Short roadmap to learn Tableau π
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 :)
π2
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 ππ
https://t.me/dataanalysisresourcestp
Hope this helps you π
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 ππ
https://t.me/dataanalysisresourcestp
Hope this helps you π
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...
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2.3 MB
BOOK
π Title: Microsoft Excel for Professionals
π Title: Microsoft Excel for Professionals
Complete Roadmap to learn Excel in 2025 ππ
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.
5οΈβ£ 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 ππ
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.
5οΈβ£ 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 ππ
β€2
If I were to start Data Analytics in 2025 π«π
β― Python
http://cs50.harvard.edu/python/2022/
https://www.freecodecamp.org/learn/data-analysis-with-python/
https://t.me/pythonresourcestp
β― 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
β― Excel
https://excel-practice-online.com/
https://t.me/dataanalysisresourcestp/35
β― Power BI
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/7
https://www.workout-wednesday.com/power-bi-challenges/
β― Tableau
https://t.me/dataanalysisresourcestp/30
https://www.tableau.com/learn/training
β― Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum
http://www.sherrytowers.com/cowan_statistical_data_analysis.pdf
β― Data Science
https://t.me/datascienceresourcestp/25
cognitiveclass.ai/courses/data-science-101
http://kaggle.com/learn
https://t.me/datascienceresourcestp/25
β― Machine Learning
http://developers.google.com/machine-learning/crash-course
https://www.freecodecamp.org/learn/machine-learning-with-python/
β― Artificial Intelligence
https://t.me/airesourcestp
https://udacity.com/course/intro-to-artificial-intelligence--cs271
introtodeeplearning.com
https://t.me/mlresourcestp
β― Data Engineering
https://tinyurl.com/fadtk827
https://t.me/datascienceresourcestp/23
Join for more free resources
Like for more β€οΈ
ENJOY LEARNINGππ
β― Python
http://cs50.harvard.edu/python/2022/
https://www.freecodecamp.org/learn/data-analysis-with-python/
https://t.me/pythonresourcestp
β― 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
β― Excel
https://excel-practice-online.com/
https://t.me/dataanalysisresourcestp/35
β― Power BI
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/7
https://www.workout-wednesday.com/power-bi-challenges/
β― Tableau
https://t.me/dataanalysisresourcestp/30
https://www.tableau.com/learn/training
β― Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum
http://www.sherrytowers.com/cowan_statistical_data_analysis.pdf
β― Data Science
https://t.me/datascienceresourcestp/25
cognitiveclass.ai/courses/data-science-101
http://kaggle.com/learn
https://t.me/datascienceresourcestp/25
β― Machine Learning
http://developers.google.com/machine-learning/crash-course
https://www.freecodecamp.org/learn/machine-learning-with-python/
β― Artificial Intelligence
https://t.me/airesourcestp
https://udacity.com/course/intro-to-artificial-intelligence--cs271
introtodeeplearning.com
https://t.me/mlresourcestp
β― Data Engineering
https://tinyurl.com/fadtk827
https://t.me/datascienceresourcestp/23
Join for more free resources
Like for more β€οΈ
ENJOY LEARNINGππ
π3
Want to become a Data Analyst?
Hereβs a roadmap with essential skills, tools & concepts youβll need to master:
1. Data Fundamentals
Statistics: Learn descriptive statistics (mean, median, mode), distributions, hypothesis testing, and correlation.
Probability: Understand basic probability theory, including conditional probability, Bayesβ theorem, and probability distributions.
2. Data Cleaning
Data Cleaning Techniques: Handling missing values, removing duplicates, and outlier detection.
Data Transformation: Data type conversions, feature engineering, and handling categorical variables.
Pandas: Master data manipulation with Pandas (merge, join, group, pivot).
3. Data Visualization (https://t.me/dataanalysisresourcestp)
Data Visualization Libraries: Master Matplotlib, Seaborn, or Plotly for Python-based visualizations.
Power BI / Tableau: Get hands-on with BI tools to create interactive dashboards and visual reports.
Design Principles: Learn best practices for designing clear, effective visualizations.
4. SQL for Data Analysis (https://t.me/sqlresourcestp)
Basic SQL: SELECT, WHERE, ORDER BY, GROUP BY, JOINs.
Advanced SQL: Window functions, Common Table Expressions (CTEs), subqueries.
Aggregation Functions: SUM, AVG, MIN, MAX, COUNT.
Data Cleaning with SQL: Filtering, transforming, and merging data in SQL databases.
5. Excel for Data Analysis (https://t.me/dataanalysisresourcestp)
Data Cleaning in Excel: Use functions like TRIM, CLEAN, SUBSTITUTE.
Advanced Functions: VLOOKUP, HLOOKUP, INDEX-MATCH, IF, SUMIF, COUNTIF.
Data Visualization in Excel: Create pivot tables, charts, and dashboards.
6. Programming for Data Analysis (Python or R) (http://t.me/pythonresourcestp)
Python: Learn data handling and manipulation with Pandas and NumPy.
R: Basic syntax, data manipulation with dplyr, and data visualization with ggplot2.
Data Analysis Libraries: Pandas, NumPy, SciPy for Python or Tidyverse for R.
7. Exploratory Data Analysis (EDA)
Pattern Recognition: Use EDA to identify patterns, trends, and correlations in data.
Visual EDA: Use pair plots, heatmaps, and distribution plots for insights.
Summary Statistics: Understand distributions, variance, and central tendencies of variables.
8. Business Acumen
Domain Knowledge: Understand the industry-specific metrics relevant to your target job (e.g., finance, marketing, e-commerce).
Data Storytelling: Learn to communicate findings clearly and effectively, connecting insights to business goals.
KPI Analysis: Identify and measure key performance indicators for informed decision-making.
9. Data Collection & Sourcing
APIs: Learn to pull data from APIs (e.g., REST APIs) using tools like Pythonβs Requests library.
Web Scraping: Use tools like BeautifulSoup and Scrapy (be mindful of ethics and legality).
Database Connections: Query databases and integrate SQL with Python or R for more extensive analyses.
10. Dashboarding and Reporting (https://t.me/dataanalysisresourcestp)
Power BI / Tableau: Master the basics of dashboard design, interactivity, and sharing insights with stakeholders.
Reporting Best Practices: Design reports that are clear, actionable, and easy for non-technical stakeholders to interpret.
11. Soft Skills
Communication: Clearly present data insights and recommendations to stakeholders.
Critical Thinking: Approach problems analytically to uncover insights.
Collaboration: Learn how to work effectively within cross-functional teams, especially with non-technical colleagues.
Top-notch Data Analytics Resources (https://topmate.io/learning_resources/1456762)
Power BI Interview Questions (https://dev.to/henryclapton/top-15-advanced-power-bi-interview-questions-2942)
Free Resources to learn Data Analytics (https://t.me/dataanalysisresourcestp/37)
Data Analyst Learning Plan (https://t.me/dataanalysisresourcestp/36)
Join for more free resources
Like for more data analytics resources β€οΈ
ENJOY LEARNINGππ
WhatsApp Channel
Hereβs a roadmap with essential skills, tools & concepts youβll need to master:
1. Data Fundamentals
Statistics: Learn descriptive statistics (mean, median, mode), distributions, hypothesis testing, and correlation.
Probability: Understand basic probability theory, including conditional probability, Bayesβ theorem, and probability distributions.
2. Data Cleaning
Data Cleaning Techniques: Handling missing values, removing duplicates, and outlier detection.
Data Transformation: Data type conversions, feature engineering, and handling categorical variables.
Pandas: Master data manipulation with Pandas (merge, join, group, pivot).
3. Data Visualization (https://t.me/dataanalysisresourcestp)
Data Visualization Libraries: Master Matplotlib, Seaborn, or Plotly for Python-based visualizations.
Power BI / Tableau: Get hands-on with BI tools to create interactive dashboards and visual reports.
Design Principles: Learn best practices for designing clear, effective visualizations.
4. SQL for Data Analysis (https://t.me/sqlresourcestp)
Basic SQL: SELECT, WHERE, ORDER BY, GROUP BY, JOINs.
Advanced SQL: Window functions, Common Table Expressions (CTEs), subqueries.
Aggregation Functions: SUM, AVG, MIN, MAX, COUNT.
Data Cleaning with SQL: Filtering, transforming, and merging data in SQL databases.
5. Excel for Data Analysis (https://t.me/dataanalysisresourcestp)
Data Cleaning in Excel: Use functions like TRIM, CLEAN, SUBSTITUTE.
Advanced Functions: VLOOKUP, HLOOKUP, INDEX-MATCH, IF, SUMIF, COUNTIF.
Data Visualization in Excel: Create pivot tables, charts, and dashboards.
6. Programming for Data Analysis (Python or R) (http://t.me/pythonresourcestp)
Python: Learn data handling and manipulation with Pandas and NumPy.
R: Basic syntax, data manipulation with dplyr, and data visualization with ggplot2.
Data Analysis Libraries: Pandas, NumPy, SciPy for Python or Tidyverse for R.
7. Exploratory Data Analysis (EDA)
Pattern Recognition: Use EDA to identify patterns, trends, and correlations in data.
Visual EDA: Use pair plots, heatmaps, and distribution plots for insights.
Summary Statistics: Understand distributions, variance, and central tendencies of variables.
8. Business Acumen
Domain Knowledge: Understand the industry-specific metrics relevant to your target job (e.g., finance, marketing, e-commerce).
Data Storytelling: Learn to communicate findings clearly and effectively, connecting insights to business goals.
KPI Analysis: Identify and measure key performance indicators for informed decision-making.
9. Data Collection & Sourcing
APIs: Learn to pull data from APIs (e.g., REST APIs) using tools like Pythonβs Requests library.
Web Scraping: Use tools like BeautifulSoup and Scrapy (be mindful of ethics and legality).
Database Connections: Query databases and integrate SQL with Python or R for more extensive analyses.
10. Dashboarding and Reporting (https://t.me/dataanalysisresourcestp)
Power BI / Tableau: Master the basics of dashboard design, interactivity, and sharing insights with stakeholders.
Reporting Best Practices: Design reports that are clear, actionable, and easy for non-technical stakeholders to interpret.
11. Soft Skills
Communication: Clearly present data insights and recommendations to stakeholders.
Critical Thinking: Approach problems analytically to uncover insights.
Collaboration: Learn how to work effectively within cross-functional teams, especially with non-technical colleagues.
Top-notch Data Analytics Resources (https://topmate.io/learning_resources/1456762)
Power BI Interview Questions (https://dev.to/henryclapton/top-15-advanced-power-bi-interview-questions-2942)
Free Resources to learn Data Analytics (https://t.me/dataanalysisresourcestp/37)
Data Analyst Learning Plan (https://t.me/dataanalysisresourcestp/36)
Join for more free resources
Like for more data analytics resources β€οΈ
ENJOY LEARNINGππ
WhatsApp Channel
Power BI Learning Plan in 2025
|-- Week 1: Introduction to Power BI
| |-- Power BI Basics
| | |-- What is Power BI?
| | |-- Components of Power BI
| | |-- Power BI Desktop vs. Power BI Service
| |-- Setting up Power BI
| | |-- Installing Power BI Desktop
| | |-- Overview of the Interface
| | |-- Connecting to Data Sources
| |-- First Power BI Report
| | |-- Creating a Simple Report
| | |-- Basic Visualizations
|
|-- Week 2: Data Transformation and Modeling
| |-- Power Query Editor
| | |-- Importing and Shaping Data
| | |-- Applied Steps
| |-- Data Modeling
| | |-- Relationships
| | |-- Calculated Columns and Measures
| | |-- DAX Basics
| |-- Data Cleaning
| | |-- Handling Missing Data
| | |-- Data Types and Formatting
|
|-- Week 3: Advanced DAX and Data Modeling
| |-- Advanced DAX Functions
| | |-- Time Intelligence
| | |-- Iterators
| | |-- Filter Functions
| |-- Advanced Data Modeling
| | |-- Star and Snowflake Schemas
| | |-- Role-playing Dimensions
| |-- Performance Optimization
| | |-- Query Performance
| | |-- Model Performance
|
|-- Week 4: Visualizations and Reports
| |-- Advanced Visualizations
| | |-- Custom Visuals
| | |-- Conditional Formatting
| | |-- Interactive Elements
| |-- Report Design
| | |-- Designing for Clarity
| | |-- Using Themes
| | |-- Report Navigation
| |-- Power BI Service
| | |-- Publishing Reports
| | |-- Workspaces and Apps
| | |-- Sharing and Collaboration
|
|-- Week 5: Dashboards and Data Analysis
| |-- Creating Dashboards
| | |-- Pinning Visuals
| | |-- Dashboard Tiles
| | |-- Alerts
| |-- Data Analysis Techniques
| | |-- Drillthrough
| | |-- Bookmarks
| | |-- What-If Parameters
| |-- Advanced Analytics
| | |-- Quick Insights
| | |-- AI Visuals
|
|-- Week 6-8: Power BI and Other Tools
| |-- Power BI and Excel
| | |-- Excel Integration
| | |-- PowerPivot and PowerQuery
| | |-- Publishing from Excel
| |-- Power BI and R
| | |-- Using R Scripts in Power BI
| | |-- R Visuals
| |-- Power BI and Python
| | |-- Using Python Scripts
| | |-- Python Visuals
| |-- Power Automate and Power BI
| | |-- Automating Workflows
| | |-- Data Alerts and Actions
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing the Model
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- SQL Databases
| | |-- Azure Data Services
|
|-- Week 12: Post-Project Learning
| |-- Power BI Administration
| | |-- Data Governance
| | |-- Security
| | |-- Monitoring and Auditing
| |-- Power BI in the Cloud
| | |-- Power BI Premium
| | |-- Power BI Embedded
| |-- Continuing Education
| | |-- Advanced Power BI Topics
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udacity)
| |-- Books (The Definitive Guide to DAX, Microsoft Power BI Cookbook)
| |-- Power BI Blogs and Resources
| |-- GitHub Repositories
| |-- Power BI Communities (Microsoft Power BI Community, Reddit)
You can refer these Power BI Interview Resources to learn more: https://t.me/dataanalysisresourcestp/27
Like this post if you want me to continue this Power BI series πβ₯οΈ
Hope it helps :)
Follow this WhatsApp Channel for More Resources
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
|-- Week 1: Introduction to Power BI
| |-- Power BI Basics
| | |-- What is Power BI?
| | |-- Components of Power BI
| | |-- Power BI Desktop vs. Power BI Service
| |-- Setting up Power BI
| | |-- Installing Power BI Desktop
| | |-- Overview of the Interface
| | |-- Connecting to Data Sources
| |-- First Power BI Report
| | |-- Creating a Simple Report
| | |-- Basic Visualizations
|
|-- Week 2: Data Transformation and Modeling
| |-- Power Query Editor
| | |-- Importing and Shaping Data
| | |-- Applied Steps
| |-- Data Modeling
| | |-- Relationships
| | |-- Calculated Columns and Measures
| | |-- DAX Basics
| |-- Data Cleaning
| | |-- Handling Missing Data
| | |-- Data Types and Formatting
|
|-- Week 3: Advanced DAX and Data Modeling
| |-- Advanced DAX Functions
| | |-- Time Intelligence
| | |-- Iterators
| | |-- Filter Functions
| |-- Advanced Data Modeling
| | |-- Star and Snowflake Schemas
| | |-- Role-playing Dimensions
| |-- Performance Optimization
| | |-- Query Performance
| | |-- Model Performance
|
|-- Week 4: Visualizations and Reports
| |-- Advanced Visualizations
| | |-- Custom Visuals
| | |-- Conditional Formatting
| | |-- Interactive Elements
| |-- Report Design
| | |-- Designing for Clarity
| | |-- Using Themes
| | |-- Report Navigation
| |-- Power BI Service
| | |-- Publishing Reports
| | |-- Workspaces and Apps
| | |-- Sharing and Collaboration
|
|-- Week 5: Dashboards and Data Analysis
| |-- Creating Dashboards
| | |-- Pinning Visuals
| | |-- Dashboard Tiles
| | |-- Alerts
| |-- Data Analysis Techniques
| | |-- Drillthrough
| | |-- Bookmarks
| | |-- What-If Parameters
| |-- Advanced Analytics
| | |-- Quick Insights
| | |-- AI Visuals
|
|-- Week 6-8: Power BI and Other Tools
| |-- Power BI and Excel
| | |-- Excel Integration
| | |-- PowerPivot and PowerQuery
| | |-- Publishing from Excel
| |-- Power BI and R
| | |-- Using R Scripts in Power BI
| | |-- R Visuals
| |-- Power BI and Python
| | |-- Using Python Scripts
| | |-- Python Visuals
| |-- Power Automate and Power BI
| | |-- Automating Workflows
| | |-- Data Alerts and Actions
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing the Model
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- SQL Databases
| | |-- Azure Data Services
|
|-- Week 12: Post-Project Learning
| |-- Power BI Administration
| | |-- Data Governance
| | |-- Security
| | |-- Monitoring and Auditing
| |-- Power BI in the Cloud
| | |-- Power BI Premium
| | |-- Power BI Embedded
| |-- Continuing Education
| | |-- Advanced Power BI Topics
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udacity)
| |-- Books (The Definitive Guide to DAX, Microsoft Power BI Cookbook)
| |-- Power BI Blogs and Resources
| |-- GitHub Repositories
| |-- Power BI Communities (Microsoft Power BI Community, Reddit)
You can refer these Power BI Interview Resources to learn more: https://t.me/dataanalysisresourcestp/27
Like this post if you want me to continue this Power BI series πβ₯οΈ
Hope it helps :)
Follow this WhatsApp Channel for More Resources
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Data Analyst Interview Questions
[Python, SQL, PowerBI]
1. Is indentation required in python?
Ans: Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.
2. What are Entities and Relationships?
Ans:
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.
Relationships: Relations or links between entities that have something to do with each other. For example β The employeeβs table in a companyβs database can be associated with the salary table in the same database.
3. What are Aggregate and Scalar functions?
Ans: An aggregate function performs operations on a collection of values to return a single scalar value. Aggregate functions are often used with the GROUP BY and HAVING clauses of the SELECT statement. A scalar function returns a single value based on the input value.
4. What are Custom Visuals in Power BI?
Ans: Custom Visuals are like any other visualizations, generated using Power BI. The only difference is that it develops the custom visuals using a custom SDK. The languages like JQuery and JavaScript are used to create custom visuals in Power BI
Join for More: (https://t.me/pythonresourcestp)
ENJOY LEARNING ππ
Follow this WhatsApp Channel for More:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
[Python, SQL, PowerBI]
1. Is indentation required in python?
Ans: Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.
2. What are Entities and Relationships?
Ans:
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.
Relationships: Relations or links between entities that have something to do with each other. For example β The employeeβs table in a companyβs database can be associated with the salary table in the same database.
3. What are Aggregate and Scalar functions?
Ans: An aggregate function performs operations on a collection of values to return a single scalar value. Aggregate functions are often used with the GROUP BY and HAVING clauses of the SELECT statement. A scalar function returns a single value based on the input value.
4. What are Custom Visuals in Power BI?
Ans: Custom Visuals are like any other visualizations, generated using Power BI. The only difference is that it develops the custom visuals using a custom SDK. The languages like JQuery and JavaScript are used to create custom visuals in Power BI
Join for More: (https://t.me/pythonresourcestp)
ENJOY LEARNING ππ
Follow this WhatsApp Channel for More:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
π1
Complete roadmap to learn Python for data analysis
Step 1: Fundamentals of Python
1. Basics of Python Programming
- Introduction to Python
- Data types (integers, floats, strings, booleans)
- Variables and constants
- Basic operators (arithmetic, comparison, logical)
2. Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- List comprehensions
3. Functions and Modules
- Defining functions
- Function arguments and return values
- Importing modules
- Built-in functions vs. user-defined functions
4. Data Structures
- Lists, tuples, sets, dictionaries
- Manipulating data structures (add, remove, update elements)
Step 2: Advanced Python
1. File Handling
- Reading from and writing to files
- Working with different file formats (txt, csv, json)
2. Error Handling
- Try, except blocks
- Handling exceptions and errors gracefully
3. Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance and polymorphism
- Encapsulation
Step 3: Libraries for Data Analysis
1. NumPy
- Understanding arrays and array operations
- Indexing, slicing, and iterating
- Mathematical functions and statistical operations
2. Pandas
- Series and DataFrames
- Reading and writing data (csv, excel, sql, json)
- Data cleaning and preparation
- Merging, joining, and concatenating data
- Grouping and aggregating data
3. Matplotlib and Seaborn
- Data visualization with Matplotlib
- Plotting different types of graphs (line, bar, scatter, histogram)
- Customizing plots
- Advanced visualizations with Seaborn
Step 4: Data Manipulation and Analysis
1. Data Wrangling
- Handling missing values
- Data transformation
- Feature engineering
2. Exploratory Data Analysis (EDA)
- Descriptive statistics
- Data visualization techniques
- Identifying patterns and outliers
3. Statistical Analysis
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions
Step 5: Advanced Topics
1. Time Series Analysis
- Working with datetime objects
- Time series decomposition
- Forecasting models
2. Machine Learning Basics
- Introduction to machine learning
- Supervised vs. unsupervised learning
- Using Scikit-Learn for machine learning
- Building and evaluating models
3. Big Data and Cloud Computing
- Introduction to big data frameworks (e.g., Hadoop, Spark)
- Using cloud services for data analysis (e.g., AWS, Google Cloud)
Step 6: Practical Projects
1. Hands-on Projects
- Analyzing datasets from Kaggle
- Building interactive dashboards with Plotly or Dash
- Developing end-to-end data analysis projects
2. Collaborative Projects
- Participating in data science competitions
- Contributing to open-source projects
Join for More
π¨βπ» FREE Resources to Learn & Practice Python
1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://t.me/pythonresourcestp
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.me/dataanalysisresourcestp
7. https://pythonbasics.org/exercises/
8. https://t.me/sqlresourcestp
9. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
Join for more free resources
ENJOY LEARNING ππ
Follow this WhatsApp Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Step 1: Fundamentals of Python
1. Basics of Python Programming
- Introduction to Python
- Data types (integers, floats, strings, booleans)
- Variables and constants
- Basic operators (arithmetic, comparison, logical)
2. Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- List comprehensions
3. Functions and Modules
- Defining functions
- Function arguments and return values
- Importing modules
- Built-in functions vs. user-defined functions
4. Data Structures
- Lists, tuples, sets, dictionaries
- Manipulating data structures (add, remove, update elements)
Step 2: Advanced Python
1. File Handling
- Reading from and writing to files
- Working with different file formats (txt, csv, json)
2. Error Handling
- Try, except blocks
- Handling exceptions and errors gracefully
3. Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance and polymorphism
- Encapsulation
Step 3: Libraries for Data Analysis
1. NumPy
- Understanding arrays and array operations
- Indexing, slicing, and iterating
- Mathematical functions and statistical operations
2. Pandas
- Series and DataFrames
- Reading and writing data (csv, excel, sql, json)
- Data cleaning and preparation
- Merging, joining, and concatenating data
- Grouping and aggregating data
3. Matplotlib and Seaborn
- Data visualization with Matplotlib
- Plotting different types of graphs (line, bar, scatter, histogram)
- Customizing plots
- Advanced visualizations with Seaborn
Step 4: Data Manipulation and Analysis
1. Data Wrangling
- Handling missing values
- Data transformation
- Feature engineering
2. Exploratory Data Analysis (EDA)
- Descriptive statistics
- Data visualization techniques
- Identifying patterns and outliers
3. Statistical Analysis
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions
Step 5: Advanced Topics
1. Time Series Analysis
- Working with datetime objects
- Time series decomposition
- Forecasting models
2. Machine Learning Basics
- Introduction to machine learning
- Supervised vs. unsupervised learning
- Using Scikit-Learn for machine learning
- Building and evaluating models
3. Big Data and Cloud Computing
- Introduction to big data frameworks (e.g., Hadoop, Spark)
- Using cloud services for data analysis (e.g., AWS, Google Cloud)
Step 6: Practical Projects
1. Hands-on Projects
- Analyzing datasets from Kaggle
- Building interactive dashboards with Plotly or Dash
- Developing end-to-end data analysis projects
2. Collaborative Projects
- Participating in data science competitions
- Contributing to open-source projects
Join for More
π¨βπ» FREE Resources to Learn & Practice Python
1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://t.me/pythonresourcestp
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.me/dataanalysisresourcestp
7. https://pythonbasics.org/exercises/
8. https://t.me/sqlresourcestp
9. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
Join for more free resources
ENJOY LEARNING ππ
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Complete Syllabus for Data Analytics interview:
SQL: https://t.me/sqlresourcestp/10
1. Basic
SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
Basic JOINS (INNER, LEFT, RIGHT, FULL)
Creating and using simple databases and tables
2. Intermediate
Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
Subqueries and nested queries
Common Table Expressions (WITH clause)
CASE statements for conditional logic in queries
3. Advanced
Advanced JOIN techniques (self-join, non-equi join)
Window functions (OVER, PARTITION BY, ROW__NUMBER, RANK, DENSE__RANK, lead, lag)
optimization with indexing
Data manipulation (INSERT, UPDATE, DELETE)
Python: https://t.me/pythonresourcestp/30
1. Basic
Syntax, variables, data types (integers, floats, strings, booleans)
Control structures (if-else, for and while loops)
Basic data structures (lists, dictionaries, sets, tuples)
Functions, lambda functions, error handling (try-except)
Modules and packages
2. Pandas & Numpy
Creating and manipulating DataFrames and Series
Indexing, selecting, and filtering data
Handling missing data (fillna, dropna)
Data aggregation with groupby, summarizing data
Merging, joining, and concatenating datasets
3. Basic Visualization
Basic plotting with Matplotlib (line plots, bar plots, histograms)
Visualization with Seaborn (scatter plots, box plots, pair plots)
PH4N745M
Customizing plots (sizes, labels, legends, color palettes)
Introduction to interactive visualizations (e.g., Plotly)
Excel: https://t.me/dataanalysisresourcestp/36
1. Basic
Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
Introduction to charts and basic data visualization
Data sorting and filtering
Conditional formatting
2. Intermediate
Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
PivotTables and PivotCharts for summarizing data
Data validation tools
What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
Array formulas and advanced functions
Data Model & Power Pivot
Advanced Filter
Slicers and Timelines in Pivot Tables
Dynamic charts and interactive dashboards
Power BI: https://t.me/dataanalysisresourcestp/39
1. Data Modeling
Importing data from various sources
Creating and managing relationships between different datasets
Data modeling basics (star schema, snowflake schema)
2. Data Transformation
Using Power Query for data cleaning and transformation
Advanced data shaping techniques
Calculated columns and measures using DAX
3. Data Visualization and Reporting
Creating interactive reports and dashboards
Visualizations (bar, line, pie charts, maps)
* Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Like for more πβ€οΈ
Share our channel link with your friends: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
SQL: https://t.me/sqlresourcestp/10
1. Basic
SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
Basic JOINS (INNER, LEFT, RIGHT, FULL)
Creating and using simple databases and tables
2. Intermediate
Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
Subqueries and nested queries
Common Table Expressions (WITH clause)
CASE statements for conditional logic in queries
3. Advanced
Advanced JOIN techniques (self-join, non-equi join)
Window functions (OVER, PARTITION BY, ROW__NUMBER, RANK, DENSE__RANK, lead, lag)
optimization with indexing
Data manipulation (INSERT, UPDATE, DELETE)
Python: https://t.me/pythonresourcestp/30
1. Basic
Syntax, variables, data types (integers, floats, strings, booleans)
Control structures (if-else, for and while loops)
Basic data structures (lists, dictionaries, sets, tuples)
Functions, lambda functions, error handling (try-except)
Modules and packages
2. Pandas & Numpy
Creating and manipulating DataFrames and Series
Indexing, selecting, and filtering data
Handling missing data (fillna, dropna)
Data aggregation with groupby, summarizing data
Merging, joining, and concatenating datasets
3. Basic Visualization
Basic plotting with Matplotlib (line plots, bar plots, histograms)
Visualization with Seaborn (scatter plots, box plots, pair plots)
PH4N745M
Customizing plots (sizes, labels, legends, color palettes)
Introduction to interactive visualizations (e.g., Plotly)
Excel: https://t.me/dataanalysisresourcestp/36
1. Basic
Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
Introduction to charts and basic data visualization
Data sorting and filtering
Conditional formatting
2. Intermediate
Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
PivotTables and PivotCharts for summarizing data
Data validation tools
What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
Array formulas and advanced functions
Data Model & Power Pivot
Advanced Filter
Slicers and Timelines in Pivot Tables
Dynamic charts and interactive dashboards
Power BI: https://t.me/dataanalysisresourcestp/39
1. Data Modeling
Importing data from various sources
Creating and managing relationships between different datasets
Data modeling basics (star schema, snowflake schema)
2. Data Transformation
Using Power Query for data cleaning and transformation
Advanced data shaping techniques
Calculated columns and measures using DAX
3. Data Visualization and Reporting
Creating interactive reports and dashboards
Visualizations (bar, line, pie charts, maps)
* Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Like for more πβ€οΈ
Share our channel link with your friends: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
π1
Essential Python Libraries for Data Analytics ππ
Python Free Resources: https://t.me/pythonresourcestp
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
SQL for Data Analytics: https://t.me/sqlresourcestp
Hope it helps :)
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Python Free Resources: https://t.me/pythonresourcestp
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
SQL for Data Analytics: https://t.me/sqlresourcestp
Hope it helps :)
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
β€1
Data visualization tools
Data visualization tools provide you with an easier way to create visual representations of large data sets. When dealing with data sets that include bunches of data points, automating the process of creating a visualization, at least in part, makes your job significantly easier.
The best data visualization tools on the market should have one important thing in common. It is their ease of use. The best tools can also handle huge sets of data. And the last but not least, they can output an array of different chart, graph, and map types.
There are hundreds, of applications, tools, and scripts available to create visualizations of large data sets. Many are very basic and have a lot of overlapping features.
- Tableau (and Tableau Public (https://www.tableau.com/)
Hundreds of data import options. Mapping capability. Free public version available. Lots of video tutorials to walk you through how to use Tableau.
- Infogram (https://infogram.com/)
Tiered pricing, including a free plan with basic features. Includes 35+ chart types and 550+ map types. Drag and drop editor. API for importing additional data sources.
- ChartBlocks (https://www.chartblocks.com/en)
Free and reasonably priced paid plans are available. Easy to use wizard for importing the necessary data.
- Datawrapper (https://www.datawrapper.de/)
Specifically designed for newsroom data visualization. Free plan is a good fit for smaller sites. Tool includes a built-in color blindness checker.
- D3.js (https://d3js.org/)
A JavaScript library for manipulating documents using data. Very powerful and customizable. Huge number of chart types possible. A focus on web standards. Tools available to let non-programmers create visualizations. Free and open source.
- Looker Studio (Google Data Studio) (https://lookerstudio.google.com/overview)
Free data visualization tool that is specifically for creating interactive charts for embedding online. Easily access a wide variety of data.
- FusionCharts (https://www.fusioncharts.com/)
A JavaScript-based option for creating web and mobile dashboards. Huge number of chart and map format options. More features than most other visualization tools. Integrates with a number of different frameworks and programming languages.
- Chart.js (https://www.chartjs.org/)
A simple but flexible JavaScript charting library. Free and open source. Responsive and cross-browser compatible output.
- Grafana (https://grafana.com/)
Open source, with free and paid options available. Large selection of data sources available. Variety of chart types available. Makes creating dynamic dashboards simple. Can work with mixed data feeds.
4 FREE Data Analytics Courses: https://tinyurl.com/m239d2s8
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Data visualization tools provide you with an easier way to create visual representations of large data sets. When dealing with data sets that include bunches of data points, automating the process of creating a visualization, at least in part, makes your job significantly easier.
The best data visualization tools on the market should have one important thing in common. It is their ease of use. The best tools can also handle huge sets of data. And the last but not least, they can output an array of different chart, graph, and map types.
There are hundreds, of applications, tools, and scripts available to create visualizations of large data sets. Many are very basic and have a lot of overlapping features.
- Tableau (and Tableau Public (https://www.tableau.com/)
Hundreds of data import options. Mapping capability. Free public version available. Lots of video tutorials to walk you through how to use Tableau.
- Infogram (https://infogram.com/)
Tiered pricing, including a free plan with basic features. Includes 35+ chart types and 550+ map types. Drag and drop editor. API for importing additional data sources.
- ChartBlocks (https://www.chartblocks.com/en)
Free and reasonably priced paid plans are available. Easy to use wizard for importing the necessary data.
- Datawrapper (https://www.datawrapper.de/)
Specifically designed for newsroom data visualization. Free plan is a good fit for smaller sites. Tool includes a built-in color blindness checker.
- D3.js (https://d3js.org/)
A JavaScript library for manipulating documents using data. Very powerful and customizable. Huge number of chart types possible. A focus on web standards. Tools available to let non-programmers create visualizations. Free and open source.
- Looker Studio (Google Data Studio) (https://lookerstudio.google.com/overview)
Free data visualization tool that is specifically for creating interactive charts for embedding online. Easily access a wide variety of data.
- FusionCharts (https://www.fusioncharts.com/)
A JavaScript-based option for creating web and mobile dashboards. Huge number of chart and map format options. More features than most other visualization tools. Integrates with a number of different frameworks and programming languages.
- Chart.js (https://www.chartjs.org/)
A simple but flexible JavaScript charting library. Free and open source. Responsive and cross-browser compatible output.
- Grafana (https://grafana.com/)
Open source, with free and paid options available. Large selection of data sources available. Variety of chart types available. Makes creating dynamic dashboards simple. Can work with mixed data feeds.
4 FREE Data Analytics Courses: https://tinyurl.com/m239d2s8
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Useful websites to practice and enhance your Data Analytics skills
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.me/sqlresourcestp/10
2. Python
https://www.learnpython.org/
https://t.me/pythonresourcestp/6
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://t.me/techpsyche/241
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/47
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://t.me/dataanalysisresourcestp/36
https://www.w3schools.com/EXCEL/index.php
ENJOY LEARNING ππ
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.me/sqlresourcestp/10
2. Python
https://www.learnpython.org/
https://t.me/pythonresourcestp/6
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://t.me/techpsyche/241
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/47
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://t.me/dataanalysisresourcestp/36
https://www.w3schools.com/EXCEL/index.php
ENJOY LEARNING ππ
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Essential tools and skills required to become a data analyst ππ
### 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:
β― SQL β t.me/sqlresourcestp
β― Data Analysis β 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 LEARNINGππ
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
### 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:
β― SQL β t.me/sqlresourcestp
β― Data Analysis β 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 LEARNINGππ
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https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R