Steps to become a data analyst
Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - https://t.iss.one/learndataanalysis
Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:
SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst
Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst
Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst
Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst
Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).
Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.
Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.
Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.
Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.
Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss
Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.
Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.
Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.
Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.
ENJOY LEARNING 👍👍
Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - https://t.iss.one/learndataanalysis
Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:
SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst
Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst
Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst
Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst
Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).
Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.
Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.
Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.
Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.
Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss
Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.
Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.
Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.
Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.
ENJOY LEARNING 👍👍
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Start your career in data analysis for freshers 😄👇
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 :)
❤4👍2
SQL is the gateway to all data jobs
You need to learn SQL to become:
• a data analyst
• a data scientist
• a data engineer
You can start your data journey today by:
• Learning SQL
• Getting familiar with SQL
• Build confidence by building projects with SQL
This is the path to become a data professional.
You need to learn SQL to become:
• a data analyst
• a data scientist
• a data engineer
You can start your data journey today by:
• Learning SQL
• Getting familiar with SQL
• Build confidence by building projects with SQL
This is the path to become a data professional.
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How to become a DIY data analyst:
Avoid formal education such as:
• Tutorials
• Bootcamps
• Certifications
• Expensive degrees
Instead your learnings on:
• SQL
• DAX
• PowerBi
• Building projects
Practical skills > Theorical skills is the DIY way.
Avoid formal education such as:
• Tutorials
• Bootcamps
• Certifications
• Expensive degrees
Instead your learnings on:
• SQL
• DAX
• PowerBi
• Building projects
Practical skills > Theorical skills is the DIY way.
🔥9👍2🥰2👏1
Follow the Data Analysts - SQL, Tableau, Excel, Power BI & Python channel on WhatsApp
👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
👍5❤4
Essential Python and SQL topics for data analysts 😄👇
Python Topics:
Python Resources - @pythonanalyst
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
SQL Topics:
SQL Resources - @sqlanalyst
1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters
2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY
3. Data Filtering
- WHERE Clause
- ORDER BY
4. Data Joins
- JOIN Operations
- Subqueries
5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization
6. Database Management
- Connecting to Databases
- SQLAlchemy
7. Database Design
- Data Types
- Normalization
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Python Topics:
Python Resources - @pythonanalyst
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
SQL Topics:
SQL Resources - @sqlanalyst
1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters
2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY
3. Data Filtering
- WHERE Clause
- ORDER BY
4. Data Joins
- JOIN Operations
- Subqueries
5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization
6. Database Management
- Connecting to Databases
- SQLAlchemy
7. Database Design
- Data Types
- Normalization
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
👍9❤7
𝗢𝗿𝗱𝗲𝗿 𝗢𝗳 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 in SQL ↓
1 → FROM (Tables selected).
2 → WHERE (Filters applied).
3 → GROUP BY (Rows grouped).
4 → HAVING (Filter on grouped data).
5 → SELECT (Columns selected).
6 → ORDER BY (Sort the data).
7 → LIMIT (Restrict number of rows).
𝗖𝗼𝗺𝗺𝗼𝗻 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗧𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ↓
↬ Find the second-highest salary:
SELECT MAX(Salary) FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees);
↬ Find duplicate records:
SELECT Name, COUNT(*)
FROM Emp
GROUP BY Name
HAVING COUNT(*) > 1;
1 → FROM (Tables selected).
2 → WHERE (Filters applied).
3 → GROUP BY (Rows grouped).
4 → HAVING (Filter on grouped data).
5 → SELECT (Columns selected).
6 → ORDER BY (Sort the data).
7 → LIMIT (Restrict number of rows).
𝗖𝗼𝗺𝗺𝗼𝗻 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗧𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ↓
↬ Find the second-highest salary:
SELECT MAX(Salary) FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees);
↬ Find duplicate records:
SELECT Name, COUNT(*)
FROM Emp
GROUP BY Name
HAVING COUNT(*) > 1;
👍7😁1
Top 10 Excel Interview Questions with Answers 😄👇
Free Resources to learn Excel: https://t.iss.one/excel_analyst
1. Question: What is the difference between CONCATENATE and "&" in Excel?
Answer: CONCATENATE and "&" both combine text, but "&" is more concise. For example,
2. Question: How can you freeze rows and columns simultaneously in Excel?
Answer: Use the "Freeze Panes" option under the "View" tab. Select the cell below and to the right of the rows and columns you want to freeze, and then click on "Freeze Panes."
3. Question: Explain the VLOOKUP function and when would you use it?
Answer: VLOOKUP searches for a value in the first column of a range and returns a corresponding value in the same row from another column. It's useful for looking up information in a table based on a specific criteria.
4. Question: What is the purpose of the IFERROR function?
Answer: IFERROR is used to handle errors in Excel formulas. It returns a specified value if a formula results in an error, and the actual result if there's no error.
5. Question: How do you create a PivotTable, and what is its purpose?
Answer: To create a PivotTable, select your data, go to the "Insert" tab, and choose "PivotTable." It summarizes and analyzes data in a spreadsheet, allowing you to make sense of large datasets.
6. Question: Explain the difference between relative and absolute cell references.
Answer: Relative references change when you copy a formula to another cell, while absolute references stay fixed. Use a
7. Question: What is the purpose of the INDEX and MATCH functions?
Answer: INDEX returns a value in a specified range based on the row and column number, while MATCH searches for a value in a range and returns its relative position. Combined, they provide a flexible way to look up data.
8. Question: How can you find and remove duplicate values in Excel?
Answer: Use the "Remove Duplicates" feature under the "Data" tab. Select the range containing duplicates, go to "Data" -> "Remove Duplicates," and choose the columns to check for duplicates.
9. Question: Explain the difference between a workbook and a worksheet.
Answer: A workbook is the entire Excel file, while a worksheet is a single sheet within that file. Workbooks can contain multiple worksheets.
10. Question: What is the purpose of the COUNTIF function?
Answer: COUNTIF counts the number of cells within a range that meet a specified condition. For example,
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Free Resources to learn Excel: https://t.iss.one/excel_analyst
1. Question: What is the difference between CONCATENATE and "&" in Excel?
Answer: CONCATENATE and "&" both combine text, but "&" is more concise. For example,
=A1&B1 achieves the same result as =CONCATENATE(A1, B1).2. Question: How can you freeze rows and columns simultaneously in Excel?
Answer: Use the "Freeze Panes" option under the "View" tab. Select the cell below and to the right of the rows and columns you want to freeze, and then click on "Freeze Panes."
3. Question: Explain the VLOOKUP function and when would you use it?
Answer: VLOOKUP searches for a value in the first column of a range and returns a corresponding value in the same row from another column. It's useful for looking up information in a table based on a specific criteria.
4. Question: What is the purpose of the IFERROR function?
Answer: IFERROR is used to handle errors in Excel formulas. It returns a specified value if a formula results in an error, and the actual result if there's no error.
5. Question: How do you create a PivotTable, and what is its purpose?
Answer: To create a PivotTable, select your data, go to the "Insert" tab, and choose "PivotTable." It summarizes and analyzes data in a spreadsheet, allowing you to make sense of large datasets.
6. Question: Explain the difference between relative and absolute cell references.
Answer: Relative references change when you copy a formula to another cell, while absolute references stay fixed. Use a
$ symbol to make a reference absolute (e.g., $A$1).7. Question: What is the purpose of the INDEX and MATCH functions?
Answer: INDEX returns a value in a specified range based on the row and column number, while MATCH searches for a value in a range and returns its relative position. Combined, they provide a flexible way to look up data.
8. Question: How can you find and remove duplicate values in Excel?
Answer: Use the "Remove Duplicates" feature under the "Data" tab. Select the range containing duplicates, go to "Data" -> "Remove Duplicates," and choose the columns to check for duplicates.
9. Question: Explain the difference between a workbook and a worksheet.
Answer: A workbook is the entire Excel file, while a worksheet is a single sheet within that file. Workbooks can contain multiple worksheets.
10. Question: What is the purpose of the COUNTIF function?
Answer: COUNTIF counts the number of cells within a range that meet a specified condition. For example,
=COUNTIF(A1:A10, ">50") counts the cells in A1 to A10 that are greater than 50.Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤6👍2
Hello friends,
I hope you all are very well, see many of you are messaging me about the roadmap and career of Python, Excel, Power BI, SQL, message everyone one by one for me, so I created a WhatsApp channel where you get all this, I give the link below, you can join it from here 👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
I hope you all are very well, see many of you are messaging me about the roadmap and career of Python, Excel, Power BI, SQL, message everyone one by one for me, so I created a WhatsApp channel where you get all this, I give the link below, you can join it from here 👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
👍4
Complete Syllabus for Data Analytics interview:
SQL:
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:
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:
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:
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 😄❤️
Share our channel link with your friends: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
SQL:
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:
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:
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:
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 😄❤️
Share our channel link with your friends: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
👍12❤4
There has never been a better time to become a data analyst.
Tackle the tools:
- Excel
- SQL
- PowerBI/Tableau
- Python/R
Sharpen these soft skills:
- Communication
- Storytelling
- Critical thinking
- Business acumen
And let your journey begin.
Tackle the tools:
- Excel
- SQL
- PowerBI/Tableau
- Python/R
Sharpen these soft skills:
- Communication
- Storytelling
- Critical thinking
- Business acumen
And let your journey begin.
👍11❤1