Common Machine Learning Algorithms!
1๏ธโฃ Linear Regression
->Used for predicting continuous values.
->Models the relationship between dependent and independent variables by fitting a linear equation.
2๏ธโฃ Logistic Regression
->Ideal for binary classification problems.
->Estimates the probability that an instance belongs to a particular class.
3๏ธโฃ Decision Trees
->Splits data into subsets based on the value of input features.
->Easy to visualize and interpret but can be prone to overfitting.
4๏ธโฃ Random Forest
->An ensemble method using multiple decision trees.
->Reduces overfitting and improves accuracy by averaging multiple trees.
5๏ธโฃ Support Vector Machines (SVM)
->Finds the hyperplane that best separates different classes.
->Effective in high-dimensional spaces and for classification tasks.
6๏ธโฃ k-Nearest Neighbors (k-NN)
->Classifies data based on the majority class among the k-nearest neighbors.
->Simple and intuitive but can be computationally intensive.
7๏ธโฃ K-Means Clustering
->Partitions data into k clusters based on feature similarity.
->Useful for market segmentation, image compression, and more.
8๏ธโฃ Naive Bayes
->Based on Bayes' theorem with an assumption of independence among predictors.
->Particularly useful for text classification and spam filtering.
9๏ธโฃ Neural Networks
->Mimic the human brain to identify patterns in data.
->Power deep learning applications, from image recognition to natural language processing.
๐ Gradient Boosting Machines (GBM)
->Combines weak learners to create a strong predictive model.
->Used in various applications like ranking, classification, and regression.
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING ๐๐
1๏ธโฃ Linear Regression
->Used for predicting continuous values.
->Models the relationship between dependent and independent variables by fitting a linear equation.
2๏ธโฃ Logistic Regression
->Ideal for binary classification problems.
->Estimates the probability that an instance belongs to a particular class.
3๏ธโฃ Decision Trees
->Splits data into subsets based on the value of input features.
->Easy to visualize and interpret but can be prone to overfitting.
4๏ธโฃ Random Forest
->An ensemble method using multiple decision trees.
->Reduces overfitting and improves accuracy by averaging multiple trees.
5๏ธโฃ Support Vector Machines (SVM)
->Finds the hyperplane that best separates different classes.
->Effective in high-dimensional spaces and for classification tasks.
6๏ธโฃ k-Nearest Neighbors (k-NN)
->Classifies data based on the majority class among the k-nearest neighbors.
->Simple and intuitive but can be computationally intensive.
7๏ธโฃ K-Means Clustering
->Partitions data into k clusters based on feature similarity.
->Useful for market segmentation, image compression, and more.
8๏ธโฃ Naive Bayes
->Based on Bayes' theorem with an assumption of independence among predictors.
->Particularly useful for text classification and spam filtering.
9๏ธโฃ Neural Networks
->Mimic the human brain to identify patterns in data.
->Power deep learning applications, from image recognition to natural language processing.
๐ Gradient Boosting Machines (GBM)
->Combines weak learners to create a strong predictive model.
->Used in various applications like ranking, classification, and regression.
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING ๐๐
โค2
Data Science Learning Plan
Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)
Step 2: Python for Data Science (Basics and Libraries)
Step 3: Data Manipulation and Analysis (Pandas, NumPy)
Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)
Step 5: Databases and SQL for Data Retrieval
Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)
Step 7: Data Cleaning and Preprocessing
Step 8: Feature Engineering and Selection
Step 9: Model Evaluation and Tuning
Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)
Step 11: Working with Big Data (Hadoop, Spark)
Step 12: Building Data Science Projects and Portfolio
Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)
Step 2: Python for Data Science (Basics and Libraries)
Step 3: Data Manipulation and Analysis (Pandas, NumPy)
Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)
Step 5: Databases and SQL for Data Retrieval
Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)
Step 7: Data Cleaning and Preprocessing
Step 8: Feature Engineering and Selection
Step 9: Model Evaluation and Tuning
Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)
Step 11: Working with Big Data (Hadoop, Spark)
Step 12: Building Data Science Projects and Portfolio
โค3
9 tips to get started with Data Analysis:
Learn Excel, SQL, and a programming language (Python or R)
Understand basic statistics and probability
Practice with real-world datasets (Kaggle, Data.gov)
Clean and preprocess data effectively
Visualize data using charts and graphs
Ask the right questions before diving into data
Use libraries like Pandas, NumPy, and Matplotlib
Focus on storytelling with data insights
Build small projects to apply what you learn
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
Learn Excel, SQL, and a programming language (Python or R)
Understand basic statistics and probability
Practice with real-world datasets (Kaggle, Data.gov)
Clean and preprocess data effectively
Visualize data using charts and graphs
Ask the right questions before diving into data
Use libraries like Pandas, NumPy, and Matplotlib
Focus on storytelling with data insights
Build small projects to apply what you learn
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
โค1
Top 10 machine Learning algorithms for beginners ๐๐
1. Linear Regression: A simple algorithm used for predicting a continuous value based on one or more input features.
2. Logistic Regression: Used for binary classification problems, where the output is a binary value (0 or 1).
3. Decision Trees: A versatile algorithm that can be used for both classification and regression tasks, based on a tree-like structure of decisions.
4. Random Forest: An ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model.
5. Support Vector Machines (SVM): Used for both classification and regression tasks, with the goal of finding the hyperplane that best separates the classes.
6. K-Nearest Neighbors (KNN): A simple algorithm that classifies a new data point based on the majority class of its k nearest neighbors in the feature space.
7. Naive Bayes: A probabilistic algorithm based on Bayes' theorem that is commonly used for text classification and spam filtering.
8. K-Means Clustering: An unsupervised learning algorithm used for clustering data points into k distinct groups based on similarity.
9. Principal Component Analysis (PCA): A dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.
10. Gradient Boosting Machines (GBM): An ensemble learning method that builds a series of weak learners to create a strong predictive model through iterative optimization.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
1. Linear Regression: A simple algorithm used for predicting a continuous value based on one or more input features.
2. Logistic Regression: Used for binary classification problems, where the output is a binary value (0 or 1).
3. Decision Trees: A versatile algorithm that can be used for both classification and regression tasks, based on a tree-like structure of decisions.
4. Random Forest: An ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model.
5. Support Vector Machines (SVM): Used for both classification and regression tasks, with the goal of finding the hyperplane that best separates the classes.
6. K-Nearest Neighbors (KNN): A simple algorithm that classifies a new data point based on the majority class of its k nearest neighbors in the feature space.
7. Naive Bayes: A probabilistic algorithm based on Bayes' theorem that is commonly used for text classification and spam filtering.
8. K-Means Clustering: An unsupervised learning algorithm used for clustering data points into k distinct groups based on similarity.
9. Principal Component Analysis (PCA): A dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.
10. Gradient Boosting Machines (GBM): An ensemble learning method that builds a series of weak learners to create a strong predictive model through iterative optimization.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
โค2
๐ ๐๐ฑ๐ฉ๐ฅ๐จ๐ซ๐ข๐ง๐ ๐๐๐ญ๐ ๐๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐ญ๐ก๐ ๐๐ ๐๐ง๐๐ฎ๐ฌ๐ญ๐ซ๐ฒ ๐
The world of data is vast and diverse, and understanding the nuances between different data roles can help both professionals and organizations thrive.
This visual breakdown offers a fantastic comparison of key data roles:
๐ ๐๐๐ญ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ โ The backbone of any data-driven team. They build robust data pipelines, manage infrastructure, and ensure data is accessible and reliable. Strong in deployment, ML-Ops, and working closely with Data Scientists.
๐ ๐๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ โ These experts bridge software engineering and data science. They focus on building and deploying machine learning models at scale, emphasizing ML Ops, experimentation, and data analysis.
โค๏ธ ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ โ The creative problem solvers. They blend statistical analysis, machine learning, and storytelling to uncover insights and predict future trends. Skilled in experimentation, ML modeling, and storytelling.
๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ โ Their strengths lie in reporting, business insights, and visualization.
The world of data is vast and diverse, and understanding the nuances between different data roles can help both professionals and organizations thrive.
This visual breakdown offers a fantastic comparison of key data roles:
๐ ๐๐๐ญ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ โ The backbone of any data-driven team. They build robust data pipelines, manage infrastructure, and ensure data is accessible and reliable. Strong in deployment, ML-Ops, and working closely with Data Scientists.
๐ ๐๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ โ These experts bridge software engineering and data science. They focus on building and deploying machine learning models at scale, emphasizing ML Ops, experimentation, and data analysis.
โค๏ธ ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ โ The creative problem solvers. They blend statistical analysis, machine learning, and storytelling to uncover insights and predict future trends. Skilled in experimentation, ML modeling, and storytelling.
๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ โ Their strengths lie in reporting, business insights, and visualization.
โค1
5 Essential Portfolio Projects for data analysts ๐๐
1. Exploratory Data Analysis (EDA) on a Real Dataset: Choose a dataset related to your interests, perform thorough EDA, visualize trends, and draw insights. This showcases your ability to understand data and derive meaningful conclusions.
Free websites to find datasets: https://t.iss.one/DataPortfolio/8
2. Predictive Modeling Project: Build a predictive model, such as a linear regression or classification model. Use a dataset to train and test your model, and evaluate its performance. Highlight your skills in machine learning and statistical analysis.
3. Data Cleaning and Transformation: Take a messy dataset and demonstrate your skills in cleaning and transforming data. Showcase your ability to handle missing values, outliers, and prepare data for analysis.
4. Dashboard Creation: Utilize tools like Tableau or Power BI to create an interactive dashboard. This project demonstrates your ability to present data insights in a visually appealing and user-friendly manner.
5. Time Series Analysis: Work with time-series data to forecast future trends. This could involve stock prices, weather data, or any other time-dependent dataset. Showcase your understanding of time-series concepts and forecasting techniques.
Share with credits: https://t.iss.one/sqlspecialist
Like it if you need more posts like this ๐โค๏ธ
Hope it helps :)
1. Exploratory Data Analysis (EDA) on a Real Dataset: Choose a dataset related to your interests, perform thorough EDA, visualize trends, and draw insights. This showcases your ability to understand data and derive meaningful conclusions.
Free websites to find datasets: https://t.iss.one/DataPortfolio/8
2. Predictive Modeling Project: Build a predictive model, such as a linear regression or classification model. Use a dataset to train and test your model, and evaluate its performance. Highlight your skills in machine learning and statistical analysis.
3. Data Cleaning and Transformation: Take a messy dataset and demonstrate your skills in cleaning and transforming data. Showcase your ability to handle missing values, outliers, and prepare data for analysis.
4. Dashboard Creation: Utilize tools like Tableau or Power BI to create an interactive dashboard. This project demonstrates your ability to present data insights in a visually appealing and user-friendly manner.
5. Time Series Analysis: Work with time-series data to forecast future trends. This could involve stock prices, weather data, or any other time-dependent dataset. Showcase your understanding of time-series concepts and forecasting techniques.
Share with credits: https://t.iss.one/sqlspecialist
Like it if you need more posts like this ๐โค๏ธ
Hope it helps :)
โค1
Data Analytics Interview Topics in structured way :
๐ตPython: Data Structures: Lists, tuples, dictionaries, sets Pandas: Data manipulation (DataFrame operations, merging, reshaping) NumPy: Numeric computing, arrays Visualization: Matplotlib, Seaborn for creating charts
๐ตSQL: Basic : SELECT, WHERE, JOIN, GROUP BY, ORDER BY Advanced : Subqueries, nested queries, window functions DBMS: Creating tables, altering schema, indexing Joins: Inner join, outer join, left/right join Data Manipulation: UPDATE, DELETE, INSERT statements Aggregate Functions: SUM, AVG, COUNT, MAX, MIN
๐ตExcel: Formulas & Functions: VLOOKUP, HLOOKUP, IF, SUMIF, COUNTIF Data Cleaning: Removing duplicates, handling errors, text-to-columns PivotTables Charts and Graphs What-If Analysis: Scenario Manager, Goal Seek, Solver
๐ตPower BI:
Data Modeling: Creating relationships between datasets
Transformation: Cleaning & shaping data using
Power Query Editor Visualization: Creating interactive reports and dashboards
DAX (Data Analysis Expressions): Formulas for calculated columns, measures Publishing and sharing reports, scheduling data refresh
๐ต Statistics Fundamentals: Mean, median, mode Variance, standard deviation Probability distributions Hypothesis testing, p-values, confidence intervals
๐ตData Manipulation and Cleaning: Data preprocessing techniques (handling missing values, outliers), Data normalization and standardization Data transformation Handling categorical data
๐ตData Visualization: Chart types (bar, line, scatter, histogram, boxplot) Data visualization libraries (matplotlib, seaborn, ggplot) Effective data storytelling through visualization
Also showcase these skills using data portfolio if possible
Like for more content like this ๐
๐ตPython: Data Structures: Lists, tuples, dictionaries, sets Pandas: Data manipulation (DataFrame operations, merging, reshaping) NumPy: Numeric computing, arrays Visualization: Matplotlib, Seaborn for creating charts
๐ตSQL: Basic : SELECT, WHERE, JOIN, GROUP BY, ORDER BY Advanced : Subqueries, nested queries, window functions DBMS: Creating tables, altering schema, indexing Joins: Inner join, outer join, left/right join Data Manipulation: UPDATE, DELETE, INSERT statements Aggregate Functions: SUM, AVG, COUNT, MAX, MIN
๐ตExcel: Formulas & Functions: VLOOKUP, HLOOKUP, IF, SUMIF, COUNTIF Data Cleaning: Removing duplicates, handling errors, text-to-columns PivotTables Charts and Graphs What-If Analysis: Scenario Manager, Goal Seek, Solver
๐ตPower BI:
Data Modeling: Creating relationships between datasets
Transformation: Cleaning & shaping data using
Power Query Editor Visualization: Creating interactive reports and dashboards
DAX (Data Analysis Expressions): Formulas for calculated columns, measures Publishing and sharing reports, scheduling data refresh
๐ต Statistics Fundamentals: Mean, median, mode Variance, standard deviation Probability distributions Hypothesis testing, p-values, confidence intervals
๐ตData Manipulation and Cleaning: Data preprocessing techniques (handling missing values, outliers), Data normalization and standardization Data transformation Handling categorical data
๐ตData Visualization: Chart types (bar, line, scatter, histogram, boxplot) Data visualization libraries (matplotlib, seaborn, ggplot) Effective data storytelling through visualization
Also showcase these skills using data portfolio if possible
Like for more content like this ๐
โค2
Common Requirements for data analyst role ๐
๐ Must be proficient in writing complex SQL Queries.
๐ Understand business requirements in BI context and design data models to transform raw data into meaningful insights.
๐ Connecting data sources, importing data, and transforming data for Business intelligence.
๐ Strong working knowledge in Excel and visualization tools like PowerBI, Tableau or QlikView
๐ Developing visual reports, KPI scorecards, and dashboards using Power BI desktop.
Nowadays, recruiters primary focus on SQL & BI skills for data analyst roles. So try practicing SQL & create some BI projects using Tableau or Power BI.
*Here are some essential WhatsApp Channels with important resources:*
โฏ Jobs โ https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
โฏ SQL โ https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
โฏ Power BI โ https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
โฏ Data Analysts โ https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
โฏ Python โ https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
I am planning to come up with interview series as well to share some essential questions based on my experience in data analytics field.
Like this post if you want me to start the interview series ๐โค๏ธ
Hope it helps :)
๐ Must be proficient in writing complex SQL Queries.
๐ Understand business requirements in BI context and design data models to transform raw data into meaningful insights.
๐ Connecting data sources, importing data, and transforming data for Business intelligence.
๐ Strong working knowledge in Excel and visualization tools like PowerBI, Tableau or QlikView
๐ Developing visual reports, KPI scorecards, and dashboards using Power BI desktop.
Nowadays, recruiters primary focus on SQL & BI skills for data analyst roles. So try practicing SQL & create some BI projects using Tableau or Power BI.
*Here are some essential WhatsApp Channels with important resources:*
โฏ Jobs โ https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
โฏ SQL โ https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
โฏ Power BI โ https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
โฏ Data Analysts โ https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
โฏ Python โ https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
I am planning to come up with interview series as well to share some essential questions based on my experience in data analytics field.
Like this post if you want me to start the interview series ๐โค๏ธ
Hope it helps :)
โค1
How to master Python from scratch๐
1. Setup and Basics ๐
- Install Python ๐ฅ๏ธ: Download Python and set it up.
- Hello, World! ๐: Write your first Hello World program.
2. Basic Syntax ๐
- Variables and Data Types ๐: Learn about strings, integers, floats, and booleans.
- Control Structures ๐: Understand if-else statements, for loops, and while loops.
- Functions ๐ ๏ธ: Write reusable blocks of code.
3. Data Structures ๐
- Lists ๐: Manage collections of items.
- Dictionaries ๐: Store key-value pairs.
- Tuples ๐ฆ: Work with immutable sequences.
- Sets ๐ข: Handle collections of unique items.
4. Modules and Packages ๐ฆ
- Standard Library ๐: Explore built-in modules.
- Third-Party Packages ๐: Install and use packages with pip.
5. File Handling ๐
- Read and Write Files ๐
- CSV and JSON ๐
6. Object-Oriented Programming ๐งฉ
- Classes and Objects ๐๏ธ
- Inheritance and Polymorphism ๐จโ๐ฉโ๐ง
7. Web Development ๐
- Flask ๐ผ: Start with a micro web framework.
- Django ๐ฆ: Dive into a full-fledged web framework.
8. Data Science and Machine Learning ๐ง
- NumPy ๐: Numerical operations.
- Pandas ๐ผ: Data manipulation and analysis.
- Matplotlib ๐ and Seaborn ๐: Data visualization.
- Scikit-learn ๐ค: Machine learning.
9. Automation and Scripting ๐ค
- Automate Tasks ๐ ๏ธ: Use Python to automate repetitive tasks.
- APIs ๐: Interact with web services.
10. Testing and Debugging ๐
- Unit Testing ๐งช: Write tests for your code.
- Debugging ๐: Learn to debug efficiently.
11. Advanced Topics ๐
- Concurrency and Parallelism ๐
- Decorators ๐ and Generators โ๏ธ
- Web Scraping ๐ธ๏ธ: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects ๐ก
- Calculator ๐งฎ
- To-Do List App ๐
- Weather App โ๏ธ
- Personal Blog ๐
13. Community and Collaboration ๐ค
- Contribute to Open Source ๐
- Join Coding Communities ๐ฌ
- Participate in Hackathons ๐
14. Keep Learning and Improving ๐
- Read Books ๐: Like "Automate the Boring Stuff with Python".
- Watch Tutorials ๐ฅ: Follow video courses and tutorials.
- Solve Challenges ๐งฉ: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge ๐ข
- Write Blogs โ๏ธ
- Create Video Tutorials ๐น
- Mentor Others ๐จโ๐ซ
I have curated the best interview resources to crack Python Interviews ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
1. Setup and Basics ๐
- Install Python ๐ฅ๏ธ: Download Python and set it up.
- Hello, World! ๐: Write your first Hello World program.
2. Basic Syntax ๐
- Variables and Data Types ๐: Learn about strings, integers, floats, and booleans.
- Control Structures ๐: Understand if-else statements, for loops, and while loops.
- Functions ๐ ๏ธ: Write reusable blocks of code.
3. Data Structures ๐
- Lists ๐: Manage collections of items.
- Dictionaries ๐: Store key-value pairs.
- Tuples ๐ฆ: Work with immutable sequences.
- Sets ๐ข: Handle collections of unique items.
4. Modules and Packages ๐ฆ
- Standard Library ๐: Explore built-in modules.
- Third-Party Packages ๐: Install and use packages with pip.
5. File Handling ๐
- Read and Write Files ๐
- CSV and JSON ๐
6. Object-Oriented Programming ๐งฉ
- Classes and Objects ๐๏ธ
- Inheritance and Polymorphism ๐จโ๐ฉโ๐ง
7. Web Development ๐
- Flask ๐ผ: Start with a micro web framework.
- Django ๐ฆ: Dive into a full-fledged web framework.
8. Data Science and Machine Learning ๐ง
- NumPy ๐: Numerical operations.
- Pandas ๐ผ: Data manipulation and analysis.
- Matplotlib ๐ and Seaborn ๐: Data visualization.
- Scikit-learn ๐ค: Machine learning.
9. Automation and Scripting ๐ค
- Automate Tasks ๐ ๏ธ: Use Python to automate repetitive tasks.
- APIs ๐: Interact with web services.
10. Testing and Debugging ๐
- Unit Testing ๐งช: Write tests for your code.
- Debugging ๐: Learn to debug efficiently.
11. Advanced Topics ๐
- Concurrency and Parallelism ๐
- Decorators ๐ and Generators โ๏ธ
- Web Scraping ๐ธ๏ธ: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects ๐ก
- Calculator ๐งฎ
- To-Do List App ๐
- Weather App โ๏ธ
- Personal Blog ๐
13. Community and Collaboration ๐ค
- Contribute to Open Source ๐
- Join Coding Communities ๐ฌ
- Participate in Hackathons ๐
14. Keep Learning and Improving ๐
- Read Books ๐: Like "Automate the Boring Stuff with Python".
- Watch Tutorials ๐ฅ: Follow video courses and tutorials.
- Solve Challenges ๐งฉ: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge ๐ข
- Write Blogs โ๏ธ
- Create Video Tutorials ๐น
- Mentor Others ๐จโ๐ซ
I have curated the best interview resources to crack Python Interviews ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
โค1
๐ ๐๐๐ฒ๐ฌ ๐ญ๐จ ๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐จ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ ๐๐จ๐๐ฌ
๐ธ๐๐ฌ๐ ๐๐จ๐ ๐๐จ๐ซ๐ญ๐๐ฅ๐ฌ
Job boards like LinkedIn & Naukari are great portals to find jobs.
Set up job alerts using keywords like โData Analystโ so youโll get notified as soon as something new comes up.
๐ธ๐๐๐ข๐ฅ๐จ๐ซ ๐๐จ๐ฎ๐ซ ๐๐๐ฌ๐ฎ๐ฆ๐
Donโt send the same resume to every job.
Take time to highlight the skills and tools that the job description asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).
๐ธ๐๐ฌ๐ ๐๐ข๐ง๐ค๐๐๐๐ง
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster
Engage with data-related content and share your own work (like project insights or dashboards).
๐ธ๐๐ก๐๐๐ค ๐๐จ๐ฆ๐ฉ๐๐ง๐ฒ ๐๐๐๐ฌ๐ข๐ญ๐๐ฌ ๐๐๐ ๐ฎ๐ฅ๐๐ซ๐ฅ๐ฒ
Most big companies post jobs directly on their websites first.
Create a list of companies youโre interested in and keep checking their careers page. Itโs a good way to find openings early before they post on job portals.
๐ธ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ ๐๐ฉ ๐๐๐ญ๐๐ซ ๐๐ฉ๐ฉ๐ฅ๐ฒ๐ข๐ง๐
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.
๐ธ๐๐ฌ๐ ๐๐จ๐ ๐๐จ๐ซ๐ญ๐๐ฅ๐ฌ
Job boards like LinkedIn & Naukari are great portals to find jobs.
Set up job alerts using keywords like โData Analystโ so youโll get notified as soon as something new comes up.
๐ธ๐๐๐ข๐ฅ๐จ๐ซ ๐๐จ๐ฎ๐ซ ๐๐๐ฌ๐ฎ๐ฆ๐
Donโt send the same resume to every job.
Take time to highlight the skills and tools that the job description asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).
๐ธ๐๐ฌ๐ ๐๐ข๐ง๐ค๐๐๐๐ง
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster
Engage with data-related content and share your own work (like project insights or dashboards).
๐ธ๐๐ก๐๐๐ค ๐๐จ๐ฆ๐ฉ๐๐ง๐ฒ ๐๐๐๐ฌ๐ข๐ญ๐๐ฌ ๐๐๐ ๐ฎ๐ฅ๐๐ซ๐ฅ๐ฒ
Most big companies post jobs directly on their websites first.
Create a list of companies youโre interested in and keep checking their careers page. Itโs a good way to find openings early before they post on job portals.
๐ธ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ ๐๐ฉ ๐๐๐ญ๐๐ซ ๐๐ฉ๐ฉ๐ฅ๐ฒ๐ข๐ง๐
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.
โค4
๐๐ข๐ฌ๐ญ ๐จ๐ ๐๐จ๐ฆ๐ฉ๐๐ง๐ข๐๐ฌ ๐ญ๐ก๐๐ญ ๐ก๐ข๐ซ๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ๐ฌ:
TMcKinsey & Company
Boston Consulting Group (BCG)
Bain & Company
Deloitte
PwC
Ernst & Young (EY)
KPMG
Accenture
Google
Amazon
Microsoft
IBM
Oracle
Tiger Analytics
Mu Sigma
Fractal Analytics
EXL Service
ZS Associates
Wells Fargo
Walmart
Target
LTIMindtree
Infosys
TCS (Tata Consultancy Services)
Wipro
HCL Technologies
Capgemini
Cognizant
These companies often hire data analysts to use data for making decisions and planning strategically for their clients.
TMcKinsey & Company
Boston Consulting Group (BCG)
Bain & Company
Deloitte
PwC
Ernst & Young (EY)
KPMG
Accenture
Amazon
Microsoft
IBM
Oracle
Tiger Analytics
Mu Sigma
Fractal Analytics
EXL Service
ZS Associates
Wells Fargo
Walmart
Target
LTIMindtree
Infosys
TCS (Tata Consultancy Services)
Wipro
HCL Technologies
Capgemini
Cognizant
These companies often hire data analysts to use data for making decisions and planning strategically for their clients.
โค3