Data Science vs ML vs Data Analytics vs Math

Visualization created by our team.


#datascience
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Artificial Neural Network for Regression

Rating ⭐️: 4.6 out of 5
Duration ⏰: 1hr 11min on-demand video
Students 👨‍🏫: 49,827
Created by: Hadelin de Ponteves, SuperDataScience Team, Ligency Team

🔗 Course link


#ai #ml #neural_networks #machine_learning #data_science #regression
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Data Science Pipeline

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*This channel belongs to @bigdataspecialist group

📊 Data Scientists vs Software Engineers 🖥

🔍 Ever wondered what sets apart Data Scientists from Software Engineers? Let's dive into the key differences!

📈 Data Scientists:

💡 Their role revolves around analyzing complex data to extract valuable insights.
🔍 They focus on data analysis, modeling, and visualization to uncover patterns and trends.
🧠 Skills include statistics, machine learning, and data mining.
🔧 Tools they commonly use are Python, R, SQL, and Jupyter Notebooks.
📋 Responsibilities include data cleaning, preprocessing, and transformation.
🌐 They often possess a strong domain knowledge in a specific industry or business area.
🎯 Their goal is to extract actionable insights from data to drive decision-making.
🔄 Workflow follows CRISP-DM, a standard process for data mining.
💼 Project examples include predictive modeling and recommendation systems.
🚀 Deployment involves integrating models and insights into existing systems or presenting them in reports.
🎯 Performance evaluation focuses on metrics like accuracy, precision, recall, and F1 score.
🤝 Collaboration involves working with cross-functional teams including domain experts and stakeholders.

💻 Software Engineers:

💡 Their role centers around designing, developing, and maintaining software systems.
🔍 They focus on software design, coding, and testing to create functional and reliable solutions.
🧠 Skills include programming languages, algorithms, and databases.
🔧 Tools they commonly use are Java, C++, JavaScript, IDEs, and version control systems.
📋 Responsibilities include developing scalable software applications.
🌐 They possess general knowledge of software engineering principles.
🎯 Their goal is to develop software that meets user needs and operates flawlessly.
🔄 Workflow follows agile or waterfall software development methodologies.
💼 Project examples include web or mobile app development and system integration.
🚀 Deployment involves delivering software for end-users to interact with directly.
🎯 Performance evaluation focuses on code efficiency, reliability, and scalability.
🤝 Collaboration involves working with other software engineers and project managers.

🚀 Whether extracting insights from data or building robust software systems, both Data Scientists and Software Engineers play essential roles in the digital landscape!

🔥 Let's celebrate their unique skills and contributions to the world of technology! 💪💻


#DataScience #SoftwareEngineering #TechComparison #DigitalWorld #DataAnalysis #SoftwareDevelopment
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🔥FREE COURSE ON GENERATIVE AI🔥

Interested in learning about GENERATIVE AI?🔥

Here's a free course from Google.

Link

#generative #ai #ml #ai

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Learn ETL using SSIS

Microsoft SQL Server Integration Services (SSIS) Training

Rating ⭐️: 4.6 out 5
Students 👨‍🎓 : 62,785
Duration ⏰ : 1hr 37min on-demand video
Created by 👨‍🏫: Rakesh Gopalakrishnan

🔗 Course Link


#ETL #SSIS
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According to this poll which has votes from 871 people by now, most of you are interested in data science. Since we created our @datascience_bds channel I am not posting much data science here. But it's truth that even that channel is a little bit neglected by me recently (due to many obligations I have).

To make it up for you I created this data science skills post today. I hope you like it.

I am also sending this ⏰ 6 hours long data science course by freecodecamp
https://www.youtube.com/watch?v=ua-CiDNNj30

I hope you will like it.

Sincerely
Yours @bigdataspecialist

Data science roadmap according to OnlineManipal team.