𝗚𝗼𝗼𝗴𝗹𝗲 𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

Ever wondered how machines describe images in words?💻

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Libraries for Data Science in Python

𝟳 𝗙𝗿𝗲𝗲 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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𝗛𝗼𝘄 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 (𝗘𝘃𝗲𝗻 𝗶𝗳 𝗬𝗼𝘂’𝗿𝗲 𝗮 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿!) 📊

Wanna break into data science but feel overwhelmed by too many courses, buzzwords, and conflicting advice? You’re not alone.

Here’s the truth: You don’t need a PhD or 10 certifications. You just need the right skills in the right order.

Let me show you a proven 5-step roadmap that actually works for landing data science roles (even entry-level) 👇

🔹 Step 1: Learn the Core Tools (This is Your Foundation)

Focus on 3 key tools first—don’t overcomplicate:

✅ Python – NumPy, Pandas, Matplotlib, Seaborn
✅ SQL – Joins, Aggregations, Window Functions
✅ Excel – VLOOKUP, Pivot Tables, Data Cleaning

🔹 Step 2: Master Data Cleaning & EDA (Your Real-World Skill)

Real data is messy. Learn how to:

✅ Handle missing data, outliers, and duplicates
✅ Visualize trends using Matplotlib/Seaborn
✅ Use groupby(), merge(), and pivot_table()

🔹 Step 3: Learn ML Basics (No Fancy Math Needed)

Stick to core algorithms first:

✅ Linear & Logistic Regression
✅ Decision Trees & Random Forest
✅ KMeans Clustering + Model Evaluation Metrics

🔹 Step 4: Build Projects That Prove Your Skills

One strong project > 5 courses. Create:

✅ Sales Forecasting using Time Series
✅ Movie Recommendation System
✅ HR Analytics Dashboard using Python + Excel
📍 Upload them on GitHub. Add visuals, write a good README, and share on LinkedIn.

🔹 Step 5: Prep for the Job Hunt (Your Personal Brand Matters)

✅ Create a strong LinkedIn profile with keywords like “Aspiring Data Scientist | Python | SQL | ML”
✅ Add GitHub link + Highlight your Projects
✅ Follow Data Science mentors, engage with content, and network for referrals

🎯 No shortcuts. Just consistent baby steps.

Every pro data scientist once started as a beginner. Stay curious, stay consistent.

Free Data Science Resources: https://whatsapp.com/channel/0029VauCKUI6WaKrgTHrRD0i

ENJOY LEARNING 👍👍

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

Whether you’re a student, fresher, or professional looking to upskill — Microsoft has dropped a series of completely free courses to get you started.

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Essential Topics to Master Data Science Interviews: 🚀

SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables

2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries

3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages

2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets

3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting

2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)

3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards

Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)

2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX

3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes

Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.

Show some ❤️ if you're ready to elevate your data science journey! 📊

ENJOY LEARNING 👍👍

𝟲 𝗙𝗿𝗲𝗲 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱😍

Whether you’re a student, aspiring data analyst, software enthusiast, or just curious about AI, now’s the perfect time to dive in.

These 6 beginner-friendly and completely free AI courses from top institutions like Google, IBM, Harvard, and more

𝗟𝗶𝗻𝗸:-👇

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Enroll for FREE & Get Certified 🎓

Time Complexity of Most Popular ML Algorithms
.
.
When selecting a machine learning model, understanding its time complexity is crucial for efficient processing, especially with large datasets.

For instance,
1️⃣ Linear Regression (OLS) is computationally expensive due to matrix multiplication, making it less suitable for big data applications.

2️⃣ Logistic Regression with Stochastic Gradient Descent (SGD) offers faster training times by updating parameters iteratively.

3️⃣ Decision Trees and Random Forests are efficient for training but can be slower for prediction due to traversing the tree structure.

4️⃣ K-Nearest Neighbours is simple but can become slow with large datasets due to distance calculations.

5️⃣ Naive Bayes is fast and scalable, making it suitable for large datasets with high-dimensional features.

𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 𝘆𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝗶𝗻 𝟮𝟬𝟮𝟱?😍

📊 These free courses are designed for learners at all levels, whether you’re a beginner or an advanced professional📌

𝐋𝐢𝐧𝐤👇:-

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Don’t Wait! Start your Learning Journey Today✅️

Many people pay too much to learn Data Science, but my mission is to break down barriers. I have shared complete learning series to learn Data Science algorithms from scratch.

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I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.

But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.

Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.

Hope it helps :)

𝗗𝗲𝗹𝗼𝗶𝘁𝘁𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 😍

If you’re eager to build real skills in data analytics before landing your first role, Deloitte is giving you a golden opportunity—completely free!

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📚 Ideal for students, freshers, and aspiring data analysts
⏰ Self-paced — complete at your convenience

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Data Science Roadmap – Step-by-Step Guide 🚀

1️⃣ Programming & Data Manipulation

Python (Pandas, NumPy, Matplotlib, Seaborn)

SQL (Joins, CTEs, Window Functions, Aggregations)

Data Wrangling & Cleaning (handling missing data, duplicates, normalization)


2️⃣ Statistics & Mathematics

Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)

Probability Theory (Bayes' Theorem, Conditional Probability)

Hypothesis Testing (T-test, ANOVA, Chi-square test)

Linear Algebra & Calculus (Matrix operations, Differentiation)


3️⃣ Data Visualization

Matplotlib & Seaborn for static visualizations

Power BI & Tableau for interactive dashboards

ggplot (R) for advanced visualizations


4️⃣ Machine Learning Fundamentals

Supervised Learning (Linear Regression, Logistic Regression, Decision Trees)

Unsupervised Learning (Clustering, PCA, Anomaly Detection)

Model Evaluation (Confusion Matrix, Precision, Recall, F1-Score, AUC-ROC)


5️⃣ Advanced Machine Learning

Ensemble Methods (Random Forest, Gradient Boosting, XGBoost)

Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV)

Deep Learning Basics (Neural Networks, TensorFlow, PyTorch)


6️⃣ Big Data & Cloud Computing

Distributed Computing (Hadoop, Spark)

Cloud Platforms (AWS, GCP, Azure)

Data Engineering Basics (ETL Pipelines, Apache Kafka, Airflow)


7️⃣ Natural Language Processing (NLP)

Text Preprocessing (Tokenization, Lemmatization, Stopword Removal)

Sentiment Analysis, Named Entity Recognition

Transformers & Large Language Models (BERT, GPT)


8️⃣ Deployment & Model Optimization

Flask & FastAPI for model deployment

Model monitoring & retraining

MLOps (CI/CD for Machine Learning)


9️⃣ Business Applications & Case Studies

A/B Testing & Experimentation

Customer Segmentation & Churn Prediction

Time Series Forecasting (ARIMA, LSTM)


🔟 Soft Skills & Career Growth

Data Storytelling & Communication

Resume & Portfolio Building (Kaggle Projects, GitHub Repos)

Networking & Job Applications (LinkedIn, Referrals)

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

𝟲 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝗸𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗦𝘁𝗮𝗻𝗱 𝗢𝘂𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍

As competition heats up across every industry, standing out to recruiters is more important than ever📄📌

The best part? You don’t need to spend a rupee to do it!💰

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👉 Start learning. Start standing out✅️

Difference between linear regression and logistic regression 👇👇

Linear regression and logistic regression are both types of statistical models used for prediction and modeling, but they have different purposes and applications.

Linear regression is used to model the relationship between a dependent variable and one or more independent variables. It is used when the dependent variable is continuous and can take any value within a range. The goal of linear regression is to find the best-fitting line that describes the relationship between the independent and dependent variables.

Logistic regression, on the other hand, is used when the dependent variable is binary or categorical. It is used to model the probability of a certain event occurring based on one or more independent variables. The output of logistic regression is a probability value between 0 and 1, which can be interpreted as the likelihood of the event happening.

Data Science Interview Resources
👇👇
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Understanding Bias and Variance in Machine Learning

Bias refers to the error in the model when the model is not able to capture the pattern in the data and what results is an underfit model (High Bias).

Variance refers to the error in the model, when the model is too much tailored to the training data and fails to generalise for unseen data which refers to an overfit model (High Variance)

There should be a tradeoff between bias and variance. An optimal model should have Low Bias and Low Variance so as to avoid underfitting and overfitting.

Techniques like cross validation can be helpful in these cases.

➖➖➖➖➖➖➖➖➖➖➖➖➖➖

Kaggle Datasets are often too perfect for real-world scenarios.

I'm about to share a method for real-life data analysis.

You see …

… most of the time, a data analyst cleans and transforms data.

So … let’s practice that.

How?

Well … you can use ChatGPT.

Just write this prompt:

Create a downloadable CSV dataset of 10,000 rows of financial credit card transactions with 10 columns of customer data so I can perform some data analysis to segment customers.

Now…

Download the dataset and start your analysis.

You'll see that, most of the time…

… numbers don’t match.

There are no patterns.

Data is incorrect and doesn’t make sense.

And that’s good.

Now you know what a data analyst deals with.

Your job is to make sense of that dataset.

To create a story that justifies the numbers.

This is how you can mimic real-life work using A.I.

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

Whether you’re a student, fresher, or professional looking to upskill — Microsoft has dropped a series of completely free courses to get you started.

Learn SQL ,Power BI & More In 2025 

𝗟𝗶𝗻𝗸:-👇

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A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

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Credits: https://t.iss.one/datasciencefun

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Hope this helps you 😊

𝟯 𝗙𝗿𝗲𝗲 𝗧𝗖𝗦 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗘𝘃𝗲𝗿𝘆 𝗙𝗿𝗲𝘀𝗵𝗲𝗿 𝗠𝘂𝘀𝘁 𝗧𝗮𝗸𝗲 𝘁𝗼 𝗚𝗲𝘁 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆😍

🎯 If You’re a Fresher, These TCS Courses Are a Must-Do📄✔️

Stepping into the job market can be overwhelming—but what if you had certified, expert-backed training that actually prepares you?👨‍🎓✨️

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“The Best Public Datasets for Machine Learning and Data Science” by Stacy Stanford

https://datasimplifier.com/best-data-analyst-projects-for-freshers/

https://toolbox.google.com/datasetsearch

https://www.kaggle.com/datasets

https://mlr.cs.umass.edu/ml/

https://www.visualdata.io/

https://guides.library.cmu.edu/machine-learning/datasets

https://www.data.gov/

https://nces.ed.gov/

https://www.ukdataservice.ac.uk/

https://datausa.io/

https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html

https://www.kaggle.com/xiuchengwang/python-dataset-download

https://www.quandl.com/

https://data.worldbank.org/

https://www.imf.org/en/Data

https://markets.ft.com/data/

https://trends.google.com/trends/?q=google&ctab=0&geo=all&date=all&sort=0

https://www.aeaweb.org/resources/data/us-macro-regional

https://xviewdataset.org/#dataset

https://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php

https://image-net.org/

https://cocodataset.org/

https://visualgenome.org/

https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html?m=1

https://vis-www.cs.umass.edu/lfw/

https://vision.stanford.edu/aditya86/ImageNetDogs/

https://web.mit.edu/torralba/www/indoor.html

https://www.cs.jhu.edu/~mdredze/datasets/sentiment/

https://ai.stanford.edu/~amaas/data/sentiment/

https://nlp.stanford.edu/sentiment/code.html

https://help.sentiment140.com/for-students/

https://www.kaggle.com/crowdflower/twitter-airline-sentiment

https://hotpotqa.github.io/

https://www.cs.cmu.edu/~./enron/

https://snap.stanford.edu/data/web-Amazon.html

https://aws.amazon.com/datasets/google-books-ngrams/

https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm

https://code.google.com/archive/p/wiki-links/downloads

https://www.dt.fee.unicamp.br/~tiago/smsspamcollection/

https://www.yelp.com/dataset

https://t.iss.one/DataPortfolio/2

https://archive.ics.uci.edu/ml/datasets/Spambase

https://bdd-data.berkeley.edu/

https://apolloscape.auto/

https://archive.org/details/comma-dataset

https://www.cityscapes-dataset.com/

https://aplicaciones.cimat.mx/Personal/jbhayet/ccsad-dataset

https://www.vision.ee.ethz.ch/~timofter/traffic_signs/

https://cvrr.ucsd.edu/LISA/datasets.html

https://hci.iwr.uni-heidelberg.de/node/6132

https://www.lara.prd.fr/benchmarks/trafficlightsrecognition

https://computing.wpi.edu/dataset.html

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