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 Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to Python
| | |-- Setting Up Development Environment (IDE: PyCharm, VSCode, etc.)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Elif Statements
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| |
| |-- Exception Handling
| | |-- Try-Except Block
| | |-- Finally Block
| | |-- Raise and Custom Exceptions
|
|-- Functions and Modules
| |-- Defining Functions
| | |-- Function Syntax
| | |-- Parameters and Arguments
| | |-- Return Statement
| |
| |-- Lambda Functions
| | |-- Syntax and Usage
| |
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Creating and Using Packages
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Classes and Objects
| | |-- Methods and Constructors
| |
| |-- Inheritance
| | |-- Single and Multiple Inheritance
| | |-- Method Overriding
| |
| |-- Polymorphism
| | |-- Method Overloading (using default arguments)
| | |-- Operator Overloading
| |
| |-- Encapsulation
| | |-- Access Modifiers (Public, Private, Protected)
| | |-- Getters and Setters
| |
| |-- Abstraction
| | |-- Abstract Base Classes
| | |-- Interfaces (using ABC module)
|
|-- Advanced Python
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON Files
| |
| |-- Iterators and Generators
| | |-- Creating Iterators
| | |-- Using Generators and Yield Statement
| |
| |-- Decorators
| | |-- Function Decorators
| | |-- Class Decorators
|
|-- Data Structures
| |-- Lists
| | |-- List Comprehensions
| | |-- Common List Methods
| |
| |-- Tuples
| | |-- Immutable Sequences
| |
| |-- Dictionaries
| | |-- Dictionary Comprehensions
| | |-- Common Dictionary Methods
| |
| |-- Sets
| | |-- Set Operations
| | |-- Set Comprehensions
|
|-- Libraries and Frameworks
| |-- Data Science
| | |-- NumPy
| | |-- Pandas
| | |-- Matplotlib
| | |-- Seaborn
| | |-- SciPy
| |
| |-- Web Development
| | |-- Flask
| | |-- Django
| |
| |-- Automation
| | |-- Selenium
| | |-- BeautifulSoup
| | |-- Scrapy
|
|-- Testing in Python
| |-- Unit Testing
| | |-- Unittest
| | |-- PyTest
| |
| |-- Mocking
| | |-- unittest.mock
| | |-- Using Mocks and Patches
|
|-- Deployment and DevOps
| |-- Containers and Microservices
| | |-- Docker (Dockerfile, Image Creation, Container Management)
| | |-- Kubernetes (Pods, Services, Deployments, Managing Python Applications on Kubernetes)
|
|-- Best Practices and Advanced Topics
| |-- Code Style
| | |-- PEP 8 Guidelines
| | |-- Code Linters (Pylint, Flake8)
| |
| |-- Performance Optimization
| | |-- Profiling and Benchmarking
| | |-- Using Cython and Numba
| |
| |-- Concurrency and Parallelism
| | |-- Threading
| | |-- Multiprocessing
| | |-- Asyncio
|
|-- Building and Distributing Packages
| |-- Creating Packages
| | |-- setuptools
| | |-- Creating environment setup
| |
| |-- Publishing Packages
| | |-- PyPI
| | |-- Versioning and Documentation

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ENJOY LEARNING 👍👍

𝐓𝐢𝐩𝐬 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐝𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:

𝘐 𝘨𝘦𝘵 𝘴𝘰 𝘮𝘢𝘯𝘺 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘧𝘳𝘰𝘮 𝘥𝘢𝘵𝘢 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 𝘢𝘴𝘱𝘪𝘳𝘢𝘯𝘵𝘴 𝘢𝘯𝘥 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭𝘴 𝘰𝘯 𝘩𝘰𝘸 𝘵𝘰 𝘨𝘢𝘪𝘯 𝘤𝘰𝘮𝘮𝘢𝘯𝘥 𝘰𝘧 𝘗𝘺𝘵𝘩𝘰𝘯.

📍𝐋𝐞𝐚𝐫𝐧 𝐂𝐨𝐫𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Master Python libraries for data analytics, like
-pandas for dataframes,
-NumPy for numerical operations,
-Matplotlib/Seaborn for plotting,
-scikit-learn for machine learning.

📍𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬: Important concepts like list comprehensions, lambda functions, object-oriented programming, and error handling to write efficient code.

📍𝐔𝐬𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦-𝐒𝐨𝐥𝐯𝐢𝐧𝐠 𝐌𝐞𝐭𝐡𝐨𝐝𝐬: Apply data wrangling techniques, efficient loops, and vectorized operations in NumPy/pandas for optimized performance.

📍𝐃𝐨 𝐌𝐨𝐜𝐤 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Work on end-to-end Python analytics projects—data loading, cleaning, analysis, and visualization.

📍𝐋𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐏𝐚𝐬𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Review your previous Python projects to see where your code can be more efficient.

Python for Business Success 💼
Python + Data Analysis = Informed Decision-Making
Python + Automation = Streamline Your Operations
Python + Web Development = Create Your Online Presence
Python + Machine Learning = Predict Trends and Behaviors
Python + APIs = Integrate Services Seamlessly
Python + Data Visualization = Present Insights Clearly
Python + E-Commerce = Enhance Your Online Store
Python + Financial Modeling = Analyze Business Performance
Python + CRM = Manage Customer Relationships Effectively
Python + Reporting Tools = Generate Insightful Reports
Python + Inventory Management = Optimize Stock Levels
Python + Social Media Analytics = Understand Your Audience

Python Tip: use enumerate() when need to loop through a list and keep track of the index DataAnalytics

enumerate(): Automatically provides the index (starting from 0) and the item in the list.

Explain the features of Python / Say something about the benefits of using Python?

Python is a MUST for students and working professionals to become a great Software Engineer specially when they are working in Web Development Domain. I will list down some of the key advantages of learning Python:

○ Simple and easy to learn:
* Learning python programming language is easy and fun.
* Compared to other language, like, Java or C++, its syntax is a way lot easier.
* You also don’t have to worry about the missing semicolons (;) in the end!
* It is more expressive means that it is more understandable and readable.
* Python is a great language for the beginner-level programmers.
* It supports the development of a wide range of applications from simple text processing to WWW browsers to games.
* Easy-to-learn − Python has few keywords, simple structure, and a clearly defined syntax. This makes it easy for Beginners to pick up the language quickly.
* Easy-to-read − Python code is more clearly defined and readable. It's almost like plain and simple English.
* Easy-to-maintain − Python's source code is fairly easy-to-maintain.


Features of Python
○ Python is Interpreted −
* Python is processed at runtime by the interpreter.
* You do not need to compile your program before executing it. This is similar to PERL and PHP.

○ Python is Interactive −
* Python has support for an interactive mode which allows interactive testing and debugging of snippets of code.
* You can open the interactive terminal also referred to as Python prompt and interact with the interpreter directly to write your programs.

○ Python is Object-Oriented −
* Python not only supports functional and structured programming methods, but Object Oriented Principles.

○ Scripting Language —
* Python can be used as a noscripting language or it can be compliled to byte-code for building large applications.

○ Dynammic language —
* It provides very high-level dynamic data types and supports dynamic type checking.

○ Garbage collection —
* Garbage collection is a process where the objects that are no longer reachable are freed from memory.
* Memory management is very important while writing programs and python supports automatic garbage collection, which is one of the main problems in writing programs using C & C++.

○ Large Open Source Community —
* Python has a large open source community and which is one of its main strength.
* And its libraries, from open source 118 thousand plus and counting.
* If you are stuck with an issue, you don’t have to worry at all because python has a huge community for help. So, if you have any queries, you can directly seek help from millions of python community members.
* A broad standard library − Python's bulk of the library is very portable and cross-platform compatible on UNIX, Windows, and Macintosh.
* Extendable − You can add low-level modules to the Python interpreter. These modules enable programmers to add to or customize their tools to be more efficient.

○ Cross-platform Language —
* Python is a Cross-platform language or Portable language.
* Python can run on a wide variety of hardware platforms and has the same interface on all platforms.
* Python can run on different platforms such as Windows, Linux, Unix and Macintosh etc.

Pandas interview questions (for data analyst):

What are the basic data structures in pandas?
How do you create a DataFrame in pandas?
How do you read a CSV file in pandas?
How can you select specific columns from a DataFrame in pandas?
How do you filter rows in a DataFrame based on a condition in pandas?
How do you handle missing values in a DataFrame using pandas?
How do you merge two DataFrames in pandas?
How do you perform groupby operation in pandas?
How do you rename columns in a DataFrame using pandas?
How do you sort a DataFrame by a specific column in pandas?
How do you aggregate data using pandas?
How do you apply a function to each element in a DataFrame in pandas?
How do you perform data visualization using pandas?
How do you handle duplicate data in a DataFrame using pandas?
How do you calculate denoscriptive statistics for a DataFrame using pandas?
How do you set the index of a DataFrame using pandas?
How do you reset the index of a DataFrame in pandas?
How do you concatenate multiple DataFrames in pandas?
How do you pivot a DataFrame in pandas?
How do you melt a DataFrame in pandas?
How do you calculate the correlation between columns in a DataFrame using pandas?
How do you handle outliers in a DataFrame using pandas?
How do you extract unique values from a column in a DataFrame using pandas?
How do you calculate cumulative sum in a DataFrame using pandas?
How do you convert data types of columns in a DataFrame using pandas?
How do you handle datetime data in a DataFrame using pandas?
How do you resample time-series data in pandas?
How do you merge and append DataFrames with different column names in pandas?
How do you handle multi-level indexing in pandas?
How do you drop columns from a DataFrame in pandas?
How do you create a pivot table in pandas?
How do you calculate rolling statistics in pandas?
How do you concatenate strings in a DataFrame column using pandas?
How do you create a cross-tabulation in pandas?
How do you handle categorical data in pandas?
How do you calculate cumulative percentage in a DataFrame column using pandas?
How do you handle data imputation in pandas?
How do you calculate percentage change in a DataFrame column using pandas?
How do you calculate the rank of values in a DataFrame column using pandas?
How do you calculate the difference between consecutive values in a DataFrame column using pandas?
How do you drop duplicate rows based on a specific column in pandas?
How do you calculate the mean, median, and mode of a DataFrame column using pandas?

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If I were to learn Python for Data Analysis again I'd focus on:

- Python Programming fundamentals.

- Pandas, Numpy, and Matplotlib for data handling/visualisation.

- Seaborn for enhanced visualisation.

- Build projects with data from Kaggle/Google Datasets.

#python