What topics would you like me to publish about during this period🤔?
Final Results
29%
programming languages🎓🪄
36%
Explanations about the computer💻
0%
Network⛓🔗
21%
Linux🐧
14%
DSA(Data Structures and Algorithms)📊
IT Engineer pinned «What topics would you like me to publish about during this period🤔?»
another roadmap here🔽🖤
Data Scientist Roadmap
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|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
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|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
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| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
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| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
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|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
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| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
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|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
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|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
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|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
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|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement
Data Scientist Roadmap
|
|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
| |
-- iv. Statistics
| |
| |-- b. Programming
| | |-- i. Python
| | | |-- 1. Syntax and Basic Concepts
| | | |-- 2. Data Structures
| | | |-- 3. Control Structures
| | | |-- 4. Functions
| | |
-- 5. Object-Oriented Programming| | |
| |
-- ii. R (optional, based on preference)
| |
| |-- c. Data Manipulation
| | |-- i. Numpy (Python)
| | |-- ii. Pandas (Python)
| |
-- iii. Dplyr (R)| |
|
-- d. Data Visualization
| |-- i. Matplotlib (Python)
| |-- ii. Seaborn (Python)
|
-- iii. ggplot2 (R)|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
|
-- e. Data Scaling and Normalization
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|-- 3. Machine Learning
| |-- a. Supervised Learning
| | |-- i. Regression
| | | |-- 1. Linear Regression
| | |
-- 2. Polynomial Regression| | |
| |
-- ii. Classification
| | |-- 1. Logistic Regression
| | |-- 2. k-Nearest Neighbors
| | |-- 3. Support Vector Machines
| | |-- 4. Decision Trees
| |
-- 5. Random Forest| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | |
-- 3. Hierarchical Clustering
| | |
| |
-- ii. Dimensionality Reduction| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| |
-- 3. Linear Discriminant Analysis (LDA)
| |
| |-- c. Reinforcement Learning
| |-- d. Model Evaluation and Validation
| | |-- i. Cross-validation
| | |-- ii. Hyperparameter Tuning
| |
-- iii. Model Selection| |
|
-- e. ML Libraries and Frameworks
| |-- i. Scikit-learn (Python)
| |-- ii. TensorFlow (Python)
| |-- iii. Keras (Python)
|
-- iv. PyTorch (Python)|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| |
-- ii. Multi-Layer Perceptron
| |
| |-- b. Convolutional Neural Networks (CNNs)
| | |-- i. Image Classification
| | |-- ii. Object Detection
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-- iii. Image Segmentation| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| |
-- iii. Sentiment Analysis
| |
| |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
| | |-- i. Time Series Forecasting
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-- ii. Language Modeling| |
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-- e. Generative Adversarial Networks (GANs)
| |-- i. Image Synthesis
| |-- ii. Style Transfer
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-- iii. Data Augmentation|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| |
-- ii. MapReduce
| |
| |-- b. Spark
| | |-- i. RDDs
| | |-- ii. DataFrames
| |
-- iii. MLlib| |
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-- c. NoSQL Databases
| |-- i. MongoDB
| |-- ii. Cassandra
| |-- iii. HBase
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-- iv. Couchbase|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| |
-- iv. Shiny (R)
| |
| |-- b. Storytelling with Data
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-- c. Effective Communication|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
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-- e. Teamwork
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-- 8. Staying Updated and Continuous Learning|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement
إبتداءًا من الشهر القادم راح ينحظر اي حساب GitHub مو مفعل التحقق بخطوتين ( 2FA ) , لذلك اي شخص ساحب على حسابه وعنده مشاريع مهمة وحاب يحافظ عليها ينطلق فوراً يضيف تحقق بخطوتين عن طريق اي تطبيق مصادقة زي Google Authenticator على الاندرويد والآيفون، وأهم شي يحفظ اكواد الاسترداد بعد مايضيف التحقق بخطوتين ، زي الي بالصورة
هنا شرح الاضافة مفصل
https://docs.github.com/en/authentication/securing-your-account-with-two-factor-authentication-2fa/configuring-two-factor-authentication
وهنا الخبر ، علمًا انه جاني اشعار ببريدي الالكتروني يجبرني على اضافة التحقق بخطوتين لحسابي
https://docs.github.com/en/authentication/securing-your-account-with-two-factor-authentication-2fa/about-mandatory-two-factor-authentication
#منقول #github
هنا شرح الاضافة مفصل
https://docs.github.com/en/authentication/securing-your-account-with-two-factor-authentication-2fa/configuring-two-factor-authentication
وهنا الخبر ، علمًا انه جاني اشعار ببريدي الالكتروني يجبرني على اضافة التحقق بخطوتين لحسابي
https://docs.github.com/en/authentication/securing-your-account-with-two-factor-authentication-2fa/about-mandatory-two-factor-authentication
#منقول #github