𝐈𝐁𝐌 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬😍

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Machine Learning Algorithms every data scientist should know:

📌 Supervised Learning:

🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression

🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)


📌 Unsupervised Learning:

🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN

🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)


📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)


📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking

Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.

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Planning for Data Science or Data Engineering Interview.

Focus on SQL & Python first. Here are some important questions which you should know.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

Join for more: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲: 𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗣𝗼𝗶𝗻𝘁 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 & 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀😍

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🧠 Technologies for Data Analysts!

📊 Data Manipulation & Analysis

▪️ Excel – Spreadsheet Data Analysis & Visualization
▪️ SQL – Structured Query Language for Data Extraction
▪️ Pandas (Python) – Data Analysis with DataFrames
▪️ NumPy (Python) – Numerical Computing for Large Datasets
▪️ Google Sheets – Online Collaboration for Data Analysis

📈 Data Visualization

▪️ Power BI – Business Intelligence & Dashboarding
▪️ Tableau – Interactive Data Visualization
▪️ Matplotlib (Python) – Plotting Graphs & Charts
▪️ Seaborn (Python) – Statistical Data Visualization
▪️ Google Data Studio – Free, Web-Based Visualization Tool

🔄 ETL (Extract, Transform, Load)

▪️ SQL Server Integration Services (SSIS) – Data Integration & ETL
▪️ Apache NiFi – Automating Data Flows
▪️ Talend – Data Integration for Cloud & On-premises

🧹 Data Cleaning & Preparation

▪️ OpenRefine – Clean & Transform Messy Data
▪️ Pandas Profiling (Python) – Data Profiling & Preprocessing
▪️ DataWrangler – Data Transformation Tool

📦 Data Storage & Databases

▪️ SQL – Relational Databases (MySQL, PostgreSQL, MS SQL)
▪️ NoSQL (MongoDB) – Flexible, Schema-less Data Storage
▪️ Google BigQuery – Scalable Cloud Data Warehousing
▪️ Redshift – Amazon’s Cloud Data Warehouse

⚙️ Data Automation

▪️ Alteryx – Data Blending & Advanced Analytics
▪️ Knime – Data Analytics & Reporting Automation
▪️ Zapier – Connect & Automate Data Workflows

📊 Advanced Analytics & Statistical Tools

▪️ R – Statistical Computing & Analysis
▪️ Python (SciPy, Statsmodels) – Statistical Modeling & Hypothesis Testing
▪️ SPSS – Statistical Software for Data Analysis
▪️ SAS – Advanced Analytics & Predictive Modeling

🌐 Collaboration & Reporting

▪️ Power BI Service – Online Sharing & Collaboration for Dashboards
▪️ Tableau Online – Cloud-Based Visualization & Sharing
▪️ Google Analytics – Web Traffic Data Insights
▪️ Trello / JIRA – Project & Task Management for Data Projects
Data-Driven Decisions with the Right Tools!

React ❤️ for more

𝟭𝟬𝟬% 𝗙𝗿𝗲𝗲 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

From data science and AI to web development and cloud computing, checkout Top 5 Websites for Free Tech Certification Courses in 2025

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𝟱 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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This list is for anyone ready to start learning ML from scratch✅️

10 ChatGPT Prompts for Marketing You'll Actually Use:

1. Crafting a Brand Story

Prompt: "Develop an engaging brand story and persona for [company name] that authentically conveys our mission, values, and competitive differentiation."

2. Optimizing Landing Pages

Prompt: "Optimize the landing page for [product/service page name] to immediately convey value, build trust, and motivate visitors to convert."

3. Generating Content Ideas

Prompt: "Suggest 10 compelling content ideas related to [topic/keyword] that will engage our target audience and promote brand awareness."

4. Improving SEO

Prompt: "Provide 3 strategic recommendations to improve SEO and increase organic traffic for [company name/website]."

5. Building an Email Nurture Sequence

Prompt: "Design a personalized, multi-message email nurture sequence focused on educating and converting leads interested in [product/service]."

6. Crafting Value Propositions

Prompt: "Create strong, benefit-driven value propositions highlighting the top 3 advantages of using [product/service] over competitors."

7. Producing Engaging Social Media Content

Prompt: "Develop 5 creative social media post ideas for [platform] that will resonate with our audience and align with broader campaign goals."

8. Planning an Influencer Marketing Partnership

Prompt: "Outline an influencer partnership strategy for [company] to build brand awareness and trust among our target demographic."

9. Crafting Paid Ads

Prompt: "Write high-converting Facebook/Google ads for [product/service] tailored to our unique selling proposition and audience motivations."

10. Building an Email Marketing Campaign

Prompt: "Construct a complete email marketing campaign for the launch of [product/service], including series copy, visual assets, send schedule, and success metrics."

Share with your friends: https://news.1rj.ru/str/Learn_Startup

ENJOY LEARNING 👍👍

𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀: 𝟱 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍

Want to break into Data Science but don’t know where to begin?👨‍💻📌

You’re not alone. Data Science is one of the most in-demand fields today, but with so many courses online, it can feel overwhelming.💫📲

𝐋𝐢𝐧𝐤👇:-

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No prior experience needed!✅️

𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄😍

𝗦𝗤𝗟:- https://pdlink.in/3SMHxaZ

𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3FJhizk

𝗝𝗮𝘃𝗮  :- https://pdlink.in/4dWkAMf

𝗗𝗦𝗔 :- https://pdlink.in/3FsDA8j

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𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 :-  https://pdlink.in/4dFem3o

𝗖𝗼𝗱𝗶𝗻𝗴 :- https://pdlink.in/3F00oMw

Get Your Dream Tech Job In Your Dream Company💫

Coding and Aptitude Round before interview

Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.

Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.

Resources for Prep:

For algorithms and data structures prep,Leetcode and Hackerrank are good resources.

For aptitude prep, you can refer to IndiaBixand Practice Aptitude.

With respect to data science challenges, practice well on GLabs and Kaggle.

Brilliant is an excellent resource for tricky math and statistics questions.

For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.

Things to Note:

Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!

In case, you are finished with the test before time, recheck your answers and then submit.

Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.