How to enter into Data Science
πStart with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.
πChoose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.
πBuild a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.
πStart with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.
πChoose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.
πBuild a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.
π3
Machine Learning Glossary
Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html
Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
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computer graphics.pdf
80.8 MB
π» Computer Graphics Handwritten Notes π π
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Python Notes .pdf
16.6 MB
π° Complete Python Notesπ
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Python Data Stracture.pdf
4 MB
πData Structure Using Python π°
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π4
Practical Python Dat... by Ashwin Pajankar.pdf
4.8 MB
Practical Python Data Visualization
ΠΠ²ΡΠΎΡ: Ashwin Pajankar
ΠΠ²ΡΠΎΡ: Ashwin Pajankar
https_coderbooks_ruIntroduction_to_Data_Science_Data_Analysis_and.pdf
73.6 MB
Introduction to Data Science
ΠΠ²ΡΠΎΡ: Rafael A. Irizarry
ΠΠ²ΡΠΎΡ: Rafael A. Irizarry
SQL-Cheat-Sheet.pdf
142.1 KB
SQL-Cheat-Sheet.pdf
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Build Data Analyst Portfolio in 1 month
Path 1 (More focus on SQL & then on Python)
ππ
Week 1: Learn Fundamentals
Days 1-3: Start with online courses or tutorials on basic data analysis concepts.
Days 4-7: Dive into SQL basics for data retrieval and manipulation.
Free Resources: https://t.iss.one/sqlanalyst/74
Week 2: Data Analysis Projects
Days 8-14: Begin working on simple data analysis projects using SQL. Analyze the data and document your findings.
Week 3: Intermediate Skills
Days 15-21: Start learning Python for data analysis. Focus on libraries like Pandas for data manipulation.
Days 22-23: Explore more advanced SQL topics.
Week 4: Portfolio Completion
Days 24-28: Continue working on your SQL-based projects, applying what you've learned.
Day 29: Transition to Python for your personal project, applying Python's data analysis capabilities.
Day 30: Create a portfolio website showcasing your projects in SQL and Python, along with explanations and code.
Hope it helps :)
Path 1 (More focus on SQL & then on Python)
ππ
Week 1: Learn Fundamentals
Days 1-3: Start with online courses or tutorials on basic data analysis concepts.
Days 4-7: Dive into SQL basics for data retrieval and manipulation.
Free Resources: https://t.iss.one/sqlanalyst/74
Week 2: Data Analysis Projects
Days 8-14: Begin working on simple data analysis projects using SQL. Analyze the data and document your findings.
Week 3: Intermediate Skills
Days 15-21: Start learning Python for data analysis. Focus on libraries like Pandas for data manipulation.
Days 22-23: Explore more advanced SQL topics.
Week 4: Portfolio Completion
Days 24-28: Continue working on your SQL-based projects, applying what you've learned.
Day 29: Transition to Python for your personal project, applying Python's data analysis capabilities.
Day 30: Create a portfolio website showcasing your projects in SQL and Python, along with explanations and code.
Hope it helps :)
π3