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- Build with Agent Builder
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- Get a free certificate of participation
Registeration link:๐
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Join a live hands-on session by GeeksforGeeks & Salesforce for working professionals
- Build with Agent Builder
- Assign real actions
- Get a free certificate of participation
Registeration link:๐
https://gfgcdn.com/tu/V4t/
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Practice | GeeksforGeeks | A computer science portal for geeks
Platform to practice programming problems. Solve company interview questions and improve your coding intellect
Data Analysis vs Data Science
Data analysis often focuses on interpreting and summarizing existing data, requiring skills like statistical analysis, SQL, and data visualization.
On the other hand, data science involves a broader set of skills, including machine learning, predictive modeling, and advanced programming.
In essence, data analysis is a subset of data science, with data scientists often having a more extensive toolkit for handling complex and unstructured data.
Free Resources to become data analyst -> https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7113004712412524545-Uw4k
Steps to become data scientist -> https://t.iss.one/learndataanalysis/559
Data analysis often focuses on interpreting and summarizing existing data, requiring skills like statistical analysis, SQL, and data visualization.
On the other hand, data science involves a broader set of skills, including machine learning, predictive modeling, and advanced programming.
In essence, data analysis is a subset of data science, with data scientists often having a more extensive toolkit for handling complex and unstructured data.
Free Resources to become data analyst -> https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7113004712412524545-Uw4k
Steps to become data scientist -> https://t.iss.one/learndataanalysis/559
๐1
TOP CONCEPTS FOR INTERVIEW PREPARATION!!
๐TOP 10 SQL Concepts for Job Interview
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
๐TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
๐TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
Like โค๏ธ the post if it was helpful to you!!!
๐TOP 10 SQL Concepts for Job Interview
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
๐TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
๐TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
Like โค๏ธ the post if it was helpful to you!!!
โค5
Steps to become data analyst when you are fresher ๐๐
1 - First try to focus 3 mandatory skills i.e. Sql, Ms excel and python -
- For sql you can refer Ankit Bansal Or Thoufiq Mohammed (techtfq) on @sqlanalyst
- For Ms excel refer Leila Gharani or @excel_analyst
- For python refer freecodecamp from YouTube or @pythonanalyst
2 - After that try to be clear with basic idea of tableau or powerbi. (Not mandatory for every job). You can refer this channel for free resources https://t.iss.one/PowerBI_analyst
3 - Add your college project in your resume, if it's a data science related project it will help a lot. If you don't have project then you can make some dashboarding projects from YouTube in tableau/powerbi.
4 - And start applying for jobs which is having 0-1 yr experience required, you can also apply for 1 yr experience required job in analytics because sometimes they may consider fresher also. You can refer this channel @jobs_sql for job opportunities
1 - First try to focus 3 mandatory skills i.e. Sql, Ms excel and python -
- For sql you can refer Ankit Bansal Or Thoufiq Mohammed (techtfq) on @sqlanalyst
- For Ms excel refer Leila Gharani or @excel_analyst
- For python refer freecodecamp from YouTube or @pythonanalyst
2 - After that try to be clear with basic idea of tableau or powerbi. (Not mandatory for every job). You can refer this channel for free resources https://t.iss.one/PowerBI_analyst
3 - Add your college project in your resume, if it's a data science related project it will help a lot. If you don't have project then you can make some dashboarding projects from YouTube in tableau/powerbi.
4 - And start applying for jobs which is having 0-1 yr experience required, you can also apply for 1 yr experience required job in analytics because sometimes they may consider fresher also. You can refer this channel @jobs_sql for job opportunities
๐4โค1
Data types are foundational in computing, and it's essential to understand them to work effectively in any programming environment.
Let's take a dive into the top ten commonly used data types:
1. Integer (int):
- Represents whole numbers.
- Examples: -2, -1, 0, 1, 2, 3
2. Floating Point (float/double):
- Represents numbers with decimals.
- Examples: -2.5, 0.0, 3.14
3. Character (char):
- Represents single characters.
- Examples: 'A', 'b', '1', '%'
4. String:
- Represents sequences of characters, basically text.
- Examples: "Hello", "ChatGPT", "1234"
5. Boolean (bool):
- Represents true or false values.
- Examples: True, False
6. Array:
- Represents a collection of elements, often of the same type.
- Examples: [1, 2, 3], ["apple", "banana", "cherry"]
7. Object:
- Used in object-oriented programming, represents a combination of data and methods to manipulate the data.
- Examples: A Car object might have data like color and speed and methods like drive() and park().
8. Date & Time:
- Represents date and time values.
- Examples: 23-10-2023, 12:30:45
9. Byte & Binary:
- Represents raw binary data.
- Examples: 01010101 (Byte), 101000111011 (Binary)
10. Enum:
- Represents a set of named constants.
- Examples: Days of the week (Monday, Tuesday...), Colors (Red, Blue, Green)
Let's take a dive into the top ten commonly used data types:
1. Integer (int):
- Represents whole numbers.
- Examples: -2, -1, 0, 1, 2, 3
2. Floating Point (float/double):
- Represents numbers with decimals.
- Examples: -2.5, 0.0, 3.14
3. Character (char):
- Represents single characters.
- Examples: 'A', 'b', '1', '%'
4. String:
- Represents sequences of characters, basically text.
- Examples: "Hello", "ChatGPT", "1234"
5. Boolean (bool):
- Represents true or false values.
- Examples: True, False
6. Array:
- Represents a collection of elements, often of the same type.
- Examples: [1, 2, 3], ["apple", "banana", "cherry"]
7. Object:
- Used in object-oriented programming, represents a combination of data and methods to manipulate the data.
- Examples: A Car object might have data like color and speed and methods like drive() and park().
8. Date & Time:
- Represents date and time values.
- Examples: 23-10-2023, 12:30:45
9. Byte & Binary:
- Represents raw binary data.
- Examples: 01010101 (Byte), 101000111011 (Binary)
10. Enum:
- Represents a set of named constants.
- Examples: Days of the week (Monday, Tuesday...), Colors (Red, Blue, Green)
๐4
Choosing the Right Chart Type
Selecting the appropriate chart can make or break your data storytelling. Here's a quick guide to help you choose the perfect visualization:
โณ ๐๐๐ซ ๐๐ก๐๐ซ๐ญ๐ฌ: Perfect for comparing quantities across categories (Think: regional sales comparison)
โณ ๐๐ข๐ง๐ ๐๐ก๐๐ซ๐ญ๐ฌ: Ideal for showing trends and changes over time (Example: monthly website traffic)
โณ ๐๐ข๐ ๐๐ก๐๐ซ๐ญ๐ฌ: Best for showing parts of a whole as percentages (Use case: market share breakdown)
โณ ๐๐ข๐ฌ๐ญ๐จ๐ ๐ซ๐๐ฆ๐ฌ: Great for showing the distribution of continuous data (Like salary ranges across your organization)
โณ ๐๐๐๐ญ๐ญ๐๐ซ ๐๐ฅ๐จ๐ญ๐ฌ: Essential for exploring relationships between variables (Perfect for marketing spend vs. sales analysis)
โณ ๐๐๐๐ญ ๐๐๐ฉ๐ฌ: Excellent for showing data density with color variation (Think: website traffic patterns by hour/day)
โณ ๐๐จ๐ฑ ๐๐ฅ๐จ๐ญ๐ฌ: Invaluable for displaying data variability and outliers (Great for analyzing performance metrics)
โณ ๐๐ซ๐๐ ๐๐ก๐๐ซ๐ญ๐ฌ: Shows cumulative totals over time (Example: sales growth across product lines)
โณ ๐๐ฎ๐๐๐ฅ๐ ๐๐ก๐๐ซ๐ญ๐ฌ: Powerful for displaying three dimensions of data (Combines size, position, and grouping)
๐๐ซ๐จ ๐๐ข๐ฉ: Always consider your audience and the story you want to tell when choosing your visualization type.
I have curated the best interview resources to crack Power BI Interviews ๐๐
https://t.iss.one/PowerBI_analyst
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
Selecting the appropriate chart can make or break your data storytelling. Here's a quick guide to help you choose the perfect visualization:
โณ ๐๐๐ซ ๐๐ก๐๐ซ๐ญ๐ฌ: Perfect for comparing quantities across categories (Think: regional sales comparison)
โณ ๐๐ข๐ง๐ ๐๐ก๐๐ซ๐ญ๐ฌ: Ideal for showing trends and changes over time (Example: monthly website traffic)
โณ ๐๐ข๐ ๐๐ก๐๐ซ๐ญ๐ฌ: Best for showing parts of a whole as percentages (Use case: market share breakdown)
โณ ๐๐ข๐ฌ๐ญ๐จ๐ ๐ซ๐๐ฆ๐ฌ: Great for showing the distribution of continuous data (Like salary ranges across your organization)
โณ ๐๐๐๐ญ๐ญ๐๐ซ ๐๐ฅ๐จ๐ญ๐ฌ: Essential for exploring relationships between variables (Perfect for marketing spend vs. sales analysis)
โณ ๐๐๐๐ญ ๐๐๐ฉ๐ฌ: Excellent for showing data density with color variation (Think: website traffic patterns by hour/day)
โณ ๐๐จ๐ฑ ๐๐ฅ๐จ๐ญ๐ฌ: Invaluable for displaying data variability and outliers (Great for analyzing performance metrics)
โณ ๐๐ซ๐๐ ๐๐ก๐๐ซ๐ญ๐ฌ: Shows cumulative totals over time (Example: sales growth across product lines)
โณ ๐๐ฎ๐๐๐ฅ๐ ๐๐ก๐๐ซ๐ญ๐ฌ: Powerful for displaying three dimensions of data (Combines size, position, and grouping)
๐๐ซ๐จ ๐๐ข๐ฉ: Always consider your audience and the story you want to tell when choosing your visualization type.
I have curated the best interview resources to crack Power BI Interviews ๐๐
https://t.iss.one/PowerBI_analyst
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
๐4
Want to practice for your next interview?
Then use this prompt and ask Chat GPT to act as an interviewer ๐๐ (Tap to copy)
Now see how it goes. All the best for your preparation
Like this post if you need more content like this๐โค๏ธ
Then use this prompt and ask Chat GPT to act as an interviewer ๐๐ (Tap to copy)
I want you to act as an interviewer. I will be the
candidate and you will ask me the
interview questions for the position position. I
want you to only reply as the interviewer.
Do not write all the conservation at once. I
want you to only do the interview with me.
Ask me the questions and wait for my answers.
Do not write explanations. Ask me the
questions one by one like an interviewer does
and wait for my answers. My first
sentence is "Hi"Now see how it goes. All the best for your preparation
Like this post if you need more content like this๐โค๏ธ
โค5
๐ฎ Data Analyst Vs Data Engineer Vs Data Scientist ๐ฎ
Skills required to become data analyst
๐ Advanced Excel, Oracle/SQL
๐ Python/R
Skills required to become data engineer
๐ Python/ Java.
๐ SQL, NoSQL technologies like Cassandra or MongoDB
๐ Big data technologies like Hadoop, Hive/ Pig/ Spark
Skills required to become data Scientist
๐ In-depth knowledge of tools like R/ Python/ SAS.
๐ Well versed in various machine learning algorithms like scikit-learn, karas and tensorflow
๐ SQL and NoSQL
Bonus skill required: Data Visualization (PowerBI/ Tableau) & Statistics
Skills required to become data analyst
๐ Advanced Excel, Oracle/SQL
๐ Python/R
Skills required to become data engineer
๐ Python/ Java.
๐ SQL, NoSQL technologies like Cassandra or MongoDB
๐ Big data technologies like Hadoop, Hive/ Pig/ Spark
Skills required to become data Scientist
๐ In-depth knowledge of tools like R/ Python/ SAS.
๐ Well versed in various machine learning algorithms like scikit-learn, karas and tensorflow
๐ SQL and NoSQL
Bonus skill required: Data Visualization (PowerBI/ Tableau) & Statistics
โค4๐2๐1
Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
๐ฎ Data Analyst Vs Data Engineer Vs Data Scientist ๐ฎ Skills required to become data analyst ๐ Advanced Excel, Oracle/SQL ๐ Python/R Skills required to become data engineer ๐ Python/ Java. ๐ SQL, NoSQL technologies like Cassandra or MongoDB ๐ Big data technologiesโฆ
Free Resources ๐
Data Science: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Data Analyst: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Data Engineers: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
Data Science: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Data Analyst: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Data Engineers: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
โค2๐1
Here are 5 key Python libraries/ concepts that are particularly important for data analysts:
1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.
3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.
4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.
5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.
By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.
Credits: https://t.iss.one/free4unow_backup
ENJOY LEARNING ๐๐
1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.
3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.
4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.
5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.
By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.
Credits: https://t.iss.one/free4unow_backup
ENJOY LEARNING ๐๐
๐3๐1
30 Days Python Roadmap for Data Analysts ๐
โค8
๐ Project Ideas for a data analyst
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
๐๐
https://t.iss.one/sqlspecialist/379
ENJOY LEARNING ๐๐
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
๐๐
https://t.iss.one/sqlspecialist/379
ENJOY LEARNING ๐๐
๐4โค2
MUST ADD these 5 POWER Bl projects to your resume to get hired
Here are 5 mini projects that not only help you to gain experience but also it will help you to build your resume stronger
๐Customer Churn Analysis
๐ https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input
๐Credit Card Fraud
๐ https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
๐Movie Sales Analysis
๐https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data
๐Airline Sector
๐https://www.kaggle.com/datasets/yuanyuwendymu/airline-
๐Financial Data Analysis
๐https://www.kaggle.com/datasets/qks1%7Cver/financial-data-
Simple guide
1. Data Utilization:
- Initiate the process by using the provided datasets for a comprehensive analysis.
2. Domain Research:
- Conduct thorough research within the domain to identify crucial metrics and KPIs for analysis.
3. Dashboard Blueprint:
- Outline the structure and aesthetics of your dashboard, drawing inspiration from existing online dashboards for enhanced design and functionality.
4. Data Handling:
- Import data meticulously, ensuring accuracy. Proceed with cleaning, modeling, and the creation of essential measures and calculations.
5. Question Formulation:
- Brainstorm a list of insightful questions your dashboard aims to answer, covering trends, comparisons, aggregations, and correlations within the data.
6. Platform Integration:
- Utilize Novypro.com as the hosting platform for your dashboard, ensuring seamless integration and accessibility.
7. LinkedIn Visibility:
- Share your dashboard on LinkedIn with a concise post providing context. Include a link to your Novypro-hosted dashboard to foster engagement and professional connections.
Join for more: https://t.iss.one/DataPortfolio
Hope this helps you :)
Here are 5 mini projects that not only help you to gain experience but also it will help you to build your resume stronger
๐Customer Churn Analysis
๐ https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input
๐Credit Card Fraud
๐ https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
๐Movie Sales Analysis
๐https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data
๐Airline Sector
๐https://www.kaggle.com/datasets/yuanyuwendymu/airline-
๐Financial Data Analysis
๐https://www.kaggle.com/datasets/qks1%7Cver/financial-data-
Simple guide
1. Data Utilization:
- Initiate the process by using the provided datasets for a comprehensive analysis.
2. Domain Research:
- Conduct thorough research within the domain to identify crucial metrics and KPIs for analysis.
3. Dashboard Blueprint:
- Outline the structure and aesthetics of your dashboard, drawing inspiration from existing online dashboards for enhanced design and functionality.
4. Data Handling:
- Import data meticulously, ensuring accuracy. Proceed with cleaning, modeling, and the creation of essential measures and calculations.
5. Question Formulation:
- Brainstorm a list of insightful questions your dashboard aims to answer, covering trends, comparisons, aggregations, and correlations within the data.
6. Platform Integration:
- Utilize Novypro.com as the hosting platform for your dashboard, ensuring seamless integration and accessibility.
7. LinkedIn Visibility:
- Share your dashboard on LinkedIn with a concise post providing context. Include a link to your Novypro-hosted dashboard to foster engagement and professional connections.
Join for more: https://t.iss.one/DataPortfolio
Hope this helps you :)
๐3