Once you've learned/mastered the fundamentals of SQL, try learning these:
- ๐๐๐๐๐ฌ: LEFT, RIGHT, INNER, OUTER joins.
- ๐๐ ๐ ๐ซ๐๐ ๐๐ญ๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Utilize SUM, COUNT, AVG, and others for efficient data summarization.
- ๐๐๐๐ ๐๐๐๐ ๐๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ๐ฌ: Use conditional logic to tailor query results.
- ๐๐๐ญ๐ ๐๐ข๐ฆ๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Master manipulating dates and times for precise analysis.
Next, explore advanced methods to structure and reuse SQL code effectively:
- ๐๐จ๐ฆ๐ฆ๐จ๐ง ๐๐๐๐ฅ๐ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ (๐๐๐๐ฌ): Simplify complex queries into manageable parts to increase the readability.
- ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ: Nest queries for more granular data retrieval.
- ๐๐๐ฆ๐ฉ๐จ๐ซ๐๐ซ๐ฒ ๐๐๐๐ฅ๐๐ฌ: Create and manipulate temporary data sets for specific tasks.
Then, move on to advanced ones:
- ๐๐ข๐ง๐๐จ๐ฐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Perform advanced calculations over sets of rows with ease.
- ๐๐ญ๐จ๐ซ๐๐ ๐๐ซ๐จ๐๐๐๐ฎ๐ซ๐๐ฌ: Create reusable SQL routines for streamlined operations.
- ๐๐ซ๐ข๐ ๐ ๐๐ซ๐ฌ: Automate database actions based on specific events.
- ๐๐๐๐ฎ๐ซ๐ฌ๐ข๐ฏ๐ ๐๐๐๐ฌ: Solve complex problems using recursive queries.
- ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ: Techniques to enhance performance and efficiency.
- ๐๐๐๐๐ฌ: LEFT, RIGHT, INNER, OUTER joins.
- ๐๐ ๐ ๐ซ๐๐ ๐๐ญ๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Utilize SUM, COUNT, AVG, and others for efficient data summarization.
- ๐๐๐๐ ๐๐๐๐ ๐๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ๐ฌ: Use conditional logic to tailor query results.
- ๐๐๐ญ๐ ๐๐ข๐ฆ๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Master manipulating dates and times for precise analysis.
Next, explore advanced methods to structure and reuse SQL code effectively:
- ๐๐จ๐ฆ๐ฆ๐จ๐ง ๐๐๐๐ฅ๐ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ (๐๐๐๐ฌ): Simplify complex queries into manageable parts to increase the readability.
- ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ: Nest queries for more granular data retrieval.
- ๐๐๐ฆ๐ฉ๐จ๐ซ๐๐ซ๐ฒ ๐๐๐๐ฅ๐๐ฌ: Create and manipulate temporary data sets for specific tasks.
Then, move on to advanced ones:
- ๐๐ข๐ง๐๐จ๐ฐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Perform advanced calculations over sets of rows with ease.
- ๐๐ญ๐จ๐ซ๐๐ ๐๐ซ๐จ๐๐๐๐ฎ๐ซ๐๐ฌ: Create reusable SQL routines for streamlined operations.
- ๐๐ซ๐ข๐ ๐ ๐๐ซ๐ฌ: Automate database actions based on specific events.
- ๐๐๐๐ฎ๐ซ๐ฌ๐ข๐ฏ๐ ๐๐๐๐ฌ: Solve complex problems using recursive queries.
- ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ: Techniques to enhance performance and efficiency.
๐4๐2
Powerful One-Liners in Python You Should Know!
1. Swap Two Numbers
n1, n2 = n2, n1
2. Reverse a String
reversed_string = input_string[::-1]
3. Factorial of a Number
fact = lambda n: [1, 0][n > 1] or fact(n - 1) * n
4. Find Prime Numbers (2 to 10)
primes = list(filter(lambda x: all(x % y != 0 for y in range(2, x)), range(2, 10)))
5. Check if a String is Palindrome
palindrome = input_string == input_string[::-1]
Free Python Resources: https://t.iss.one/pythonproz
1. Swap Two Numbers
n1, n2 = n2, n1
2. Reverse a String
reversed_string = input_string[::-1]
3. Factorial of a Number
fact = lambda n: [1, 0][n > 1] or fact(n - 1) * n
4. Find Prime Numbers (2 to 10)
primes = list(filter(lambda x: all(x % y != 0 for y in range(2, x)), range(2, 10)))
5. Check if a String is Palindrome
palindrome = input_string == input_string[::-1]
Free Python Resources: https://t.iss.one/pythonproz
๐4
๐ Free Power BI Course by Microsoft
https://learn.microsoft.com/en-us/power-bi/
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
https://learn.microsoft.com/en-us/power-bi/
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
โค1
How much Statistics must I know to become a Data Scientist?
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐
โ Bayes' Theorem & conditional probability
โ Permutations & combinations
โ Card & die roll problem-solving
๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐๐ฒ ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ & ๐ฑ๐ถ๐๐๐ฟ๐ถ๐ฏ๐๐๐ถ๐ผ๐ป๐
โ Mean, median, mode
โ Standard deviation and variance
โ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐๐ถ๐ฎ๐น ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐
โ A/B experimentation
โ T-test, Z-test, Chi-squared tests
โ Type 1 & 2 errors
โ Sampling techniques & biases
โ Confidence intervals & p-values
โ Central Limit Theorem
โ Causal inference techniques
๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
โ Logistic & Linear regression
โ Decision trees & random forests
โ Clustering models
โ Feature engineering
โ Feature selection methods
โ Model testing & validation
โ Time series analysis
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content ๐๐
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐
โ Bayes' Theorem & conditional probability
โ Permutations & combinations
โ Card & die roll problem-solving
๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐๐ฒ ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ & ๐ฑ๐ถ๐๐๐ฟ๐ถ๐ฏ๐๐๐ถ๐ผ๐ป๐
โ Mean, median, mode
โ Standard deviation and variance
โ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐๐ถ๐ฎ๐น ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐
โ A/B experimentation
โ T-test, Z-test, Chi-squared tests
โ Type 1 & 2 errors
โ Sampling techniques & biases
โ Confidence intervals & p-values
โ Central Limit Theorem
โ Causal inference techniques
๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
โ Logistic & Linear regression
โ Decision trees & random forests
โ Clustering models
โ Feature engineering
โ Feature selection methods
โ Model testing & validation
โ Time series analysis
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content ๐๐
๐4๐1
Here are 5 key Python libraries/ concepts that are particularly important for data analysts:
1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.
3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.
4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.
5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.
By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.
Credits: https://t.iss.one/free4unow_backup
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.
3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.
4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.
5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.
By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.
Credits: https://t.iss.one/free4unow_backup
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
๐4โค2
Learning data analytics in 2025 can be an exciting and rewarding journey. Here are some steps you can take to start learning data analytics:
1. Understand the Basics: Begin by familiarizing yourself with the basic concepts of data analytics, such as data types, data visualization, statistical analysis, and machine learning.
2. Take Online Courses: There are many online platforms that offer courses in data analytics, such as Coursera, Udemy, and edX. Look for courses that cover topics like data manipulation, data visualization, and predictive modeling.
3. Practice with Real Data: To truly understand data analytics, you need to practice with real datasets. You can find datasets on websites like Kaggle or UCI Machine Learning Repository to work on real-world projects.
4. Learn Tools and Software: Familiarize yourself with popular data analytics tools and software like Python, R, SQL, Tableau, and Power BI. These tools are commonly used in the industry for data analysis.
5. Join Data Analytics Communities: Join online communities like Reddit, LinkedIn groups, or local meetups to connect with other data analysts and learn from their experiences.
6. Build a Portfolio: Create a portfolio of your data analytics projects to showcase your skills to potential employers. Include detailed descriptions of the problem you solved, the data analysis techniques you used, and the results you achieved.
7. Stay Updated: Data analytics is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow industry blogs, attend webinars, and participate in online forums to stay informed.
Give credits while sharing: https://t.iss.one/learndataanalysis
1. Understand the Basics: Begin by familiarizing yourself with the basic concepts of data analytics, such as data types, data visualization, statistical analysis, and machine learning.
2. Take Online Courses: There are many online platforms that offer courses in data analytics, such as Coursera, Udemy, and edX. Look for courses that cover topics like data manipulation, data visualization, and predictive modeling.
3. Practice with Real Data: To truly understand data analytics, you need to practice with real datasets. You can find datasets on websites like Kaggle or UCI Machine Learning Repository to work on real-world projects.
4. Learn Tools and Software: Familiarize yourself with popular data analytics tools and software like Python, R, SQL, Tableau, and Power BI. These tools are commonly used in the industry for data analysis.
5. Join Data Analytics Communities: Join online communities like Reddit, LinkedIn groups, or local meetups to connect with other data analysts and learn from their experiences.
6. Build a Portfolio: Create a portfolio of your data analytics projects to showcase your skills to potential employers. Include detailed descriptions of the problem you solved, the data analysis techniques you used, and the results you achieved.
7. Stay Updated: Data analytics is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow industry blogs, attend webinars, and participate in online forums to stay informed.
Give credits while sharing: https://t.iss.one/learndataanalysis
๐2
Power BI DAX Cheatsheet ๐
1๏ธโฃ Basics of DAX (Data Analysis Expressions)
DAX is used to create custom calculations in Power BI.
It works with tables and columns, not individual cells.
Functions in DAX are similar to Excel but optimized for relational data.
2๏ธโฃ Aggregation Functions
SUM(ColumnName): Adds all values in a column.
AVERAGE(ColumnName): Finds the mean of values.
MIN(ColumnName): Returns the smallest value.
MAX(ColumnName): Returns the largest value.
COUNT(ColumnName): Counts non-empty values.
COUNTROWS(TableName): Counts rows in a table.
3๏ธโฃ Logical Functions
IF(condition, result_if_true, result_if_false): Conditional statement.
SWITCH(expression, value1, result1, value2, result2, default): Alternative to nested IF.
AND(condition1, condition2): Returns TRUE if both conditions are met.
OR(condition1, condition2): Returns TRUE if either condition is met.
4๏ธโฃ Time Intelligence Functions
TODAY(): Returns the current date.
YEAR(TODAY()): Extracts the year from a date.
TOTALYTD(SUM(Sales[Amount]), Date[Date]): Year-to-date total.
SAMEPERIODLASTYEAR(Date[Date]): Returns values from the same period last year.
DATEADD(Date[Date], -1, MONTH): Shifts dates by a specified interval.
5๏ธโฃ Filtering Functions
FILTER(Table, Condition): Returns a filtered table.
ALL(TableName): Removes all filters from a table.
ALLEXCEPT(TableName, Column1, Column2): Removes all filters except specified columns.
KEEPFILTERS(FilterExpression): Keeps filters applied while using other functions.
6๏ธโฃ Ranking & Row Context Functions
RANKX(Table, Expression, [Value], [Order]): Ranks values in a column.
TOPN(N, Table, OrderByExpression): Returns the top N rows based on an expression.
7๏ธโฃ Iterators (Row-by-Row Calculations)
SUMX(Table, Expression): Iterates over a table and sums calculated values.
AVERAGEX(Table, Expression): Iterates over a table and finds the average.
MAXX(Table, Expression): Finds the maximum value based on an expression.
8๏ธโฃ Relationships & Lookup Functions
RELATED(ColumnName): Fetches a related column from another table.
LOOKUPVALUE(ColumnName, SearchColumn, SearchValue): Returns a value from a column where another column matches a value.
9๏ธโฃ Variables in DAX
VAR variableName = Expression RETURN variableName
Improves performance by reducing redundant calculations.
๐ Advanced DAX Concepts
Calculated Columns: Created at the column level, stored in the data model.
Measures: Dynamic calculations based on user interactions in Power BI visuals.
Row Context vs. Filter Context: Understanding how DAX applies calculations at different levels.
Free Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
React with โค๏ธ for free cheatsheets
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1๏ธโฃ Basics of DAX (Data Analysis Expressions)
DAX is used to create custom calculations in Power BI.
It works with tables and columns, not individual cells.
Functions in DAX are similar to Excel but optimized for relational data.
2๏ธโฃ Aggregation Functions
SUM(ColumnName): Adds all values in a column.
AVERAGE(ColumnName): Finds the mean of values.
MIN(ColumnName): Returns the smallest value.
MAX(ColumnName): Returns the largest value.
COUNT(ColumnName): Counts non-empty values.
COUNTROWS(TableName): Counts rows in a table.
3๏ธโฃ Logical Functions
IF(condition, result_if_true, result_if_false): Conditional statement.
SWITCH(expression, value1, result1, value2, result2, default): Alternative to nested IF.
AND(condition1, condition2): Returns TRUE if both conditions are met.
OR(condition1, condition2): Returns TRUE if either condition is met.
4๏ธโฃ Time Intelligence Functions
TODAY(): Returns the current date.
YEAR(TODAY()): Extracts the year from a date.
TOTALYTD(SUM(Sales[Amount]), Date[Date]): Year-to-date total.
SAMEPERIODLASTYEAR(Date[Date]): Returns values from the same period last year.
DATEADD(Date[Date], -1, MONTH): Shifts dates by a specified interval.
5๏ธโฃ Filtering Functions
FILTER(Table, Condition): Returns a filtered table.
ALL(TableName): Removes all filters from a table.
ALLEXCEPT(TableName, Column1, Column2): Removes all filters except specified columns.
KEEPFILTERS(FilterExpression): Keeps filters applied while using other functions.
6๏ธโฃ Ranking & Row Context Functions
RANKX(Table, Expression, [Value], [Order]): Ranks values in a column.
TOPN(N, Table, OrderByExpression): Returns the top N rows based on an expression.
7๏ธโฃ Iterators (Row-by-Row Calculations)
SUMX(Table, Expression): Iterates over a table and sums calculated values.
AVERAGEX(Table, Expression): Iterates over a table and finds the average.
MAXX(Table, Expression): Finds the maximum value based on an expression.
8๏ธโฃ Relationships & Lookup Functions
RELATED(ColumnName): Fetches a related column from another table.
LOOKUPVALUE(ColumnName, SearchColumn, SearchValue): Returns a value from a column where another column matches a value.
9๏ธโฃ Variables in DAX
VAR variableName = Expression RETURN variableName
Improves performance by reducing redundant calculations.
๐ Advanced DAX Concepts
Calculated Columns: Created at the column level, stored in the data model.
Measures: Dynamic calculations based on user interactions in Power BI visuals.
Row Context vs. Filter Context: Understanding how DAX applies calculations at different levels.
Free Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
React with โค๏ธ for free cheatsheets
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐6โค2
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)
- 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.
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)
- 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.
โค5๐1
๐๐Data Analytics skills and projects to add in a resume to get shortlisted
1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.
2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.
3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.
4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.
5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.
6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.
7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.
8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.
9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.
10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.
11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.
12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.
๐ผTailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.
2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.
3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.
4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.
5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.
6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.
7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.
8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.
9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.
10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.
11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.
12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.
๐ผTailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
๐4
Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources:
๐๏ธWeek 1: Foundation of Data Analytics
โพDay 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.
โพDay 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
โพDay 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
๐๏ธWeek 2: Intermediate Data Analytics Skills
โพDay 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
โพDay 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
โพDay 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
๐๏ธWeek 3: Advanced Techniques and Tools
โพDay 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
โพDay 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
โพDay 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
๐๏ธWeek 4: Projects and Practice
โพDay 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
โพDay 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
โพDay 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
๐Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
๐๏ธWeek 1: Foundation of Data Analytics
โพDay 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.
โพDay 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
โพDay 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
๐๏ธWeek 2: Intermediate Data Analytics Skills
โพDay 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
โพDay 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
โพDay 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
๐๏ธWeek 3: Advanced Techniques and Tools
โพDay 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
โพDay 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
โพDay 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
๐๏ธWeek 4: Projects and Practice
โพDay 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
โพDay 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
โพDay 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
๐Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
๐2๐1
Data Analytics Interview Topics in structured way :
๐ตPython: Data Structures: Lists, tuples, dictionaries, sets Pandas: Data manipulation (DataFrame operations, merging, reshaping) NumPy: Numeric computing, arrays Visualization: Matplotlib, Seaborn for creating charts
๐ตSQL: Basic : SELECT, WHERE, JOIN, GROUP BY, ORDER BY Advanced : Subqueries, nested queries, window functions DBMS: Creating tables, altering schema, indexing Joins: Inner join, outer join, left/right join Data Manipulation: UPDATE, DELETE, INSERT statements Aggregate Functions: SUM, AVG, COUNT, MAX, MIN
๐ตExcel: Formulas & Functions: VLOOKUP, HLOOKUP, IF, SUMIF, COUNTIF Data Cleaning: Removing duplicates, handling errors, text-to-columns PivotTables Charts and Graphs What-If Analysis: Scenario Manager, Goal Seek, Solver
๐ตPower BI:
Data Modeling: Creating relationships between datasets
Transformation: Cleaning & shaping data using
Power Query Editor Visualization: Creating interactive reports and dashboards
DAX (Data Analysis Expressions): Formulas for calculated columns, measures Publishing and sharing reports, scheduling data refresh
๐ต Statistics Fundamentals: Mean, median, mode Variance, standard deviation Probability distributions Hypothesis testing, p-values, confidence intervals
๐ตData Manipulation and Cleaning: Data preprocessing techniques (handling missing values, outliers), Data normalization and standardization Data transformation Handling categorical data
๐ตData Visualization: Chart types (bar, line, scatter, histogram, boxplot) Data visualization libraries (matplotlib, seaborn, ggplot) Effective data storytelling through visualization
Also showcase these skills using data portfolio if possible
Like for more content like this ๐
๐ตPython: Data Structures: Lists, tuples, dictionaries, sets Pandas: Data manipulation (DataFrame operations, merging, reshaping) NumPy: Numeric computing, arrays Visualization: Matplotlib, Seaborn for creating charts
๐ตSQL: Basic : SELECT, WHERE, JOIN, GROUP BY, ORDER BY Advanced : Subqueries, nested queries, window functions DBMS: Creating tables, altering schema, indexing Joins: Inner join, outer join, left/right join Data Manipulation: UPDATE, DELETE, INSERT statements Aggregate Functions: SUM, AVG, COUNT, MAX, MIN
๐ตExcel: Formulas & Functions: VLOOKUP, HLOOKUP, IF, SUMIF, COUNTIF Data Cleaning: Removing duplicates, handling errors, text-to-columns PivotTables Charts and Graphs What-If Analysis: Scenario Manager, Goal Seek, Solver
๐ตPower BI:
Data Modeling: Creating relationships between datasets
Transformation: Cleaning & shaping data using
Power Query Editor Visualization: Creating interactive reports and dashboards
DAX (Data Analysis Expressions): Formulas for calculated columns, measures Publishing and sharing reports, scheduling data refresh
๐ต Statistics Fundamentals: Mean, median, mode Variance, standard deviation Probability distributions Hypothesis testing, p-values, confidence intervals
๐ตData Manipulation and Cleaning: Data preprocessing techniques (handling missing values, outliers), Data normalization and standardization Data transformation Handling categorical data
๐ตData Visualization: Chart types (bar, line, scatter, histogram, boxplot) Data visualization libraries (matplotlib, seaborn, ggplot) Effective data storytelling through visualization
Also showcase these skills using data portfolio if possible
Like for more content like this ๐
๐3
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
๐จโ๐ป 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.iss.one/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.iss.one/pythonfreebootcamp/134
7. https://t.iss.one/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://t.iss.one/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://t.iss.one/pythonspecialist/33
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
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
๐จโ๐ป 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.iss.one/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.iss.one/pythonfreebootcamp/134
7. https://t.iss.one/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://t.iss.one/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://t.iss.one/pythonspecialist/33
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
๐3โค1๐ฅ1
Questions & Answers for Data Analyst Interview
Question 1: Describe a time when you used data analysis to solve a business problem.
Ideal answer: This is your opportunity to showcase your data analysis skills in a real-world context. Be specific and provide examples of your work. For example, you could talk about a time when you used data analysis to identify customer churn, improve marketing campaigns, or optimize product development.
Question 2: What are some of the challenges you have faced in previous data analysis projects, and how did you overcome them?
Ideal answer: This question is designed to assess your problem-solving skills and your ability to learn from your experiences. Be honest and upfront about the challenges you have faced, but also focus on how you overcame them. For example, you could talk about a time when you had to deal with a large and messy dataset, or a time when you had to work with a tight deadline.
Question 3: How do you handle missing values in a dataset?
Ideal answer: Missing values are a common problem in data analysis, so it is important to know how to handle them properly. There are a variety of different methods that you can use, depending on the specific situation. For example, you could delete the rows with missing values, impute the missing values using a statistical method, or assign a default value to the missing values.
Question 4: How do you identify and remove outliers?
Ideal answer: Outliers are data points that are significantly different from the rest of the data. They can be caused by data errors or by natural variation in the data. It is important to identify and remove outliers before performing data analysis, as they can skew the results. There are a variety of different methods that you can use to identify outliers, such as the interquartile range (IQR) method or the standard deviation method.
Question 5: How do you interpret and communicate the results of your data analysis to non-technical audiences?
Ideal answer: It is important to be able to communicate your data analysis findings to both technical and non-technical audiences. When communicating to non-technical audiences, it is important to avoid using jargon and to focus on the key takeaways from your analysis. You can use data visualization tools to help you communicate your findings in a clear and concise way.
In addition to providing specific examples and answers to the questions, it is also important to be enthusiastic and demonstrate your passion for data analysis. Show the interviewer that you are excited about the opportunity to use your skills to solve real-world problems.
Question 1: Describe a time when you used data analysis to solve a business problem.
Ideal answer: This is your opportunity to showcase your data analysis skills in a real-world context. Be specific and provide examples of your work. For example, you could talk about a time when you used data analysis to identify customer churn, improve marketing campaigns, or optimize product development.
Question 2: What are some of the challenges you have faced in previous data analysis projects, and how did you overcome them?
Ideal answer: This question is designed to assess your problem-solving skills and your ability to learn from your experiences. Be honest and upfront about the challenges you have faced, but also focus on how you overcame them. For example, you could talk about a time when you had to deal with a large and messy dataset, or a time when you had to work with a tight deadline.
Question 3: How do you handle missing values in a dataset?
Ideal answer: Missing values are a common problem in data analysis, so it is important to know how to handle them properly. There are a variety of different methods that you can use, depending on the specific situation. For example, you could delete the rows with missing values, impute the missing values using a statistical method, or assign a default value to the missing values.
Question 4: How do you identify and remove outliers?
Ideal answer: Outliers are data points that are significantly different from the rest of the data. They can be caused by data errors or by natural variation in the data. It is important to identify and remove outliers before performing data analysis, as they can skew the results. There are a variety of different methods that you can use to identify outliers, such as the interquartile range (IQR) method or the standard deviation method.
Question 5: How do you interpret and communicate the results of your data analysis to non-technical audiences?
Ideal answer: It is important to be able to communicate your data analysis findings to both technical and non-technical audiences. When communicating to non-technical audiences, it is important to avoid using jargon and to focus on the key takeaways from your analysis. You can use data visualization tools to help you communicate your findings in a clear and concise way.
In addition to providing specific examples and answers to the questions, it is also important to be enthusiastic and demonstrate your passion for data analysis. Show the interviewer that you are excited about the opportunity to use your skills to solve real-world problems.
๐5
Python for Data Analysis: Must-Know Libraries ๐๐
Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.
๐ฅ Essential Python Libraries for Data Analysis:
โ Pandas โ The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.
๐ Example: Loading a CSV file and displaying the first 5 rows:
โ NumPy โ Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.
๐ Example: Creating an array and performing basic operations:
โ Matplotlib & Seaborn โ These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.
๐ Example: Creating a basic bar chart:
โ Scikit-Learn โ A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.
โ OpenPyXL โ Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.
๐ก Challenge for You!
Try writing a Python script that:
1๏ธโฃ Reads a CSV file
2๏ธโฃ Cleans missing data
3๏ธโฃ Creates a simple visualization
React with โฅ๏ธ if you want me to post the script for above challenge! โฌ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.
๐ฅ Essential Python Libraries for Data Analysis:
โ Pandas โ The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.
๐ Example: Loading a CSV file and displaying the first 5 rows:
import pandas as pd df = pd.read_csv('data.csv') print(df.head()) โ NumPy โ Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.
๐ Example: Creating an array and performing basic operations:
import numpy as np arr = np.array([10, 20, 30]) print(arr.mean()) # Calculates the average
โ Matplotlib & Seaborn โ These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.
๐ Example: Creating a basic bar chart:
import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [5, 7, 3]) plt.show()
โ Scikit-Learn โ A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.
โ OpenPyXL โ Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.
๐ก Challenge for You!
Try writing a Python script that:
1๏ธโฃ Reads a CSV file
2๏ธโฃ Cleans missing data
3๏ธโฃ Creates a simple visualization
React with โฅ๏ธ if you want me to post the script for above challenge! โฌ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐5๐1
This is how data analytics teams work!
Example:
1) Senior Management at Swiggy/Infosys/HDFC/XYZ company needs data-driven insights to solve a critical business challenge.
So, they onboard a data analytics team to provide support.
2) A team from Analytics Team/Consulting Firm/Internal Data Science Division is onboarded.
The team typically consists of a Lead Analyst/Manager and 2-3 Data Analysts/Junior Analysts.
3) This data analytics team (1 manager + 2-3 analysts) is part of a bigger ecosystem that they can rely upon:
- A Senior Data Scientist/Analytics Lead who has industry knowledge and experience solving similar problems.
- Subject Matter Experts (SMEs) from various domains like AI, Machine Learning, or industry-specific fields (e.g., Marketing, Supply Chain, Finance).
- Business Intelligence (BI) Experts and Data Engineers who ensure that the data is well-structured and easy to interpret.
- External Tools & Platforms (e.g., Power BI, Tableau, Google Analytics) that can be leveraged for advanced analytics.
- Data Experts who specialize in various data sources, research, and methods to get the right information.
4) Every member of this ecosystem collaborates to create value for the client:
- The entire team works toward solving the clientโs business problem using data-driven insights.
- The Manager & Analysts may not be industry experts but have access to the right tools and people to bring the expertise required.
- If help is needed from a Data Scientist sitting in New York or a Cloud Engineer in Singapore, itโs availableโcollaboration is key!
End of the day:
1) Data analytics teams arenโt just about crunching numbersโtheyโre about solving problems using data-driven insights.
2) EVERYONE in this ecosystem plays a vital role and is rewarded well because the value they create helps the business make informed decisions!
3) You should consider working in this field for a few years, at least. Itโll teach you how to break down complex business problems and solve them with data. And trust me, data-driven decision-making is one of the most powerful skills to have today!
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Example:
1) Senior Management at Swiggy/Infosys/HDFC/XYZ company needs data-driven insights to solve a critical business challenge.
So, they onboard a data analytics team to provide support.
2) A team from Analytics Team/Consulting Firm/Internal Data Science Division is onboarded.
The team typically consists of a Lead Analyst/Manager and 2-3 Data Analysts/Junior Analysts.
3) This data analytics team (1 manager + 2-3 analysts) is part of a bigger ecosystem that they can rely upon:
- A Senior Data Scientist/Analytics Lead who has industry knowledge and experience solving similar problems.
- Subject Matter Experts (SMEs) from various domains like AI, Machine Learning, or industry-specific fields (e.g., Marketing, Supply Chain, Finance).
- Business Intelligence (BI) Experts and Data Engineers who ensure that the data is well-structured and easy to interpret.
- External Tools & Platforms (e.g., Power BI, Tableau, Google Analytics) that can be leveraged for advanced analytics.
- Data Experts who specialize in various data sources, research, and methods to get the right information.
4) Every member of this ecosystem collaborates to create value for the client:
- The entire team works toward solving the clientโs business problem using data-driven insights.
- The Manager & Analysts may not be industry experts but have access to the right tools and people to bring the expertise required.
- If help is needed from a Data Scientist sitting in New York or a Cloud Engineer in Singapore, itโs availableโcollaboration is key!
End of the day:
1) Data analytics teams arenโt just about crunching numbersโtheyโre about solving problems using data-driven insights.
2) EVERYONE in this ecosystem plays a vital role and is rewarded well because the value they create helps the business make informed decisions!
3) You should consider working in this field for a few years, at least. Itโll teach you how to break down complex business problems and solve them with data. And trust me, data-driven decision-making is one of the most powerful skills to have today!
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค4๐1๐1
SQL Basics for Beginners: Must-Know Concepts
1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.
2. SQL Syntax
SQL is written using statements, which consist of keywords like
- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g.,
3. SQL Data Types
Databases store data in different formats. The most common data types are:
-
-
-
-
4. Basic SQL Queries
Here are some fundamental SQL operations:
- SELECT Statement: Used to retrieve data from a database.
- WHERE Clause: Filters data based on conditions.
- ORDER BY: Sorts data in ascending (
- LIMIT: Limits the number of rows returned.
5. Filtering Data with WHERE Clause
The
You can use comparison operators like:
-
-
-
-
6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.
- SUM(): Adds up values in a column.
- AVG(): Calculates the average value.
- GROUP BY: Groups rows that have the same values into summary rows.
7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.
- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
8. Inserting Data
To add new data to a table, you use the
9. Updating Data
You can update existing data in a table using the
10. Deleting Data
To remove data from a table, use the
Here you can find essential SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Like this post if you need more ๐โค๏ธ
Hope it helps :)
1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.
2. SQL Syntax
SQL is written using statements, which consist of keywords like
SELECT, FROM, WHERE, etc., to perform operations on the data.- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g.,
SELECT, FROM).3. SQL Data Types
Databases store data in different formats. The most common data types are:
-
INT (Integer): For whole numbers.-
VARCHAR(n) or TEXT: For storing text data.-
DATE: For dates.-
DECIMAL: For precise decimal values, often used in financial calculations.4. Basic SQL Queries
Here are some fundamental SQL operations:
- SELECT Statement: Used to retrieve data from a database.
SELECT column1, column2 FROM table_name;
- WHERE Clause: Filters data based on conditions.
SELECT * FROM table_name WHERE condition;
- ORDER BY: Sorts data in ascending (
ASC) or descending (DESC) order.SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
- LIMIT: Limits the number of rows returned.
SELECT * FROM table_name LIMIT 5;
5. Filtering Data with WHERE Clause
The
WHERE clause helps you filter data based on a condition:SELECT * FROM employees WHERE salary > 50000;
You can use comparison operators like:
-
=: Equal to-
>: Greater than-
<: Less than-
LIKE: For pattern matching6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.
SELECT COUNT(*) FROM table_name;
- SUM(): Adds up values in a column.
SELECT SUM(salary) FROM employees;
- AVG(): Calculates the average value.
SELECT AVG(salary) FROM employees;
- GROUP BY: Groups rows that have the same values into summary rows.
SELECT department, AVG(salary) FROM employees GROUP BY department;
7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.
SELECT employees.name, departments.department
FROM employees
INNER JOIN departments
ON employees.department_id = departments.id;
- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
SELECT employees.name, departments.department
FROM employees
LEFT JOIN departments
ON employees.department_id = departments.id;
8. Inserting Data
To add new data to a table, you use the
INSERT INTO statement: INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
9. Updating Data
You can update existing data in a table using the
UPDATE statement:UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
10. Deleting Data
To remove data from a table, use the
DELETE statement:DELETE FROM employees WHERE name = 'John Doe';
Here you can find essential SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Like this post if you need more ๐โค๏ธ
Hope it helps :)
๐4โค2๐1
Roadmap to master SQL:
๐ *Basic SQL Concepts*
โ๐ Understand Databases & Tables
โ๐ Learn SQL Syntax & Structure
โ๐ Learn Data Types in SQL
โ๐ Learn Basic SELECT Queries
โ๐ Learn WHERE Clause for Filtering Data
โ๐ Learn ORDER BY for Sorting Data
๐ *Advanced SQL Queries*
โ๐ Learn JOINs (INNER, LEFT, RIGHT, FULL, SELF)
โ๐ Learn Aggregation Functions (SUM, AVG, COUNT, MIN, MAX)
โ๐ Learn GROUP BY and HAVING Clauses
โ๐ Learn Subqueries (Nested Queries)
โ๐ Learn UNION and INTERSECT
โ๐ Learn LIKE, IN, and BETWEEN Operators
๐ *Advanced Data Manipulation*
โ๐ Learn Data Manipulation (INSERT, UPDATE, DELETE)
โ๐ Learn Data Constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL)
โ๐ Learn Normalization & Denormalization
โ๐ Learn Transactions & COMMIT/ROLLBACK
๐ *Performance Optimization*
โ๐ Learn Indexing
โ๐ Learn Query Optimization Techniques
โ๐ Learn EXPLAIN Plan
๐ *Common SQL Functions*
โ๐ Learn Date & Time Functions
โ๐ Learn String Functions (CONCAT, SUBSTRING, TRIM, etc.)
โ๐ Learn Mathematical Functions
โ๐ Learn Window Functions (ROW_NUMBER, RANK, PARTITION BY)
๐ *Working with Views and Stored Procedures*
โ๐ Learn Creating and Using Views
โ๐ Learn Creating and Using Stored Procedures
โ๐ Learn Triggers and Functions
๐ *Build Projects*
โ๐ Create Data Analytics Reports using SQL
โ๐ Build a Database from Scratch
โ๐ Work on Data Cleaning and Transformation Projects
๐ โ *Apply for Jobs*
โ๐ Apply for Data Analyst Roles
โ๐ Highlight SQL Skills & Projects in Resume
React โค๏ธ for detailed explanation of each topic
Data Analyst Roadmap: https://t.iss.one/sqlspecialist/1414
Data Analyst Jobs: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
For all resources and cheat sheets, check out our Telegram channel
๐๐
https://t.iss.one/mysqldata
Hope it helps :)
๐ *Basic SQL Concepts*
โ๐ Understand Databases & Tables
โ๐ Learn SQL Syntax & Structure
โ๐ Learn Data Types in SQL
โ๐ Learn Basic SELECT Queries
โ๐ Learn WHERE Clause for Filtering Data
โ๐ Learn ORDER BY for Sorting Data
๐ *Advanced SQL Queries*
โ๐ Learn JOINs (INNER, LEFT, RIGHT, FULL, SELF)
โ๐ Learn Aggregation Functions (SUM, AVG, COUNT, MIN, MAX)
โ๐ Learn GROUP BY and HAVING Clauses
โ๐ Learn Subqueries (Nested Queries)
โ๐ Learn UNION and INTERSECT
โ๐ Learn LIKE, IN, and BETWEEN Operators
๐ *Advanced Data Manipulation*
โ๐ Learn Data Manipulation (INSERT, UPDATE, DELETE)
โ๐ Learn Data Constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL)
โ๐ Learn Normalization & Denormalization
โ๐ Learn Transactions & COMMIT/ROLLBACK
๐ *Performance Optimization*
โ๐ Learn Indexing
โ๐ Learn Query Optimization Techniques
โ๐ Learn EXPLAIN Plan
๐ *Common SQL Functions*
โ๐ Learn Date & Time Functions
โ๐ Learn String Functions (CONCAT, SUBSTRING, TRIM, etc.)
โ๐ Learn Mathematical Functions
โ๐ Learn Window Functions (ROW_NUMBER, RANK, PARTITION BY)
๐ *Working with Views and Stored Procedures*
โ๐ Learn Creating and Using Views
โ๐ Learn Creating and Using Stored Procedures
โ๐ Learn Triggers and Functions
๐ *Build Projects*
โ๐ Create Data Analytics Reports using SQL
โ๐ Build a Database from Scratch
โ๐ Work on Data Cleaning and Transformation Projects
๐ โ *Apply for Jobs*
โ๐ Apply for Data Analyst Roles
โ๐ Highlight SQL Skills & Projects in Resume
React โค๏ธ for detailed explanation of each topic
Data Analyst Roadmap: https://t.iss.one/sqlspecialist/1414
Data Analyst Jobs: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
For all resources and cheat sheets, check out our Telegram channel
๐๐
https://t.iss.one/mysqldata
Hope it helps :)
๐3โ1โค1
Reality check on Data Analytics jobs:
โถ Most recruiters & employers are open to different backgrounds
โถ The "essential skills" are usually a mix of hard and soft skills
Desired hard skills:
โถ Excel - every job needs it
โถ SQL - data retrieval and manipulation
โถ Data Visualization - Tableau, Power BI, or Excel (Advanced)
โถ Python - Basics, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, etc
Desired soft skills:
โถ Communication
โถ Teamwork & Collaboration
โถ Problem Solver
โถ Critical Thinking
If you're lacking in some of the hard skills, start learning them through online courses or engaging in personal projects.
But don't forget to highlight your soft skills in your job application - they're equally important.
In short: Excel + SQL + Data Viz + Python + Communication + Teamwork + Problem Solver + Critical Thinking = Data Analytics
โถ Most recruiters & employers are open to different backgrounds
โถ The "essential skills" are usually a mix of hard and soft skills
Desired hard skills:
โถ Excel - every job needs it
โถ SQL - data retrieval and manipulation
โถ Data Visualization - Tableau, Power BI, or Excel (Advanced)
โถ Python - Basics, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, etc
Desired soft skills:
โถ Communication
โถ Teamwork & Collaboration
โถ Problem Solver
โถ Critical Thinking
If you're lacking in some of the hard skills, start learning them through online courses or engaging in personal projects.
But don't forget to highlight your soft skills in your job application - they're equally important.
In short: Excel + SQL + Data Viz + Python + Communication + Teamwork + Problem Solver + Critical Thinking = Data Analytics
๐2๐1๐1
Building Your Personal Brand as a Data Analyst ๐
A strong personal brand can help you land better job opportunities, attract freelance clients, and position you as a thought leader in data analytics.
Hereโs how to build and grow your brand effectively:
1๏ธโฃ Optimize Your LinkedIn Profile ๐
Use a clear, professional profile picture and a compelling headline (e.g., Data Analyst | SQL | Power BI | Python Enthusiast).
Write an engaging "About" section showcasing your skills, experience, and passion for data analytics.
Share projects, case studies, and insights to demonstrate expertise.
Engage with industry leaders, recruiters, and fellow analysts.
2๏ธโฃ Share Valuable Content Consistently โ๏ธ
Post insightful LinkedIn posts, Medium articles, or Twitter threads on SQL, Power BI, Python, and industry trends.
Write about real-world case studies, common mistakes, and career advice.
Share data visualization tips, SQL tricks, or step-by-step tutorials.
3๏ธโฃ Contribute to Open-Source & GitHub ๐ป
Publish SQL queries, Python scripts, Jupyter notebooks, and dashboards.
Share projects with real datasets to showcase your hands-on skills.
Collaborate on open-source data analytics projects to gain exposure.
4๏ธโฃ Engage in Online Data Analytics Communities ๐
Join and contribute to Reddit (r/dataanalysis, r/SQL), Stack Overflow, and Data Science Discord groups.
Participate in Kaggle competitions to gain practical experience.
Answer questions on Quora, LinkedIn, or Twitter to establish credibility.
5๏ธโฃ Speak at Webinars & Meetups ๐ค
Host or participate in webinars on LinkedIn, YouTube, or data conferences.
Join local meetups or online communities like DataCamp and Tableau User Groups.
Share insights on career growth, best practices, and analytics trends.
6๏ธโฃ Create a Portfolio Website ๐
Build a personal website showcasing your projects, resume, and blog.
Include interactive dashboards, case studies, and problem-solving examples.
Use Wix, WordPress, or GitHub Pages to get started.
7๏ธโฃ Network & Collaborate ๐ค
Connect with hiring managers, recruiters, and senior analysts.
Collaborate on guest blog posts, podcasts, or YouTube interviews.
Attend data science and analytics conferences to expand your reach.
8๏ธโฃ Start a YouTube Channel or Podcast ๐ฅ
Share short tutorials on SQL, Power BI, Python, and Excel.
Interview industry experts and discuss data analytics career paths.
Offer career guidance, resume tips, and interview prep content.
9๏ธโฃ Offer Free Value Before Monetizing ๐ก
Give away free e-books, templates, or mini-courses to attract an audience.
Provide LinkedIn Live Q&A sessions, career guidance, or free tutorials.
Once you build trust, you can monetize through consulting, courses, and coaching.
๐ Stay Consistent & Keep Learning
Building a brand takes timeโstay consistent with content creation and engagement.
Keep learning new skills and sharing your journey to stay relevant.
Follow industry leaders, subscribe to analytics blogs, and attend workshops.
A strong personal brand in data analytics can open unlimited opportunitiesโfrom job offers to freelance gigs and consulting projects.
Start small, be consistent, and showcase your expertise! ๐ฅ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalyst
A strong personal brand can help you land better job opportunities, attract freelance clients, and position you as a thought leader in data analytics.
Hereโs how to build and grow your brand effectively:
1๏ธโฃ Optimize Your LinkedIn Profile ๐
Use a clear, professional profile picture and a compelling headline (e.g., Data Analyst | SQL | Power BI | Python Enthusiast).
Write an engaging "About" section showcasing your skills, experience, and passion for data analytics.
Share projects, case studies, and insights to demonstrate expertise.
Engage with industry leaders, recruiters, and fellow analysts.
2๏ธโฃ Share Valuable Content Consistently โ๏ธ
Post insightful LinkedIn posts, Medium articles, or Twitter threads on SQL, Power BI, Python, and industry trends.
Write about real-world case studies, common mistakes, and career advice.
Share data visualization tips, SQL tricks, or step-by-step tutorials.
3๏ธโฃ Contribute to Open-Source & GitHub ๐ป
Publish SQL queries, Python scripts, Jupyter notebooks, and dashboards.
Share projects with real datasets to showcase your hands-on skills.
Collaborate on open-source data analytics projects to gain exposure.
4๏ธโฃ Engage in Online Data Analytics Communities ๐
Join and contribute to Reddit (r/dataanalysis, r/SQL), Stack Overflow, and Data Science Discord groups.
Participate in Kaggle competitions to gain practical experience.
Answer questions on Quora, LinkedIn, or Twitter to establish credibility.
5๏ธโฃ Speak at Webinars & Meetups ๐ค
Host or participate in webinars on LinkedIn, YouTube, or data conferences.
Join local meetups or online communities like DataCamp and Tableau User Groups.
Share insights on career growth, best practices, and analytics trends.
6๏ธโฃ Create a Portfolio Website ๐
Build a personal website showcasing your projects, resume, and blog.
Include interactive dashboards, case studies, and problem-solving examples.
Use Wix, WordPress, or GitHub Pages to get started.
7๏ธโฃ Network & Collaborate ๐ค
Connect with hiring managers, recruiters, and senior analysts.
Collaborate on guest blog posts, podcasts, or YouTube interviews.
Attend data science and analytics conferences to expand your reach.
8๏ธโฃ Start a YouTube Channel or Podcast ๐ฅ
Share short tutorials on SQL, Power BI, Python, and Excel.
Interview industry experts and discuss data analytics career paths.
Offer career guidance, resume tips, and interview prep content.
9๏ธโฃ Offer Free Value Before Monetizing ๐ก
Give away free e-books, templates, or mini-courses to attract an audience.
Provide LinkedIn Live Q&A sessions, career guidance, or free tutorials.
Once you build trust, you can monetize through consulting, courses, and coaching.
๐ Stay Consistent & Keep Learning
Building a brand takes timeโstay consistent with content creation and engagement.
Keep learning new skills and sharing your journey to stay relevant.
Follow industry leaders, subscribe to analytics blogs, and attend workshops.
A strong personal brand in data analytics can open unlimited opportunitiesโfrom job offers to freelance gigs and consulting projects.
Start small, be consistent, and showcase your expertise! ๐ฅ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalyst
โค5๐3