๐ Machine Learning Cheat Sheet ๐
1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.
2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)
3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.
4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.
5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.
6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.
7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.
๐ Dive into Machine Learning and transform data into insights! ๐
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.
2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)
3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.
4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.
5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.
6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.
7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.
๐ Dive into Machine Learning and transform data into insights! ๐
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
โค4
A step-by-step guide to land a job as a data analyst
Landing your first data analyst job is toughhhhh.
Here are 11 tips to make it easier:
- Master SQL.
- Next, learn a BI tool.
- Drink lots of tea or coffee.
- Tackle relevant data projects.
- Create a relevant data portfolio.
- Focus on actionable data insights.
- Remember imposter syndrome is normal.
- Find ways to prove youโre a problem-solver.
- Develop compelling data visualization stories.
- Engage with LinkedIn posts from fellow analysts.
- Illustrate your analytical impact with metrics & KPIs.
- Share your career story & insights via LinkedIn posts.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
Landing your first data analyst job is toughhhhh.
Here are 11 tips to make it easier:
- Master SQL.
- Next, learn a BI tool.
- Drink lots of tea or coffee.
- Tackle relevant data projects.
- Create a relevant data portfolio.
- Focus on actionable data insights.
- Remember imposter syndrome is normal.
- Find ways to prove youโre a problem-solver.
- Develop compelling data visualization stories.
- Engage with LinkedIn posts from fellow analysts.
- Illustrate your analytical impact with metrics & KPIs.
- Share your career story & insights via LinkedIn posts.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
๐2โค1๐1
Commonly used Python functions and methods:
### STRING FUNCTIONS:
- len(): Returns the length of a string.
- str.upper(): Converts a string to upper-case.
- str.lower(): Converts a string to lower-case.
- str.capitalize(): Capitalizes the first character of a string.
- str.split(): Splits a string into a list.
- str.join(): Joins elements of a list into a string.
- str.replace(): Replaces a specified phrase with another specified phrase.
- str.strip(): Removes whitespace from the beginning and end of a string.
### LIST FUNCTIONS:
- len(): Returns the length of a list.
- list.append(): Adds an item to the end of the list.
- list.extend(): Adds the elements of a list (or any iterable) to the end of the current list.
- list.insert(): Adds an item at a specified position.
- list.remove(): Removes the first item with the specified value.
- list.pop(): Removes the item at the specified position.
- list.index(): Returns the index of the first element with the specified value.
- list.sort(): Sorts the list.
- list.reverse(): Reverses the order of the list.
### DICTIONARY FUNCTIONS:
- dict.keys(): Returns a list of all the keys in the dictionary.
- dict.values(): Returns a list of all the values in the dictionary.
- dict.items(): Returns a list of tuples, each tuple containing a key and a value.
- dict.get(): Returns the value of the specified key.
- dict.update(): Updates the dictionary with the specified key-value pairs.
- dict.pop(): Removes the element with the specified key.
### TUPLE FUNCTIONS:
- len(): Returns the length of a tuple.
- tuple.count(): Returns the number of times a specified value appears in a tuple.
- tuple.index(): Searches the tuple for a specified value and returns the position of where it was found.
### SET FUNCTIONS:
- len(): Returns the length of a set.
- set.add(): Adds an element to the set.
- set.remove(): Removes the specified element.
- set.union(): Returns a set containing the union of sets.
- set.intersection(): Returns a set containing the intersection of sets.
- set.difference(): Returns a set containing the difference of sets.
- set.symmetric_difference(): Returns a set with elements in either the set or the specified set, but not both.
### NUMERIC FUNCTIONS:
- abs(): Returns the absolute value of a number.
- round(): Rounds a number to a specified number of digits.
- max(): Returns the largest item in an iterable.
- min(): Returns the smallest item in an iterable.
- sum(): Sums the items of an iterable.
### DATE AND TIME FUNCTIONS (datetime module):
- datetime.datetime.now(): Returns the current date and time.
- datetime.datetime.today(): Returns the current local date.
- datetime.datetime.strftime(): Formats a datetime object as a string.
- datetime.datetime.strptime(): Parses a string to a datetime object.
### FILE I/O FUNCTIONS:
- open(): Opens a file and returns a file object.
- file.read(): Reads the contents of a file.
- file.write(): Writes data to a file.
- file.readlines(): Reads all the lines of a file into a list.
- file.close(): Closes the file.
### GENERAL FUNCTIONS:
- print(): Prints to the console.
- input(): Reads a string from standard input.
- type(): Returns the type of an object.
- isinstance(): Checks if an object is an instance of a class or a tuple of classes.
- id(): Returns the identity of an object.
Here you can find essential Python Interview Resources๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
### STRING FUNCTIONS:
- len(): Returns the length of a string.
- str.upper(): Converts a string to upper-case.
- str.lower(): Converts a string to lower-case.
- str.capitalize(): Capitalizes the first character of a string.
- str.split(): Splits a string into a list.
- str.join(): Joins elements of a list into a string.
- str.replace(): Replaces a specified phrase with another specified phrase.
- str.strip(): Removes whitespace from the beginning and end of a string.
### LIST FUNCTIONS:
- len(): Returns the length of a list.
- list.append(): Adds an item to the end of the list.
- list.extend(): Adds the elements of a list (or any iterable) to the end of the current list.
- list.insert(): Adds an item at a specified position.
- list.remove(): Removes the first item with the specified value.
- list.pop(): Removes the item at the specified position.
- list.index(): Returns the index of the first element with the specified value.
- list.sort(): Sorts the list.
- list.reverse(): Reverses the order of the list.
### DICTIONARY FUNCTIONS:
- dict.keys(): Returns a list of all the keys in the dictionary.
- dict.values(): Returns a list of all the values in the dictionary.
- dict.items(): Returns a list of tuples, each tuple containing a key and a value.
- dict.get(): Returns the value of the specified key.
- dict.update(): Updates the dictionary with the specified key-value pairs.
- dict.pop(): Removes the element with the specified key.
### TUPLE FUNCTIONS:
- len(): Returns the length of a tuple.
- tuple.count(): Returns the number of times a specified value appears in a tuple.
- tuple.index(): Searches the tuple for a specified value and returns the position of where it was found.
### SET FUNCTIONS:
- len(): Returns the length of a set.
- set.add(): Adds an element to the set.
- set.remove(): Removes the specified element.
- set.union(): Returns a set containing the union of sets.
- set.intersection(): Returns a set containing the intersection of sets.
- set.difference(): Returns a set containing the difference of sets.
- set.symmetric_difference(): Returns a set with elements in either the set or the specified set, but not both.
### NUMERIC FUNCTIONS:
- abs(): Returns the absolute value of a number.
- round(): Rounds a number to a specified number of digits.
- max(): Returns the largest item in an iterable.
- min(): Returns the smallest item in an iterable.
- sum(): Sums the items of an iterable.
### DATE AND TIME FUNCTIONS (datetime module):
- datetime.datetime.now(): Returns the current date and time.
- datetime.datetime.today(): Returns the current local date.
- datetime.datetime.strftime(): Formats a datetime object as a string.
- datetime.datetime.strptime(): Parses a string to a datetime object.
### FILE I/O FUNCTIONS:
- open(): Opens a file and returns a file object.
- file.read(): Reads the contents of a file.
- file.write(): Writes data to a file.
- file.readlines(): Reads all the lines of a file into a list.
- file.close(): Closes the file.
### GENERAL FUNCTIONS:
- print(): Prints to the console.
- input(): Reads a string from standard input.
- type(): Returns the type of an object.
- isinstance(): Checks if an object is an instance of a class or a tuple of classes.
- id(): Returns the identity of an object.
Here you can find essential Python Interview Resources๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค4๐1
Many people ask this common question โCan I get a job with just SQL and Excel?โ or โCan I get a job with just Power BI and Python?โ.
The answer to all of those questions is yes.
There are jobs that use only SQL, Tableau, Power BI, Excel, Python, or R or some combination of those.
However, the combination of tools you learn impacts the total number of jobs you are qualified for.
For example, letโs say with just SQL and Excel you are qualified for 10 jobs, but if you add Tableau to that, you are qualified for 50 jobs.
If you have a success rate of landing a job youโre qualified for of 4%, having 5 times as many jobs to go for greatly improves your odds of landing a job.
Does this mean you should go out there and learn every single skill any data analyst job requires?
NO!
Itโs about finding the core tools that many jobs want.
And, in my opinion, those tools are SQL, Excel, and a visualization tool.
With these three tools, you are qualified for the majority of entry level data jobs and many higher level jobs.
So, you can land a job with whatever tools youโre comfortable with.
But if you have the three tools above in your toolbelt, you will have many more jobs to apply for and greatly improve your chances of snagging one.
The answer to all of those questions is yes.
There are jobs that use only SQL, Tableau, Power BI, Excel, Python, or R or some combination of those.
However, the combination of tools you learn impacts the total number of jobs you are qualified for.
For example, letโs say with just SQL and Excel you are qualified for 10 jobs, but if you add Tableau to that, you are qualified for 50 jobs.
If you have a success rate of landing a job youโre qualified for of 4%, having 5 times as many jobs to go for greatly improves your odds of landing a job.
Does this mean you should go out there and learn every single skill any data analyst job requires?
NO!
Itโs about finding the core tools that many jobs want.
And, in my opinion, those tools are SQL, Excel, and a visualization tool.
With these three tools, you are qualified for the majority of entry level data jobs and many higher level jobs.
So, you can land a job with whatever tools youโre comfortable with.
But if you have the three tools above in your toolbelt, you will have many more jobs to apply for and greatly improve your chances of snagging one.
โค2
Top 5 Case Studies for Data Analytics: You Must Know Before Attending an Interview
1. Retail: Target's Predictive Analytics for Customer Behavior
Company: Target
Challenge: Target wanted to identify customers who were expecting a baby to send them personalized promotions.
Solution:
Target used predictive analytics to analyze customers' purchase history and identify patterns that indicated pregnancy.
They tracked purchases of items like unscented lotion, vitamins, and cotton balls.
Outcome:
The algorithm successfully identified pregnant customers, enabling Target to send them relevant promotions.
This personalized marketing strategy increased sales and customer loyalty.
2. Healthcare: IBM Watson's Oncology Treatment Recommendations
Company: IBM Watson
Challenge: Oncologists needed support in identifying the best treatment options for cancer patients.
Solution:
IBM Watson analyzed vast amounts of medical data, including patient records, clinical trials, and medical literature.
It provided oncologists with evidencebased treatment recommendations tailored to individual patients.
Outcome:
Improved treatment accuracy and personalized care for cancer patients.
Reduced time for doctors to develop treatment plans, allowing them to focus more on patient care.
3. Finance: JP Morgan Chase's Fraud Detection System
Company: JP Morgan Chase
Challenge: The bank needed to detect and prevent fraudulent transactions in realtime.
Solution:
Implemented advanced machine learning algorithms to analyze transaction patterns and detect anomalies.
The system flagged suspicious transactions for further investigation.
Outcome:
Significantly reduced fraudulent activities.
Enhanced customer trust and satisfaction due to improved security measures.
4. Sports: Oakland Athletics' Use of Sabermetrics
Team: Oakland Athletics (Moneyball)
Challenge: Compete with larger teams with higher budgets by optimizing player performance and team strategy.
Solution:
Used sabermetrics, a form of advanced statistical analysis, to evaluate player performance and potential.
Focused on undervalued players with high onbase percentages and other key metrics.
Outcome:
Achieved remarkable success with a limited budget.
Revolutionized the approach to team building and player evaluation in baseball and other sports.
5. Ecommerce: Amazon's Recommendation Engine
Company: Amazon
Challenge: Enhance customer shopping experience and increase sales through personalized recommendations.
Solution:
Implemented a recommendation engine using collaborative filtering, which analyzes user behavior and purchase history.
The system suggests products based on what similar users have bought.
Outcome:
Increased average order value and customer retention.
Significantly contributed to Amazon's revenue growth through crossselling and upselling.
Like if it helps ๐
1. Retail: Target's Predictive Analytics for Customer Behavior
Company: Target
Challenge: Target wanted to identify customers who were expecting a baby to send them personalized promotions.
Solution:
Target used predictive analytics to analyze customers' purchase history and identify patterns that indicated pregnancy.
They tracked purchases of items like unscented lotion, vitamins, and cotton balls.
Outcome:
The algorithm successfully identified pregnant customers, enabling Target to send them relevant promotions.
This personalized marketing strategy increased sales and customer loyalty.
2. Healthcare: IBM Watson's Oncology Treatment Recommendations
Company: IBM Watson
Challenge: Oncologists needed support in identifying the best treatment options for cancer patients.
Solution:
IBM Watson analyzed vast amounts of medical data, including patient records, clinical trials, and medical literature.
It provided oncologists with evidencebased treatment recommendations tailored to individual patients.
Outcome:
Improved treatment accuracy and personalized care for cancer patients.
Reduced time for doctors to develop treatment plans, allowing them to focus more on patient care.
3. Finance: JP Morgan Chase's Fraud Detection System
Company: JP Morgan Chase
Challenge: The bank needed to detect and prevent fraudulent transactions in realtime.
Solution:
Implemented advanced machine learning algorithms to analyze transaction patterns and detect anomalies.
The system flagged suspicious transactions for further investigation.
Outcome:
Significantly reduced fraudulent activities.
Enhanced customer trust and satisfaction due to improved security measures.
4. Sports: Oakland Athletics' Use of Sabermetrics
Team: Oakland Athletics (Moneyball)
Challenge: Compete with larger teams with higher budgets by optimizing player performance and team strategy.
Solution:
Used sabermetrics, a form of advanced statistical analysis, to evaluate player performance and potential.
Focused on undervalued players with high onbase percentages and other key metrics.
Outcome:
Achieved remarkable success with a limited budget.
Revolutionized the approach to team building and player evaluation in baseball and other sports.
5. Ecommerce: Amazon's Recommendation Engine
Company: Amazon
Challenge: Enhance customer shopping experience and increase sales through personalized recommendations.
Solution:
Implemented a recommendation engine using collaborative filtering, which analyzes user behavior and purchase history.
The system suggests products based on what similar users have bought.
Outcome:
Increased average order value and customer retention.
Significantly contributed to Amazon's revenue growth through crossselling and upselling.
Like if it helps ๐
โค3
How to revolutionize Hollywood with AI.
Unlock new possibilities:
1. Voice Cloning
Clone voices of Hollywood icons:
โข Legally clone and use voices with permission.
โข Recreate iconic voices for new projects.
โข Preserve legendary performances for future generations.
2. Custom Voices
Create unique voices for your projects:
โข Generate up to 20 seconds of dialogue.
โข Select from preset voice options or create your own.
3. Lip Sync Tool
Bring still characters to life:
โข Use ElevenLabs's Lip Sync tool.
โข Select a face and add a script.
โข Generate videos with synchronized lip movements.
AI is reshaping the industry, voice cloning is part of a broader trend.
Filmmakers can now recreate voices of iconic actors.
Unlock new possibilities:
1. Voice Cloning
Clone voices of Hollywood icons:
โข Legally clone and use voices with permission.
โข Recreate iconic voices for new projects.
โข Preserve legendary performances for future generations.
2. Custom Voices
Create unique voices for your projects:
โข Generate up to 20 seconds of dialogue.
โข Select from preset voice options or create your own.
3. Lip Sync Tool
Bring still characters to life:
โข Use ElevenLabs's Lip Sync tool.
โข Select a face and add a script.
โข Generate videos with synchronized lip movements.
AI is reshaping the industry, voice cloning is part of a broader trend.
Filmmakers can now recreate voices of iconic actors.
โค2
Data Analytics isn't rocket science. It's just a different language.
Here's a beginner's guide to the world of data analytics:
1) Understand the fundamentals:
- Mathematics
- Statistics
- Technology
2) Learn the tools:
- SQL
- Python
- Excel (yes, it's still relevant!)
3) Understand the data:
- What do you want to measure?
- How are you measuring it?
- What metrics are important to you?
4) Data Visualization:
- A picture is worth a thousand words
5) Practice:
- There's no better way to learn than to do it yourself.
Data Analytics is a valuable skill that can help you make better decisions, understand your audience better, and ultimately grow your business.
It's never too late to start learning!
Here's a beginner's guide to the world of data analytics:
1) Understand the fundamentals:
- Mathematics
- Statistics
- Technology
2) Learn the tools:
- SQL
- Python
- Excel (yes, it's still relevant!)
3) Understand the data:
- What do you want to measure?
- How are you measuring it?
- What metrics are important to you?
4) Data Visualization:
- A picture is worth a thousand words
5) Practice:
- There's no better way to learn than to do it yourself.
Data Analytics is a valuable skill that can help you make better decisions, understand your audience better, and ultimately grow your business.
It's never too late to start learning!
โค2
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 :)
โค9
Essential NumPy Functions for Data Analysis
Array Creation:
np.array() - Create an array from a list.
np.zeros((rows, cols)) - Create an array filled with zeros.
np.ones((rows, cols)) - Create an array filled with ones.
np.arange(start, stop, step) - Create an array with a range of values.
Array Operations:
np.sum(array) - Calculate the sum of array elements.
np.mean(array) - Compute the mean.
np.median(array) - Calculate the median.
np.std(array) - Compute the standard deviation.
Indexing and Slicing:
array[start:stop] - Slice an array.
array[row, col] - Access a specific element.
array[:, col] - Select all rows for a column.
Reshaping and Transposing:
array.reshape(new_shape) - Reshape an array.
array.T - Transpose an array.
Random Sampling:
np.random.rand(rows, cols) - Generate random numbers in [0, 1).
np.random.randint(low, high, size) - Generate random integers.
Mathematical Operations:
np.dot(A, B) - Compute the dot product.
np.linalg.inv(A) - Compute the inverse of a matrix.
Here you can find essential Python Interview Resources๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Array Creation:
np.array() - Create an array from a list.
np.zeros((rows, cols)) - Create an array filled with zeros.
np.ones((rows, cols)) - Create an array filled with ones.
np.arange(start, stop, step) - Create an array with a range of values.
Array Operations:
np.sum(array) - Calculate the sum of array elements.
np.mean(array) - Compute the mean.
np.median(array) - Calculate the median.
np.std(array) - Compute the standard deviation.
Indexing and Slicing:
array[start:stop] - Slice an array.
array[row, col] - Access a specific element.
array[:, col] - Select all rows for a column.
Reshaping and Transposing:
array.reshape(new_shape) - Reshape an array.
array.T - Transpose an array.
Random Sampling:
np.random.rand(rows, cols) - Generate random numbers in [0, 1).
np.random.randint(low, high, size) - Generate random integers.
Mathematical Operations:
np.dot(A, B) - Compute the dot product.
np.linalg.inv(A) - Compute the inverse of a matrix.
Here you can find essential Python Interview Resources๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค1
Data Analyst Learning Plan in 2025
|-- Week 1: Introduction to Data Analytics
| |-- What is Data Analytics?
| |-- Roles & Responsibilities of a Data Analyst
| |-- Data Analytics Workflow
| |-- Types of Data (Structured, Unstructured, Semi-structured)
|
|-- Week 2: Excel for Data Analysis
| |-- Excel Basics & Interface
| |-- Data Cleaning & Preparation
| |-- Formulas, Functions, Pivot Tables
| |-- Dashboards & Reporting in Excel
|
|-- Week 3: SQL for Data Analysts
| |-- SQL Basics: SELECT, WHERE, ORDER BY
| |-- Aggregations & GROUP BY
| |-- Joins: INNER, LEFT, RIGHT, FULL
| |-- CTEs, Subqueries & Window Functions
|
|-- Week 4: Python for Data Analysis
| |-- Python Basics (Variables, Data Types, Loops)
| |-- Data Analysis with Pandas
| |-- Data Visualization with Matplotlib & Seaborn
| |-- Exploratory Data Analysis (EDA)
|
|-- Week 5: Statistics & Probability
| |-- Descriptive Statistics
| |-- Probability Theory Basics
| |-- Distributions (Normal, Binomial, Poisson)
| |-- Hypothesis Testing & A/B Testing
|
|-- Week 6: Data Cleaning & Transformation
| |-- Handling Missing Values
| |-- Duplicates, Outliers, and Data Formatting
| |-- Data Parsing & Regex
| |-- Data Normalization
|
|-- Week 7: Data Visualization Tools
| |-- Power BI Basics
| |-- Creating Reports and Dashboards
| |-- Data Modeling in Power BI
| |-- Filters, Slicers, DAX Basics
|
|-- Week 8: Advanced Excel & Power BI
| |-- Advanced Charts & Dashboards
| |-- Time Intelligence in Power BI
| |-- Calculated Columns & Measures (DAX)
| |-- Performance Optimization Tips
|
|-- Week 9: Business Acumen & Domain Knowledge
| |-- KPIs & Business Metrics
| |-- Understanding Financial, Marketing, Sales Data
| |-- Creating Insightful Reports
| |-- Storytelling with Data
|
|-- Week 10: Real-World Projects & Portfolio
| |-- End-to-End Project on E-commerce/Sales
| |-- Collecting & Cleaning Data
| |-- Analyzing Trends & Presenting Insights
| |-- Uploading Projects on GitHub
|
|-- Week 11: Tools for Data Analysts
| |-- Jupyter Notebooks
| |-- Google Sheets & Google Data Studio
| |-- Tableau Overview
| |-- APIs & Web Scraping (Intro only)
|
|-- Week 12: Career Preparation
| |-- Resume & LinkedIn for Data Analysts
| |-- Common Interview Questions (SQL, Python, Case Studies)
| |-- Mock Interviews & Peer Reviews
Join our WhatsApp channel: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
|-- Week 1: Introduction to Data Analytics
| |-- What is Data Analytics?
| |-- Roles & Responsibilities of a Data Analyst
| |-- Data Analytics Workflow
| |-- Types of Data (Structured, Unstructured, Semi-structured)
|
|-- Week 2: Excel for Data Analysis
| |-- Excel Basics & Interface
| |-- Data Cleaning & Preparation
| |-- Formulas, Functions, Pivot Tables
| |-- Dashboards & Reporting in Excel
|
|-- Week 3: SQL for Data Analysts
| |-- SQL Basics: SELECT, WHERE, ORDER BY
| |-- Aggregations & GROUP BY
| |-- Joins: INNER, LEFT, RIGHT, FULL
| |-- CTEs, Subqueries & Window Functions
|
|-- Week 4: Python for Data Analysis
| |-- Python Basics (Variables, Data Types, Loops)
| |-- Data Analysis with Pandas
| |-- Data Visualization with Matplotlib & Seaborn
| |-- Exploratory Data Analysis (EDA)
|
|-- Week 5: Statistics & Probability
| |-- Descriptive Statistics
| |-- Probability Theory Basics
| |-- Distributions (Normal, Binomial, Poisson)
| |-- Hypothesis Testing & A/B Testing
|
|-- Week 6: Data Cleaning & Transformation
| |-- Handling Missing Values
| |-- Duplicates, Outliers, and Data Formatting
| |-- Data Parsing & Regex
| |-- Data Normalization
|
|-- Week 7: Data Visualization Tools
| |-- Power BI Basics
| |-- Creating Reports and Dashboards
| |-- Data Modeling in Power BI
| |-- Filters, Slicers, DAX Basics
|
|-- Week 8: Advanced Excel & Power BI
| |-- Advanced Charts & Dashboards
| |-- Time Intelligence in Power BI
| |-- Calculated Columns & Measures (DAX)
| |-- Performance Optimization Tips
|
|-- Week 9: Business Acumen & Domain Knowledge
| |-- KPIs & Business Metrics
| |-- Understanding Financial, Marketing, Sales Data
| |-- Creating Insightful Reports
| |-- Storytelling with Data
|
|-- Week 10: Real-World Projects & Portfolio
| |-- End-to-End Project on E-commerce/Sales
| |-- Collecting & Cleaning Data
| |-- Analyzing Trends & Presenting Insights
| |-- Uploading Projects on GitHub
|
|-- Week 11: Tools for Data Analysts
| |-- Jupyter Notebooks
| |-- Google Sheets & Google Data Studio
| |-- Tableau Overview
| |-- APIs & Web Scraping (Intro only)
|
|-- Week 12: Career Preparation
| |-- Resume & LinkedIn for Data Analysts
| |-- Common Interview Questions (SQL, Python, Case Studies)
| |-- Mock Interviews & Peer Reviews
Join our WhatsApp channel: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค3
๐๐๐Preparing for a Data science/ Data Analytics interview can be challenging, but with the right strategy, you can enhance your chances of success. Here are some key tips to assist you in getting ready:
Review Fundamental Concepts: Ensure you have a strong grasp of statistics, probability, linear algebra, data structures, algorithms, and programming languages like Python, R, and SQL.
Refresh Machine Learning Knowledge: Familiarize yourself with various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
Practice Coding: Sharpen your coding skills by solving data science-related problems on platforms like HackerRank, LeetCode, and Kaggle.
Build a Project Portfolio: Showcase your proficiency by creating a portfolio highlighting projects covering data cleaning, wrangling, exploratory data analysis, and machine learning.
Hone Communication Skills: Practice articulating complex technical ideas in simple terms, as effective communication is vital for data scientists when interacting with non-technical stakeholders.
Research the Company: Gain insights into the company's operations, industry, and how they leverage data to solve challenges.
๐ง ๐By adhering to these guidelines, you'll be well-prepared for your upcoming data science interview. Best of luck!
Hope this helps ๐โค๏ธ:โ -โ )
๐๐Be the first one to know the latest Job openings
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Review Fundamental Concepts: Ensure you have a strong grasp of statistics, probability, linear algebra, data structures, algorithms, and programming languages like Python, R, and SQL.
Refresh Machine Learning Knowledge: Familiarize yourself with various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
Practice Coding: Sharpen your coding skills by solving data science-related problems on platforms like HackerRank, LeetCode, and Kaggle.
Build a Project Portfolio: Showcase your proficiency by creating a portfolio highlighting projects covering data cleaning, wrangling, exploratory data analysis, and machine learning.
Hone Communication Skills: Practice articulating complex technical ideas in simple terms, as effective communication is vital for data scientists when interacting with non-technical stakeholders.
Research the Company: Gain insights into the company's operations, industry, and how they leverage data to solve challenges.
๐ง ๐By adhering to these guidelines, you'll be well-prepared for your upcoming data science interview. Best of luck!
Hope this helps ๐โค๏ธ:โ -โ )
๐๐Be the first one to know the latest Job openings
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
โค1
๐ช๐ฎ๐ป๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐ธ๐ถ๐น๐น๐ ๐ง๐ต๐ฎ๐ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐๐ฟ๐ฒ ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ผ๐ฟ?๐
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If youโre looking to land a job in tech or simply want to upskill without spending money, this is your golden chanceโจ๏ธ๐
Weโve handpicked 5 YouTube channels that teach 5 in-demand tech skills for FREE. These skills are widely sought after by employers in 2025 โ from startups to top MNCs๐งโ๐ป
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