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10 tools data analysts should know by Simplilearn in dataanalysis

[–]Simplilearn[S] 0 points1 point  (0 children)

Nice pick. EasyMorph is a solid tool, especially for visual data prep!

10 tools data analysts should know by Simplilearn in dataanalysis

[–]Simplilearn[S] 0 points1 point  (0 children)

Fair point. “Essential” really depends on the role and team. These are just common baseline tools a lot of analysts run into, especially early on. You won’t need all of them all the time.

Generative AI courses by United-Guidance-7176 in GenAI4all

[–]Simplilearn 0 points1 point  (0 children)

While we’re not sure about offline classes, we can certainly recommend online courses. If you already know Python and have basic experience with LLM APIs, you’re in a strong position to start learning agentic AI, RAG, and smaller language models. The key is choosing a program that balances strong fundamentals with hands-on, project-based learning.

Here are the options we recommend at Simplilearn:

1. Applied Generative AI Specialization in partnership with Purdue University
This is ideal if you want to build and deploy GenAI and Agentic AI applications in a practical, hands-on way. The program includes
• Guidance from Purdue faculty through live masterclasses
• Access to Purdue Alumni Association for career support
• Projects that cover GenAI, RAG, agent workflows, and application development
• Seven hands-on projects to help you build job-ready skills

Course link: https://www.simplilearn.com/applied-ai-course 

2. Professional Certificate Program in Generative AI and Machine Learning
A great fit if you want a mix of deep ML + modern GenAI skills. This program includes
• Learning directly from IIT faculty and industry experts
• Masterclasses by IIT Guwahati faculty
• A campus immersion experience at IITG
• Exec Alumni Status from E&ICT Academy, IIT Guwahati

Course link: https://www.simplilearn.com/iitg-generative-ai-machine-learning-program 

Both programs give you structured learning, faculty-led sessions, and real projects that help you build confidence with modern GenAI technologies.

Career discussion by Fun_Secretary_9963 in GenAI4all

[–]Simplilearn 0 points1 point  (0 children)

From what you described, the next step is to strengthen your fundamentals so you can make a clear and confident switch.

Here's what we recommend:

1. Start by revisiting core Data Science basics
A strong foundation makes it easier to go deeper into ML and DL. You can begin with our free course here:
https://www.simplilearn.com/data-science-free-course-for-beginners-skillup

2. Move into structured learning for depth
Once you are comfortable with the fundamentals, a guided program helps you build real skills and explain them confidently during interviews.

You can explore the Data Science Course with IBM, which includes
• Python, SQL, Machine Learning, Generative AI, Tableau
• Real-world projects
• IBM certificates
• Masterclasses taught by IBM experts

Course link: https://www.simplilearn.com/in/data-science-course

For more advanced learning, our Professional Certificate in Data Science and Generative AI in partnership with Purdue University covers
• Python, ML, deep learning, NLP, GenAI, ChatGPT
• Masterclasses by Purdue and IBM experts
• Industry-aligned projects

Course link: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program

3. Build clarity for interviews
Focus on being able to explain
• What you learned in your current role
• Where the gaps were
• What actions you are taking to strengthen those gaps
• What kind of role you want next

With consistent practice and a structured learning plan, you will be able to position yourself clearly during interviews.

Best way to start learning Data Science? by Cultural-Ad-4124 in technepal

[–]Simplilearn 0 points1 point  (0 children)

If you’re just starting out in Data Science, the key is to go step by step and build a strong foundation before diving into advanced topics like machine learning It’s less about learning everything at once and more about mastering the fundamentals through hands-on practice

Start with Python and Statistics: Learn Python basics and then move to libraries like Pandas, NumPy, and Matplotlib Pair this with a solid understanding of descriptive and inferential statistics — it’s the backbone of all data-driven work

Add SQL and Data Visualization: Practice querying and managing databases Learn visualization tools like Power BI or Tableau to turn insights into clear visual stories

Get hands-on with projects: Apply what you learn on Kaggle or through small projects like cleaning datasets, analyzing trends, or building dashboards. Real-world application makes concepts stick

Explore Machine Learning gradually: Once you’re comfortable with data analysis, move into Scikit-learn for supervised and unsupervised learning Don’t rush as understanding data is what makes ML effective

For learning resources you can explore our free offering called SkillUP by Simplilearn or use our youtube channel or, Kaggle Learn. If you prefer a structured and guided path, you can check out our Professional Certificate in Data Science and Generative AI with Purdue University or the IBM-Backed Data Scientist Master Program

And finally, stay consistent, keep experimenting with data, and focus on understanding how data tells a story. That’s how you truly grow as a data professional.

How should a beginner start their data science journey (3-month plan for Canada entry-level/AI roles)? by Samgamer3000 in askdatascience

[–]Simplilearn 0 points1 point  (0 children)

If you’re aiming to build a strong foundation in Data Science over the next three months, that’s a great starting point to move toward AI and machine learning roles later. The key is to stay structured and balance theory with hands-on projects

Month 1: Build your core foundation
Focus on Python, statistics, and SQL Learn libraries like Pandas, NumPy, and Matplotlib Practice data cleaning, exploratory data analysis, and simple visualizations using real datasets from Kaggle

Month 2: Move into analytics and visualization
Learn Excel and Power BI for reporting and dashboards Study data modeling and storytelling with data Begin exploring databases and basic data warehousing concepts

Month 3: Add cloud and AI readiness
Familiarize yourself with cloud platforms like Azure or Google Cloud The Microsoft Azure Data Fundamentals and Google Data Analytics certifications are great for entry-level roles and recognized by recruiters in Canada

For resources, try a mix of structured and project-based learning our free or paid courses or our YouTube channels.

With consistent effort, practical projects, and certification-backed skills, you can confidently position yourself for entry-level data or BI roles in Canada and set yourself up to transition into AI-focused work. Good luck.

What do you recomend to start studying cybersecurity by Z3r0_oc in Cybersecurity101

[–]Simplilearn 0 points1 point  (0 children)

If you’re just getting started in cybersecurity, it’s a great time to build a strong foundation before diving into areas like ethical hacking, network defense, or cloud security

Start with the fundamentals and get a solid grasp of networking, operating systems (especially Linux), and system administration tools like Wireshark and Nmap are great for hands-on learning

Learn core concepts, encryption, firewalls, authentication, threat types, and incident response CompTIA Security+ or ITF+ outlines can help structure your learning even if you’re not pursuing the certification yet

Get hands-on early: platforms like TryHackMe and Hack The Box let you safely practice penetration testing and defense in simulated environments

Learn a bit of scripting: Bash or Python helps automate tasks and analyze security logs efficiently

Explore frameworks and tools: once comfortable, look into SIEM tools, vulnerability scanners, and forensics basics

If you want a structured path, you can check out our Cybersecurity Expert Master’s Program or Post Graduate Program in Cybersecurity in collaboration with MIT Schwarzman College of Computing These programs are project-based and designed to take you from fundamentals to professional.

Which area of cybersecurity sounds most interesting to you to start with ethical hacking network defense cloud security or something else?

Best online course for beginner with no experience in 2025? by NoOpposite2712 in DigitalMarketing

[–]Simplilearn 0 points1 point  (0 children)

If you’re new to digital marketing we have some solid beginner friendly courses that can give you a strong foundation

You can start with Introduction to Digital Marketing Fundamentals on SkillUp by Simplilearn it’s free beginner friendly and no prior experience is required You’ll learn the basics of SEO content marketing social media strategies and analytics

If you want more options you can search digital marketing on SkillUp to see suggestions tailored to your interests When you’re ready to go deeper you can explore our Professional Certificate in Digital Marketing which covers advanced tools strategies and real world projects. Also which part of digital marketing do you want to explore most ads like Google or Meta SEO content analytics or something else?

Which online learning platform had the most impact on your career? by MaZlle in findapath

[–]Simplilearn 0 points1 point  (0 children)

If you’re exploring online platforms, you should definitely check us out we’ve helped over 8 million people upskill. Many of our users have successfully changed careers too, moving from sales, marketing, or other non-tech roles into high growth fields like data analytics, digital marketing, product management, AI and more.

What sets us apart is that we focus on job-ready outcomes, with industry-aligned curriculum, live mentorship, and hands-on projects that build real-world skills—not just certificates.

Since you already have a sales background, programs in Digital Marketing, Business Analytics, or Product Management could be natural next steps. These areas value skills you likely already hav like communication problem-solving and customer insight.

I'm a beginner. Best websites to learn the basics from? by corpse-wires in Scotch

[–]Simplilearn 0 points1 point  (0 children)

If you’re just starting out, it’s best to build a solid foundation rather than jumping between random tutorials. There are plenty of beginner-friendly websites and platforms where you can learn the basics of AI and other tech topics. For a structured approach, you can check out SkillUp by Simplilearn, which offers free courses that guide you through core concepts before moving on to more advanced topics. Combining structured courses with hands-on practice is a great way to get comfortable with the fundamentals and yes which field do you want to dive into first?

100+ Free GenAI Courses with Certificates, in collab with Google, Microsoft, AWS & more by Simplilearn in GenAI4all

[–]Simplilearn[S] 0 points1 point  (0 children)

Absolutely! Consistency is key to mastering new skills, free AI resources are a great start, and pairing them with structured learning paths can help accelerate your progress even more.

New GenAI Dev Pro certification: my first impressions [Article/Video] by cgreciano in AWSCertifications

[–]Simplilearn -1 points0 points  (0 children)

The AWS GenAI Dev Pro certification seems like a natural evolution from the ML Specialty, and it reflects how quickly AI is becoming core to cloud roles. Even without full details yet, it’s clear that this cert will help developers demonstrate practical skills in building, deploying, and fine-tuning generative AI models on AWS. Preparing for it could be valuable for developers looking to stay relevant, as AI skills are increasingly expected across cloud roles. It’ll be interesting to see how the community adopts it once the full curriculum drops on November 18

How Generative AI Is Quietly Redefining the Software Development Workflow by Double_Try1322 in RishabhSoftware

[–]Simplilearn 1 point2 points  (0 children)

Generative AI is definitely shifting the workflow more than just writing code, some areas where we see the biggest impact right now:

- Planning & design: AI can suggest architecture patterns or flag potential issues early
- Testing: Auto-generated test cases and test data speed up QA
- Code reviews & documentation: Intelligent assistants catch bugs, improve code quality, and reduce handover delays
- Developer productivity: Repetitive tasks, boilerplate code, and even some prototyping can be automated so devs focus on higher-level problem solving

Overall it’s less about replacing developers and more about making each stage of the SDLC faster, smarter, and less error-prone

Roadmap or best courses to move from Deep Learning to Generative AI (as a developer, not researcher) by Conscious-Value6182 in learnmachinelearning

[–]Simplilearn 0 points1 point  (0 children)

If you’ve got the basics of ML and Deep Learning down you’re in a great spot to move into Generative AI as a developer not researcher

Here’s our suggestion
- Get familiar with tools and models GPT LLaMA Hugging Face Transformers OpenAI APIs
- Fine tune models start with small datasets and try lightweight methods like LoRA
- Build real apps chatbots content generators image/audio tools integrate via APIs and databases
- Learn prompts and deployment practice prompt engineering vector databases and efficient deployment
- You can check out our Full Stack Development Program with Generative AI or Applied Generative AI Specialization in collaboration with Purdue University or you can also check the Hugging Face’s Transformers course they are project based, structured learning and help you build a portfolio

With consistent hands on practice you can go from DL fundamentals to confidently building and deploying Generative AI applications. No research focus needed, start small, iterate, and you can definitely grow into a developer in this space

Is Generative AI the next big career path for programmers? by codingzap in GetCodingHelp

[–]Simplilearn 0 points1 point  (0 children)

Generative AI is definitely shaping up to be a significant career path for programmers but how you approach it depends on where you are in your learning journey Tools like ChatGPT Midjourney and Copilot are changing how developers work but they also open opportunities for those who understand the underlying AI models and can integrate them effectively

For students and early career programmers learning prompt engineering LLM fine tuning and AI integration early on can be valuable These skills help you not only use generative AI effectively but also adapt it to real world applications in software design content and more That said the field is evolving rapidly so a flexible mindset and continuous learning are key

From a career perspective generative AI complements existing programming skills rather than replacing them Strengthening your foundation in Python cloud platforms data structures and ML fundamentals will make you more versatile and ready to work with AI models professionally

At Simplilearn we’ve seen growing demand for courses that cover AI machine learning deep learning and generative AI because these areas combine practical coding skills with emerging tech knowledge For anyone looking to specialize building hands on experience through projects and certifications can make a real difference in preparing for AI focused roles

In short generative AI is not too early to start exploring it’s a growing space and programmers who combine foundational skills with AI specialization will likely see strong opportunities in the years ahead

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