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

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Looking for Data Scientist professionals

Skills required - Proficient in R & Python with statistical modelling and ML techniques along with deep learning and NLP concepts.

Experience - 5-7 Years

Location - Bengaluru

Interested candidates can mail their cv at nidhig@symphonihr.com

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁’𝘀 𝗙𝗥𝗘𝗘 𝗣𝗼𝘄𝗲𝗿𝗕𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲😍

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Sony Hiring Data Science Intern

Internship Duration: 6 months

Graduation Year: 2025 / 2026

Location: Bengaluru / Mumbai / Remote

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

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Data Scientist Hiring in Noida, India

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Airbus hiring Data Scientist - Generative AI

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Experience : 1+ Year
Location : Gurugram

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Experience : 2+ Years
Location : Bangalore

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Senior Data Science Professionals hiring

Location: Mumbai


https://www.linkedin.com/jobs/view/4222202564

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Microsoft Learn is offering 5 must-do courses for aspiring data scientists, absolutely free🔥📊

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What is PCA

PCA is a commonly used tool in statistics for making complex data more manageable. Here are some essential points to get started with PCA in R:

🔹 What is PCA? PCA transforms a large set of variables into a smaller one that still contains most of the information in the original set. This process is crucial for analyzing data more efficiently.

🔸 Why R? R is a statistical powerhouse, favored for its versatility in data analysis and visualization capabilities. Its comprehensive packages and functions make PCA straightforward and effective.

🔹 Getting Started: Utilize R's prcomp() function to perform PCA. This function is robust, offering a standardized method to carry out PCA with ease, providing you with principal components, variance captured, and more.

🔸 Visualizing PCA Results: With R, you can leverage powerful visualization libraries like ggplot2 and factoextra. Visualize your PCA results through scree plots to decide how many principal components to retain, or use biplots to understand the relationship between variables and components.

🔹 Interpreting Results: The output of PCA in R includes the variance explained by each principal component, helping you understand the significance of each component in your analysis. This is crucial for making informed decisions based on your data.

🔸 Applications: Whether it's in market research, genomics, or any field dealing with large data sets, PCA in R can help you identify patterns, reduce noise, and focus on the variables that truly matter.

🔹 Key Packages: Beyond base R, packages like factoextra offer additional functions for enhanced PCA analysis and visualization, making your data analysis journey smoother and more insightful.

Embark on your PCA journey in R and transform vast, complicated data sets into simplified, insightful information. Ready to go from data to insights? Our comprehensive course on PCA in R programming covers everything from the basics to advanced applications.

Jupiter AI Labs hiring Machine Learning Engineer ✔: https://www.linkedin.com/jobs/view/4218685035

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Explore top-notch courses to build expertise in cloud computing, data analysis, and visualization—all for FREE!

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Policybazaar is hiring Business Analyst 🚀

Experience : 2+ Years
Location : Gurugram

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𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝗜𝗻 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀😍

1️⃣ BCG Data Science & Analytics Virtual Experience
2️⃣ TATA Data Visualization Internship
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Key Concepts for Data Science Interviews

1. Data Cleaning and Preprocessing: Master techniques for cleaning, transforming, and preparing data for analysis, including handling missing data, outlier detection, data normalization, and feature engineering.

2. Statistics and Probability: Have a solid understanding of descriptive and inferential statistics, including distributions, hypothesis testing, p-values, confidence intervals, and Bayesian probability.

3. Linear Algebra and Calculus: Understand the mathematical foundations of data science, including matrix operations, eigenvalues, derivatives, and gradients, which are essential for algorithms like PCA and gradient descent.

4. Machine Learning Algorithms: Know the fundamentals of machine learning, including supervised and unsupervised learning. Be familiar with key algorithms like linear regression, logistic regression, decision trees, random forests, SVMs, and k-means clustering.

5. Model Evaluation and Validation: Learn how to evaluate model performance using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, and confusion matrices. Understand techniques like cross-validation and overfitting prevention.

6. Feature Engineering: Develop the ability to create meaningful features from raw data that improve model performance. This includes encoding categorical variables, scaling features, and creating interaction terms.

7. Deep Learning: Understand the basics of neural networks and deep learning. Familiarize yourself with architectures like CNNs, RNNs, and frameworks like TensorFlow and PyTorch.

8. Natural Language Processing (NLP): Learn key NLP techniques such as tokenization, stemming, lemmatization, and sentiment analysis. Understand the use of models like BERT, Word2Vec, and LSTM for text data.

9. Big Data Technologies: Gain knowledge of big data frameworks and tools like Hadoop, Spark, and NoSQL databases that are used to process large datasets efficiently.

10. Data Visualization and Storytelling: Develop the ability to create compelling visualizations using tools like Matplotlib, Seaborn, or Tableau. Practice conveying your data findings clearly to both technical and non-technical audiences through visual storytelling.

11. Python and R: Be proficient in Python and R for data manipulation, analysis, and model building. Familiarity with libraries like Pandas, NumPy, Scikit-learn, and tidyverse is essential.

12. Domain Knowledge: Develop a deep understanding of the specific industry or domain you're working in, as this context helps you make more informed decisions during the data analysis and modeling process.

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Oracle hiring Data Scientist

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𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍

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