certifications have their own merit and can be valuable depending on your career path and interests.

[optjobsinusas-blog]

For freshers and entry-level data analyst positions, employers typically sought candidates with strong analytical. And problem-solving abilities, along with relevant educational background or certifications in data-related fields.

[safdarrizvi]

Accenture hiring engineering and non-engineering graduates with 0-23 months of work experience

Use the link below and apply. Please let me know if anyone facing issue:

[aionlinemoney]

India’s Tech Sector to Create 1.2 Lakh AI Job Vacancies in Two Years

India’s technology sector is set to experience a hiring boom with job vacancies for artificial intelligence (AI) roles projected to reach 1.2 lakh over the next two years. As the demand for AI latest technology increases across industries, companies are rapidly adopting advanced tools to stay competitive. These new roles will span across tech services, Global Capability Centres (GCCs), pure-play AI and analytics firms, startups, and product companies.

Following a slowdown in tech hiring, the focus is shifting toward the development of AI. Market analysts estimate that Indian companies are moving beyond Proof of Concept (PoC) and deploying large-scale AI systems, generating high demand for roles such as AI researchers, product managers, and data application specialists. “We foresee about 120,000 to 150,000 AI-related job vacancies emerging as Indian IT services ramp up AI applications,” noted Gaurav Vasu, CEO of UnearthInsight.

India currently has 4 lakh AI professionals, but the gap between demand and supply is widening, with job requirements expected to reach 6 lakh soon. By 2026, experts predict the number of AI specialists required will hit 1 million, reflecting the deep integration of AI latest technology into industries like healthcare, e-commerce, and manufacturing.

The transition to AI-driven operations is also altering the nature of job vacancies. Unlike traditional software engineering roles, artificial intelligence positions focus on advanced algorithms, automation, and machine learning. Companies are recruiting experts in fields like deep learning, robotics, and natural language processing to meet the growing demand for innovative AI solutions. The development of AI has led to the rise of specialised roles such as Machine Learning Engineers, Data Scientists, and Prompt Engineers.

Krishna Vij, Vice President of TeamLease Digital, remarked that new AI roles are evolving across industries as AI latest technology becomes an essential tool for product development, operations, and consulting. “We expect close to 120,000 new job vacancies in AI across different sectors like finance, healthcare, and autonomous systems,” he said.

AI professionals also enjoy higher compensation compared to their traditional tech counterparts. Around 80% of AI-related job vacancies offer premium salaries, with packages 40%-80% higher due to the limited pool of trained talent. “The low availability of experienced AI professionals ensures that artificial intelligence roles will command attractive pay for the next 2-3 years,” noted Krishna Gautam, Business Head of Xpheno.

Candidates aiming for AI roles need to master key competencies. Proficiency in programming languages like Python, R, Java, or C++ is essential, along with knowledge of AI latest technology such as large language models (LLMs). Expertise in statistics, machine learning algorithms, and cloud computing platforms adds value to applicants. As companies adopt AI latest technology across domains, candidates with critical thinking and  AI adaptability will stay ahead so it is important to learn and stay updated with AI informative blogs & news.

Although companies are prioritising experienced professionals for mid-to-senior roles, entry-level job vacancies are also rising, driven by the increased use of AI in enterprises. Bootcamps, certifications, and academic programs are helping freshers gain the skills required for artificial intelligence roles. As AI development progresses, entry-level roles are expected to expand in the near future. AI is reshaping the industries providing automation & the techniques to save time , to increase work efficiency. 

India’s tech sector is entering a transformative phase, with a surge in job vacancies linked to AI latest technology adoption. The next two years will witness fierce competition for AI talent, reshaping hiring trends across industries and unlocking new growth opportunities in artificial intelligence. Both startups and established companies are racing to secure talent, fostering a dynamic landscape where artificial intelligence expertise will be help in innovation and growth. AI will help organizations and businesses to actively participate in new trends.

#aionlinemoney.com

[flexispheres]

Logistics Assistant at WFP in Port Sudan, Sudan

The World Food Programme (WFP), the world’s largest humanitarian organization and 2020 Nobel Peace Prize Laureate, is offering a fantastic opportunity for a Logistics Assistant (FTC) based in Port Sudan, Sudan. This role is not only a gateway to enhancing your career in logistics and supply chain management but also a chance to be part of a team committed to providing life-saving assistance to…

[mvishnukumar]

Is it possible to transition to a data scientist from a non-tech background at the age of 28?

Hi,

You can certainly shift to become a data scientist from a nontechnical background at 28. As a matter of fact, very many do. Most data scientists have actually shifted to this field from different academic and professional backgrounds, with some of them having changed careers even in their midlife years. 

Build a Strong Foundation:

Devour some of the core knowledge about statistics, programming, and data analysis. Online classes, bootcamps—those are good and many, many convenient resources. Give it a whirl with Coursera and Lejhro for specific courses related to data science, machine learning and programming languages like Python and R.

A data scientist needs to be proficient in at least one or two programming languages. Python is the most used language for data science, for it is simple, and it has many libraries. R is another language that might come in handy for a data scientist, mostly in cases connected with statistical analysis. The study of manipulation libraries for study data and visualization tools includes Pandas for Python and Matplotlib and Seaborn for data, respectively.

Develop Analytical Skills:

The field of data science includes much analytics and statistics. Probability, hypothesis testing, regression analysis would be essential. These skills will help you derive meaningful information out of the data and also allow you to use statistical methods for real-world problems.

Practical experience is very important in the field of data science. In order to gain experience, one might work on personal projects or contribute to open-source projects in the same field. For instance, data analysis on publicly available datasets, machine learning, and creating models to solve particular problems, all these steps help to make the field more aware of skills with one's profile.

Though formal education in data science is by no means a requirement, earning a degree or certification in the discipline you are considering gives you great credibility. Many reputed universities and institutions offer courses on data science, machine learning, and analytics.

Connect with professionals in the same field: try to be part of communities around data science and attend events as well. You would be able to find these opportunities through networking and mentoring on platforms like LinkedIn, Kaggle, and local meetups. This will keep you abreast of the latest developments in this exciting area of research and help you land job opportunities while getting support.

Look out for entry-level job opportunities or internships in the field of data science; this, in effect, would be a great way to exercise your acquired experience so far. Such positions will easily expose one to a real-world problem related to data and allow seizing the occasion to develop practical skills. These might be entry-level positions, such as data analysts or junior data scientists, to begin with.

Stay Current with Industry Trends: Data science keeps on evolving with new techniques, tools, and technologies. Keep up to date with the latest trends and developments in the industry by reading blogs and research papers online and through courses.

Conclusion: 

It is definitely possible to move into a data scientist role if one belongs to a non-tech profile and is eyeing this target at the age of 28. Proper approach in building the base of strong, relevant skills, gaining practical experience, and networking with industry professionals helps a lot in being successful in the transition. This is because data science as a field is more about skills and the ability to solve problems, which opens its doors to people from different backgrounds.

[walkawaytall]

Can you talk more about your job? What did you do? How did you end up in that career and industry?

So, I'm not going to disclose my exact industry, but it honestly doesn't really matter because I'm a data analyst, and data professionals are necessary in just about any industry you can think of. I kind of backward engineered my way into this career. Like, I got laid off two jobs in a row back when I was working as an executive assistant (EA is truthfully not a great fit for me -- I always thrived with any special project I was handed, but the day-to-day stuff is a drag. That being said, I wasn't fired; it really was two layoffs in a row. I remember thinking no one would believe me :D), and a family member worked for a company that I knew was just a really good company to work for. I applied for an entry-level tech position there and didn't get it, but one of the managers who interviewed me remembered me when she was looking for some contractors to do data clean-up work for a project. I came in as a contractor, worked my ass off to learn about the company's data and sort of became an unofficial team lead for the group of contractors I was working with. I also learned some skills like how to write SQL that came in handy when a permanent position opened up with the company, and I was able to come in as an actual employee. I've continued to learn and grow my skillset, but my job years later actually also centers on data quality -- though I do less cleaning up and more building auditing tools, finding data issues that need to be fixed, etc.

(Just a note: "data analyst" can involve a wide range of responsibilities and skills. I've done a lot of data quality work in part because I genuinely like it, but a lot of my colleagues are doing more work with statistics and analyzing trends in the work my company does, etc.)

[vivekavicky12]

Charting Your Course: A Roadmap to a Successful Data Science Career

Embarking on a career in data science is a thrilling journey marked by continuous learning, skill development, and real-world applications. Whether you're a beginner or looking to specialize, understanding the types of data science courses available is crucial. Choosing the best Data Science Institute can further accelerate your journey into this thriving industry. This comprehensive guide outlines the typical path a data scientist takes, from educational foundations to leadership roles, providing insights for aspiring professionals.

1. Educational Foundation:

A strong educational background forms the bedrock of a data scientist's career. Degrees in statistics, mathematics, or computer science, whether at the bachelor's, master's, or Ph.D. level, set the stage for a deep understanding of data principles.

2. Specialized Training:

The journey intensifies with specialized training. Online courses, bootcamps, and workshops offer hands-on experience in programming languages (Python, R), data manipulation, statistical analysis, machine learning, and data visualization.

3. Building a Skill Set:

Versatility defines a data scientist's skill set. Proficiency in programming languages, expertise in data manipulation libraries, knowledge of machine learning algorithms, and familiarity with data visualization tools contribute to a well-rounded skill set.

4. Gaining Practical Experience:

Transitioning theory into practice is paramount. Engaging in real-world projects or internships provides the practical experience necessary to apply skills in a professional setting and develop problem-solving capabilities.

5. Developing a Portfolio:

A robust portfolio becomes a calling card. Showcasing completed projects, data analyses, and machine learning models offers tangible proof of skills and accomplishments, enhancing prospects during job applications and interviews.

6. Networking and Community Engagement:

Data science thrives on community collaboration. Networking through events, conferences, and online forums fosters connections, provides learning opportunities, and opens doors to career advancement.

7. Job Entry and Specialization:

Entry-level positions like data analyst or junior data scientist serve as entry points. With experience, professionals may specialize in areas such as machine learning, data engineering, or natural language processing based on their interests.

8. Continuous Learning:

Data science is synonymous with constant evolution. A commitment to continuous learning ensures professionals stay abreast of industry trends, new technologies, and emerging methodologies, contributing to ongoing success.

9. Leadership and Advanced Roles:

Seasoned data scientists may progress into leadership roles, such as data science manager or director. Specialization in niche areas or transitions to roles like Chief Data Officer represent the pinnacle of a data scientist's career journey.

The journey of a data scientist is a dynamic expedition that demands a commitment to education, hands-on experience, and continuous growth. As technology evolves, so does the role of a data scientist. Adapting to change, staying connected with the community, and embracing lifelong learning are the keys to unlocking the vast potential within the realm of data science. Whether you're starting your journey or looking to ascend to leadership roles, the path is rich with opportunities for those willing to explore and innovate. Choosing the best Data Science courses in Chennai is a crucial step in acquiring the necessary expertise for a successful career in the evolving landscape of data science.

[itsolutionsworld]

Data Analyst Entry Level Jobs in USA

To begin your career in the right direction in an industry you must look for an entry level data analyst jobs. Data analysts work in a wide range of fields and industries. Data analysts are increasingly valuable in many other sectors as well as businesses and organisations. They take messy data and make it neat and organised. Then, they use computer tools to find interesting things in the data, like trends and patterns. They work with experienced analysts to learn and help the company make smart decisions using data. 

[optjobsinusas-blog]

Apart from being good at the technical requirements of Data Analyst jobs of data handling, data management, and data analysis. They must be good at communication, problem-solving, and critical decision-making, and they must have good financial knowledge. They process the data and visualize the data using statistical and mathematical approaches. visit at - https://www.optnation.com/blog/your-guide-to-becoming-a-data-analyst/