Professional_Android_Application_Development.pdf
5.5 MB
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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.
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Python WhatsApp Community: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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.
Like for more 😄❤️
Python WhatsApp Community: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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Spend $0 to master new skills in 2024:
1. HTML - w3schools.com
2. CSS - css-tricks.com
3. JavaScript - learnjavascript.online
4. React - react-tutorial.app
5. Tailwind - scrimba.com
6. Vue - vueschool.io
7. Python - pythontutorial.net
8. SQL - t.me/sqlanalyst
9. Git - atlassian.com/git/tutorials
10. Power BI - t.me/PowerBI_analyst
📌Join our Community
[https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226]
Do react ❤️ if you want more content like this
1. HTML - w3schools.com
2. CSS - css-tricks.com
3. JavaScript - learnjavascript.online
4. React - react-tutorial.app
5. Tailwind - scrimba.com
6. Vue - vueschool.io
7. Python - pythontutorial.net
8. SQL - t.me/sqlanalyst
9. Git - atlassian.com/git/tutorials
10. Power BI - t.me/PowerBI_analyst
📌Join our Community
[https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226]
Do react ❤️ if you want more content like this
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Top 10 Programming Languages to learn in 2025 (With Free Resources to learn) :-
1. Python
- learnpython.org
- t.me/pythonfreebootcamp
2. Java
- learnjavaonline.org
- t.me/free4unow_backup/550
3. C#
- learncs.org
- w3schools.com
4. JavaScript
- learnjavascript.online
- t.me/javascript_courses
5. Rust
- rust-lang.org
- exercism.org
6. Go Programming
- go.dev
- learn-golang.org
7. Kotlin
- kotlinlang.org
- w3schools.com/KOTLIN
8. TypeScript
- Typescriptlang.org
- learntypescript.dev
9. SQL
- datasimplifier.com
- t.me/sqlanalyst
10. R Programming
- w3schools.com/r/
- r-coder.com
ENJOY LEARNING 👍👍
1. Python
- learnpython.org
- t.me/pythonfreebootcamp
2. Java
- learnjavaonline.org
- t.me/free4unow_backup/550
3. C#
- learncs.org
- w3schools.com
4. JavaScript
- learnjavascript.online
- t.me/javascript_courses
5. Rust
- rust-lang.org
- exercism.org
6. Go Programming
- go.dev
- learn-golang.org
7. Kotlin
- kotlinlang.org
- w3schools.com/KOTLIN
8. TypeScript
- Typescriptlang.org
- learntypescript.dev
9. SQL
- datasimplifier.com
- t.me/sqlanalyst
10. R Programming
- w3schools.com/r/
- r-coder.com
ENJOY LEARNING 👍👍
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30 Python libraries .pdf
9.3 MB
30 Python libraries .pdf
Share it: https://t.me/pythonanalyst
Share it: https://t.me/pythonanalyst
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DeepLearning Notes.pdf
19.1 MB
DeepLearning Notes
Matrix Theory and Linear Algebra, 2018.pdf
8.7 MB
Matrix Theory and Linear Algebra
Peter Selinger, 2018
Peter Selinger, 2018
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Lists 🆚 Tuples 🆚 Dictionaries
What's the difference?
Lists are mutable.
Tuples are immutable.
Dictionaries are associative.
When should you use each?
Lists:
⟶ When you want to add or remove elements
⟶ When you want to sort elements
⟶ When you want to slice elements
Tuples:
⟶ When you want a constant object
⟶ When you want to send multiple in a function
⟶ When you want to return multiple from a function
Dictionaries:
⟶ When you want to map keys to values
⟶ When you want to loop over the keys
⟶ When you want to validate if key exists
Now, pick your weapon of mass data analysis and become a Python pro!
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
What's the difference?
Lists are mutable.
Tuples are immutable.
Dictionaries are associative.
When should you use each?
Lists:
⟶ When you want to add or remove elements
⟶ When you want to sort elements
⟶ When you want to slice elements
Tuples:
⟶ When you want a constant object
⟶ When you want to send multiple in a function
⟶ When you want to return multiple from a function
Dictionaries:
⟶ When you want to map keys to values
⟶ When you want to loop over the keys
⟶ When you want to validate if key exists
Now, pick your weapon of mass data analysis and become a Python pro!
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
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100 DSA QUESTIONS.pdf
3.1 MB
100 DSA interview questions ⭐
Do not forget to React ❤️ to this Message for More Content Like this!
Do not forget to React ❤️ to this Message for More Content Like this!
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