Top 10 Python functions that are commonly used in data analysis
import pandas as pd: This function is used to import the Pandas library, which is essential for data manipulation and analysis.
read_csv(): This function from Pandas is used to read data from CSV files into a DataFrame, a primary data structure for data analysis.
head(): It allows you to quickly preview the first few rows of a DataFrame to understand its structure.
describe(): This function provides summary statistics of the numeric columns in a DataFrame, such as mean, standard deviation, and percentiles.
groupby(): It's used to group data by one or more columns, enabling aggregation and analysis within those groups.
pivot_table(): This function helps in creating pivot tables, allowing you to summarize and reshape data for analysis.
fillna(): Useful for filling missing values in a DataFrame with a specified value or a calculated one (e.g., mean or median).
apply(): This function is used to apply custom functions to DataFrame columns or rows, which is handy for data transformation.
plot(): It's part of the Matplotlib library and is used for creating various data visualizations, such as line plots, bar charts, and scatter plots.
merge(): This function is used for combining two or more DataFrames based on a common column or index, which is crucial for joining datasets during analysis.
These functions are essential tools for any data analyst working with Python for data analysis tasks.
Hope it helps :)
import pandas as pd: This function is used to import the Pandas library, which is essential for data manipulation and analysis.
read_csv(): This function from Pandas is used to read data from CSV files into a DataFrame, a primary data structure for data analysis.
head(): It allows you to quickly preview the first few rows of a DataFrame to understand its structure.
describe(): This function provides summary statistics of the numeric columns in a DataFrame, such as mean, standard deviation, and percentiles.
groupby(): It's used to group data by one or more columns, enabling aggregation and analysis within those groups.
pivot_table(): This function helps in creating pivot tables, allowing you to summarize and reshape data for analysis.
fillna(): Useful for filling missing values in a DataFrame with a specified value or a calculated one (e.g., mean or median).
apply(): This function is used to apply custom functions to DataFrame columns or rows, which is handy for data transformation.
plot(): It's part of the Matplotlib library and is used for creating various data visualizations, such as line plots, bar charts, and scatter plots.
merge(): This function is used for combining two or more DataFrames based on a common column or index, which is crucial for joining datasets during analysis.
These functions are essential tools for any data analyst working with Python for data analysis tasks.
Hope it helps :)
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Forwarded from SQL For Data Analytics
Essentials for Acing any Data Analytics Interviews-
SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
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. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution
❤8👍3
Python Programming Interview Questions for Entry Level Data Analyst
1. What is Python, and why is it popular in data analysis?
2. Differentiate between Python 2 and Python 3.
3. Explain the importance of libraries like NumPy and Pandas in data analysis.
4. How do you read and write data from/to files using Python?
5. Discuss the role of Matplotlib and Seaborn in data visualization with Python.
6. What are list comprehensions, and how do you use them in Python?
7. Explain the concept of object-oriented programming (OOP) in Python.
8. Discuss the significance of libraries like SciPy and Scikit-learn in data analysis.
9. How do you handle missing or NaN values in a DataFrame using Pandas?
10. Explain the difference between loc and iloc in Pandas DataFrame indexing.
11. Discuss the purpose and usage of lambda functions in Python.
12. What are Python decorators, and how do they work?
13. How do you handle categorical data in Python using the Pandas library?
14. Explain the concept of data normalization and its importance in data preprocessing.
15. Discuss the role of regular expressions (regex) in data cleaning with Python.
16. What are Python virtual environments, and why are they useful?
17. How do you handle outliers in a dataset using Python?
18. Explain the usage of the map and filter functions in Python.
19. Discuss the concept of recursion in Python programming.
20. How do you perform data analysis and visualization using Jupyter Notebooks?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
1. What is Python, and why is it popular in data analysis?
2. Differentiate between Python 2 and Python 3.
3. Explain the importance of libraries like NumPy and Pandas in data analysis.
4. How do you read and write data from/to files using Python?
5. Discuss the role of Matplotlib and Seaborn in data visualization with Python.
6. What are list comprehensions, and how do you use them in Python?
7. Explain the concept of object-oriented programming (OOP) in Python.
8. Discuss the significance of libraries like SciPy and Scikit-learn in data analysis.
9. How do you handle missing or NaN values in a DataFrame using Pandas?
10. Explain the difference between loc and iloc in Pandas DataFrame indexing.
11. Discuss the purpose and usage of lambda functions in Python.
12. What are Python decorators, and how do they work?
13. How do you handle categorical data in Python using the Pandas library?
14. Explain the concept of data normalization and its importance in data preprocessing.
15. Discuss the role of regular expressions (regex) in data cleaning with Python.
16. What are Python virtual environments, and why are they useful?
17. How do you handle outliers in a dataset using Python?
18. Explain the usage of the map and filter functions in Python.
19. Discuss the concept of recursion in Python programming.
20. How do you perform data analysis and visualization using Jupyter Notebooks?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
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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
Best Resource to learn Python
Python Interview Questions with Answers
Freecodecamp Python ML Course with FREE Certificate
Python for Data Analysis
Python course for beginners by Microsoft
Scientific Computing with Python
Python course by Google
Python Free Resources
Please give us credits while sharing: -> https://t.iss.one/free4unow_backup
ENJOY LEARNING 👍👍
|
|-- 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
Best Resource to learn Python
Python Interview Questions with Answers
Freecodecamp Python ML Course with FREE Certificate
Python for Data Analysis
Python course for beginners by Microsoft
Scientific Computing with Python
Python course by Google
Python Free Resources
Please give us credits while sharing: -> https://t.iss.one/free4unow_backup
ENJOY LEARNING 👍👍
👍6❤3
Python Programming Interview Questions for Entry Level Data Analyst
1. What is Python, and why is it popular in data analysis?
2. Differentiate between Python 2 and Python 3.
3. Explain the importance of libraries like NumPy and Pandas in data analysis.
4. How do you read and write data from/to files using Python?
5. Discuss the role of Matplotlib and Seaborn in data visualization with Python.
6. What are list comprehensions, and how do you use them in Python?
7. Explain the concept of object-oriented programming (OOP) in Python.
8. Discuss the significance of libraries like SciPy and Scikit-learn in data analysis.
9. How do you handle missing or NaN values in a DataFrame using Pandas?
10. Explain the difference between loc and iloc in Pandas DataFrame indexing.
11. Discuss the purpose and usage of lambda functions in Python.
12. What are Python decorators, and how do they work?
13. How do you handle categorical data in Python using the Pandas library?
14. Explain the concept of data normalization and its importance in data preprocessing.
15. Discuss the role of regular expressions (regex) in data cleaning with Python.
16. What are Python virtual environments, and why are they useful?
17. How do you handle outliers in a dataset using Python?
18. Explain the usage of the map and filter functions in Python.
19. Discuss the concept of recursion in Python programming.
20. How do you perform data analysis and visualization using Jupyter Notebooks?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
1. What is Python, and why is it popular in data analysis?
2. Differentiate between Python 2 and Python 3.
3. Explain the importance of libraries like NumPy and Pandas in data analysis.
4. How do you read and write data from/to files using Python?
5. Discuss the role of Matplotlib and Seaborn in data visualization with Python.
6. What are list comprehensions, and how do you use them in Python?
7. Explain the concept of object-oriented programming (OOP) in Python.
8. Discuss the significance of libraries like SciPy and Scikit-learn in data analysis.
9. How do you handle missing or NaN values in a DataFrame using Pandas?
10. Explain the difference between loc and iloc in Pandas DataFrame indexing.
11. Discuss the purpose and usage of lambda functions in Python.
12. What are Python decorators, and how do they work?
13. How do you handle categorical data in Python using the Pandas library?
14. Explain the concept of data normalization and its importance in data preprocessing.
15. Discuss the role of regular expressions (regex) in data cleaning with Python.
16. What are Python virtual environments, and why are they useful?
17. How do you handle outliers in a dataset using Python?
18. Explain the usage of the map and filter functions in Python.
19. Discuss the concept of recursion in Python programming.
20. How do you perform data analysis and visualization using Jupyter Notebooks?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
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Top 10 Python Libraries for Data Science & Machine Learning
1. 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.
2. Pandas: Pandas is a powerful data manipulation library that provides data structures like DataFrame and Series, which make it easy to work with structured data. It offers tools for data cleaning, reshaping, merging, and slicing data.
3. Matplotlib: Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python. It allows you to generate various types of plots, including line plots, bar charts, histograms, scatter plots, and more.
4. Scikit-learn: Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It enables you to build and train deep learning models using high-level APIs and tools for neural networks, natural language processing, computer vision, and more.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It allows you to quickly prototype deep learning models with minimal code and easily experiment with different architectures.
7. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of creating complex visualizations like heatmaps, violin plots, and pair plots.
8. Statsmodels: Statsmodels is a library that focuses on statistical modeling and hypothesis testing in Python. It offers a wide range of statistical models, including linear regression, logistic regression, time series analysis, and more.
9. XGBoost: XGBoost is an optimized gradient boosting library that provides an efficient implementation of the gradient boosting algorithm. It is widely used in machine learning competitions and has become a popular choice for building accurate predictive models.
10. NLTK (Natural Language Toolkit): NLTK is a library for natural language processing (NLP) that provides tools for text processing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and more. It is a valuable resource for working with textual data in data science projects.
Data Science Resources for Beginners
👇👇
https://drive.google.com/drive/folders/1uCShXgmol-fGMqeF2hf9xA5XPKVSxeTo
Share with credits: https://t.iss.one/datasciencefun
ENJOY LEARNING 👍👍
1. 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.
2. Pandas: Pandas is a powerful data manipulation library that provides data structures like DataFrame and Series, which make it easy to work with structured data. It offers tools for data cleaning, reshaping, merging, and slicing data.
3. Matplotlib: Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python. It allows you to generate various types of plots, including line plots, bar charts, histograms, scatter plots, and more.
4. Scikit-learn: Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It enables you to build and train deep learning models using high-level APIs and tools for neural networks, natural language processing, computer vision, and more.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It allows you to quickly prototype deep learning models with minimal code and easily experiment with different architectures.
7. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of creating complex visualizations like heatmaps, violin plots, and pair plots.
8. Statsmodels: Statsmodels is a library that focuses on statistical modeling and hypothesis testing in Python. It offers a wide range of statistical models, including linear regression, logistic regression, time series analysis, and more.
9. XGBoost: XGBoost is an optimized gradient boosting library that provides an efficient implementation of the gradient boosting algorithm. It is widely used in machine learning competitions and has become a popular choice for building accurate predictive models.
10. NLTK (Natural Language Toolkit): NLTK is a library for natural language processing (NLP) that provides tools for text processing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and more. It is a valuable resource for working with textual data in data science projects.
Data Science Resources for Beginners
👇👇
https://drive.google.com/drive/folders/1uCShXgmol-fGMqeF2hf9xA5XPKVSxeTo
Share with credits: https://t.iss.one/datasciencefun
ENJOY LEARNING 👍👍
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