Anyone with an Internet connection can learn ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ ๐ณ๐ผ๐ฟ ๐ณ๐ฟ๐ฒ๐ฒ:
No more excuses now.
SQL - https://lnkd.in/gQkjdAWP
Python - https://lnkd.in/gQk8siKn
Excel - https://lnkd.in/d-txjPJn
Power BI - https://lnkd.in/gs6RgH2m
Tableau - https://lnkd.in/dDFdyS8y
Data Visualization - https://lnkd.in/dcHqhgn4
Data Cleaning - https://lnkd.in/dCXspR4p
Google Sheets - https://lnkd.in/d7eDi8pn
Statistics - https://lnkd.in/dgaw6KMW
Projects - https://lnkd.in/g2Fjzbma
Portfolio - https://t.iss.one/DataPortfolio
If you've read so far, do LIKE and share this channel with your friends & loved ones โฅ๏ธ
Hope it helps :)
No more excuses now.
SQL - https://lnkd.in/gQkjdAWP
Python - https://lnkd.in/gQk8siKn
Excel - https://lnkd.in/d-txjPJn
Power BI - https://lnkd.in/gs6RgH2m
Tableau - https://lnkd.in/dDFdyS8y
Data Visualization - https://lnkd.in/dcHqhgn4
Data Cleaning - https://lnkd.in/dCXspR4p
Google Sheets - https://lnkd.in/d7eDi8pn
Statistics - https://lnkd.in/dgaw6KMW
Projects - https://lnkd.in/g2Fjzbma
Portfolio - https://t.iss.one/DataPortfolio
If you've read so far, do LIKE and share this channel with your friends & loved ones โฅ๏ธ
Hope it helps :)
โค8๐3
A Day in the Life of a Data Analyst
๐๐
https://medium.com/@data_analyst/a-day-in-the-life-of-a-data-analyst-8a6ab433a186?sk=38f05d4c415ed394eba19ef45bcf08e0
๐๐
https://medium.com/@data_analyst/a-day-in-the-life-of-a-data-analyst-8a6ab433a186?sk=38f05d4c415ed394eba19ef45bcf08e0
Medium
A Day in the Life of a Data Analyst
Ever wondered what itโs like to be a data analyst?
๐2โค1
Avoid directly copying YouTube projects onto your resume because if everyone looks the same, recruiters might discard resumes.
Instead, for eg, let's say you are working on a SQL case study, download a dataset from Kaggle (usually a CSV file), set up a Postgre/MySQL database, connect it with the data, and prompt ChatGPT with questions ranging from basic to advanced SQL.
Solve the questions step by step. When using PowerBI, connect to the database and create a compelling dashboard. Don't just upload the dataset; employ DAX queries, statistical functions, and avoid relying solely on drag-and-drop features. Use Formatting section to do creative stuff and add your unique element in the project.
ENJOY LEARNING ๐๐
Instead, for eg, let's say you are working on a SQL case study, download a dataset from Kaggle (usually a CSV file), set up a Postgre/MySQL database, connect it with the data, and prompt ChatGPT with questions ranging from basic to advanced SQL.
Solve the questions step by step. When using PowerBI, connect to the database and create a compelling dashboard. Don't just upload the dataset; employ DAX queries, statistical functions, and avoid relying solely on drag-and-drop features. Use Formatting section to do creative stuff and add your unique element in the project.
ENJOY LEARNING ๐๐
๐9โค2
Want to become a Data Analyst?
Hereโs a roadmap with essential skills, tools & concepts youโll 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, Bayesโ 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
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
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
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)
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 Pythonโs 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
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
How to become a Data Analyst in 2025
Free Resources to learn Data Analytics
Data Analyst Learning Plan
Join @free4unow_backup for more free courses
Like for more data analytics resources โค๏ธ
ENJOY LEARNING๐๐
Hereโs a roadmap with essential skills, tools & concepts youโll 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, Bayesโ 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
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
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
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)
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 Pythonโs 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
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
How to become a Data Analyst in 2025
Free Resources to learn Data Analytics
Data Analyst Learning Plan
Join @free4unow_backup for more free courses
Like for more data analytics resources โค๏ธ
ENJOY LEARNING๐๐
๐5โค2
โ๏ธ๐A beginner's roadmap for learning SQL:
๐บUnderstand Basics:
Learn what SQL is and its purpose in managing relational databases.
Understand basic database concepts like tables, rows, columns, and relationships.
๐บLearn SQL Syntax:
Familiarize yourself with SQL syntax for common commands like SELECT, INSERT, UPDATE, DELETE.
Understand clauses like WHERE, ORDER BY, GROUP BY, and JOIN.
๐บSetup a Database:
Install a relational database management system (RDBMS) like MySQL, SQLite, or PostgreSQL.
Practice creating databases, tables, and inserting data.
๐บRetrieve Data (SELECT):
Learn to retrieve data from a database using SELECT statements.
Practice filtering data using WHERE clause and sorting using ORDER BY.
๐บModify Data (INSERT, UPDATE, DELETE):
Understand how to insert new records, update existing ones, and delete data.
Be cautious with DELETE to avoid unintentional data loss.
๐บWorking with Functions:
Explore SQL functions like COUNT, AVG, SUM, MAX, MIN for data analysis.
Understand string functions, date functions, and mathematical functions.
๐บData Filtering and Sorting:
Learn advanced filtering techniques using AND, OR, and IN operators.
Practice sorting data using multiple columns.
๐บTable Relationships (JOIN):
Understand the concept of joining tables to retrieve data from multiple tables.
Learn about INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
๐บGrouping and Aggregation:
Explore GROUP BY clause to group data based on specific columns.
Understand aggregate functions for summarizing data (SUM, AVG, COUNT).
๐บSubqueries:
Learn to use subqueries to perform complex queries.
Understand how to use subqueries in SELECT, WHERE, and FROM clauses.
๐บIndexes and Optimization:
Gain knowledge about indexes and their role in optimizing queries.
Understand how to optimize SQL queries for better performance.
๐บTransactions and ACID Properties:
Learn about transactions and the ACID properties (Atomicity, Consistency, Isolation, Durability).
Understand how to use transactions to maintain data integrity.
๐บNormalization:
Understand the basics of database normalization to design efficient databases.
Learn about 1NF, 2NF, 3NF, and BCNF.
๐บBackup and Recovery:
Understand the importance of database backups.
Learn how to perform backups and recovery operations.
๐บPractice and Projects:
Apply your knowledge through hands-on projects.
Practice on platforms like LeetCode, HackerRank, or build your own small database-driven projects.
๐๐Remember to practice regularly and build real-world projects to reinforce your learning.
Happy Learning ๐ฅณ ๐
๐บUnderstand Basics:
Learn what SQL is and its purpose in managing relational databases.
Understand basic database concepts like tables, rows, columns, and relationships.
๐บLearn SQL Syntax:
Familiarize yourself with SQL syntax for common commands like SELECT, INSERT, UPDATE, DELETE.
Understand clauses like WHERE, ORDER BY, GROUP BY, and JOIN.
๐บSetup a Database:
Install a relational database management system (RDBMS) like MySQL, SQLite, or PostgreSQL.
Practice creating databases, tables, and inserting data.
๐บRetrieve Data (SELECT):
Learn to retrieve data from a database using SELECT statements.
Practice filtering data using WHERE clause and sorting using ORDER BY.
๐บModify Data (INSERT, UPDATE, DELETE):
Understand how to insert new records, update existing ones, and delete data.
Be cautious with DELETE to avoid unintentional data loss.
๐บWorking with Functions:
Explore SQL functions like COUNT, AVG, SUM, MAX, MIN for data analysis.
Understand string functions, date functions, and mathematical functions.
๐บData Filtering and Sorting:
Learn advanced filtering techniques using AND, OR, and IN operators.
Practice sorting data using multiple columns.
๐บTable Relationships (JOIN):
Understand the concept of joining tables to retrieve data from multiple tables.
Learn about INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
๐บGrouping and Aggregation:
Explore GROUP BY clause to group data based on specific columns.
Understand aggregate functions for summarizing data (SUM, AVG, COUNT).
๐บSubqueries:
Learn to use subqueries to perform complex queries.
Understand how to use subqueries in SELECT, WHERE, and FROM clauses.
๐บIndexes and Optimization:
Gain knowledge about indexes and their role in optimizing queries.
Understand how to optimize SQL queries for better performance.
๐บTransactions and ACID Properties:
Learn about transactions and the ACID properties (Atomicity, Consistency, Isolation, Durability).
Understand how to use transactions to maintain data integrity.
๐บNormalization:
Understand the basics of database normalization to design efficient databases.
Learn about 1NF, 2NF, 3NF, and BCNF.
๐บBackup and Recovery:
Understand the importance of database backups.
Learn how to perform backups and recovery operations.
๐บPractice and Projects:
Apply your knowledge through hands-on projects.
Practice on platforms like LeetCode, HackerRank, or build your own small database-driven projects.
๐๐Remember to practice regularly and build real-world projects to reinforce your learning.
Happy Learning ๐ฅณ ๐
๐3โค1
Can AI Completely Replace Data Analysts?
Despite AIโs capabilities, it has limitations:
1. AI Lacks Business Context & Critical Thinking
- AI cannot understand business goals, market trends, or human emotions.
- AI suggests patterns, but it cannot determine strategic actions based on insights.
Example: AI can identify a sales drop, but only a human analyst can explain why it happened.
2. AI is Only as Good as the Data It Learns From
- AI depends on quality dataโpoor data leads to inaccurate results.
- AI models cannot detect bias in datasets without human supervision.
Example: If an AI-driven hiring model is trained on biased data, it will continue biased hiring decisions unless humans correct it.
3. AI Cannot Replace Human Creativity & Soft Skills
- AI lacks creativity, problem-solving, and negotiation skills.
- AI cannot collaborate, lead teams, or interpret business goals.
Example: In a business meeting, a data analyst explains insights to leadership, whereas AI just provides numbers.
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
๐ Biggest Data Analytics Telegram Channel: https://t.iss.one/sqlspecialist
Like for more โค๏ธ
All the best ๐ ๐
Despite AIโs capabilities, it has limitations:
1. AI Lacks Business Context & Critical Thinking
- AI cannot understand business goals, market trends, or human emotions.
- AI suggests patterns, but it cannot determine strategic actions based on insights.
Example: AI can identify a sales drop, but only a human analyst can explain why it happened.
2. AI is Only as Good as the Data It Learns From
- AI depends on quality dataโpoor data leads to inaccurate results.
- AI models cannot detect bias in datasets without human supervision.
Example: If an AI-driven hiring model is trained on biased data, it will continue biased hiring decisions unless humans correct it.
3. AI Cannot Replace Human Creativity & Soft Skills
- AI lacks creativity, problem-solving, and negotiation skills.
- AI cannot collaborate, lead teams, or interpret business goals.
Example: In a business meeting, a data analyst explains insights to leadership, whereas AI just provides numbers.
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
๐ Biggest Data Analytics Telegram Channel: https://t.iss.one/sqlspecialist
Like for more โค๏ธ
All the best ๐ ๐
โค4๐3
๐ฎ Data Analyst Vs Data Engineer Vs Data Scientist ๐ฎ
Skills required to become data analyst
๐ Advanced Excel, Oracle/SQL
๐ Python/R
Skills required to become data engineer
๐ Python/ Java.
๐ SQL, NoSQL technologies like Cassandra or MongoDB
๐ Big data technologies like Hadoop, Hive/ Pig/ Spark
Skills required to become data Scientist
๐ In-depth knowledge of tools like R/ Python/ SAS.
๐ Well versed in various machine learning algorithms like scikit-learn, karas and tensorflow
๐ SQL and NoSQL
Bonus skill required: Data Visualization (PowerBI/ Tableau) & Statistics
Skills required to become data analyst
๐ Advanced Excel, Oracle/SQL
๐ Python/R
Skills required to become data engineer
๐ Python/ Java.
๐ SQL, NoSQL technologies like Cassandra or MongoDB
๐ Big data technologies like Hadoop, Hive/ Pig/ Spark
Skills required to become data Scientist
๐ In-depth knowledge of tools like R/ Python/ SAS.
๐ Well versed in various machine learning algorithms like scikit-learn, karas and tensorflow
๐ SQL and NoSQL
Bonus skill required: Data Visualization (PowerBI/ Tableau) & Statistics
๐2
How to start your career in data analysis for freshers ๐๐
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค1๐1
Recruiter: โWeโre hiring a Data Analyst!โ
Job description: SQL, Python, R, Excel, Power BI, Tableau, machine learning, business communication, stakeholder mgmt, ETL tools, APIs...
Salary: โน25,000/month.
Also recruiter: โWeโre looking for a fresher.โ
Job description: SQL, Python, R, Excel, Power BI, Tableau, machine learning, business communication, stakeholder mgmt, ETL tools, APIs...
Salary: โน25,000/month.
Also recruiter: โWeโre looking for a fresher.โ
๐11โค5
Complete Power BI Topics for Data Analysts ๐๐
1. Introduction to Power BI
- Overview and architecture
- Installation and setup
2. Loading and Transforming Data
- Connecting to various data sources
- Data loading techniques
- Data cleaning and transformation using Power Query
3. Data Modeling
- Creating relationships between tables
- DAX (Data Analysis Expressions) basics
- Calculated columns and measures
4. Data Visualization
- Building reports and dashboards
- Visualization best practices
- Custom visuals and formatting options
5. Advanced DAX
- Time intelligence functions
- Advanced DAX functions and scenarios
- Row context vs. filter context
6. Power BI Service
- Publishing and sharing reports
- Power BI workspaces and apps
- Power BI mobile app
7. Power BI Integration
- Integrating Power BI with other Microsoft tools (Excel, SharePoint, Teams)
- Embedding Power BI reports in websites and applications
8. Power BI Security
- Row-level security
- Data source permissions
- Power BI service security features
9. Power BI Governance
- Monitoring and managing usage
- Best practices for deployment
- Version control and deployment pipelines
10. Advanced Visualizations
- Drillthrough and bookmarks
- Hierarchies and custom visuals
- Geo-spatial visualizations
11. Power BI Tips and Tricks
- Productivity shortcuts
- Data exploration techniques
- Troubleshooting common issues
12. Power BI and AI Integration
- AI-powered features in Power BI
- Azure Machine Learning integration
- Advanced analytics in Power BI
13. Power BI Report Server
- On-premises deployment
- Managing and securing on-premises reports
- Power BI Report Server vs. Power BI Service
14. Real-world Use Cases
- Case studies and examples
- Industry-specific applications
- Practical scenarios and solutions
Like this post if you want me to continue this Power BI series ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1. Introduction to Power BI
- Overview and architecture
- Installation and setup
2. Loading and Transforming Data
- Connecting to various data sources
- Data loading techniques
- Data cleaning and transformation using Power Query
3. Data Modeling
- Creating relationships between tables
- DAX (Data Analysis Expressions) basics
- Calculated columns and measures
4. Data Visualization
- Building reports and dashboards
- Visualization best practices
- Custom visuals and formatting options
5. Advanced DAX
- Time intelligence functions
- Advanced DAX functions and scenarios
- Row context vs. filter context
6. Power BI Service
- Publishing and sharing reports
- Power BI workspaces and apps
- Power BI mobile app
7. Power BI Integration
- Integrating Power BI with other Microsoft tools (Excel, SharePoint, Teams)
- Embedding Power BI reports in websites and applications
8. Power BI Security
- Row-level security
- Data source permissions
- Power BI service security features
9. Power BI Governance
- Monitoring and managing usage
- Best practices for deployment
- Version control and deployment pipelines
10. Advanced Visualizations
- Drillthrough and bookmarks
- Hierarchies and custom visuals
- Geo-spatial visualizations
11. Power BI Tips and Tricks
- Productivity shortcuts
- Data exploration techniques
- Troubleshooting common issues
12. Power BI and AI Integration
- AI-powered features in Power BI
- Azure Machine Learning integration
- Advanced analytics in Power BI
13. Power BI Report Server
- On-premises deployment
- Managing and securing on-premises reports
- Power BI Report Server vs. Power BI Service
14. Real-world Use Cases
- Case studies and examples
- Industry-specific applications
- Practical scenarios and solutions
Like this post if you want me to continue this Power BI series ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค5๐1
Here are 10 project ideas to work on for Data Analytics
1. Customer Churn Prediction: Predict customer churn for subscription-based services. Skills: EDA, classification models. Tools: Python, Scikit-Learn.
2. Retail Sales Forecasting: Forecast sales using historical data. Skills: Time series analysis. Tools: Python, Statsmodels.
3. Sentiment Analysis: Analyze sentiments in product reviews or tweets. Skills: Text processing, NLP. Tools: Python, NLTK.
4. Loan Approval Prediction: Predict loan approvals based on credit risk. Skills: Classification models. Tools: Python, Scikit-Learn.
5. COVID-19 Data Analysis: Explore and visualize COVID-19 trends. Skills: EDA, visualization. Tools: Python, Tableau.
6. Traffic Accident Analysis: Discover patterns in traffic accidents. Skills: Clustering, heatmaps. Tools: Python, Folium.
7. Movie Recommendation System: Build a recommendation system using user ratings. Skills: Collaborative filtering. Tools: Python, Scikit-Learn.
8. E-commerce Analysis: Analyze top-performing products in e-commerce. Skills: EDA, association rules. Tools: Python, Apriori.
9. Stock Market Analysis: Analyze stock trends using historical data. Skills: Moving averages, sentiment analysis. Tools: Python, Matplotlib.
10. Employee Attrition Analysis: Predict employee turnover. Skills: Classification models, HR analytics. Tools: Python, Scikit-Learn.
And this is how you can work on
Hereโs a compact list of free resources for working on data analytics projects:
1. Datasets
โข Kaggle Datasets: Wide range of datasets and community discussions.
โข UCI Machine Learning Repository: Great for educational datasets.
โข Data.gov: U.S. government datasets (e.g., traffic, COVID-19).
2. Learning Platforms
โข YouTube: Channels like Data School and freeCodeCamp for tutorials.
โข 365DataScience: Data Science & AI Related Courses
3. Tools
โข Google Colab: Free Jupyter Notebooks for Python coding.
โข Tableau Public & Power BI Desktop: Free data visualization tools.
4. Project Resources
โข Kaggle Notebooks & GitHub: Code examples and project walk-throughs.
โข Data Analytics on Medium: Project guides and tutorials.
ENJOY LEARNING โ ๏ธโ ๏ธ
#datascienceprojects
1. Customer Churn Prediction: Predict customer churn for subscription-based services. Skills: EDA, classification models. Tools: Python, Scikit-Learn.
2. Retail Sales Forecasting: Forecast sales using historical data. Skills: Time series analysis. Tools: Python, Statsmodels.
3. Sentiment Analysis: Analyze sentiments in product reviews or tweets. Skills: Text processing, NLP. Tools: Python, NLTK.
4. Loan Approval Prediction: Predict loan approvals based on credit risk. Skills: Classification models. Tools: Python, Scikit-Learn.
5. COVID-19 Data Analysis: Explore and visualize COVID-19 trends. Skills: EDA, visualization. Tools: Python, Tableau.
6. Traffic Accident Analysis: Discover patterns in traffic accidents. Skills: Clustering, heatmaps. Tools: Python, Folium.
7. Movie Recommendation System: Build a recommendation system using user ratings. Skills: Collaborative filtering. Tools: Python, Scikit-Learn.
8. E-commerce Analysis: Analyze top-performing products in e-commerce. Skills: EDA, association rules. Tools: Python, Apriori.
9. Stock Market Analysis: Analyze stock trends using historical data. Skills: Moving averages, sentiment analysis. Tools: Python, Matplotlib.
10. Employee Attrition Analysis: Predict employee turnover. Skills: Classification models, HR analytics. Tools: Python, Scikit-Learn.
And this is how you can work on
Hereโs a compact list of free resources for working on data analytics projects:
1. Datasets
โข Kaggle Datasets: Wide range of datasets and community discussions.
โข UCI Machine Learning Repository: Great for educational datasets.
โข Data.gov: U.S. government datasets (e.g., traffic, COVID-19).
2. Learning Platforms
โข YouTube: Channels like Data School and freeCodeCamp for tutorials.
โข 365DataScience: Data Science & AI Related Courses
3. Tools
โข Google Colab: Free Jupyter Notebooks for Python coding.
โข Tableau Public & Power BI Desktop: Free data visualization tools.
4. Project Resources
โข Kaggle Notebooks & GitHub: Code examples and project walk-throughs.
โข Data Analytics on Medium: Project guides and tutorials.
ENJOY LEARNING โ ๏ธโ ๏ธ
#datascienceprojects
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Types Of Database YOU MUST KNOW
1. Relational Databases (e.g., MySQL, Oracle, SQL Server):
- Uses structured tables to store data.
- Offers data integrity and complex querying capabilities.
- Known for ACID compliance, ensuring reliable transactions.
- Includes features like foreign keys and security control, making them ideal for applications needing consistent data relationships.
2. Document Databases (e.g., CouchDB, MongoDB):
- Stores data as JSON documents, providing flexible schemas that can adapt to varying structures.
- Popular for semi-structured or unstructured data.
- Commonly used in content management and automated sharding for scalability.
3. In-Memory Databases (e.g., Apache Geode, Hazelcast):
- Focuses on real-time data processing with low-latency and high-speed transactions.
- Frequently used in scenarios like gaming applications and high-frequency trading where speed is critical.
4. Graph Databases (e.g., Neo4j, OrientDB):
- Best for handling complex relationships and networks, such as social networks or knowledge graphs.
- Features like pattern recognition and traversal make them suitable for analyzing connected data structures.
5. Time-Series Databases (e.g., Timescale, InfluxDB):
- Optimized for temporal data, IoT data, and fast retrieval.
- Ideal for applications requiring data compression and trend analysis over time, such as monitoring logs.
6. Spatial Databases (e.g., PostGIS, Oracle, Amazon Aurora):
- Specializes in geographic data and location-based queries.
- Commonly used for applications involving maps, GIS, and geospatial data analysis, including earth sciences.
Different types of databases are optimized for specific tasks. Relational databases excel in structured data management, while document, graph, in-memory, time-series, and spatial databases each have distinct strengths suited for modern data-driven applications.
1. Relational Databases (e.g., MySQL, Oracle, SQL Server):
- Uses structured tables to store data.
- Offers data integrity and complex querying capabilities.
- Known for ACID compliance, ensuring reliable transactions.
- Includes features like foreign keys and security control, making them ideal for applications needing consistent data relationships.
2. Document Databases (e.g., CouchDB, MongoDB):
- Stores data as JSON documents, providing flexible schemas that can adapt to varying structures.
- Popular for semi-structured or unstructured data.
- Commonly used in content management and automated sharding for scalability.
3. In-Memory Databases (e.g., Apache Geode, Hazelcast):
- Focuses on real-time data processing with low-latency and high-speed transactions.
- Frequently used in scenarios like gaming applications and high-frequency trading where speed is critical.
4. Graph Databases (e.g., Neo4j, OrientDB):
- Best for handling complex relationships and networks, such as social networks or knowledge graphs.
- Features like pattern recognition and traversal make them suitable for analyzing connected data structures.
5. Time-Series Databases (e.g., Timescale, InfluxDB):
- Optimized for temporal data, IoT data, and fast retrieval.
- Ideal for applications requiring data compression and trend analysis over time, such as monitoring logs.
6. Spatial Databases (e.g., PostGIS, Oracle, Amazon Aurora):
- Specializes in geographic data and location-based queries.
- Commonly used for applications involving maps, GIS, and geospatial data analysis, including earth sciences.
Different types of databases are optimized for specific tasks. Relational databases excel in structured data management, while document, graph, in-memory, time-series, and spatial databases each have distinct strengths suited for modern data-driven applications.
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Here is an A-Z list of essential programming terms:
1. Array: A data structure that stores a collection of elements of the same type in contiguous memory locations.
2. Boolean: A data type that represents true or false values.
3. Conditional Statement: A statement that executes different code based on a condition.
4. Debugging: The process of identifying and fixing errors or bugs in a program.
5. Exception: An event that occurs during the execution of a program that disrupts the normal flow of instructions.
6. Function: A block of code that performs a specific task and can be called multiple times in a program.
7. GUI (Graphical User Interface): A visual way for users to interact with a computer program using graphical elements like windows, buttons, and menus.
8. HTML (Hypertext Markup Language): The standard markup language used to create web pages.
9. Integer: A data type that represents whole numbers without any fractional part.
10. JSON (JavaScript Object Notation): A lightweight data interchange format commonly used for transmitting data between a server and a web application.
11. Loop: A programming construct that allows repeating a block of code multiple times.
12. Method: A function that is associated with an object in object-oriented programming.
13. Null: A special value that represents the absence of a value.
14. Object-Oriented Programming (OOP): A programming paradigm based on the concept of "objects" that encapsulate data and behavior.
15. Pointer: A variable that stores the memory address of another variable.
16. Queue: A data structure that follows the First-In-First-Out (FIFO) principle.
17. Recursion: A programming technique where a function calls itself to solve a problem.
18. String: A data type that represents a sequence of characters.
19. Tuple: An ordered collection of elements, similar to an array but immutable.
20. Variable: A named storage location in memory that holds a value.
21. While Loop: A loop that repeatedly executes a block of code as long as a specified condition is true.
Best Programming Resources: https://topmate.io/coding/898340
Join for more: https://t.iss.one/programming_guide
ENJOY LEARNING ๐๐
1. Array: A data structure that stores a collection of elements of the same type in contiguous memory locations.
2. Boolean: A data type that represents true or false values.
3. Conditional Statement: A statement that executes different code based on a condition.
4. Debugging: The process of identifying and fixing errors or bugs in a program.
5. Exception: An event that occurs during the execution of a program that disrupts the normal flow of instructions.
6. Function: A block of code that performs a specific task and can be called multiple times in a program.
7. GUI (Graphical User Interface): A visual way for users to interact with a computer program using graphical elements like windows, buttons, and menus.
8. HTML (Hypertext Markup Language): The standard markup language used to create web pages.
9. Integer: A data type that represents whole numbers without any fractional part.
10. JSON (JavaScript Object Notation): A lightweight data interchange format commonly used for transmitting data between a server and a web application.
11. Loop: A programming construct that allows repeating a block of code multiple times.
12. Method: A function that is associated with an object in object-oriented programming.
13. Null: A special value that represents the absence of a value.
14. Object-Oriented Programming (OOP): A programming paradigm based on the concept of "objects" that encapsulate data and behavior.
15. Pointer: A variable that stores the memory address of another variable.
16. Queue: A data structure that follows the First-In-First-Out (FIFO) principle.
17. Recursion: A programming technique where a function calls itself to solve a problem.
18. String: A data type that represents a sequence of characters.
19. Tuple: An ordered collection of elements, similar to an array but immutable.
20. Variable: A named storage location in memory that holds a value.
21. While Loop: A loop that repeatedly executes a block of code as long as a specified condition is true.
Best Programming Resources: https://topmate.io/coding/898340
Join for more: https://t.iss.one/programming_guide
ENJOY LEARNING ๐๐
๐4
If you want a data role THIS year, don't just create value, CAPTURE it.
๐ Creating value
- Build end-to-end data projects
- Work with cloud providers (AWS, Azure, GCP)
- Learn fundamentals (SQL, Excel, Power BI, Python)
๐ข Capture value
- Show your projects online (GitHub, LinkedIn)
- Network with data pros and hiring managers
- Quantify your achievements on your resume + interviews
๐ Creating value
- Build end-to-end data projects
- Work with cloud providers (AWS, Azure, GCP)
- Learn fundamentals (SQL, Excel, Power BI, Python)
๐ข Capture value
- Show your projects online (GitHub, LinkedIn)
- Network with data pros and hiring managers
- Quantify your achievements on your resume + interviews
๐1
Want to make a transition to a career in data?
Here is a 7-step plan for each data role
Data Scientist
Statistics and Math: Advanced statistics, linear algebra, calculus.
Machine Learning: Supervised and unsupervised learning algorithms.
xData Wrangling: Cleaning and transforming datasets.
Big Data: Hadoop, Spark, SQL/NoSQL databases.
Data Visualization: Matplotlib, Seaborn, D3.js.
Domain Knowledge: Industry-specific data science applications.
Data Analyst
Data Visualization: Tableau, Power BI, Excel for visualizations.
SQL: Querying and managing databases.
Statistics: Basic statistical analysis and probability.
Excel: Data manipulation and analysis.
Python/R: Programming for data analysis.
Data Cleaning: Techniques for data preprocessing.
Business Acumen: Understanding business context for insights.
Data Engineer
SQL/NoSQL Databases: MySQL, PostgreSQL, MongoDB, Cassandra.
ETL Tools: Apache NiFi, Talend, Informatica.
Big Data: Hadoop, Spark, Kafka.
Programming: Python, Java, Scala.
Data Warehousing: Redshift, BigQuery, Snowflake.
Cloud Platforms: AWS, GCP, Azure.
Data Modeling: Designing and implementing data models.
#data
Here is a 7-step plan for each data role
Data Scientist
Statistics and Math: Advanced statistics, linear algebra, calculus.
Machine Learning: Supervised and unsupervised learning algorithms.
xData Wrangling: Cleaning and transforming datasets.
Big Data: Hadoop, Spark, SQL/NoSQL databases.
Data Visualization: Matplotlib, Seaborn, D3.js.
Domain Knowledge: Industry-specific data science applications.
Data Analyst
Data Visualization: Tableau, Power BI, Excel for visualizations.
SQL: Querying and managing databases.
Statistics: Basic statistical analysis and probability.
Excel: Data manipulation and analysis.
Python/R: Programming for data analysis.
Data Cleaning: Techniques for data preprocessing.
Business Acumen: Understanding business context for insights.
Data Engineer
SQL/NoSQL Databases: MySQL, PostgreSQL, MongoDB, Cassandra.
ETL Tools: Apache NiFi, Talend, Informatica.
Big Data: Hadoop, Spark, Kafka.
Programming: Python, Java, Scala.
Data Warehousing: Redshift, BigQuery, Snowflake.
Cloud Platforms: AWS, GCP, Azure.
Data Modeling: Designing and implementing data models.
#data
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