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How much Statistics must I know to become a Data Scientist?

This is one of the most common questions

Here are the must-know Statistics concepts every Data Scientist should know:

๐—ฃ๐—ฟ๐—ผ๐—ฏ๐—ฎ๐—ฏ๐—ถ๐—น๐—ถ๐˜๐˜†

โ†— Bayes' Theorem & conditional probability
โ†— Permutations & combinations
โ†— Card & die roll problem-solving

๐——๐—ฒ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฝ๐˜๐—ถ๐˜ƒ๐—ฒ ๐˜€๐˜๐—ฎ๐˜๐—ถ๐˜€๐˜๐—ถ๐—ฐ๐˜€ & ๐—ฑ๐—ถ๐˜€๐˜๐—ฟ๐—ถ๐—ฏ๐˜‚๐˜๐—ถ๐—ผ๐—ป๐˜€

โ†— Mean, median, mode
โ†— Standard deviation and variance
โ†—  Bernoulli's, Binomial, Normal, Uniform, Exponential distributions

๐—œ๐—ป๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜๐—ถ๐—ฎ๐—น ๐˜€๐˜๐—ฎ๐˜๐—ถ๐˜€๐˜๐—ถ๐—ฐ๐˜€

โ†— A/B experimentation
โ†— T-test, Z-test, Chi-squared tests
โ†— Type 1 & 2 errors
โ†— Sampling techniques & biases
โ†— Confidence intervals & p-values
โ†— Central Limit Theorem
โ†— Causal inference techniques

๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—น๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด

โ†— Logistic & Linear regression
โ†— Decision trees & random forests
โ†— Clustering models
โ†— Feature engineering
โ†— Feature selection methods
โ†— Model testing & validation
โ†— Time series analysis

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American Express Global Business Travel hiring Data Scientist

Apply link: https://travelhrportal.wd1.myworkdayjobs.com/Jobs/job/India/Associate-Data-Scientist_J-74018

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All the best ๐Ÿ‘๐Ÿ‘
๐Ÿ”ฅ1
๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ ๐—ข๐—ฝ๐—ฝ๐—ผ๐—ฟ๐˜๐˜‚๐—ป๐—ถ๐˜๐˜† ๐Ÿ˜

Company Name: Khatabook 

Role:- Analytics - Intern

Location: Bangalore

Experience: 0 to 1 Year 

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๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—™๐—ฟ๐—ผ๐—บ ๐—ง๐—ผ๐—ฝ ๐—–๐—ผ๐—บ๐—ฝ๐—ฎ๐—ป๐—ถ๐—ฒ๐˜€๐Ÿ˜

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ZS hiring Data Scientist

Apply link: https://www.linkedin.com/jobs/view/4226874462

๐Ÿ‘‰WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J

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๐Ÿ‘2
๐—–๐—ถ๐˜€๐—ฐ๐—ผ ๐—™๐—ฅ๐—˜๐—˜ ๐—ฃ๐—ฟ๐—ผ๐—ณ๐—ฒ๐˜€๐˜€๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿ˜

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Uber hiring Data Scientist (Product Analytics)

Location: Hyderabad & Bangalore

Apply link: https://www.uber.com/global/en/careers/list/138137/?uclick_id=37269e91-9c24-429a-aa24-5fbd5a7519f7

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IBM hiring Data Scientist

Apply link: https://ibmglobal.avature.net/en_US/careers/JobDetail?jobId=29148&source=WEB_Search_INDIA

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๐Ÿฑ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—ฆ๐—ธ๐˜†๐—ฟ๐—ผ๐—ฐ๐—ธ๐—ฒ๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜

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๐Ÿš€ Top 10 Tools Data Scientists Love! ๐Ÿง 

In the ever-evolving world of data science, staying updated with the right tools is crucial to solving complex problems and deriving meaningful insights.

๐Ÿ” Hereโ€™s a quick breakdown of the most popular tools:

1. Python ๐Ÿ: The go-to language for data science, favored for its versatility and powerful libraries.
2. SQL ๐Ÿ› ๏ธ: Essential for querying databases and manipulating data.
3. Jupyter Notebooks ๐Ÿ““: An interactive environment that makes data analysis and visualization a breeze.
4. TensorFlow/PyTorch ๐Ÿค–: Leading frameworks for deep learning and neural networks.
5. Tableau ๐Ÿ“Š: A user-friendly tool for creating stunning visualizations and dashboards.
6. Git & GitHub ๐Ÿ’ป: Version control systems that every data scientist should master.
7. Hadoop & Spark ๐Ÿ”ฅ: Big data frameworks that help process massive datasets efficiently.
8. Scikit-learn ๐Ÿงฌ: A powerful library for machine learning in Python.
9. R ๐Ÿ“ˆ: A statistical programming language that is still a favorite among many analysts.
10. Docker ๐Ÿ‹: A must-have for containerization and deploying applications.

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5 Handy Tips to Master Data Science โฌ‡๏ธ

1๏ธโƒฃ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel

2๏ธโƒฃ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.

3๏ธโƒฃ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.

4๏ธโƒฃ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.

5๏ธโƒฃ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.
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