NOC:Python for Data Science, IIT Madras
π Free Online Course
π» 40 Lecture Videos
β° 5 Module
πββοΈ Self paced
Teacher π¨βπ« : Prof. Ragunathan Rengasamy
π https://nptel.ac.in/courses/106106212
#Data_Science #IIT
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π Free Online Course
π» 40 Lecture Videos
β° 5 Module
πββοΈ Self paced
Teacher π¨βπ« : Prof. Ragunathan Rengasamy
π https://nptel.ac.in/courses/106106212
#Data_Science #IIT
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Applied Data Science
by Daniel Krasner
π 141 pages
π Book link
#BigData #DataScience #MachineLearning #Statistics
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by Daniel Krasner
π 141 pages
π Book link
#BigData #DataScience #MachineLearning #Statistics
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Visualisation: visual representations of data and information
Modern society is often referred to as 'the information society' - but how can we make sense of all the information we are bombarded with? In this free course, Visualisation: visual representations of data and information, you will learn how to interpret, and in some cases create, visual representations of data and information that help us to see things in a different way.
β° Free Online Course
β° 9 Module
β° Duration : 8 hours
πββοΈ Self paced
Offered by: openlearn
π Course link
#Data #Visualization #data_science
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Modern society is often referred to as 'the information society' - but how can we make sense of all the information we are bombarded with? In this free course, Visualisation: visual representations of data and information, you will learn how to interpret, and in some cases create, visual representations of data and information that help us to see things in a different way.
β° Free Online Course
β° 9 Module
β° Duration : 8 hours
πββοΈ Self paced
Offered by: openlearn
π Course link
#Data #Visualization #data_science
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data-science-ipython-notebooks
Creator: Donne Martin
Stars βοΈ: 22.6k
Forked By: 7k
GithubRepo: https://github.com/donnemartin/data-science-ipython-notebooks
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Creator: Donne Martin
Stars βοΈ: 22.6k
Forked By: 7k
GithubRepo: https://github.com/donnemartin/data-science-ipython-notebooks
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GitHub
GitHub - donnemartin/data-science-ipython-notebooks: Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras)β¦
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials,...
Data Science vs ML vs Data Analytics vs Math
Visualization created by our team.
#datascience
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Visualization created by our team.
#datascience
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Artificial Neural Network for Regression
Rating βοΈ: 4.6 out of 5
Duration β°: 1hr 11min on-demand video
Students π¨βπ«: 49,827
Created by: Hadelin de Ponteves, SuperDataScience Team, Ligency Team
π Course link
#ai #ml #neural_networks #machine_learning #data_science #regression
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Rating βοΈ: 4.6 out of 5
Duration β°: 1hr 11min on-demand video
Students π¨βπ«: 49,827
Created by: Hadelin de Ponteves, SuperDataScience Team, Ligency Team
π Course link
#ai #ml #neural_networks #machine_learning #data_science #regression
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Udemy
Data Manipulation in Python: Master Python, Numpy & Pandas
Learn Python, NumPy & Pandas for Data Science: Master essential data manipulation for data science in python
Data Science Pipeline
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*This channel belongs to @bigdataspecialist group
π Data Scientists vs Software Engineers π₯
π Ever wondered what sets apart Data Scientists from Software Engineers? Let's dive into the key differences!
π Data Scientists:
π‘ Their role revolves around analyzing complex data to extract valuable insights.
π They focus on data analysis, modeling, and visualization to uncover patterns and trends.
π§ Skills include statistics, machine learning, and data mining.
π§ Tools they commonly use are Python, R, SQL, and Jupyter Notebooks.
π Responsibilities include data cleaning, preprocessing, and transformation.
π They often possess a strong domain knowledge in a specific industry or business area.
π― Their goal is to extract actionable insights from data to drive decision-making.
π Workflow follows CRISP-DM, a standard process for data mining.
πΌ Project examples include predictive modeling and recommendation systems.
π Deployment involves integrating models and insights into existing systems or presenting them in reports.
π― Performance evaluation focuses on metrics like accuracy, precision, recall, and F1 score.
π€ Collaboration involves working with cross-functional teams including domain experts and stakeholders.
π» Software Engineers:
π‘ Their role centers around designing, developing, and maintaining software systems.
π They focus on software design, coding, and testing to create functional and reliable solutions.
π§ Skills include programming languages, algorithms, and databases.
π§ Tools they commonly use are Java, C++, JavaScript, IDEs, and version control systems.
π Responsibilities include developing scalable software applications.
π They possess general knowledge of software engineering principles.
π― Their goal is to develop software that meets user needs and operates flawlessly.
π Workflow follows agile or waterfall software development methodologies.
πΌ Project examples include web or mobile app development and system integration.
π Deployment involves delivering software for end-users to interact with directly.
π― Performance evaluation focuses on code efficiency, reliability, and scalability.
π€ Collaboration involves working with other software engineers and project managers.
π Whether extracting insights from data or building robust software systems, both Data Scientists and Software Engineers play essential roles in the digital landscape!
π₯ Let's celebrate their unique skills and contributions to the world of technology! πͺπ»
#DataScience #SoftwareEngineering #TechComparison #DigitalWorld #DataAnalysis #SoftwareDevelopment
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π Ever wondered what sets apart Data Scientists from Software Engineers? Let's dive into the key differences!
π Data Scientists:
π‘ Their role revolves around analyzing complex data to extract valuable insights.
π They focus on data analysis, modeling, and visualization to uncover patterns and trends.
π§ Skills include statistics, machine learning, and data mining.
π§ Tools they commonly use are Python, R, SQL, and Jupyter Notebooks.
π Responsibilities include data cleaning, preprocessing, and transformation.
π They often possess a strong domain knowledge in a specific industry or business area.
π― Their goal is to extract actionable insights from data to drive decision-making.
π Workflow follows CRISP-DM, a standard process for data mining.
πΌ Project examples include predictive modeling and recommendation systems.
π Deployment involves integrating models and insights into existing systems or presenting them in reports.
π― Performance evaluation focuses on metrics like accuracy, precision, recall, and F1 score.
π€ Collaboration involves working with cross-functional teams including domain experts and stakeholders.
π» Software Engineers:
π‘ Their role centers around designing, developing, and maintaining software systems.
π They focus on software design, coding, and testing to create functional and reliable solutions.
π§ Skills include programming languages, algorithms, and databases.
π§ Tools they commonly use are Java, C++, JavaScript, IDEs, and version control systems.
π Responsibilities include developing scalable software applications.
π They possess general knowledge of software engineering principles.
π― Their goal is to develop software that meets user needs and operates flawlessly.
π Workflow follows agile or waterfall software development methodologies.
πΌ Project examples include web or mobile app development and system integration.
π Deployment involves delivering software for end-users to interact with directly.
π― Performance evaluation focuses on code efficiency, reliability, and scalability.
π€ Collaboration involves working with other software engineers and project managers.
π Whether extracting insights from data or building robust software systems, both Data Scientists and Software Engineers play essential roles in the digital landscape!
π₯ Let's celebrate their unique skills and contributions to the world of technology! πͺπ»
#DataScience #SoftwareEngineering #TechComparison #DigitalWorld #DataAnalysis #SoftwareDevelopment
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π₯FREE COURSE ON GENERATIVE AIπ₯
Interested in learning about GENERATIVE AI?π₯
Here's a free course from Google.
Link
#generative #ai #ml #ai
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Interested in learning about GENERATIVE AI?π₯
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 βοΈ: 4.6 out 5
Students π¨βπ : 62,785
Duration β° : 1hr 37min on-demand video
Created by π¨βπ«: Rakesh Gopalakrishnan
π Course Link
#ETL #SSIS
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Microsoft SQL Server Integration Services (SSIS) Training
Rating βοΈ: 4.6 out 5
Students π¨βπ : 62,785
Duration β° : 1hr 37min on-demand video
Created by π¨βπ«: Rakesh Gopalakrishnan
π Course Link
#ETL #SSIS
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Udemy
Free ETL Tutorial - Learn ETL using SSIS
Microsoft SQL Server Integration Services (SSIS) Training - Free Course
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 β° 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
To make it up for you I created this data science skills post today. I hope you like it.
I am also sending this β° 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