Top 10 machine Learning algorithms for beginners 👇👇

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

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🔍 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐈𝐓 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 🔍

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:

💚 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 – 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.

💜 𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 – 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.

❤️ 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 – 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.

💛 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 – Their strengths lie in reporting, business insights, and visualization.

5 Essential Portfolio Projects for data analysts 😄👇

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

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Hope it helps :)

Data Analytics Interview Topics in structured way :

🔵Python: Data Structures: Lists, tuples, dictionaries, sets Pandas: Data manipulation (DataFrame operations, merging, reshaping) NumPy: Numeric computing, arrays Visualization: Matplotlib, Seaborn for creating charts

🔵SQL: 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

🔵Excel: 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

🔵Power 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

🔵 Statistics Fundamentals: Mean, median, mode Variance, standard deviation Probability distributions Hypothesis testing, p-values, confidence intervals

🔵Data Manipulation and Cleaning: Data preprocessing techniques (handling missing values, outliers), Data normalization and standardization Data transformation Handling categorical data

🔵Data 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

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Common Requirements for data analyst role 👇

👉 Must be proficient in writing complex SQL Queries.

👉 Understand business requirements in BI context and design data models to transform raw data into meaningful insights.

👉 Connecting data sources, importing data, and transforming data for Business intelligence.

👉 Strong working knowledge in Excel and visualization tools like PowerBI, Tableau or QlikView

👉 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:*

❯ Jobs ➟ https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J

❯ SQL ➟ https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

❯ Power BI ➟ https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c

❯ Data Analysts ➟ https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

❯ Python ➟ 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 👍❤️

Hope it helps :)

How to master Python from scratch🚀

1. Setup and Basics 🏁
   - Install Python 🖥️: Download Python and set it up.
   - Hello, World! 🌍: Write your first Hello World program.

2. Basic Syntax 📜
   - Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
   - Control Structures 🔄: Understand if-else statements, for loops, and while loops.
   - Functions 🛠️: Write reusable blocks of code.

3. Data Structures 📂
   - Lists 📋: Manage collections of items.
   - Dictionaries 📖: Store key-value pairs.
   - Tuples 📦: Work with immutable sequences.
   - Sets 🔢: Handle collections of unique items.

4. Modules and Packages 📦
   - Standard Library 📚: Explore built-in modules.
   - Third-Party Packages 🌐: Install and use packages with pip.

5. File Handling 📁
   - Read and Write Files 📝
   - CSV and JSON 📑

6. Object-Oriented Programming 🧩
   - Classes and Objects 🏛️
   - Inheritance and Polymorphism 👨‍👩‍👧

7. Web Development 🌐
   - Flask 🍼: Start with a micro web framework.
   - Django 🦄: Dive into a full-fledged web framework.

8. Data Science and Machine Learning 🧠
   - NumPy 📊: Numerical operations.
   - Pandas 🐼: Data manipulation and analysis.
   - Matplotlib 📈 and Seaborn 📊: Data visualization.
   - Scikit-learn 🤖: Machine learning.

9. Automation and Scripting 🤖
   - Automate Tasks 🛠️: Use Python to automate repetitive tasks.
   - APIs 🌐: Interact with web services.

10. Testing and Debugging 🐞
    - Unit Testing 🧪: Write tests for your code.
    - Debugging 🔍: Learn to debug efficiently.

11. Advanced Topics 🚀
    - Concurrency and Parallelism 🕒
    - Decorators 🌀 and Generators ⚙️
    - Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.

12. Practice Projects 💡
    - Calculator 🧮
    - To-Do List App 📋
    - Weather App ☀️
    - Personal Blog 📝

13. Community and Collaboration 🤝
    - Contribute to Open Source 🌍
    - Join Coding Communities 💬
    - Participate in Hackathons 🏆

14. Keep Learning and Improving 📈
    - Read Books 📖: Like "Automate the Boring Stuff with Python".
    - Watch Tutorials 🎥: Follow video courses and tutorials.
    - Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.

15. Teach and Share Knowledge 📢
    - Write Blogs ✍️
    - Create Video Tutorials 📹
    - Mentor Others 👨‍🏫

I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340

Hope you'll like it

Like this post if you need more resources like this 👍❤️

𝟓 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐀𝐩𝐩𝐥𝐲 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐉𝐨𝐛𝐬

🔸𝐔𝐬𝐞 𝐉𝐨𝐛 𝐏𝐨𝐫𝐭𝐚𝐥𝐬
Job boards like LinkedIn & Naukari are great portals to find jobs.

Set up job alerts using keywords like “Data Analyst” so you’ll get notified as soon as something new comes up.

🔸𝐓𝐚𝐢𝐥𝐨𝐫 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐮𝐦𝐞
Don’t 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).

🔸𝐔𝐬𝐞 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧
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).

🔸𝐂𝐡𝐞𝐜𝐤 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐖𝐞𝐛𝐬𝐢𝐭𝐞𝐬 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲
Most big companies post jobs directly on their websites first.

Create a list of companies you’re interested in and keep checking their careers page. It’s a good way to find openings early before they post on job portals.

🔸𝐅𝐨𝐥𝐥𝐨𝐰 𝐔𝐩 𝐀𝐟𝐭𝐞𝐫 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠
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.

𝐋𝐢𝐬𝐭 𝐨𝐟 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐭𝐡𝐚𝐭 𝐡𝐢𝐫𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐭𝐬:
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.

Data Analyst Cheatsheet 💪

How to Merge Pandas DataFrames?

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!