Essential Python topics for data analysts ๐๐
Python Topics:
Python Resources - https://t.me/PythonResourcesTP
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Share with credits: https://t.me/PythonResourcesTP
Hope it helps :)
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Python Topics:
Python Resources - https://t.me/PythonResourcesTP
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Share with credits: https://t.me/PythonResourcesTP
Hope it helps :)
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐3
Learn Data Science in 2025
๐ญ. ๐๐ฝ๐ฝ๐น๐ ๐ฃ๐ฎ๐ฟ๐ฒ๐๐ผ'๐ ๐๐ฎ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐๐ ๐๐ป๐ผ๐๐ด๐ต ๐
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
๐ฎ. ๐๐ถ๐ป๐ฑ ๐ฎ ๐ ๐ฒ๐ป๐๐ผ๐ฟ โก๏ธ
Thereโs a Japanese proverb that says โBetter than a thousand days of diligent study is one day with a great teacher.โ This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you donโt often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
๐ฏ. ๐๐ฒ๐น๐ถ๐ฏ๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ โ๏ธ
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join https://t.me/DataScienceResourcesTP for more
ENJOY LEARNING ๐๐
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐ญ. ๐๐ฝ๐ฝ๐น๐ ๐ฃ๐ฎ๐ฟ๐ฒ๐๐ผ'๐ ๐๐ฎ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐๐ ๐๐ป๐ผ๐๐ด๐ต ๐
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
๐ฎ. ๐๐ถ๐ป๐ฑ ๐ฎ ๐ ๐ฒ๐ป๐๐ผ๐ฟ โก๏ธ
Thereโs a Japanese proverb that says โBetter than a thousand days of diligent study is one day with a great teacher.โ This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you donโt often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
๐ฏ. ๐๐ฒ๐น๐ถ๐ฏ๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ โ๏ธ
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join https://t.me/DataScienceResourcesTP for more
ENJOY LEARNING ๐๐
WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
๐2
5 Game-Changing Habits to Master Your Data Science Journey
Read Here: https://dev.to/justdetermined/5-game-changing-habits-to-master-your-data-science-journey-2nd4
Read Here: https://dev.to/justdetermined/5-game-changing-habits-to-master-your-data-science-journey-2nd4
DEV Community
5 Game-Changing Habits to Master Your Data Science Journey
The journey to becoming a data scientist isnโt for the faint of heart. Itโs a demanding but rewarding...
Forwarded from Artificial Intelligence Resources TP . AI Tools . AI Updates
ChatGPT cheat sheet for Data Science.pdf
29 MB
Title: ChatGPT Cheat Sheet for Data Science (2025)
Source: DataCamp
Description:
This comprehensive cheat sheet serves as an essential guide for leveraging ChatGPT in data science workflows. Designed for both beginners and seasoned practitioners, it provides actionable prompts, code examples, and best practices to streamline tasks such as data generation, analysis, modeling, and automation. Key features include:
- Code Generation: Scripts for creating sample datasets in Python using Pandas and NumPy
- Data Analysis: Techniques for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, including visualization recommendations.
- Machine Learning: Guidance on algorithm selection, hyperparameter tuning, and model interpretation.
- NLP Applications: Tools for text classification, sentiment analysis, and named entity recognition, leveraging ChatGPTโs natural language processing capabilities .
Tg: https://t.me/AIResourcesTP
Source: DataCamp
Description:
This comprehensive cheat sheet serves as an essential guide for leveraging ChatGPT in data science workflows. Designed for both beginners and seasoned practitioners, it provides actionable prompts, code examples, and best practices to streamline tasks such as data generation, analysis, modeling, and automation. Key features include:
- Code Generation: Scripts for creating sample datasets in Python using Pandas and NumPy
- Data Analysis: Techniques for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, including visualization recommendations.
- Machine Learning: Guidance on algorithm selection, hyperparameter tuning, and model interpretation.
- NLP Applications: Tools for text classification, sentiment analysis, and named entity recognition, leveraging ChatGPTโs natural language processing capabilities .
Tg: https://t.me/AIResourcesTP
๐2โค1
There has never been a better time to become a data analyst.
Tackle the tools:
- Excel
- SQL
- PowerBI/Tableau
- Python/R
Sharpen these soft skills:
- Communication
- Storytelling
- Critical thinking
- Business acumen
And let your journey begin.
Tackle the tools:
- Excel
- SQL
- PowerBI/Tableau
- Python/R
Sharpen these soft skills:
- Communication
- Storytelling
- Critical thinking
- Business acumen
And let your journey begin.
Essential tools and skills required to become a data analyst ๐๐
### Data Analysis and Visualization:
1. Microsoft Excel: Essential for data manipulation, analysis, and basic modeling.
2. SQL (Structured Query Language): Crucial for querying databases and extracting data for analysis.
3. Tableau or Power BI: Powerful tools for creating interactive dashboards and visualizing data.
### Programming and Data Manipulation:(Optional)
4. Python: Used for data manipulation, scripting, and automation.
5. R: Useful for statistical computing, data visualization, and basic analytics.
### Statistical Analysis:
6. Statistical Software (SPSS, SAS): Tools for advanced statistical analysis and modeling.(Optional)
7. Advanced Excel Functions: Proficiency in pivot tables, VLOOKUP, statistical functions, and data cleaning techniques.
### Project Management and Collaboration:(Optional)
8. Jira or Trello: Tools for project management, task tracking, and collaboration.
9. Confluence or SharePoint: Platforms for documentation, collaboration, and knowledge sharing.
### Business Process Management:(Optional)
10. Business Process Modeling Tools (Visio, Lucidchart): Used for modeling, analyzing, and optimizing business processes.
### Additional Skills:
11. Google Analytics: Important for understanding website traffic and user behavior. (Optional)
12. CRM Systems (Salesforce, HubSpot): Knowledge of these systems aids in analyzing sales data and customer interactions.(Optional)
13. Version Control (Git): Helps manage changes in analytical projects and ensures versioning control. (Optional)
### Data Warehousing and Database Management:
14. Data Warehousing (Amazon Redshift, Google BigQuery): Knowledge of these platforms for handling large-scale datasets and optimizing queries. (Optional)
### Soft Skills:
15. Communication: Clear and concise communication of findings and recommendations.
16. Problem-Solving & Critical Thinking: Ability to analyze complex problems and derive actionable insights.
I know this list might seem extensive, so it's best to begin with mastering Excel, Power BI, and SQL. As you progress, you can gradually add other tools from the list based on specific project needs and requirements.
Here are some essential telegram channels with important resources:
โฏ SQL โ t.me/sqlresourcestp
โฏ Data Analysis โ t.me/dataanalysisresourcestp
Also, try building projects & data portfolio while learning these skills. Creating data analytics projects will help you in showcasing the skills while giving job interviews.
Join t.me/techpsyche for more resources
ENJOY LEARNING๐๐
### Data Analysis and Visualization:
1. Microsoft Excel: Essential for data manipulation, analysis, and basic modeling.
2. SQL (Structured Query Language): Crucial for querying databases and extracting data for analysis.
3. Tableau or Power BI: Powerful tools for creating interactive dashboards and visualizing data.
### Programming and Data Manipulation:(Optional)
4. Python: Used for data manipulation, scripting, and automation.
5. R: Useful for statistical computing, data visualization, and basic analytics.
### Statistical Analysis:
6. Statistical Software (SPSS, SAS): Tools for advanced statistical analysis and modeling.(Optional)
7. Advanced Excel Functions: Proficiency in pivot tables, VLOOKUP, statistical functions, and data cleaning techniques.
### Project Management and Collaboration:(Optional)
8. Jira or Trello: Tools for project management, task tracking, and collaboration.
9. Confluence or SharePoint: Platforms for documentation, collaboration, and knowledge sharing.
### Business Process Management:(Optional)
10. Business Process Modeling Tools (Visio, Lucidchart): Used for modeling, analyzing, and optimizing business processes.
### Additional Skills:
11. Google Analytics: Important for understanding website traffic and user behavior. (Optional)
12. CRM Systems (Salesforce, HubSpot): Knowledge of these systems aids in analyzing sales data and customer interactions.(Optional)
13. Version Control (Git): Helps manage changes in analytical projects and ensures versioning control. (Optional)
### Data Warehousing and Database Management:
14. Data Warehousing (Amazon Redshift, Google BigQuery): Knowledge of these platforms for handling large-scale datasets and optimizing queries. (Optional)
### Soft Skills:
15. Communication: Clear and concise communication of findings and recommendations.
16. Problem-Solving & Critical Thinking: Ability to analyze complex problems and derive actionable insights.
I know this list might seem extensive, so it's best to begin with mastering Excel, Power BI, and SQL. As you progress, you can gradually add other tools from the list based on specific project needs and requirements.
Here are some essential telegram channels with important resources:
โฏ SQL โ t.me/sqlresourcestp
โฏ Data Analysis โ t.me/dataanalysisresourcestp
Also, try building projects & data portfolio while learning these skills. Creating data analytics projects will help you in showcasing the skills while giving job interviews.
Join t.me/techpsyche for more resources
ENJOY LEARNING๐๐
๐2
Top 15 advanced Power BI interview questions
1. Explain the concept of row-level security in Power BI and how to implement it.
2. What are calculated tables in Power BI, and when would you use them?
3. Describe the differences between DirectQuery, Live Connection, and Import Data storage modes in Power BI.
4. How can you optimize the performance of a Power BI report or dashboard with large datasets?
5. What is the DAX language, and how is it used in Power BI? Provide an example of a complex DAX calculation.
6. Explain the role of Power Query in data transformation within Power BI. What are some common data cleansing techniques in Power Query?
7. What is the purpose of the Power BI Data Model, and how do relationships between tables impact report development?
8. How can you create custom visuals or extensions in Power BI? Provide an example of when you would use custom visuals.
9. Describe the steps involved in setting up Power BI Gateway and its significance in a corporate environment.
10. What are the differences between Power BI Desktop, Power BI Service, and Power BI Mobile? How do they work together in a typical Power BI workflow?
11. Discuss the process of incremental data refresh in Power BI and its benefits.
12. How can you implement dynamic security roles in Power BI, and why might you need them in a multi-user environment?
13. What are Power BI paginated reports, and when would you choose to use them over standard interactive reports?
14. Explain the concept of drill-through in Power BI, including its configuration and use cases.
15. How can you integrate Power BI with other Microsoft products, such as Azure Data Lake Storage or SharePoint?
Hope you'll like it
More Resources Here: https://t.me/DataAnalysisResourcesTP
Like this post if you need more resources like this ๐โค๏ธ
1. Explain the concept of row-level security in Power BI and how to implement it.
2. What are calculated tables in Power BI, and when would you use them?
3. Describe the differences between DirectQuery, Live Connection, and Import Data storage modes in Power BI.
4. How can you optimize the performance of a Power BI report or dashboard with large datasets?
5. What is the DAX language, and how is it used in Power BI? Provide an example of a complex DAX calculation.
6. Explain the role of Power Query in data transformation within Power BI. What are some common data cleansing techniques in Power Query?
7. What is the purpose of the Power BI Data Model, and how do relationships between tables impact report development?
8. How can you create custom visuals or extensions in Power BI? Provide an example of when you would use custom visuals.
9. Describe the steps involved in setting up Power BI Gateway and its significance in a corporate environment.
10. What are the differences between Power BI Desktop, Power BI Service, and Power BI Mobile? How do they work together in a typical Power BI workflow?
11. Discuss the process of incremental data refresh in Power BI and its benefits.
12. How can you implement dynamic security roles in Power BI, and why might you need them in a multi-user environment?
13. What are Power BI paginated reports, and when would you choose to use them over standard interactive reports?
14. Explain the concept of drill-through in Power BI, including its configuration and use cases.
15. How can you integrate Power BI with other Microsoft products, such as Azure Data Lake Storage or SharePoint?
Hope you'll like it
More Resources Here: https://t.me/DataAnalysisResourcesTP
Like this post if you need more resources like this ๐โค๏ธ
โค2๐1
Why Tabular Data Reigns Supreme in the Real World of AI
Read Here: https://dev.to/justdetermined/why-tabular-data-reigns-supreme-in-the-real-world-of-ai-5ena
Read Here: https://dev.to/justdetermined/why-tabular-data-reigns-supreme-in-the-real-world-of-ai-5ena
DEV Community
Why Tabular Data Reigns Supreme in the Real World of AI
Artificial Intelligence (AI) often brings to mind futuristic robots, sophisticated deep learning...
4 Most Useful Charts to Show Trends: Data Visualization
Link: https://dev.to/justdetermined/4-most-useful-charts-to-show-trends-data-visualization-3pdg
Link: https://dev.to/justdetermined/4-most-useful-charts-to-show-trends-data-visualization-3pdg
DEV Community
4 Most Useful Charts to Show Trends: Data Visualization
In today's data-driven world, the ability to effectively visualize data is a superpower. Whether...
Forwarded from Free Courses: Google | Microsoft | Udemy | Coursera | IBM | NVIDIA | LinkedIn Learning | MIT | Udemy Coupons & PDF Books
14th ๐ฅ Feb 2025 Free Udemy Coupons New Coupons Added
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#01 Hands-On Python Machine Learning with Real World Projects
https://techurl.in/kcjul
#02 CCDAK: Confluent Developer for Apache Kafka Skills
https://techurl.in/jsmwH
#03 2025 Data Visualization in Tableau & Python (2 Courses in 1)
https://techurl.in/OHTso
#04 AWS and Linode: The Ultimate Guide to Cloud Computing [IaaS]
https://techurl.in/IYNxH
#05 2025 Data Structures Using Python
https://techurl.in/Jbgxh
#06 Logistic Regression in R Studio
https://techurl.in/SCSPx
#07 Master Amazon EC2 Storage: Complete Guide for EBS, EFS & AMI
https://techurl.in/IaJOC
#08 CCA-500: Cloudera Administrator Apache Hadoop Professional
https://techurl.in/ruqSv
#09 AWS: Elastic Load Balancing with Auto Scaling Groups
https://techurl.in/MDMnp
#10 LookML A-Z: Google Looker for Developers
https://techurl.in/Mehbk
Generatice AI Resources: https://t.me/airesourcestp/27
About Machine Learning: https://t.me/mlresourcestp/11
Data Science Cheat Sheet: https://t.me/datascienceresourcestp/19
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Do share in your groups.โจ
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โ Free Certificate upon Completion๐ฅณ
โโโโโโโโโโโโโโโโโโ
#01 Hands-On Python Machine Learning with Real World Projects
https://techurl.in/kcjul
#02 CCDAK: Confluent Developer for Apache Kafka Skills
https://techurl.in/jsmwH
#03 2025 Data Visualization in Tableau & Python (2 Courses in 1)
https://techurl.in/OHTso
#04 AWS and Linode: The Ultimate Guide to Cloud Computing [IaaS]
https://techurl.in/IYNxH
#05 2025 Data Structures Using Python
https://techurl.in/Jbgxh
#06 Logistic Regression in R Studio
https://techurl.in/SCSPx
#07 Master Amazon EC2 Storage: Complete Guide for EBS, EFS & AMI
https://techurl.in/IaJOC
#08 CCA-500: Cloudera Administrator Apache Hadoop Professional
https://techurl.in/ruqSv
#09 AWS: Elastic Load Balancing with Auto Scaling Groups
https://techurl.in/MDMnp
#10 LookML A-Z: Google Looker for Developers
https://techurl.in/Mehbk
Generatice AI Resources: https://t.me/airesourcestp/27
About Machine Learning: https://t.me/mlresourcestp/11
Data Science Cheat Sheet: https://t.me/datascienceresourcestp/19
โโโโโโโโโโโโโโโโโโ
Udemy Coupons Expire After 1000 Redemptions, So Please Join Our Telegram Or Whatsapp Channel To Get An Instant Alert For Coupons.
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KenyaTrends.co.ke
Kenya Trends - Jobs | Opportunities | Free Resources
Sharing free learning resources, jobs & opportunities.
๐1
Short roadmap to learn Tableau ๐
1. Getting Started:
- Download and install Tableau Public (free) or Tableau Desktop (trial version).
- Explore the Tableau interface to get familiar with its components.
2. Data Connection:
- Learn to connect Tableau to your data sources like Excel, CSV, databases, or cloud services.
3. Data Preparation:
- Understand how to clean and shape data in Tableau using the Data Source tab.
4. Basic Visualization:
- Create simple visualizations like bar charts, line charts, and scatter plots.
5. Calculations:
- Learn about calculated fields and basic functions for more complex data transformations.
6. Dashboards and Stories:
- Explore creating interactive dashboards and stories to present your insights effectively.
7. Advanced Visualizations:
- Dive into more advanced charts and graphs, such as heat maps, treemaps, and dual-axis charts.
8. Advanced Calculations:
- Master advanced calculations, such as level of detail (LOD) expressions and table calculations.
9. Mapping:
- Learn how to create maps and geospatial visualizations using Tableau's mapping features.
10. Data Blending:
- Understand how to blend data from multiple sources for comprehensive analysis.
11. Performance Optimization:
- Optimize the performance of your Tableau workbooks for larger datasets.
12. Tableau Server (Optional):
- If needed, explore Tableau Server for collaboration and sharing.
13. Online Resources:
- Utilize online tutorials, documentation, and forums to expand your knowledge.
14. Practice:
- Work on real-world projects to apply what you've learned. Remember to practice and apply your knowledge as you progress through each stage.
15. Certification (Optional):
- Consider pursuing Tableau certification for formal recognition of your skills.
Data Analytics Resources: https://t.me/dataanalysisresourcestp
Hope it helps :)
1. Getting Started:
- Download and install Tableau Public (free) or Tableau Desktop (trial version).
- Explore the Tableau interface to get familiar with its components.
2. Data Connection:
- Learn to connect Tableau to your data sources like Excel, CSV, databases, or cloud services.
3. Data Preparation:
- Understand how to clean and shape data in Tableau using the Data Source tab.
4. Basic Visualization:
- Create simple visualizations like bar charts, line charts, and scatter plots.
5. Calculations:
- Learn about calculated fields and basic functions for more complex data transformations.
6. Dashboards and Stories:
- Explore creating interactive dashboards and stories to present your insights effectively.
7. Advanced Visualizations:
- Dive into more advanced charts and graphs, such as heat maps, treemaps, and dual-axis charts.
8. Advanced Calculations:
- Master advanced calculations, such as level of detail (LOD) expressions and table calculations.
9. Mapping:
- Learn how to create maps and geospatial visualizations using Tableau's mapping features.
10. Data Blending:
- Understand how to blend data from multiple sources for comprehensive analysis.
11. Performance Optimization:
- Optimize the performance of your Tableau workbooks for larger datasets.
12. Tableau Server (Optional):
- If needed, explore Tableau Server for collaboration and sharing.
13. Online Resources:
- Utilize online tutorials, documentation, and forums to expand your knowledge.
14. Practice:
- Work on real-world projects to apply what you've learned. Remember to practice and apply your knowledge as you progress through each stage.
15. Certification (Optional):
- Consider pursuing Tableau certification for formal recognition of your skills.
Data Analytics Resources: https://t.me/dataanalysisresourcestp
Hope it helps :)
๐2
Breaking into Data Analysis can be very confusing in 2025!
Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R?
Fundamental principles are more important than tools:
Understanding data cleaning and preprocessing is more important than SQL vs NoSQL.
Understanding data visualization concepts is more important than Tableau vs Power BI.
Understanding statistical analysis is more important than Excel vs R.
Understanding programming for data manipulation is more important than Python vs R.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
Best Top-Notch Data Analytics Resources ๐๐
https://t.me/dataanalysisresourcestp
Hope this helps you ๐
Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R?
Fundamental principles are more important than tools:
Understanding data cleaning and preprocessing is more important than SQL vs NoSQL.
Understanding data visualization concepts is more important than Tableau vs Power BI.
Understanding statistical analysis is more important than Excel vs R.
Understanding programming for data manipulation is more important than Python vs R.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
Best Top-Notch Data Analytics Resources ๐๐
https://t.me/dataanalysisresourcestp
Hope this helps you ๐
31 Essential Functions to Supercharge Your Data Analysis
Read Here:
https://dev.to/henryclapton/31-essential-functions-to-supercharge-your-data-analysis-30in
Read Here:
https://dev.to/henryclapton/31-essential-functions-to-supercharge-your-data-analysis-30in
DEV Community
31 Essential Functions to Supercharge Your Data Analysis
Data Analysis Expressions (DAX) is the backbone of Power BI, Excel Power Pivot, and other data...
๐๐ข๐๐ซ๐จ๐ฌ๐จ๐๐ญ_๐๐ฑ๐๐๐ฅ_๐
๐จ๐ซ_๐๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐จ๐ง๐๐ฅ๐ฌ!_.pdf
2.3 MB
BOOK
๐ Title: Microsoft Excel for Professionals
๐ Title: Microsoft Excel for Professionals
Complete Roadmap to learn Excel in 2025 ๐๐
1. Basic Excel Skills:
- Familiarize yourself with Excel's interface and navigation.
- Learn basic formulas (SUM, AVERAGE, COUNT, etc.).
- Understand cell referencing (absolute vs. relative).
2. Data Entry and Formatting:
- Practice entering and formatting data efficiently.
- Explore cell formatting options for a clean and organized dataset.
3. Advanced Formulas:
- Master more advanced formulas like VLOOKUP, HLOOKUP, INDEX-MATCH.
- Learn logical functions (IF, AND, OR).
- Understand array formulas for complex calculations.
4. Pivot Tables:
- Gain proficiency in creating Pivot Tables for data summarization.
- Learn to customize and format Pivot Tables effectively.
5. Data Cleaning:
- Acquire skills in cleaning and transforming data.
- Explore text-to-columns, remove duplicates, and data validation.
6. Charts and Graphs:
- Learn to create various charts (bar, line, pie) for data visualization.
- Understand chart formatting and customization.
7. Dashboard Creation:
- Combine charts and tables to build basic dashboards.
- Explore dynamic dashboards using Excel features.
8. Macros and VBA:
- Dive into basic automation using Excel macros.
- Learn Visual Basic for Applications (VBA) for more advanced automation.
9. Power Query:
- Introduce yourself to Power Query for enhanced data manipulation.
- Learn to import, transform, and load data efficiently.
10. Advanced Excel Techniques:
- Explore advanced features like Goal Seek, Solver, and Scenario Manager.
- Master the use of data tables for sensitivity analysis.
11. Real-world Projects:
- Apply your skills to real-world projects or datasets.
- Practice solving analytical problems using Excel.
Remember to practice consistently, as hands-on experience is crucial for mastering Excel. This roadmap will provide a solid foundation for your journey into data analysis using Excel.
5๏ธโฃ Free resources to practice Excel
W3Schools Excel Tutorial
SimpliLearn Intruduction to Ms Excel
https://bit.ly/3PSorPT
http://learn.microsoft.com/en-gb/training/paths/modern-analytics/
https://t.me/dataanalysisresourcestp/35
https://excel-practice-online.com/
Join for more
ENJOY LEARNING ๐๐
1. Basic Excel Skills:
- Familiarize yourself with Excel's interface and navigation.
- Learn basic formulas (SUM, AVERAGE, COUNT, etc.).
- Understand cell referencing (absolute vs. relative).
2. Data Entry and Formatting:
- Practice entering and formatting data efficiently.
- Explore cell formatting options for a clean and organized dataset.
3. Advanced Formulas:
- Master more advanced formulas like VLOOKUP, HLOOKUP, INDEX-MATCH.
- Learn logical functions (IF, AND, OR).
- Understand array formulas for complex calculations.
4. Pivot Tables:
- Gain proficiency in creating Pivot Tables for data summarization.
- Learn to customize and format Pivot Tables effectively.
5. Data Cleaning:
- Acquire skills in cleaning and transforming data.
- Explore text-to-columns, remove duplicates, and data validation.
6. Charts and Graphs:
- Learn to create various charts (bar, line, pie) for data visualization.
- Understand chart formatting and customization.
7. Dashboard Creation:
- Combine charts and tables to build basic dashboards.
- Explore dynamic dashboards using Excel features.
8. Macros and VBA:
- Dive into basic automation using Excel macros.
- Learn Visual Basic for Applications (VBA) for more advanced automation.
9. Power Query:
- Introduce yourself to Power Query for enhanced data manipulation.
- Learn to import, transform, and load data efficiently.
10. Advanced Excel Techniques:
- Explore advanced features like Goal Seek, Solver, and Scenario Manager.
- Master the use of data tables for sensitivity analysis.
11. Real-world Projects:
- Apply your skills to real-world projects or datasets.
- Practice solving analytical problems using Excel.
Remember to practice consistently, as hands-on experience is crucial for mastering Excel. This roadmap will provide a solid foundation for your journey into data analysis using Excel.
5๏ธโฃ Free resources to practice Excel
W3Schools Excel Tutorial
SimpliLearn Intruduction to Ms Excel
https://bit.ly/3PSorPT
http://learn.microsoft.com/en-gb/training/paths/modern-analytics/
https://t.me/dataanalysisresourcestp/35
https://excel-practice-online.com/
Join for more
ENJOY LEARNING ๐๐
โค2
If I were to start Data Analytics in 2025 ๐ซ๐
โฏ Python
http://cs50.harvard.edu/python/2022/
https://www.freecodecamp.org/learn/data-analysis-with-python/
https://t.me/pythonresourcestp
โฏ SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql
https://www.freecodecamp.org/learn/relational-database/
https://topmate.io/learning_resources/1456762
https://tinyurl.com/4rwc9v5a
โฏ Excel
https://excel-practice-online.com/
https://t.me/dataanalysisresourcestp/35
โฏ Power BI
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/7
https://www.workout-wednesday.com/power-bi-challenges/
โฏ Tableau
https://t.me/dataanalysisresourcestp/30
https://www.tableau.com/learn/training
โฏ Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum
http://www.sherrytowers.com/cowan_statistical_data_analysis.pdf
โฏ Data Science
https://t.me/datascienceresourcestp/25
cognitiveclass.ai/courses/data-science-101
http://kaggle.com/learn
https://t.me/datascienceresourcestp/25
โฏ Machine Learning
http://developers.google.com/machine-learning/crash-course
https://www.freecodecamp.org/learn/machine-learning-with-python/
โฏ Artificial Intelligence
https://t.me/airesourcestp
https://udacity.com/course/intro-to-artificial-intelligence--cs271
introtodeeplearning.com
https://t.me/mlresourcestp
โฏ Data Engineering
https://tinyurl.com/fadtk827
https://t.me/datascienceresourcestp/23
Join for more free resources
Like for more โค๏ธ
ENJOY LEARNING๐๐
โฏ Python
http://cs50.harvard.edu/python/2022/
https://www.freecodecamp.org/learn/data-analysis-with-python/
https://t.me/pythonresourcestp
โฏ SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql
https://www.freecodecamp.org/learn/relational-database/
https://topmate.io/learning_resources/1456762
https://tinyurl.com/4rwc9v5a
โฏ Excel
https://excel-practice-online.com/
https://t.me/dataanalysisresourcestp/35
โฏ Power BI
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/7
https://www.workout-wednesday.com/power-bi-challenges/
โฏ Tableau
https://t.me/dataanalysisresourcestp/30
https://www.tableau.com/learn/training
โฏ Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum
http://www.sherrytowers.com/cowan_statistical_data_analysis.pdf
โฏ Data Science
https://t.me/datascienceresourcestp/25
cognitiveclass.ai/courses/data-science-101
http://kaggle.com/learn
https://t.me/datascienceresourcestp/25
โฏ Machine Learning
http://developers.google.com/machine-learning/crash-course
https://www.freecodecamp.org/learn/machine-learning-with-python/
โฏ Artificial Intelligence
https://t.me/airesourcestp
https://udacity.com/course/intro-to-artificial-intelligence--cs271
introtodeeplearning.com
https://t.me/mlresourcestp
โฏ Data Engineering
https://tinyurl.com/fadtk827
https://t.me/datascienceresourcestp/23
Join for more free resources
Like for more โค๏ธ
ENJOY LEARNING๐๐
๐3