Data Analysis free courses
The Analytics Edge (Spring 2017)
by MIT
🎬 193 video lessons
⏰ 16 hours worth of material
🔗 Courses link
Statistics and data literacy for non-statisticians
Rating ⭐️: 4.7 out of 5
Students 👨🎓: 13,320
Duration ⏰: 1h 36min
Teacher: Mike X Cohen
🔗 Courses link
Data Analysis with Python courses
by freeCodeCamp
[
Data Analysis with Python
🎬 28 video lessons
Numpy
🎬 9 video lessons
Data Analysis with Python Projects
🔖 5 projects
🔗 Courses link
]
Data Analysis w/ Python 3 and Pandas
by sentdex
🎬 6 video lessons
⏰ 2-3 hours worth of material
🔗 Course link
Master Data Analysis with Python - Intro to Pandas 2022
Rating ⭐️: 4.6 out of 5
Students 👨🎓: 3,828
Duration ⏰: 1hr 49min
Teacher: Ted Petrou
🔗 Courses link
Learn to code for data analysis
by OpenLearn
⏳ 8 weeks
🔗 Course link
Lecture notes from Statistical Thinking and Data Analysis
by MIT
🔗 Notes link
Python for Data Analysis
Rating ⭐️: 4.2 out of 5
Students 👨🎓: 14,168
Duration ⏰: 1h 10min
Teacher: Bob Wakefield
🔗 Courses link
Prepare data for analysis
by Microsoft
📁2 modules
Get data in Power BI - 12 Units
Clean, transform, and load data in Power BI - 10 Units
Duration ⏰: 3 hr 26 min
🔗 Course link
NOC:Data Analysis and Decision Making - I, IIT Kanpur
NOC:Data Analysis & Decision Making - II, IIT Kanpur
NOC:Data Analysis & Decision Making - III, IIT Kanpur
👨🏫 Prof. Raghunandan Sengupta
Each of 3 parts lasts ⏳12 weeks!
#datanalysis #dataanalysis #datascience #powerbi #dataanalytics
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👉Join @bigdataspecialist for more👈
The Analytics Edge (Spring 2017)
by MIT
🎬 193 video lessons
⏰ 16 hours worth of material
🔗 Courses link
Statistics and data literacy for non-statisticians
Rating ⭐️: 4.7 out of 5
Students 👨🎓: 13,320
Duration ⏰: 1h 36min
Teacher: Mike X Cohen
🔗 Courses link
Data Analysis with Python courses
by freeCodeCamp
[
Data Analysis with Python
🎬 28 video lessons
Numpy
🎬 9 video lessons
Data Analysis with Python Projects
🔖 5 projects
🔗 Courses link
]
Data Analysis w/ Python 3 and Pandas
by sentdex
🎬 6 video lessons
⏰ 2-3 hours worth of material
🔗 Course link
Master Data Analysis with Python - Intro to Pandas 2022
Rating ⭐️: 4.6 out of 5
Students 👨🎓: 3,828
Duration ⏰: 1hr 49min
Teacher: Ted Petrou
🔗 Courses link
Learn to code for data analysis
by OpenLearn
⏳ 8 weeks
🔗 Course link
Lecture notes from Statistical Thinking and Data Analysis
by MIT
🔗 Notes link
Python for Data Analysis
Rating ⭐️: 4.2 out of 5
Students 👨🎓: 14,168
Duration ⏰: 1h 10min
Teacher: Bob Wakefield
🔗 Courses link
Prepare data for analysis
by Microsoft
📁2 modules
Get data in Power BI - 12 Units
Clean, transform, and load data in Power BI - 10 Units
Duration ⏰: 3 hr 26 min
🔗 Course link
NOC:Data Analysis and Decision Making - I, IIT Kanpur
NOC:Data Analysis & Decision Making - II, IIT Kanpur
NOC:Data Analysis & Decision Making - III, IIT Kanpur
👨🏫 Prof. Raghunandan Sengupta
Each of 3 parts lasts ⏳12 weeks!
#datanalysis #dataanalysis #datascience #powerbi #dataanalytics
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👉Join @bigdataspecialist for more👈
📊 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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👉Join @bigdataspecialist for more👈
🔍 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
➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
Learn Statistical Data Analysis with Python
Perform Statistical Data Analysis Techniques with the Python Programming Language. Practice Notebook included.
Rating ⭐️: 4.1 out 5
Students 👨🎓 : 4,234
Duration ⏰ : 1hr 2min of on-demand video
Created by 👨🏫: Valentine Mwangi
🔗 Course Link
#datascience #dataanalysis #python
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👉Join @datascience_bds for more👈
Perform Statistical Data Analysis Techniques with the Python Programming Language. Practice Notebook included.
Rating ⭐️: 4.1 out 5
Students 👨🎓 : 4,234
Duration ⏰ : 1hr 2min of on-demand video
Created by 👨🏫: Valentine Mwangi
🔗 Course Link
#datascience #dataanalysis #python
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Udemy
Free Data Science Tutorial - Learn Statistical Data Analysis with Python
Perform Statistical Data Analysis Techniques with the Python Programming Language. Practice Notebook included. - Free Course