Please go through this top 10 SQL projects with Datasets that you can practice and can add in your resume
📌1. Social Media Analytics:
(https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset)
🚀2. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)
📌3. HR Analytics:
(https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-
attrition-dataset)
🚀4. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)
📌5. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)
🚀6. Inventory Management:
(https://www.kaggle.com/datasets?
search=inventory+management)
📌 7.Customer Relationship Management:
(https://www.kaggle.com/pankajjsh06/ibm-watson-
marketing-customer-value-data)
🚀8. Financial Data Analysis:
(https://www.kaggle.com/awaiskalia/banking-database)
📌9. Supply Chain Management:
(https://www.kaggle.com/shashwatwork/procurement-analytics)
🚀10. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)
Small suggestion from my side for non tech students: kindly pick those datasets which you like the subject in general, that way you will be more excited to practice it, instead of just doing it for the sake of resume, you will learn SQL more passionately, since it’s a programming language try to make it more exciting for yourself.
Join for more: https://news.1rj.ru/str/DataPortfolio
Hope this piece of information helps you
📌1. Social Media Analytics:
(https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset)
🚀2. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)
📌3. HR Analytics:
(https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-
attrition-dataset)
🚀4. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)
📌5. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)
🚀6. Inventory Management:
(https://www.kaggle.com/datasets?
search=inventory+management)
📌 7.Customer Relationship Management:
(https://www.kaggle.com/pankajjsh06/ibm-watson-
marketing-customer-value-data)
🚀8. Financial Data Analysis:
(https://www.kaggle.com/awaiskalia/banking-database)
📌9. Supply Chain Management:
(https://www.kaggle.com/shashwatwork/procurement-analytics)
🚀10. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)
Small suggestion from my side for non tech students: kindly pick those datasets which you like the subject in general, that way you will be more excited to practice it, instead of just doing it for the sake of resume, you will learn SQL more passionately, since it’s a programming language try to make it more exciting for yourself.
Join for more: https://news.1rj.ru/str/DataPortfolio
Hope this piece of information helps you
👍1
𝟯 𝗙𝗥𝗘𝗘 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟱😍
Taught by industry leaders (like Microsoft - 100% online and beginner-friendly
* Generative AI for Data Analysts
* Generative AI: Enhance Your Data Analytics Career
* Microsoft Generative AI for Data Analysis
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/3R7asWB
Enroll Now & Get Certified 🎓
Taught by industry leaders (like Microsoft - 100% online and beginner-friendly
* Generative AI for Data Analysts
* Generative AI: Enhance Your Data Analytics Career
* Microsoft Generative AI for Data Analysis
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/3R7asWB
Enroll Now & Get Certified 🎓
👍1
7 Free Kaggle Micro-Courses for Data Science Beginners with Certification
Python
https://www.kaggle.com/learn/python
Pandas
https://www.kaggle.com/learn/pandas
Data visualization
https://www.kaggle.com/learn/data-visualization
Intro to sql
https://www.kaggle.com/learn/intro-to-sql
Advanced Sql
https://www.kaggle.com/learn/advanced-sql
Intro to ML
https://www.kaggle.com/learn/intro-to-machine-learning
Advanced ML
https://www.kaggle.com/learn/intermediate-machine-learning
#datascienceprojects #kaggle
Python
https://www.kaggle.com/learn/python
Pandas
https://www.kaggle.com/learn/pandas
Data visualization
https://www.kaggle.com/learn/data-visualization
Intro to sql
https://www.kaggle.com/learn/intro-to-sql
Advanced Sql
https://www.kaggle.com/learn/advanced-sql
Intro to ML
https://www.kaggle.com/learn/intro-to-machine-learning
Advanced ML
https://www.kaggle.com/learn/intermediate-machine-learning
#datascienceprojects #kaggle
👍2
AI Myths vs. Reality
1️⃣ AI Can Think Like Humans – ❌ Myth
🤖 AI doesn’t "think" or "understand" like humans. It predicts based on patterns in data but lacks reasoning or emotions.
2️⃣ AI Will Replace All Jobs – ❌ Myth
👨💻 AI automates repetitive tasks but creates new job opportunities in AI development, ethics, and oversight.
3️⃣ AI is 100% Accurate – ❌ Myth
⚠ AI can generate incorrect or biased outputs because it learns from imperfect human data.
4️⃣ AI is the Same as AGI – ❌ Myth
🧠 Generative AI is task-specific, while AGI (which doesn’t exist yet) would have human-like intelligence.
5️⃣ AI is Only for Big Tech – ❌ Myth
💡 Startups, small businesses, and individuals use AI for marketing, automation, and content creation.
6️⃣ AI Models Don’t Need Human Supervision – ❌ Myth
🔍 AI requires human oversight to ensure ethical use and prevent misinformation.
7️⃣ AI Will Keep Getting Smarter Forever – ❌ Myth
📉 AI is limited by its training data and doesn’t improve on its own without new data and updates.
AI is powerful but not magic. Knowing its limits helps us use it wisely. 🚀
1️⃣ AI Can Think Like Humans – ❌ Myth
🤖 AI doesn’t "think" or "understand" like humans. It predicts based on patterns in data but lacks reasoning or emotions.
2️⃣ AI Will Replace All Jobs – ❌ Myth
👨💻 AI automates repetitive tasks but creates new job opportunities in AI development, ethics, and oversight.
3️⃣ AI is 100% Accurate – ❌ Myth
⚠ AI can generate incorrect or biased outputs because it learns from imperfect human data.
4️⃣ AI is the Same as AGI – ❌ Myth
🧠 Generative AI is task-specific, while AGI (which doesn’t exist yet) would have human-like intelligence.
5️⃣ AI is Only for Big Tech – ❌ Myth
💡 Startups, small businesses, and individuals use AI for marketing, automation, and content creation.
6️⃣ AI Models Don’t Need Human Supervision – ❌ Myth
🔍 AI requires human oversight to ensure ethical use and prevent misinformation.
7️⃣ AI Will Keep Getting Smarter Forever – ❌ Myth
📉 AI is limited by its training data and doesn’t improve on its own without new data and updates.
AI is powerful but not magic. Knowing its limits helps us use it wisely. 🚀
👍2
Q. Explain the data preprocessing steps in data analysis.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
👍1
Machine Learning (17.4%)
Models: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes, Neural Networks (including Deep Learning)
Techniques: Training/testing data splitting, cross-validation, feature scaling, model evaluation metrics (accuracy, precision, recall, F1-score)
Data Manipulation (13.9%)
Techniques: Data cleaning (handling missing values, outliers), data wrangling (sorting, filtering, aggregating), data transformation (scaling, normalization), merging datasets
Programming Skills (11.7%)
Languages: Python (widely used in data science for its libraries like pandas, NumPy, scikit-learn), R (another popular choice for statistical computing), SQL (for querying relational databases)
Statistics and Probability (11.7%)
Concepts: Denoscriptive statistics (mean, median, standard deviation), hypothesis testing, probability distributions (normal, binomial, Poisson), statistical inference
Big Data Technologies (9.3%)
Tools: Apache Spark, Hadoop, Kafka (for handling large and complex datasets)
Data Visualization (9.3%)
Techniques: Creating charts and graphs (scatter plots, bar charts, heatmaps), storytelling with data, choosing the right visualizations for the data
Model Deployment (9.3%)
Techniques: Cloud platforms (AWS SageMaker, Google Cloud AI Platform, Microsoft Azure Machine Learning), containerization (Docker), model monitoring
Models: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes, Neural Networks (including Deep Learning)
Techniques: Training/testing data splitting, cross-validation, feature scaling, model evaluation metrics (accuracy, precision, recall, F1-score)
Data Manipulation (13.9%)
Techniques: Data cleaning (handling missing values, outliers), data wrangling (sorting, filtering, aggregating), data transformation (scaling, normalization), merging datasets
Programming Skills (11.7%)
Languages: Python (widely used in data science for its libraries like pandas, NumPy, scikit-learn), R (another popular choice for statistical computing), SQL (for querying relational databases)
Statistics and Probability (11.7%)
Concepts: Denoscriptive statistics (mean, median, standard deviation), hypothesis testing, probability distributions (normal, binomial, Poisson), statistical inference
Big Data Technologies (9.3%)
Tools: Apache Spark, Hadoop, Kafka (for handling large and complex datasets)
Data Visualization (9.3%)
Techniques: Creating charts and graphs (scatter plots, bar charts, heatmaps), storytelling with data, choosing the right visualizations for the data
Model Deployment (9.3%)
Techniques: Cloud platforms (AWS SageMaker, Google Cloud AI Platform, Microsoft Azure Machine Learning), containerization (Docker), model monitoring
👍1
Choose the Visualization tool that fits your business needs
𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 (𝗧𝗼𝗽 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆)
✓ Row-Level Security (RLS)
✓ Column-Level Security (CLS)
✓ Plot-Level Security
✓ Dashboard-Level Security
✓ Data Masking & Anonymization
✓ Audit Logging & User Activity Tracking
𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀
✓ Global Filters
✓ Local Filters
✓ Cross-Filtering
✓ Cascading Filters – One filter should dynamically adjust available options in other filters.
✓ Consistent Coloring After Filtering – Colors inside plots should remain the same after applying filters.
𝗔𝗹𝗲𝗿𝘁𝗶𝗻𝗴 & 𝗡𝗼𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺
✓ Threshold-Based Alerts
✓ Anomaly Detection Alerts
✓ Scheduled Reports & Notifications
✓ Real-Time Alerts – Instant notifications for critical data updates.
𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀
✓ Embedding in Web Apps – Ability to integrate dashboards in external applications.
✓ APIs for Custom Queries – Fetch & manipulate visualization data programmatically.
✓ SSO & Authentication Integration – Support for OAuth, SAML, LDAP for secure embedding.
✓ SDK or iFrame Support – Ease of embedding with minimal coding.
𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀
✓ Wide Range of Chart Types
✓ Custom Chart Creation – Ability to extend with JavaScript/Python based visualizations.
✓ Interactive & Drill-Down Support – Clicking on elements should allow further exploration.
✓ Time-Series & Forecasting Support – Built-in trend analysis and forecasting models.
𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳𝗶𝗻𝗴 & 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆
✓ Cloud vs. On-Premise Support – Flexibility to deploy on different infrastructures.
✓ Multi-Tenant Support – Ability to manage multiple client environments separately.
✓ Performance on Large Datasets – Efficient handling of millions/billions of rows.
✓ AI & ML Capabilities – Support for AI-driven insights and predictive analytics.
Benefits of Metabase
Limitations
𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 (𝗧𝗼𝗽 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆)
✓ Row-Level Security (RLS)
✓ Column-Level Security (CLS)
✓ Plot-Level Security
✓ Dashboard-Level Security
✓ Data Masking & Anonymization
✓ Audit Logging & User Activity Tracking
𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀
✓ Global Filters
✓ Local Filters
✓ Cross-Filtering
✓ Cascading Filters – One filter should dynamically adjust available options in other filters.
✓ Consistent Coloring After Filtering – Colors inside plots should remain the same after applying filters.
𝗔𝗹𝗲𝗿𝘁𝗶𝗻𝗴 & 𝗡𝗼𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺
✓ Threshold-Based Alerts
✓ Anomaly Detection Alerts
✓ Scheduled Reports & Notifications
✓ Real-Time Alerts – Instant notifications for critical data updates.
𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀
✓ Embedding in Web Apps – Ability to integrate dashboards in external applications.
✓ APIs for Custom Queries – Fetch & manipulate visualization data programmatically.
✓ SSO & Authentication Integration – Support for OAuth, SAML, LDAP for secure embedding.
✓ SDK or iFrame Support – Ease of embedding with minimal coding.
𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀
✓ Wide Range of Chart Types
✓ Custom Chart Creation – Ability to extend with JavaScript/Python based visualizations.
✓ Interactive & Drill-Down Support – Clicking on elements should allow further exploration.
✓ Time-Series & Forecasting Support – Built-in trend analysis and forecasting models.
𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳𝗶𝗻𝗴 & 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆
✓ Cloud vs. On-Premise Support – Flexibility to deploy on different infrastructures.
✓ Multi-Tenant Support – Ability to manage multiple client environments separately.
✓ Performance on Large Datasets – Efficient handling of millions/billions of rows.
✓ AI & ML Capabilities – Support for AI-driven insights and predictive analytics.
Benefits of Metabase
1. Affordable Pricing
↳ On-Prem: Free | Starter: $85 | Pro: $500
2. Easy to Get Started
↳ Only SQL knowledge required
3. Built-in Alerts
↳ Supports Email and Slack notifications
4. Conditional Formatting
↳ Customize table row/cell colors based on conditions
5. Drill-Through Charts
↳ Click data points to explore deeper insights
6. User-Friendly Interface
Limitations
1. Filters Placement
↳ Only available at the top of dashboards
2. Limited Selection for Filtering
↳ Can select only a single cell; global/local filters update based on that value
👍2
𝗝𝗣 𝗠𝗼𝗿𝗴𝗮𝗻 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍
JPMorgan offers free virtual internships to help you develop industry-specific tech, finance, and research skills.
- Software Engineering Internship
- Investment Banking Program
- Quantitative Research Internship
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4gHGofl
Enroll For FREE & Get Certified 🎓
JPMorgan offers free virtual internships to help you develop industry-specific tech, finance, and research skills.
- Software Engineering Internship
- Investment Banking Program
- Quantitative Research Internship
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4gHGofl
Enroll For FREE & Get Certified 🎓
👍1
Jupyter Notebooks are essential for data analysts working with Python.
Here’s how to make the most of this great tool:
1. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲 𝘄𝗶𝘁𝗵 𝗖𝗹𝗲𝗮𝗿 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲:
Break your notebook into logical sections using markdown headers. This helps you and your colleagues navigate the notebook easily and understand the flow of analysis. You could use headings (#, ##, ###) and bullet points to create a table of contents.
2. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀:
Add markdown cells to explain your methodology, code, and guidelines for the user. This Enhances the readability and makes your notebook a great reference for future projects. You might want to include links to relevant resources and detailed docs where necessary.
3. 𝗨𝘀𝗲 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗪𝗶𝗱𝗴𝗲𝘁𝘀:
Leverage ipywidgets to create interactive elements like sliders, dropdowns, and buttons. With those, you can make your analysis more dynamic and allow users to explore different scenarios without changing the code. Create widgets for parameter tuning and real-time data visualization.
𝟰. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗖𝗹𝗲𝗮𝗻 𝗮𝗻𝗱 𝗠𝗼𝗱𝘂𝗹𝗮𝗿:
Write reusable functions and classes instead of long, monolithic code blocks. This will improve the code maintainability and efficiency of your notebook. You should store frequently used functions in separate Python noscripts and import them when needed.
5. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆:
Utilize libraries like Matplotlib, Seaborn, and Plotly for your data visualizations. These clear and insightful visuals will help you to communicate your findings. Make sure to customize your plots with labels, noscripts, and legends to make them more informative.
6. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Jupyter Notebooks are great for exploration, but they often lack systematic version control. Use tools like Git and nbdime to track changes, collaborate effectively, and ensure that your work is reproducible.
7. 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Clean and secure your notebooks by removing sensitive information before sharing. This helps to prevent the leakage of private data. You should consider using environment variables for credentials.
Keeping these techniques in mind will help to transform your Jupyter Notebooks into great tools for analysis and communication.
I have curated the best interview resources to crack Python Interviews 👇👇
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Hope you'll like it
Like this post if you need more resources like this 👍❤️
Here’s how to make the most of this great tool:
1. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲 𝘄𝗶𝘁𝗵 𝗖𝗹𝗲𝗮𝗿 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲:
Break your notebook into logical sections using markdown headers. This helps you and your colleagues navigate the notebook easily and understand the flow of analysis. You could use headings (#, ##, ###) and bullet points to create a table of contents.
2. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀:
Add markdown cells to explain your methodology, code, and guidelines for the user. This Enhances the readability and makes your notebook a great reference for future projects. You might want to include links to relevant resources and detailed docs where necessary.
3. 𝗨𝘀𝗲 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗪𝗶𝗱𝗴𝗲𝘁𝘀:
Leverage ipywidgets to create interactive elements like sliders, dropdowns, and buttons. With those, you can make your analysis more dynamic and allow users to explore different scenarios without changing the code. Create widgets for parameter tuning and real-time data visualization.
𝟰. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗖𝗹𝗲𝗮𝗻 𝗮𝗻𝗱 𝗠𝗼𝗱𝘂𝗹𝗮𝗿:
Write reusable functions and classes instead of long, monolithic code blocks. This will improve the code maintainability and efficiency of your notebook. You should store frequently used functions in separate Python noscripts and import them when needed.
5. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆:
Utilize libraries like Matplotlib, Seaborn, and Plotly for your data visualizations. These clear and insightful visuals will help you to communicate your findings. Make sure to customize your plots with labels, noscripts, and legends to make them more informative.
6. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Jupyter Notebooks are great for exploration, but they often lack systematic version control. Use tools like Git and nbdime to track changes, collaborate effectively, and ensure that your work is reproducible.
7. 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Clean and secure your notebooks by removing sensitive information before sharing. This helps to prevent the leakage of private data. You should consider using environment variables for credentials.
Keeping these techniques in mind will help to transform your Jupyter Notebooks into great tools for analysis and communication.
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🚀👉Data Analytics skills and projects to add in a resume to get shortlisted
1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.
2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.
3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.
4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.
5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.
6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.
7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.
8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.
9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.
10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.
11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.
12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.
💼Tailor your resume to the specific job denoscription, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.
2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.
3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.
4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.
5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.
6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.
7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.
8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.
9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.
10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.
11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.
12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.
💼Tailor your resume to the specific job denoscription, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
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Analytics are the analysis of raw data to draw meaningful insights from it. In other words, applying algorithms, statistical models, or even machine learning on large volumes of data will seek to discover patterns, trends, and correlations. In this way, the bottom line is to support businesses in making much more informed, data-driven decisions.
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Analytics are the analysis of raw data to draw meaningful insights from it. In other words, applying algorithms, statistical models, or even machine learning on large volumes of data will seek to discover patterns, trends, and correlations. In this way, the bottom line is to support businesses in making much more informed, data-driven decisions.
In simple words, think about running a retail store. You’ve got years of sales data, customer feedback, and inventory reports. However, do you know which are the best-sellers or where you’re losing money? By applying data analytics, you would find out some hidden opportunities, adjust your strategies, and improve your business outcome accordingly.
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