A naukri.com initiative
Data Science News
Medium
3w
411
Image Credit: Medium
How To Start Strong In Your First Week As An Engineering Manager
- Starting as an engineering manager (EM) can be challenging, requiring thorough preparation and effective communication in the first week.
- Prepare for initial meetings by gathering context and asking insightful questions to understand team dynamics and ongoing projects.
- Initiate a strong foundation with your manager by suggesting ideas and setting up regular check-ins to align on priorities.
- Establish clear communication channels with your manager, adapt to their preferences, and ensure clarity in all discussions.
- Build strong relationships with your team by reintroducing yourself, showing genuine curiosity, and proactively addressing any power dynamics.
- Maintain a professional demeanor, be mindful of your words, and set new boundaries as you navigate the transition from peer to manager.
- Schedule regular one-on-one meetings with your engineers to build trust, understand their challenges, and provide support.
- Embrace the shift from hands-on coding to managerial responsibilities, understanding that your team's technical capacity will rely on your guidance.
- Reflect on your progress at the end of the week, identify obstacles, and focus on continuous improvement to enhance team productivity.
- Encourage a culture of continuous improvement within your team by seeking small but impactful changes and fostering proactive problem-solving.
- Invest time in learning about your team, company, and aligning technical direction while prioritizing relationships and team growth.
Read Full Article
24 Likes
Medium
3w
196
Image Credit: Medium
Roadmap to Become a Big Data Engineer: Your Path to Mastering Data
- 1. Master Python, Java, and Scala for big data applications.
- 2. Gain expertise in Hadoop, Apache Spark, and Apache Flink for big data frameworks.
- 3. Learn SQL, NoSQL, and data warehousing with Amazon Redshift and Google BigQuery for databases and data warehousing.
- 4. Utilize Apache Kafka, Apache NiFi, and AWS Kinesis for efficient data ingestion with data ingestion tools.
Read Full Article
11 Likes
Medium
3w
244
Image Credit: Medium
Catch Breakouts Smarter: Build a CVD Divergence Detector in Pine Script
- Breakouts often fail due to lack of real buying pressure, not bad patterns.
- Using Cumulative Volume Delta (CVD) can help in detecting fakeouts early.
- Building a CVD Divergence Detector in Pine Script can assist in spotting breakout weakness signs.
- CVD divergence is a useful warning sign that something might be out of sync in the market.
Read Full Article
14 Likes
Medium
3w
402
Image Credit: Medium
"Skincare for Oily Skin: Controlling Shine and Reducing Acne:
- Summary of recommended skincare products for oily skin: Kiehl’s Rare Earth Deep Pore Daily Cleanser, PanOxyl Acne Creamy Wash, Cetaphil DermaControl Oil Removing Foam Wash, Paula’s Choice Skin Balancing Pore-Reducing Toner, Neutrogena Pore Refining Toner, Mario Badescu Glycolic Acid Toner, CeraVe PM Facial Moisturizing Lotion, SkinMedica Ultra Sheer Moisturizer, Origins Clear Improvement Moisturizer, The INKEY List Niacinamide Oil Control Serum, Primally Pure Clarifying Serum, Paula’s Choice Defense Antioxidant Pore Purifier, Skinceuticals Clarifying Clay Masque, Neutrogena Pink Grapefruit 100% Hydrogel Mask, Andalou Naturals Pumpkin Honey Glycolic Mask, Origins Clear Improvement Active Charcoal Mask, and Paula’s Choice... (list continues)
Read Full Article
24 Likes
Analyticsindiamag
3w
27
Image Credit: Analyticsindiamag
Google Cloud to Collaborate with IndiaAI Mission to Ramp Up AI Infrastructure
- Google Cloud to collaborate with IndiaAI Mission to ramp up AI infrastructure, as announced by Bikram Singh Bedi, VP and MD of Google Cloud India.
- Google Cloud aims to work closely with the Indian government and the electronics and information technology ministry to serve public sector needs and support the AI mission opportunity.
- The company's focus on public sector, particularly companies associated with the Indian government, remains strong with plans to introduce new versions of technology locally.
- Google Cloud is actively expanding its presence in India, offering cloud services, with data centres in Delhi and Mumbai and considerations for investment in Navi Mumbai to cater to digital needs.
Read Full Article
1 Like
Dev
3w
48
Image Credit: Dev
3337. Total Characters in String After Transformations II
- Given a string s, an integer t, and an array nums of size 26 representing transformations on s.
- Each character in s is replaced by the next nums[s[i] - 'a'] consecutive characters, wrapping around if needed.
- Return the length of the resulting string after t transformations, modulo 10^9 + 7.
- Implementing matrix multiplication efficiently handles large values of t.
- Strategy involves representing transformations as matrices, using matrix exponentiation, and calculating dot products.
- Example transformations and their impact on the length of the string are illustrated.
- Functions in PHP for matrix operations like matrix multiplication and exponentiation are provided.
- Matrix Construction builds a matrix indicating characters generated during transformations.
- Matrix Exponentiation efficiently handles large exponents using exponentiation by squaring.
- Contribution Calculation computes character contributions to the final length using precomputed matrices.
Read Full Article
2 Likes
Medium
3w
407
Your Next Sales Rep Might Be an AI Avatar. But Your Best One Will Still Be Human.
- AI is increasingly being utilized in sales and customer service, with chatbots and virtual agents streamlining operations and reducing costs.
- Functions like prioritizing high-value leads, predicting churn, and personalizing offers are now being efficiently managed by AI tools.
- However, AI still falls short in areas like empathy, building client relationships, storytelling, and handling complex situations with the finesse of humans.
- The future of sales lies in a symbiotic relationship between AI and human intelligence, leveraging the strengths of both to drive business growth and enhance customer experiences.
Read Full Article
24 Likes
Medium
3w
8
The Math Behind the Magic: Why Data Science Needs More Than Code
- Data science relies heavily on mathematics to power insights and predictions, going beyond just coding and tools like Python and Tableau.
- Statistics helps in making sense of real-world data, while linear algebra is essential for machine learning techniques like training models.
- Calculus plays a key role in optimizing models for efficiency, and probability theory is crucial for making informed predictions in areas like spam detection.
- Understanding the math behind data science tools builds intuition, allowing data scientists to trust, tune, and troubleshoot models effectively.
Read Full Article
Like
Medium
3w
376
Image Credit: Medium
Linear Algebra in Machine Learning: Smoothie Analogy for Learning Math
- Linear algebra, often perceived as complex, is fundamental to machine learning and can be understood through a fun analogy like making smoothies.
- Linear algebra serves as the backbone of many machine learning algorithms, helping in calculations for input-output relationships.
- Using the example of a smoothie shop with different recipes, the concept of matrices and vectors is explained in relation to linear algebra.
- By visualizing linear algebra concepts through everyday analogies, such as smoothie-making, one can develop a better understanding of its applications in machine learning.
Read Full Article
22 Likes
Towards Data Science
3w
60
Get Started with Rust: Installation and Your First CLI Tool – A Beginner’s Guide
- Rust is a popular programming language known for its security and high performance, combining features of C, C++, and simplicity of modern languages like Python.
- Installation of Rust is made easy through the official installer rustup, available for free on the Rust website.
- For Windows installation, downloading rustup-init.exe and running it through command line completes the process.
- On Linux, Rust can be installed through the terminal using a specific command.
- For macOS, installation via Homebrew or a script is possible.
- Using cargo, the official package manager and build system of Rust, a new project can be initiated with ease.
- Cargo assists in project management by handling dependencies, compilation, tests, and builds.
- Dependencies like serde and serde_json facilitate working with data formats like JSON.
- By following set-up steps and writing Rust code, a simple CLI tool to parse and display JSON content can be created.
- The process includes creating a project, defining dependencies, writing code for JSON parsing, and testing the CLI tool.
Read Full Article
3 Likes
Towards Data Science
3w
361
Non-Parametric Density Estimation: Theory and Applications
- Density estimation is essential in statistical analysis for inferring the probability density function of a random variable given a sample data. It can be used for distribution analysis, classification tasks, and more.
- Histograms and Kernel Density Estimators (KDEs) are popular non-parametric methods for density estimation, with KDEs being a smoother alternative to histograms.
- Density estimation methods may be parametric (assuming a known distribution) or non-parametric (making no rigid assumptions about the distribution). Non-parametric methods like KDEs typically have lower bias and higher variance.
- Histograms partition data into bins, while KDEs compute weighted sums of neighboring points. KDEs generalize the histogram approach and are commonly used in practice.
- Kernels play a crucial role in KDE, with choices like Gaussian, Epanechnikov, rectangular, and triangular, influencing the smoothness of the density estimate.
- The accuracy of density estimators is influenced by bias and variance trade-offs, with bandwidth selection impacting the estimation quality.
- In classification tasks, density estimation can be used to build classifiers like Naive Bayes, where parametric and non-parametric density estimates affect decision boundaries and classification accuracy.
- Non-parametric Naive Bayes classifiers may provide more flexible decision boundaries but could introduce roughness, compared to smoother decision boundaries from parametric approaches.
- Understanding density estimation theory, methods like histograms and KDEs, and their applications in classification tasks offer valuable insights for statistical analysis.
- Resources like notes on nonparametric statistics, statistical learning textbooks, and datasets like the famous Iris dataset can aid in further exploration of density estimation.
- The choice between parametric and non-parametric density estimation depends on the dataset characteristics, with parametric assumptions often offering smoother decision boundaries in classification tasks.
Read Full Article
21 Likes
Medium
3w
205
Image Credit: Medium
Advanced Data Analysis: Elevating Your Skills with Modern Techniques
- Using advanced data analysis techniques like crPlots, influence.measures, splines, gam, glm, lmer, rlm, bootstrapping, mice, selection, and cross-validation can lead to unbiased and reliable results.
- Ignoring influential points or nonlinearity can result in biased estimates and overconfidence in results.
- Assuming linearity can obscure true effects, leading to incorrect policy or business decisions.
- Using OLS on counts or proportions violates distributional assumptions, producing nonsensical predictions.
Read Full Article
12 Likes
Towards Data Science
3w
54
Rethinking the Environmental Costs of Training AI — Why We Should Look Beyond Hardware
- Hardware choices and training time significantly impact energy, water, and carbon footprints during AI model training, while architecture-related factors have a minimal effect.
- Energy efficiency during AI model training has improved slightly over the years, showing a 0.13% improvement annually.
- Longer training times can gradually reduce overall energy efficiency by 0.03% per hour.
- The study analyzed AI models' architectural and hardware choices' impact on resource consumption during training.
- Data from Epoch AI's Notable AI Models dataset was used for estimation and analysis methods.
- Results indicated that hardware choices and training time were significant predictors of energy consumption during AI training.
- AI models have become slightly more energy-efficient over time, with an estimated 0.13% improvement annually.
- Training time was identified as a significant factor influencing energy efficiency, decreasing by 0.03% per hour.
- The study highlighted the significant environmental impacts of AI model training and the importance of considering hardware choices and training practices.
- Further research is recommended to explore interactions between hardware types and training practices for more comprehensive insights.
Read Full Article
3 Likes
VentureBeat
3w
389
Image Credit: VentureBeat
AI power rankings upended: OpenAI, Google rise as Anthropic falls, Poe report finds
- OpenAI and Google have strengthened their positions in key AI categories according to the latest report by Poe.
- The report highlights shifts in market share, with rapid innovation and an increasingly diverse competitive landscape.
- In core text generation, OpenAI's GPT-4o maintained dominance, while Google's Gemini 2.5 Pro gained share.
- Specialized reasoning models gained importance, with Gemini 2.5 Pro leading the category.
- OpenAI released multiple reasoning models, showing rapid innovation in the space.
- Hybrid reasoning models like Gemini 2.5 Flash Preview and Qwen 3 are emerging.
- The image generation market saw increased competition, with Google's Imagen 3 family growing substantially.
- In video generation, Kuaishou's Kling models disrupted the market, while Google's Veo 2 maintained a strong position.
- ElevenLabs led the audio generation category, facing emerging competition from players offering differentiated voice options.
- Reasoning capabilities are becoming crucial in the AI market, signaling a shift in how businesses evaluate and deploy models.
Read Full Article
23 Likes
Towards Data Science
3w
322
TDS Authors Can Now Receive Payments Via Stripe
- TDS Author Payment Program introduced in February has now integrated with Stripe for simplifying payment process.
- Authors can now connect their Stripe account to their author profile on the Contributor Portal for timely and streamlined payments.
- Eligibility conditions and article criteria for earning on TDS remain the same.
- Payments to authors will be processed on the 15th of each month following a 30-day earning window of the article.
Read Full Article
19 Likes
For uninterrupted reading, download the app