Creative ways to craft your data analytics portfolio
Free Data sets for Data Analytics Projects: https://t.iss.one/DataPortfolio
1. Storytelling with Data Projects: Craft narratives around real-world scenarios, demonstrating your ability to extract insights from data. Use visuals, such as charts and graphs, to make your analysis more engaging.
2. Interactive Dashboards: Build interactive dashboards using tools like Tableau or Power BI. Showcase your skills in creating user-friendly interfaces that allow for dynamic exploration of data.
3. Predictive Modeling Showcase: Develop projects that involve predictive modeling, such as machine learning algorithms. Highlight your ability to make data-driven predictions and explain the implications of your findings.
4. Data Visualization Blog: Start a blog to share your insights and showcase your projects. Explain your analysis process, display visualizations, and discuss the impact of your findings. This demonstrates your ability to communicate complex ideas.
5. Open Source Contributions: Contribute to data-related open-source projects on platforms like GitHub. This not only adds to your portfolio but also demonstrates collaboration skills and engagement with the broader data science community.
6. Kaggle Competitions: Participate in Kaggle competitions and document your approach and results. Employ a variety of algorithms and techniques to solve different types of problems, showcasing your versatility.
7. Industry-specific Analyses: Tailor projects to specific industries of interest. For example, analyze trends in healthcare, finance, or marketing. This demonstrates your understanding of domain-specific challenges and your ability to provide actionable insights.
8. Portfolio Website: Create a professional portfolio website to showcase your projects. Include project descriptions, methodologies, visualizations, and the impact of your analyses. Make it easy for potential employers to navigate and understand your work.
9. Skill Diversification: Showcase a range of skills by incorporating data cleaning, feature engineering, and other pre-processing steps into your projects. Highlighting a holistic approach to data analysis enhances your portfolio.
10. Continuous Learning Projects: Demonstrate your commitment to ongoing learning by including projects that showcase new tools, techniques, or methodologies you've recently acquired. This shows adaptability and a proactive attitude toward staying current in the field.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Free Data sets for Data Analytics Projects: https://t.iss.one/DataPortfolio
1. Storytelling with Data Projects: Craft narratives around real-world scenarios, demonstrating your ability to extract insights from data. Use visuals, such as charts and graphs, to make your analysis more engaging.
2. Interactive Dashboards: Build interactive dashboards using tools like Tableau or Power BI. Showcase your skills in creating user-friendly interfaces that allow for dynamic exploration of data.
3. Predictive Modeling Showcase: Develop projects that involve predictive modeling, such as machine learning algorithms. Highlight your ability to make data-driven predictions and explain the implications of your findings.
4. Data Visualization Blog: Start a blog to share your insights and showcase your projects. Explain your analysis process, display visualizations, and discuss the impact of your findings. This demonstrates your ability to communicate complex ideas.
5. Open Source Contributions: Contribute to data-related open-source projects on platforms like GitHub. This not only adds to your portfolio but also demonstrates collaboration skills and engagement with the broader data science community.
6. Kaggle Competitions: Participate in Kaggle competitions and document your approach and results. Employ a variety of algorithms and techniques to solve different types of problems, showcasing your versatility.
7. Industry-specific Analyses: Tailor projects to specific industries of interest. For example, analyze trends in healthcare, finance, or marketing. This demonstrates your understanding of domain-specific challenges and your ability to provide actionable insights.
8. Portfolio Website: Create a professional portfolio website to showcase your projects. Include project descriptions, methodologies, visualizations, and the impact of your analyses. Make it easy for potential employers to navigate and understand your work.
9. Skill Diversification: Showcase a range of skills by incorporating data cleaning, feature engineering, and other pre-processing steps into your projects. Highlighting a holistic approach to data analysis enhances your portfolio.
10. Continuous Learning Projects: Demonstrate your commitment to ongoing learning by including projects that showcase new tools, techniques, or methodologies you've recently acquired. This shows adaptability and a proactive attitude toward staying current in the field.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
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Complete SQL road map
👇👇
1.Intro to SQL
• Definition
• Purpose
• Relational DBs
• DBMS
2.Basic SQL Syntax
• SELECT
• FROM
• WHERE
• ORDER BY
• GROUP BY
3. Data Types
• Integer
• Floating-Point
• Character
• Date
• VARCHAR
• TEXT
• BLOB
• BOOLEAN
4.Sub languages
• DML
• DDL
• DQL
• DCL
• TCL
5. Data Manipulation
• INSERT
• UPDATE
• DELETE
6. Data Definition
• CREATE
• ALTER
• DROP
• Indexes
7.Query Filtering and Sorting
• WHERE
• AND
• OR Conditions
• Ascending
• Descending
8. Data Aggregation
• SUM
• AVG
• COUNT
• MIN
• MAX
9.Joins and Relationships
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• Self-Joins
• Cross Joins
• FULL OUTER JOIN
10.Subqueries
• Subqueries used in
• Filtering data
• Aggregating data
• Joining tables
• Correlated Subqueries
11.Views
• Creating
• Modifying
• Dropping Views
12.Transactions
• ACID Properties
• COMMIT
• ROLLBACK
• SAVEPOINT
• ROLLBACK TO SAVEPOINT
13.Stored Procedures
• CREATE PROCEDURE
• ALTER PROCEDURE
• DROP PROCEDURE
• EXECUTE PROCEDURE
• User-Defined Functions (UDFs)
14.Triggers
• Trigger Events
• Trigger Execution and Syntax
15. Security and Permissions
• CREATE USER
• GRANT
• REVOKE
• ALTER USER
• DROP USER
16.Optimizations
• Indexing Strategies
• Query Optimization
17.Normalization
• 1NF(Normal Form)
• 2NF
• 3NF
• BCNF
18.Backup and Recovery
• Database Backups
• Point-in-Time Recovery
19.NoSQL Databases
• MongoDB
• Cassandra etc...
• Key differences
20. Data Integrity
• Primary Key
• Foreign Key
21.Advanced SQL Queries
• Window Functions
• Common Table Expressions (CTEs)
22.Full-Text Search
• Full-Text Indexes
• Search Optimization
23. Data Import and Export
• Importing Data
• Exporting Data (CSV, JSON)
• Using SQL Dump Files
24.Database Design
• Entity-Relationship Diagrams
• Normalization Techniques
25.Advanced Indexing
• Composite Indexes
• Covering Indexes
26.Database Transactions
• Savepoints
• Nested Transactions
• Two-Phase Commit Protocol
27.Performance Tuning
• Query Profiling and Analysis
• Query Cache Optimization
------------------ END -------------------
Some good resources to learn SQL
1.Tutorial & Courses
• Learn SQL: https://bit.ly/3FxxKPz
• Udacity: imp.i115008.net/AoAg7K
2. YouTube Channel's
• FreeCodeCamp:rb.gy/pprz73
• Programming with Mosh: rb.gy/g62hpe
3. Books
• SQL in a Nutshell: https://t.iss.one/DataAnalystInterview/158
4. SQL Interview Questions
https://t.iss.one/sqlanalyst/72?single
Join @free4unow_backup for more free resourses
ENJOY LEARNING 👍👍
👇👇
1.Intro to SQL
• Definition
• Purpose
• Relational DBs
• DBMS
2.Basic SQL Syntax
• SELECT
• FROM
• WHERE
• ORDER BY
• GROUP BY
3. Data Types
• Integer
• Floating-Point
• Character
• Date
• VARCHAR
• TEXT
• BLOB
• BOOLEAN
4.Sub languages
• DML
• DDL
• DQL
• DCL
• TCL
5. Data Manipulation
• INSERT
• UPDATE
• DELETE
6. Data Definition
• CREATE
• ALTER
• DROP
• Indexes
7.Query Filtering and Sorting
• WHERE
• AND
• OR Conditions
• Ascending
• Descending
8. Data Aggregation
• SUM
• AVG
• COUNT
• MIN
• MAX
9.Joins and Relationships
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• Self-Joins
• Cross Joins
• FULL OUTER JOIN
10.Subqueries
• Subqueries used in
• Filtering data
• Aggregating data
• Joining tables
• Correlated Subqueries
11.Views
• Creating
• Modifying
• Dropping Views
12.Transactions
• ACID Properties
• COMMIT
• ROLLBACK
• SAVEPOINT
• ROLLBACK TO SAVEPOINT
13.Stored Procedures
• CREATE PROCEDURE
• ALTER PROCEDURE
• DROP PROCEDURE
• EXECUTE PROCEDURE
• User-Defined Functions (UDFs)
14.Triggers
• Trigger Events
• Trigger Execution and Syntax
15. Security and Permissions
• CREATE USER
• GRANT
• REVOKE
• ALTER USER
• DROP USER
16.Optimizations
• Indexing Strategies
• Query Optimization
17.Normalization
• 1NF(Normal Form)
• 2NF
• 3NF
• BCNF
18.Backup and Recovery
• Database Backups
• Point-in-Time Recovery
19.NoSQL Databases
• MongoDB
• Cassandra etc...
• Key differences
20. Data Integrity
• Primary Key
• Foreign Key
21.Advanced SQL Queries
• Window Functions
• Common Table Expressions (CTEs)
22.Full-Text Search
• Full-Text Indexes
• Search Optimization
23. Data Import and Export
• Importing Data
• Exporting Data (CSV, JSON)
• Using SQL Dump Files
24.Database Design
• Entity-Relationship Diagrams
• Normalization Techniques
25.Advanced Indexing
• Composite Indexes
• Covering Indexes
26.Database Transactions
• Savepoints
• Nested Transactions
• Two-Phase Commit Protocol
27.Performance Tuning
• Query Profiling and Analysis
• Query Cache Optimization
------------------ END -------------------
Some good resources to learn SQL
1.Tutorial & Courses
• Learn SQL: https://bit.ly/3FxxKPz
• Udacity: imp.i115008.net/AoAg7K
2. YouTube Channel's
• FreeCodeCamp:rb.gy/pprz73
• Programming with Mosh: rb.gy/g62hpe
3. Books
• SQL in a Nutshell: https://t.iss.one/DataAnalystInterview/158
4. SQL Interview Questions
https://t.iss.one/sqlanalyst/72?single
Join @free4unow_backup for more free resourses
ENJOY LEARNING 👍👍
❤4
Machine Learning Algorithms every data scientist should know:
📌 Supervised Learning:
🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression
🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)
📌 Unsupervised Learning:
🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN
🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)
📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)
📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking
Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
📌 Supervised Learning:
🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression
🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)
📌 Unsupervised Learning:
🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN
🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)
📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)
📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking
Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
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