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
Essential Power BI Interview Resources 👇👇
https://t.iss.one/PowerBI_analyst/498
https://t.iss.one/PowerBI_analyst/498
👍1
How to do confidence as a Data Analyst
You’re unqualified because you haven’t applied your learning
2025 OUTs:
• less tutorials
• less boot camps
• less certification
2025 INs:
• Build SQL projects
• Build Excel reports
• Build PowerBi dashboards
Apply your learning by building to gain confidence.
#dataanalytics
You’re unqualified because you haven’t applied your learning
2025 OUTs:
• less tutorials
• less boot camps
• less certification
2025 INs:
• Build SQL projects
• Build Excel reports
• Build PowerBi dashboards
Apply your learning by building to gain confidence.
#dataanalytics
👍16❤1
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
👨💻 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 👍👍
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
👨💻 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 👍👍
👍6
Python Pandas Beginner's Guide
👍4
Useful websites to practice and enhance your Data Analytics skills
👇👇
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.iss.one/sqlspecialist/232?single
2. Python
https://www.learnpython.org/
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
👇👇
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.iss.one/sqlspecialist/232?single
2. Python
https://www.learnpython.org/
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
👍6❤2