data-scientist-roadmap related contents

alro10 / roadmap-data-scientist

data-scientist-roadmap, The basic roadmap to become a data scientist

From user alro10

anurag421 / data-scientist-roadmap-and-important-skills.

data-scientist-roadmap, This is a simple infographics contains all the important skills required to begin with the journey of data scientist.

From user anurag421

bogordevelopers / introduction-to-data

data-scientist-roadmap, 📊 Introduction for Data Scientist skill path containing links and roadmaps

From organization bogordevelopers

bytesofintelligences / become-data-scientist-a-complete-roadmap

data-scientist-roadmap, To become a data scientist, follow these steps: 1. Learn programming (Python, R). 2. Acquire math and statistics skills. 3. Master data analysis and visualization. 4. Study machine learning and deep learning. 5. Gain domain knowledge. 6. Build a portfolio and seek job opportunities.

From user bytesofintelligences

Home Page: https://www.youtube.com/@BytesOfIntelligences

cantuvi / data-scientist-roadmap

data-scientist-roadmap, null

From user cantuvi

codebasics / roadmaps

data-scientist-roadmap, This repo is to add pages on various career paths and roadmaps such as data scientist, software engineer etc.

From organization codebasics

djdprogramming / adfa2

data-scientist-roadmap, # David's Personal Roadmap to Learning Data Science #### Based on the article [Learn Data Science for free in 2021](https://www.kdnuggets.com/2021/01/learn-data-science-free-2021.html) from KDnuggets. Some additions have been made. ###### I'm new to data science and programming. Some areas of study in this roadmap may be researched to a point of redundancy while materials for other topics could be seriously lacking. As I progress through this learning path, I'll be able to gauge which areas need more (or less) focus and will add and remove resources as needed. ## Schoolwork ##### Required readings for my Data Science classes. - [ ] [Doing Data Science: Straight Talk from the Frontline](https://www.amazon.com/Doing-Data-Science-Straight-Frontline/dp/1449358659) by Cathy O'Neil & Rachel Schutt - [ ] 1. Introduction: What is Data Science? - [ ] 2. Statistical Inference, Exploratory Data Analysis, and the Data Science Process - [ ] 3. Algorithms - [ ] 4. Spam Filters, Naive Bayes, and Wrangling - [ ] 5. Logistic Regression - [ ] 6. Time Stamps and Financial Modeling - [ ] 7. Extracting Meaning from Data - [ ] 8. Recommendation Engines: Building a User-Facing Data Product at Scale - [ ] 9. Data Visualization and Fraud Detection - [ ] 10. Social Networks and Data Journalism - [ ] 11. Causality - [ ] 12. Epidemiology - [ ] 13. Lessons Learned from Data Competitions: Data Leakage and Model Evaluation - [ ] 14. Data Engineering: MapReduce, Pregel, and Hadoop - [ ] 15. The Students Speak - [ ] 16. Next-Generation Data Scientists, Hubris, and Ethics - [ ] [Practical Statistics for Data Scientists: 50 Essential Concepts](https://www.amazon.com/Practical-Statistics-Data-Scientists-Essential/dp/149207294X/ref=sr_1_1?dchild=1&keywords=Practical+Statistics+for+Data+Scientists&qid=1609991269&s=books&sr=1-1) by Peter Bruce, Andrew Bruce & Peter Gedeck - [ ] 1. Exploratory Data Analysis - [ ] 2. Data and Sampling Distributions - [ ] 3. Statistical Experiments and Significance Testing - [ ] 4. Regression and Prediction - [ ] 5. Classification - [ ] 6. Statistical Machine Learning - [ ] 7. Unsupervised Learning ## Programming Skills ##### Learn programming basics. - [ ] [Python 3 Basics Tutorial Series](https://www.youtube.com/playlist?list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M) by sentdex - [ ] 1. Python 3 Programming Tutorial: Why Python 3? Python 2 vs Python 3 `7:36` - [ ] 2. Python 3 Programming Tutorial: Installing Python 3 - How to Install Both Python 2 and Python 3 `15:47` - [ ] 3. Python 3 Programming Tutorial: Print Function and Strings `9:31` - [ ] 4. Python 3 Programming Tutorial: Math `4:49` - [ ] 5. Python 3 Programming Tutorial: Variables `4:26` - [ ] 6. Python 3 Programming Tutorial: While Loop `5:55` - [ ] 7. Python 3 Programming Tutorial: For Loop `9:05` - [ ] 8. Python 3 Programming Tutorial: If Statement `4:54` - [ ] 9. Python 3 Programming Tutorial: If Else `3:20` - [ ] 10. Python 3 Programming Tutorial: If Elif Else `4:19` - [ ] 11. Python 3 Programming Tutorial: Functions `3:05` - [ ] 12. Python 3 Programming Tutorial: Function Parameters `4:00` - [ ] 13. Python 3 Programming Tutorial: Function Parameter Defaults `6:06` - [ ] 14. Python 3 Programming Tutorial: Global and Local Variables `6:31` - [ ] 15. Python 3 Programming Tutorial: Installing Modules `7:44` - [ ] 16. Python 3 Programming Tutorial: How to Download and Install Python Packages and Modules with Pip `8:32` - [ ] 17. Python 3 Programming Tutorial: Common Errors `4:49` - [ ] 18. Python 3 Programming Tutorial: Writing to File `3:35` - [ ] 19. Python 3 Programming Tutorial: Appending Files `2:42` - [ ] 20. Python 3 Programming Tutorial: Read from a File `1:49` - [ ] 21. Python 3 Programming Tutorial: Classes `4:56` - [ ] 22. Python 3 Programming Tutorial: Frequently Asked Questions `5:33` - [ ] 23. Python 3 Programming Tutorial: Getting User Input `1:43` - [ ] 24. Python 3 Programming Tutorial: Statistics (Mean, Standard Deviation) `2:36` - [ ] 25. Python 3 Programming Tutorial: Module Import Syntax `5:31` - [ ] 26. Python 3 Programming Tutorial: Making Modules `4:58` - [ ] 27. Python 3 Programming Tutorial: Lists and Tuples `5:51` - [ ] 28. Python 3 Programming Tutorial: List Manipulation `9:35` - [ ] 29. Python 3 Programming Tutorial: Multi-Dimensional List `5:45` - [ ] 30. Python 3 Programming Tutorial: Reading from a CSV Spreadsheet `9:24` - [ ] 31. Python 3 Programming Tutorial: Try and Except Error Handlings `7:04` - [ ] 32. Python 3 Programming Tutorial: Multi-Line Print `3:19` - [ ] 33. Python 3 Programming Tutorial: Dictionaries `7:11` - [ ] 34. Python 3 Programming Tutorial: Built-in Functions `10:58` - [ ] 35. Python 3 Programming Tutorial: OS Module `5:01` - [ ] 36. Python 3 Programming Tutorial: Sys Module `11:00` - [ ] 37. Python 3 Programming Tutorial: urllib Module `24:04` - [ ] 38. Python 3 Programming Tutorial: Regular Expressions/Regex with re `19:58` - [ ] 39. Python 3 Programming Tutorial: Parsing Websites with re and urllib `7:29` - [ ] 40. Python 3 Programming Tutorial: Tkinter Module Making Windows `8:03` - [ ] 41. Python 3 Programming Tutorial: Tkinter Adding Buttons `6:29` - [ ] 42. Python 3 Programming Tutorial: Tkinter Event Handling `5:40` - [ ] 43. Python 3 Programming Tutorial: Tkinter Menu Bar `10:25` - [ ] 44. Python 3 Programming Tutorial: Tkinter Adding Images and Text `11:59` - [ ] 45. Python 3 Programming Tutorial: Threading Module `18:43` - [ ] 46. Python 3 Programming Tutorial: cx_freeze Python to .exe `12:08` - [ ] 47. Python 3 Programming Tutorial: Subprocess Module `13:17` - [ ] 48. Python 3 Programming Tutorial: Matplotlib Graphing Intro `10:25` - [ ] 49. Python 3 Programming Tutorial: Matplotlib Labels and Titles `5:03` - [ ] 50. Python 3 Programming Tutorial: Matplotlib Styles `10:38` - [ ] 51. Python 3 Programming Tutorial: Matplotlib Legends `4:07` - [ ] 52. Python 3 Programming Tutorial: Scatter Plots and Bar Charts `6:38` - [ ] 53. Python 3 Programming Tutorial: Matplotlib Plotting from a CSV `7:21` - [ ] 54. Python 3 Programming Tutorial: ftplib FTP Transfers Python `8:47` - [ ] 55. Python 3 Programming Tutorial: Sockets Intro `10:48` - [ ] 56. Python 3 Programming Tutorial: Sockets Simple Port Scanner `5:08` - [ ] 57. Python 3 Programming Tutorial: Threaded Port Scanner `9:36` - [ ] 58. Python 3 Programming Tutorial: Sockets Binding and Listening `5:53` - [ ] 59. Python 3 Programming Tutorial: Sockets Client Server System `10:27` - [ ] [Intermediate Python Programming](https://www.youtube.com/playlist?list=PLQVvvaa0QuDfju7ADVp5W1GF9jVhjbX-_) by sentdex - [ ] 1. Intermediate Python Programming: Introduction `7:48` - [ ] 2. Intermediate Python Programming: String Concatenation and Formatting `13:40` - [ ] 3. Intermediate Python Programming: Argparse for CLI `10:49` - [ ] 4. Intermediate Python Programming: List Comprehension and Generator Expressions `6:52` - [ ] 5. Intermediate Python Programming: More on List Comp and Generators `15:28` - [ ] 6. Intermediate Python Programming: Timeit Module `11:28` - [ ] 7. Intermediate Python Programming: Enumerate `4:48` - [ ] 8. Intermediate Python Programming: Zip `7:23` - [ ] 9. Intermediate Python Programming: Writing Our Own Generator `11:08` - [ ] 10. Intermediate Python Programming: Multiprocessing `11:30` - [ ] 11. Intermediate Python Programming: Getting Returned Values from Processes `4:22` - [ ] 12. Intermediate Python Programming: Multiprocessing Spider Example `24:18` - [ ] 13. Intermediate Python Programming: Object Oriented Programming Introductions `11:35` - [ ] 14. Intermediate Python Programming: Creating an Environment for Our Project `11:49` - [ ] 15. Intermediate Python Programming: Many Blob Objects `8:30` - [ ] 16. Intermediate Python Programming: Object Modularity Thoughts `16:41` - [ ] 17. Intermediate Python Programming: OOP Inheritance `10:17` - [ ] 18. Intermediate Python Programming: Decorators `8:50` - [ ] 19. Intermediate Python Programming: Operator Overloading `10:19` - [ ] 20. Intermediate Python Programming: Detecting Collisions `15:20` - [ ] 21. Intermediate Python Programming: Special Methods, OOP, Iteration `13:30` - [ ] 22. Intermediate Python Programming: Logging `15:00` - [ ] 23. Intermediate Python Programming: Error Handling `6:11` - [ ] 24. Intermediate Python Programming: --str-- and --repr-- `11:32` - [ ] 25. Intermediate Python Programming: Args and Kwargs `11:58` - [ ] 26. Intermediate Python Programming: Asyncio - Asynchronous Programming with Coroutines `28:37` - [ ] [2021 Complete Python Bootcamp From Zero to Hero in Python](https://www.udemy.com/course/complete-python-bootcamp/) by Jose Portilla - [ ] 1. Course Overview - [ ] 2. Python Setup - [ ] 3. Python Object and Data Structure Basics - [ ] 4. Python Comparison Operators - [ ] 5. Python Statements - [ ] 6. Methods and Functions - [ ] 7. Milestone Project 1 - [ ] 8. Object Oriented Programming - [ ] 9. Modules and Packages - [ ] 10. Errors and Exceptions Handlings - [ ] 11. Milestone Project 2 - [ ] 12. Python Decorators - [ ] 13. Python Generators - [ ] 14. Advanced Python Modules - [ ] 15. Web Scraping with Python - [ ] 16. Working with Images with Python - [ ] 17. Working with PDFs and Spreadsheet CSV Files - [ ] 18. Emails with Python - [ ] 19. Final Capstone Python Project - [ ] 20. Advanced Python Objects and Data Structures - [ ] 21. Bonus Material - Introduction to GUIs - [ ] Build the 5 projects listed in the [5 Intermediate Python Projects](https://www.youtube.com/watch?v=o5sb8ehRSYA&ab_channel=TechWithTim) video by Tech With Tim - [ ] 1. Build a Website with Django/Flask - [ ] 2. Use a WebScraper - [ ] 3. Create a Game with PyGame - [ ] 4. Build a GUI with Tkinter/PyQt5 - [ ] 5. Robotics/Raspberry Pi Project ## Data Analysis and Visualization ##### Learn NumPy, Pandas and Matplotlib. - [ ] [Python NumPy Tutorial for Beginners](https://www.youtube.com/watch?v=QUT1VHiLmmI&ab_channel=freeCodeCamp.org) by freeCodeCamp.org `58:09` - [ ] Read the [Introduction to NumPy](https://jakevdp.github.io/PythonDataScienceHandbook/02.00-introduction-to-numpy.html) chapter from the Python Data Science Handbook by Jake VanderPlas - [ ] 1. Introduction to NumPy - [ ] 2. Understanding Data Types in Python - [ ] 3. The Basics of NumPy Arrays - [ ] 4. Computation on NumPy Arrays: Universal Functions - [ ] 5. Aggregations: Min, Max, and Everything in Between - [ ] 6. Computation on Arrays: Broadcasting - [ ] 7. Comparisons, Masks, and Boolean Logic - [ ] 8. Fancy Indexing - [ ] 9. Sorting Arrays - [ ] 10. Structured Data: NumPy's Structured Arrays - [ ] [Pandas Tutorials](https://www.youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS) by Corey Schafer - [ ] 1. Python Pandas Tutorial: Getting Started with Data Analysis - Installation and Loading Data `23:01` - [ ] 2. Python Pandas Tutorial: DataFrame and Series Basics - Selecting Rows and Columns `33:35` - [ ] 3. Python Pandas Tutorial: Indexes - How to Set, Reset, and Use Indexes `17:27` - [ ] 4. Python Pandas Tutorial: Filtering - Using Conditionals to Filter Rows and Columns `23:04` - [ ] 5. Python Pandas Tutorial: Updating Rows and Columns - Modifying Data within DataFrames `40:03` - [ ] 6. Python Pandas Tutorial: Add/Remove Rows and Columns from DataFrames `16:55` - [ ] 7. Python Pandas Tutorial: Sorting Data `15:40` - [ ] 8. Python Pandas Tutorial: Grouping and Aggregating - Analyzing and Exploring Your Data `49:06` - [ ] 9. Python Pandas Tutorial: Cleaning Data - Casting Data Types and Handling Missing Values `31:54` - [ ] 10. Python Pandas Tutorial: Working with Dates and Time Series Data `35:41` - [ ] 11. Python Pandas Tutorial: Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc. `32:45` - [ ] Read the [Data Manipulation with Pandas](https://jakevdp.github.io/PythonDataScienceHandbook/03.00-introduction-to-pandas.html) chapter from the Python Data Science Handbook by Jake VanderPlas - [ ] 1. Data Manipulation with Pandas - [ ] 2. Introducing Pandas Objects - [ ] 3. Data Indexing and Selection - [ ] 4. Operating on Data in Pandas - [ ] 5. Handling Missing Data - [ ] 6. Hierarchical Indexing - [ ] 7. Combining Datasets: Concat and Append - [ ] 8. Combining Datasets: Merge and Join - [ ] 9. Aggregation and Grouping - [ ] 10. Pivot Tables - [ ] 11. Vectorized String Operations - [ ] 12. Working with Time Series - [ ] 13. High-Performance Pandas: eval() and query() - [ ] [Matplotlib Tutorials](https://www.youtube.com/playlist?list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_) by Corey Schafer - [ ] 1. Matplotlib Tutorial: Creating and Customizing Our First Plots `35:01` - [ ] 2. Matplotlib Tutorial: Bar Charts and Analyzing Data from CSVs `34:26` - [ ] 3. Matplotlib Tutorial: Pie Charts `17:02` - [ ] 4. Matplotlib Tutorial: Stack Plots `14:49` - [ ] 5. Matplotlib Tutorial: Filling Area on Line Plots `15:18` - [ ] 6. Matplotlib Tutorial: Histograms `16:36` - [ ] 7. Matplotlib Tutorial: Scatter Plots `21:24` - [ ] 8. Matplotlib Tutorial: Plotting Time Series Data `17:09` - [ ] 9. Matplotlib Tutorial: Plotting Live Data in Real-Time `20:34` - [ ] 10. Matplotlib Tutorial: Subplots `21:22` - [ ] Read the [Visualization with Matplotlib](https://jakevdp.github.io/PythonDataScienceHandbook/04.00-introduction-to-matplotlib.html) chapter from the Python Data Science Handbook by Jake VanderPlas - [ ] 1. Visualization with Matplotlib - [ ] 2. Simple Line Plots - [ ] 3. Simple Scatter Plots - [ ] 4. Visualizing Errors - [ ] 5. Density and Contour Plots - [ ] 6. Histograms, Binnings, and Density - [ ] 7. Customizing Plot Legends - [ ] 8. Customizing Colorbars - [ ] 9. Multiple Subplots - [ ] 10. Text and Annotation - [ ] 11. Customizing Ticks - [ ] 12. Customizing Matplotlib: Configurations and Stylesheets - [ ] 13. Three-Dimensional Plotting in Matplotlib - [ ] 14. Geographic Data with Basemap - [ ] 15. Visualization with Seaborn - [ ] [Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib)](https://www.youtube.com/watch?v=LHBE6Q9XlzI&t=2s&ab_channel=freeCodeCamp.org) by freeCodeCamp.org `12:19:51` ## Data Preprocessing ##### Learn the basics of data preprocessing. - [ ] [Data Cleaning](https://www.kaggle.com/learn/data-cleaning) by Kaggle - [ ] 1. Handling Missing Values - [ ] 2. Scaling and Normalization - [ ] 3. Parsing Dates - [ ] 4. Character Encodings - [ ] 5. Inconsistent Data Entry - [ ] Do the [Titanic - Machine Learning from Disaster](https://www.kaggle.com/c/titanic) competition by Kaggle - [ ] Do the [Housing Prices](https://www.kaggle.com/c/home-data-for-ml-course) competition by Kaggle - [ ] [Feature Engineering](https://www.kaggle.com/learn/feature-engineering) by Kaggle - [ ] 1. Baseline Model - [ ] 2. Categorical Encodings - [ ] 3. Feature Generation - [ ] 4. Feature Selection ## Databases ##### Learn about databases. - [ ] [Intro to SQL](https://www.kaggle.com/learn/intro-to-sql) by Kaggle - [ ] 1. Getting Started with SQL and BigQuery - [ ] 2. Select, From & Where - [ ] 3. Group By, Having & Count - [ ] 4. Order By - [ ] 5. As & With - [ ] 6. Joining Data - [ ] [Advanced SQL](https://www.kaggle.com/learn/advanced-sql) by Kaggle - [ ] 1. JOINs and UNIONs - [ ] 2. Analytic Functions - [ ] 3. Nested and Repeated Data - [ ] 4. Writing Efficient Queries - [ ] [MongoDB with Python Crash Course - Tutorial for Beginners](https://www.youtube.com/watch?v=E-1xI85Zog8&ab_channel=freeCodeCamp.org) by freeCodeCamp.org `1:57:33` ## Machine Learning ##### Taking our first steps into the world of ML. - [ ] [Machine Learning](https://www.coursera.org/learn/machine-learning#syllabus) by Andrew Ng (skipping the MATLAB section) - [ ] 1. Introduction - [ ] 2. Linear Regression with One Variable - [ ] 3. Linear Algebra Review - [ ] 4. Linear Regression with Multiple Variables - [ ] 5. Logistic Regression - [ ] 6. Regularization - [ ] 7. Neural Networks: Representation - [ ] 8. Neural Networks: Learning - [ ] 9: Advice for Applying Machine Learning - [ ] 10. Machine Learning System Design - [ ] 11. Support Vector Machines - [ ] 12. Unsupervised Learning - [ ] 13. Dimensionality Reduction - [ ] 14. Anomaly Detection - [ ] 15. Recommender Systems - [ ] 16. Large Scale Machine Learning - [ ] 17. Application Example: Photo OCR - [ ] [Coursera Machine Learning MOOC by Andrew Ng Python Programming Assignments](https://github.com/dibgerge/ml-coursera-python-assignments) - [ ] Exercise 1 - [ ] Exercise 2 - [ ] Exercise 3 - [ ] Exercise 4 - [ ] Exercise 5 - [ ] Exercise 6 - [ ] Exercise 7 - [ ] Exercise 8 - [ ] Do any [Kaggle](https://www.kaggle.com/) competition - [ ] [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) by Kaggle - [ ] 1. Introduction - [ ] 2. Missing Values - [ ] 3. Categorical Variables - [ ] 4. Pipelines - [ ] 5. Cross-Validation - [ ] 6. XGBoost - [ ] 7. Data Leakage ## Linear Algebra and Statistics ##### Learn linear algebra and statistics. - [ ] [Linear Algebra](https://www.khanacademy.org/math/linear-algebra) on Khan Academy - [ ] 1. Vectors and Spaces - [ ] 2. Matrix Transformations - [ ] 3. Alternate Coordinate Systems (Bases) - [ ] [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/index.htm) on MIT OpenCourseWare - [ ] Problem Set 1 - [ ] Problem Set 2 - [ ] Problem Set 3 - [ ] Problem Set 4 - [ ] Problem Set 5 - [ ] Problem Set 6 - [ ] Problem Set 7 - [ ] Problem Set 8 - [ ] Problem Set 9 - [ ] Problem Set 10 - [ ] Exam 1 - [ ] Exam 2 - [ ] Exam 3 - [ ] Final Exam - [ ] [Statistics and Probability](https://www.khanacademy.org/math/statistics-probability) on Khan Academy - [ ] 1. Analyzing Categorical Data - [ ] 2. Displaying and Comparing Quantitative Data - [ ] 3. Summarizing Quantitative Data - [ ] 4. Modeling Data Distributions - [ ] 5. Exploring Bivariate Numerical Data - [ ] 6. Study Design - [ ] 7. Probability - [ ] 8. Counting, Permutations, and Combinations - [ ] 9. Random Variables - [ ] 10. Sampling Distributions - [ ] 11. Confidence Intervals - [ ] 12. Significance Tests (Hypothesis Testing) - [ ] 13. Two-Sample Inference for the Difference Between Groups - [ ] 14. Inference for Categorical Data (Chi-Square Tests) - [ ] 15. Advanced Regression (Inference and Transforming) - [ ] 16. Analysis of Variance (ANOVA) - [ ] [Introduction to Probability and Statistics](https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/index.htm) on MIT OpenCourseWare - [ ] Problem Set 1 - [ ] Problem Set 2 - [ ] Problem Set 3 - [ ] Problem Set 4 - [ ] Problem Set 5 - [ ] Problem Set 6 - [ ] Problem Set 7 - [ ] Problem Set 8 - [ ] Problem Set 9 - [ ] Exam 1 Practice Questions I - [ ] Exam 1 Practice Questions II - [ ] Exam 1 Practice Questions: Long List - [ ] Exam 1 - [ ] Exam 2 Practice Questions - [ ] Exam 2 - [ ] Final Exam Practice Questions - [ ] Final Exam - [ ] [Deep Learning Book](https://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio & Aaron Courville - [ ] 1. Introduction - [ ] 2. Linear Algebra - [ ] 3. Probability and Information Theory - [ ] 4. Numerical Computation - [ ] 5. Machine Learning Basics - [ ] 6. Deep Feedforward Networks - [ ] 7. Regularization for Deep Learning - [ ] 8. Optimization for Training Deep Models - [ ] 9. Convolutional Networks - [ ] 10. Sequence Modeling: Recurrent and Recursive Nets - [ ] 11. Practical Methodology - [ ] 12. Applications - [ ] 13. Linear Factor Models - [ ] 14. Autoencoders - [ ] 15. Representation Learning - [ ] 16. Structured Probabilistic Models for Deep Learning - [ ] 17. Monte Carlo Methods - [ ] 18. Confronting the Partition Function - [ ] 19. Approximate Inference - [ ] 20. Deep Generative Models ## Deep Learning ##### Learning about deep learning. - [ ] [Practical Deep Learning for Coders](https://course.fast.ai/) by fast.ai - [ ] Lesson 1 - [ ] Lesson 2 - [ ] Lesson 3 - [ ] Lesson 4 - [ ] Lesson 5 - [ ] Lesson 6 - [ ] Lesson 7 - [ ] Lesson 8 - [ ] [Part 2: Deep Learning from the Foundations](https://course19.fast.ai/part2) by fast.ai - [ ] Lesson 1 - [ ] Lesson 2 - [ ] Lesson 3 - [ ] Lesson 4 - [ ] Lesson 5 - [ ] Lesson 6 - [ ] Lesson 7 - [ ] Lesson 8 - [ ] Lesson 9 - [ ] Lesson 10 - [ ] Lesson 11 - [ ] Lesson 12 - [ ] Lesson 13 - [ ] Lesson 14 - [ ] [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) by Andrew Ng - [ ] Course 1: Neural Networks and Deep Learning - [ ] 1. Introduction to Deep Learning - [ ] 2. Neural Network Basics - [ ] 3. Shallow Neural Networks - [ ] 4. Deep Neural Networks - [ ] Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - [ ] 1. Practical Aspects of Deep Learning - [ ] 2. Optimization Algorithms - [ ] 3. Hyperparameter tuning, Batch Normalization and Programming Frameworks - [ ] Course 3: Structuring Machine Learning Projects - [ ] 1. ML Strategy (1) - [ ] 2. ML Strategy (2) - [ ] Course 4: Convolutional Neural Networks - [ ] 1. Foundations of Convolutional Neural Networks - [ ] 2. Deep Convolutional Models: Case Studies - [ ] 3. Object Detection - [ ] 4. Special applications: Face recognition & Neural style transfer - [ ] Course 5: Sequence Models - [ ] 1. Recurrent Neural Networks - [ ] 2. Natural Language Processing & Word Embeddings - [ ] 3. Sequence Models & Attention Mechanism - [ ] [DeepLearning.AI TensorFlow Developer Professional Certificate](https://www.coursera.org/professional-certificates/tensorflow-in-practice?) by Laurence Moroney - [ ] Course 1: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning - [ ] 1. A New Programming Paradigm - [ ] 2. Introduction to Computer Vision - [ ] 3. Enhancing Vision with Convolutional Neural Networks - [ ] 4. Using Real-World Images - [ ] Course 2: Convolutional Neural Networks in TensorFlow - [ ] 1. Exploring a Larger Dataset - [ ] 2. Augmentation: A Technique to Avoid Overfitting - [ ] 3. Transfer Learning - [ ] 4. Multiclass Classifications - [ ] Course 3: Natural Language Processing in TensorFlow - [ ] 1. Sentiment in Text - [ ] 2. Word Embeddings - [ ] 3. Sequence Models - [ ] 4. Sequence Models and Literature - [ ] Course 4: Sequences, Time Series and Prediction - [ ] 1. Sequences and Prediction - [ ] 2. Deep Neural Networks for Time Series - [ ] 3. Recurrent Neural Networks for Time Series - [ ] 4. Real-World Time Series Data ## Cloud for Model Deployment ##### Learn how to build, train, test, and deploy a machine learning model on AWS. - [ ] [AWS Machine Learning Specialty](https://www.youtube.com/playlist?list=PLEF5xKHm-3ZNDvdJpMCLu9xa1oDNvAmMr) by Amazon - [ ] 1. AWS Training and Certification: Machine Learning `1:31` - [ ] 2. Build, Train and Deploy Machine Learning Models on AWS with Amazon SageMaker - AWS Online `35:51` - [ ] 3. AWS re:Invent 2018: Leadership Session: Machine Learning (AIM202-L) `58:01` - [ ] 4. Machine Learning Models with TensorFlow Using Amazon SageMaker - AWS Online Tech Talks `40:16` - [ ] 5. AWS re:Invent 2018: Build & Deploy ML Models Quickly & Easily with Amazon SageMaker `57:53` - [ ] 6. AWS re:Invent 2018: CI/CD for Your Machine Learning Pipeline with Amazon SageMaker `57:13` - [ ] 7. AWS Berlin Summit 2018 - Building and Running Your First ML Application on Amazon SageMaker `52:54` - [ ] 8. Predictive Analytics with Amazon SageMaker `1:03:29` - [ ] 9. AWS re:Invent 2018: AI/ML with Data Lakes: Counterintuitive Consumer Insights in Retail `1:00:10` - [ ] 10. AWS re:Invent 2018: Industrialize Machine Learning Using CI/CD Techniques (FSV304-i) `45:34` - [ ] 11. AWS re:Invent 2018: Driving Machine Learning and Analytics Use Cases with AWS Storage (STG302) `40:16` - [ ] 12. AWS re:Invent 2018: Deep Learning Applications Using TensorFlow (AIM401-R) `1:02:29` - [ ] 13. AWS re:Invent 2017: Machine Learning State of the Union (MCL210) `1:00:55` - [ ] 14. AWS re:Invent 2017: Containerized Machine Learning on AWS (CON309) `1:03:21` - [ ] 15. AWS re:Invent 2017: Introduction to Deep Learning (MCL205) `46:17` - [ ] 16. Continuous Delivery with AWS CodePipeline and Amazon SageMaker `25:24` - [ ] 17. AWS re:Invent 2017: Best Practices for Distributed Machine Learning and Predictive A (ABD403) `1:16:16` - [ ] 18. AWS re:Invent 2017: GPS: Enhancing Customer Security Using AI/ML on AWS (GPSTEC311) `50:21` - [ ] 19. How to Wrangle Data for Machine Learning on AWS `59:24` - [ ] 20. Extract Data from Images and Videos with Amazon Rekognition (Level 300) `26:52` - [ ] 21. Exploring the Business Use Cases for Amazon Machine Learning - 2017 AWS Online Tech Talks `30:35` - [ ] 22. AWS re:Invent 2017: Orchestrating Machine Learning Training for Netflix Recommendation (MCL317) `54:21` - [ ] 23. AWS re:Invent 2017: Reinforcement Learning - The Ultimate AI (ARC320) `1:00:00` - [ ] 24. Amazon Machine Learning: Empowering Developers to Build Smart Applications `55:09` - [ ] 25. Amazon SageMaker's Built-in Algorithm Webinar Series: DeepAR Forecasting `53:41` - [ ] 26. Amazon SageMaker's Built-in Algorithm Webinar Series: Linear Learner `58:55` - [ ] 27. Amazon SageMaker's Built-in Algorithm Webinar Series: Clustering with K Means `58:52` - [ ] 28. Amazon SageMaker's Built-in Algorithm Webinar Series: Latent Dirichlet Allocation (LDA) `57:25` - [ ] 29. Amazon SageMaker's Built-in Algorithm Webinar Series: XGBoost `1:01:02` - [ ] 30. Amazon SageMaker's Built-in Algorithm Webinar Series: ResNet `55:56` - [ ] 31. Amazon SageMaker-s Built-in Algorithm Webinar Series: Blazing Text `1:14:37` - [ ] 32. AWS re:Invent 2017: NEW LAUNCH! Introducing Amazon SageMaker (MCL365) `1:02:08` - [ ] 33. Fully Managed Notebook Instances with Amazon SageMaker - a Deep Dive `16:45` - [ ] 34. Built-in Machine Learning Algorithms with Amazon SageMaker - a Deep Dive `15:38` - [ ] [Machine Learning with TensorFlow on Google Cloud Platform Specialization](https://www.coursera.org/specializations/machine-learning-tensorflow-gcp) by Google Cloud Training - [ ] Course 1: How Google does Machine Learning - [ ] 1. Introduction to Course - [ ] 2. What It Means to Be AI First - [ ] 3. How Google Does ML - [ ] 4. Inclusive ML - [ ] 5. Python Notebooks in the Cloud - [ ] 6. Summary - [ ] Course 2: Launching into Machine Learning - [ ] 1. Introduction to Course - [ ] 2. Improve Data Quality and Exploratory Data Analysis - [ ] 3. Practical ML - [ ] 4. Optimization - [ ] 5. Generalization and Sampling - [ ] 6. Summary - [ ] Course 3: Introduction to TensorFlow - [ ] 1. Introduction to Course - [ ] 2. Introduction to TensorFlow - [ ] 3. Design and Build a TensorFlow Input Data Pipeline - [ ] 4. Training Neural Networks with TensorFlow 2 and the Keras Sequential API - [ ] 5. Training Neural Networks with TensorFlow 2 and the Keras Functional API - [ ] 6. Summary - [ ] Course 4: Feature Engineering - [ ] 1. Introduction to Course - [ ] 2. Raw Data to Features - [ ] 3. Preprocessing and Feature Creation - [ ] 4. Feature Crosses - [ ] 5. TensorFlow Transform - [ ] 6. Summary - [ ] Course 5: Art and Science of Machine Learning - [ ] 1. Introduction - [ ] 2. The Art of ML - [ ] 3. Hyperparameter Tuning - [ ] 4. A Pinch of Science - [ ] 5. The Science of Neural Networks - [ ] 6. Embeddings - [ ] 7. Summary

From user djdprogramming

efekanalpsa / datascientist-roadmap

data-scientist-roadmap, DATA SCIENCE ROADMAP

From user efekanalpsa

fatihilhan42 / data_science_journey

data-scientist-roadmap, "Data Science Journey" is a repository for those interested in learning about data science and AI. It covers fundamentals and provides a roadmap for advancing knowledge and skills. It includes step-by-step studies on topics such as machine learning, NLP, deep learning, and data visualization. Suitable for beginners and experienced data scientists.

From user fatihilhan42

gauravkr0071 / data-scientist-roadmap

data-scientist-roadmap, A complete road map for entry level data scientist

From user gauravkr0071

gouthamiexcelr / internet-of-things-training-in-malaysia

data-scientist-roadmap, IoT – Internet of Things is a relatively new concept in Malaysia. As the potential of IoT is vast, the government of Malaysia decided to encourage the IoT technologies across different industries. With strong support from the government, locally based enterprises would find it easier to adopt and develop IoT technologies in their productions and daily operations. Firstly, as Malaysia is preparing to join the wave of countries currently immersed in Industry 4.0, especially in its manufacturing and heavy industry sector, the government is pushing for more budget allocation for Industry 4.0. Moreover, with the current strong support from the government, more enterprises, especially SMEs, will be encouraged to adopt IoT technologies in their production, such as remote monitoring or automation. To create a national ecosystem to enable the proliferation of use and industrialization of IoT as a new source of economic growth. IoT has the ability to transfer data over a network without requiring interaction by human-to-human or human-to-computer and interrelated mechanical and digital machines, computing devices, objects, animals or people that are provided with sole identifiers. It creates the information and knowledge to boost human intelligence, and productivity to enhance the quality of life. IoT technology in Malaysia is based on two major market trends market and technology. The effect of these trends will enhance productivity, optimize resources by implementing new technology while maintaining individual needs. The economic potential of IoT for Malaysia is expected to experience exponential growth beyond 2020. Technology opportunities can be created by IoT in Malaysia with forecast technology opportunities for application and services, and device producers. Agriculture, healthcare, manufacturing, and transportation are sectors that are identified to have the most potential for IoT deployment in Malaysia. National IoT Strategic Roadmap aimed at nurturing a national ecosystem, Malaysia has articulated plans to get in on the IoT game and make Malaysia a regional IoT development hub. The roadmap estimates that Malaysia has a strong base in giving the well-developed telecommunications infrastructure, vibrant electrical and electronics industry, policy incentives and frameworks for intellectual property and cybersecurity protection to spur IoT domestically. With knowledge of IoT becoming a necessary skill set for data scientists in Malaysia, the IoT training in Malaysia is ideal for professionals and fresher’s looking to enter the field of automated products and communication interfaces. Focusing on data capture, storage and data analytics, embedded electronics and client-server architecture, the course guides students to be adept at carving practical applications for modern day industries. In IoT training, participants will discuss the Internet of Things (IoT) framework for the digital transformation and IoT strategic leadership. With ExcelR, You will gain an intensive overview of IoT in this new evolution in the sensor, cloud, and big data. In Internet of Things training, you will be able to design independent IoT devices for sectors like retail, manufacturing and construction, understand the advantages of cloud storage, understand IoT communication protocols, comprehend IoT architecture and modern microcontrollers for data capture and signal relaying, make hardware compatible MCUs, set up a HTTP server and able to deploy python modules for basic data analytics. The Internet of Things training (IoT training) in Malaysia is suitable for the people with good exposure towards IoT technology will be the best catch for the Industry and the people who are business analysts, technical architects, software engineers, IT managers, executives, and entrepreneurs can do this IoT training.

From user gouthamiexcelr

hemansnation / full-stack-data-science-ml-2022-roadmap

data-scientist-roadmap, Roadmap for all those who want to get a kick start as Data Scientist.

From user hemansnation

ikhwankhaliddd / roadmap-data-scientist

data-scientist-roadmap, null

From user ikhwankhaliddd

jack341 / roadmap---to---data-science

data-scientist-roadmap, "Roadmap to Data Science" GitHub repository offers a detailed guide to becoming a data scientist through free online resources. Includes a step-by-step roadmap and free projects for hands-on practice. Join the community and start your journey today!

From user jack341

jerin06 / data-science-days

data-scientist-roadmap, A list of comprehensive guides, cheatsheets, roadmaps, and hands-on labs to become a data scientist.

From user jerin06

joaopaulolndev / my-data-scientist-roadmap

data-scientist-roadmap, Description about my roadmap to become Data Scientist and Engineer Machine Learning

From user joaopaulolndev

kushal334 / roadmap-data-scientist-master

data-scientist-roadmap, null

From user kushal334

machinelp / codefun

data-scientist-roadmap, DataStructure(SwordOffer、LeetCode)、Deep Learning(Tensorflow、Keras、Pytorch)、Machine Learning(sklearn、spark)、AutoML、AutoDL、ModelDeploying、SQL

From user machinelp

mikeffendii / data-scientist-roadmap

data-scientist-roadmap, Discover a highly effective roadmap that guides you to become a Data Scientist within a span of 3 months.

From user mikeffendii

mrmimic / data-scientist-roadmap

data-scientist-roadmap, Toturials coming with the "data science roadmap" picture.

From user mrmimic

Home Page: http://nirvacana.com/thoughts/becoming-a-data-scientist/

nilesh9975 / https-github.com-mrmimic-data-scientist-roadmap

data-scientist-roadmap, null

From user nilesh9975

rbhatia46 / zero-to-fullstack-data-science

data-scientist-roadmap, This repo is a roadmap for any beginner who wishes to learn Data Science, covering all the basic concepts needed, from all the programming to the math, and including relevant resources to go from zero to being a full stack Data Scientist.

From user rbhatia46

sdhigbee / becoming_a_data_scientist

data-scientist-roadmap, One roadmap for what you need to learn to become a data scientist

From user sdhigbee

shamilashamila / mrmimic-data-scientist-roadmap

data-scientist-roadmap, null

From user shamilashamila

sonukumarraj007 / data-scientist-roadmap

data-scientist-roadmap, null

From user sonukumarraj007

sreedhardata / data-science-mrmimic--data-scientist-roadmap

data-scientist-roadmap, null

From user sreedhardata

tahminashoaib / my-data-scientist-roadmap

data-scientist-roadmap, My roadmap to become a Data Scientist and an Engineer Machine Learning.

From user tahminashoaib

vida42 / my-data-scientist-roadmap

data-scientist-roadmap, 路漫漫其修远兮，吾将上下而求索。

From user vida42

xandie985 / data-scientist-roadmap2024

data-scientist-roadmap, null

From user xandie985

yashdev9274 / data-scientist-roadmap-main

data-scientist-roadmap, This repo contains all my learning related to Data Science and Data Engineering. A complete path to become Data Scientist / Data Engineer

From user yashdev9274

Home Page: https://yashdev9274.github.io/Data-Scientist-Roadmap-main/#/