𝟯 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗶𝗻 𝟮𝟬𝟮𝟱😍

👩‍💻 Want to Break into Data Science but Don’t Know Where to Start?🚀

The best way to begin your data science journey is with hands-on projects using real-world datasets.👨‍💻📌

𝐋𝐢𝐧𝐤👇:-

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Enjoy Learning ✅️

Important Machine Learning Algorithms 👇👇

- Linear Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- k-Nearest Neighbors (kNN)
- Naive Bayes
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Neural Networks (Deep Learning)
- Gradient Boosting algorithms (e.g., XGBoost, LightGBM)

Like this post if you want me to explain each algorithm in detail

Share with credits: https://news.1rj.ru/str/datasciencefun

ENJOY LEARNING 👍👍

𝗚𝗼𝗼𝗴𝗹𝗲 𝗧𝗼𝗽 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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For data analysts working with Python, mastering these top 10 concepts is essential:

1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation.

2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.

3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables.

4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling.

5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data.

6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn.

7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets.

8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.

9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.

10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.

Give credits while sharing: https://news.1rj.ru/str/pythonanalyst

ENJOY LEARNING 👍👍

𝟳 𝗕𝗲𝘀𝘁 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗡𝗼 𝗖𝗼𝘀𝘁, 𝗡𝗼 𝗖𝗮𝘁𝗰𝗵!)😍

Want to become a Data Scientist in 2025 without spending a single rupee? You’re in the right place📌

From Python and machine learning to hands-on projects and challenges🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4dAuymr

Enjoy Learning ✅️

In a data science project, using multiple scalers can be beneficial when dealing with features that have different scales or distributions. Scaling is important in machine learning to ensure that all features contribute equally to the model training process and to prevent certain features from dominating others.

Here are some scenarios where using multiple scalers can be helpful in a data science project:

1. Standardization vs. Normalization: Standardization (scaling features to have a mean of 0 and a standard deviation of 1) and normalization (scaling features to a range between 0 and 1) are two common scaling techniques. Depending on the distribution of your data, you may choose to apply different scalers to different features.

2. RobustScaler vs. MinMaxScaler: RobustScaler is a good choice when dealing with outliers, as it scales the data based on percentiles rather than the mean and standard deviation. MinMaxScaler, on the other hand, scales the data to a specific range. Using both scalers can be beneficial when dealing with mixed types of data.

3. Feature engineering: In feature engineering, you may create new features that have different scales than the original features. In such cases, applying different scalers to different sets of features can help maintain consistency in the scaling process.

4. Pipeline flexibility: By using multiple scalers within a preprocessing pipeline, you can experiment with different scaling techniques and easily switch between them to see which one works best for your data.

5. Domain-specific considerations: Certain domains may require specific scaling techniques based on the nature of the data. For example, in image processing tasks, pixel values are often scaled differently than numerical features.

When using multiple scalers in a data science project, it's important to evaluate the impact of scaling on the model performance through cross-validation or other evaluation methods. Try experimenting with different scaling techniques to you find the optimal approach for your specific dataset and machine learning model.

𝗕𝗿𝗲𝗮𝗸 𝗜𝗻𝘁𝗼 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟱 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗥𝗘𝗘 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲😍

If you’re serious about AI, you can’t skip Deep Learning—and this FREE course from MIT is one of the best ways to start👨‍💻📌

Offered by MIT’s top researchers and engineers, this online course is open to everyone, no matter where you live or work🎯

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Why wait to get started when you can learn from MIT for free?✅️

𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱 😍

Data Analytics :- https://pdlink.in/3Fq7E4p

Data Science :- https://pdlink.in/4iSWjaP

SQL :- https://pdlink.in/3EyjUPt

Python :- https://pdlink.in/4c7hGDL

Web Dev :- https://bit.ly/4ffFnJZ

AI :- https://pdlink.in/4d0SrTG

Enroll For FREE & Get Certified 🎓

𝟰 𝗙𝗿𝗲𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗸𝗶𝗹𝗹𝘀 𝗗𝗮𝗶𝗹𝘆 (𝗡𝗼 𝗦𝗶𝗴𝗻𝘂𝗽 𝗡𝗲𝗲𝗱𝗲𝗱!)😍

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Enjoy Learning ✅️

5 GitHub Repo to Master Python

1. The Algorithms: https://github.com/TheAlgorithms/Python
2. Vinta: https://github.com/vinta/awesome-python
3. Avinash Kranjan: https://tinyurl.com/Amazing-Python-Scripts
4. Geek Computers: https://github.com/geekcomputers/Python
5. Practical Tutorials: https://tinyurl.com/project-based-learningg

Don’t forget to react ❤️ if you’d like to see more content like this!

Thank you all for joining! ❤️🙏

𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Google :- https://pdlink.in/3H2YJX7

Microsoft :- https://pdlink.in/4iq8QlM

Infosys :- https://pdlink.in/4jsHZXf

IBM :- https://pdlink.in/3QyJyqk

Cisco :- https://pdlink.in/4fYr1xO

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If you're serious about getting into Data Science with Python, follow this 5-step roadmap.

Each phase builds on the previous one, so don’t rush.

Take your time, build projects, and keep moving forward.

Step 1: Python Fundamentals
Before anything else, get your hands dirty with core Python.
This is the language that powers everything else.

✅ What to learn:
type(), int(), float(), str(), list(), dict()
if, elif, else, for, while, range()
def, return, function arguments
List comprehensions: [x for x in list if condition]
– Mini Checkpoint:
Build a mini console-based data calculator (inputs, basic operations, conditionals, loops).

Step 2: Data Cleaning with Pandas
Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios.

✅ What to learn:
Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates()
Merging & reshaping: pd.merge(), df.pivot(), df.melt()
Grouping & aggregation: df.groupby(), df.agg()
– Mini Checkpoint:
Build a data cleaning noscript for a messy CSV file. Add comments to explain every step.

Step 3: Data Visualization with Matplotlib
Nobody wants raw tables.
Learn to tell stories through charts.

✅ What to learn:
Basic charts: plt.plot(), plt.scatter()
Advanced plots: plt.hist(), plt.kde(), plt.boxplot()
Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.noscript(), plt.legend(), plt.xlabel()
– Mini Checkpoint:
Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots.

Step 4: Exploratory Data Analysis (EDA)
This is where your analytical skills kick in.
You’ll draw insights, detect trends, and prepare for modeling.

✅ What to learn:
Denoscriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile()
Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr()
— Mini Checkpoint:
Write an EDA report (Markdown or PDF) based on your findings from a public dataset.

Step 5: Intro to Machine Learning with Scikit-Learn
Now that your data skills are sharp, it's time to model and predict.

✅ What to learn:
Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score()
Regression: LinearRegression(), mean_squared_error(), r2_score()
Classification: LogisticRegression(), accuracy_score(), confusion_matrix()
Clustering: KMeans(), silhouette_score()

– Final Checkpoint:

Build your first ML project end-to-end
✅ Load data
✅ Clean it
✅ Visualize it
✅ Run EDA
✅ Train & test a model
✅ Share the project with visuals and explanations on GitHub

Don’t just complete tutorialsm create things.

Explain your work.
Build your GitHub.
Write a blog.

That’s how you go from “learning” to “landing a job

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

All the best 👍👍