10 際際滷 Intro to ML
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Akvis Sketch is a software 2025 application designed crack to convert digital photos into sketches or drawings. It provides users with the ability to transform their photographs into artwork with a variety of artistic styles, including pencil sketches, watercolor, and oil painting effects. Akvis Sketch is particularly known for its easy-to-use interface and the ability to achieve high-quality, customizable results. It is popular among both professional photographers and amateur artists who want to enhance their images with artistic effects.
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Scaling Data Workflows with Azure Synapse Analytics and PySparkNasir Arafat
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Understanding Azure Synapse Analytics for big data processing
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Elastic Kafka Meetup Singapore_Privacy Protected Data Management.pdfNaveen Nandan
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Regulated industries typically look for techniques such as encryption, masking, tokenization to ensure customer PII and other sensitive information are classified and protected when data moves across multiple systems and LoBs. In this talk let's explore how some of these methods can be applied early on at ingestion to make it easier for teams to manage and govern datasets as it flows through multiple systems across and outside of their organisation.
Here's my talk at the SG Elastic Meetup titled Privacy Protected Data Management with Kafka and Elasticsearch.
A Simple Introduction to data Science- what is it and what does it do
A Simple Introduction to data Science- what is it and what does it dosarah mabrouk
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introduction to data science100 questions on Data Science to Master interview
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# **Crack Your Data Science Interview with Confidence: A Comprehensive Guide by Tutort Academy**
## **Introduction**
Data Science has emerged as one of the most sought-after fields in the tech industry. With its blend of statistics, programming, machine learning, and business acumen, the role of a data scientist is both challenging and rewarding. However, cracking a data science interview can be intimidating due to its multidisciplinary nature.
In this comprehensive guide by **Tutort Academy**, we break down everything you need to know to ace your next data science interviewfrom core concepts and technical rounds to behavioral questions and interview tips.
---
## **1. Understanding the Data Science Interview Process**
Most data science interviews typically consist of the following stages:
### **1.1 Resume Shortlisting**
Ensure your resume highlights relevant skills such as Python, SQL, Machine Learning, and project experience. Certifications and courses (like those offered by Tutort Academy) can add extra credibility.
### **1.2 Initial Screening**
Usually conducted by a recruiter or HR. It focuses on your background, motivation, and basic fit for the role.
### **1.3 Technical Assessment**
This can include:
- Online coding tests (HackerRank, Codility)
- SQL queries
- Statistics and Probability questions
- Machine Learning concepts
### **1.4 Case Studies or Business Problems**
You may be asked to solve real-world problems such as churn prediction, customer segmentation, or A/B testing.
### **1.5 Technical Interview Rounds**
Youll interact with data scientists or engineers and answer questions on algorithms, data preprocessing, model evaluation, etc.
### **1.6 Behavioral and HR Round**
Test your cultural fit, communication skills, and team collaboration.
---
## **2. Core Skills Required**
### **2.1 Programming (Python/R)**
- Data structures and algorithms
- Libraries like Pandas, NumPy, Matplotlib, Seaborn
- Web scraping, APIs
### **2.2 SQL and Databases**
- Joins, subqueries, window functions
- Data extraction and transformation
- Writing efficient queries
### **2.3 Statistics and Probability**
- Descriptive and inferential statistics
- Hypothesis testing
- Probability distributions
### **2.4 Machine Learning**
- Supervised vs Unsupervised Learning
- Algorithms: Linear Regression, Decision Trees, SVM, Random Forest, XGBoost
- Model evaluation metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC
### **2.5 Data Visualization**
- Storytelling with data
- Tools: Tableau, Power BI, or Python libraries
### **2.6 Communication and Business Acumen**
- Explaining complex results to non-technical stakeholders
- Understanding KPIs and business objectives
---
## **3. Important Interview Questions**
### **3.1 Python/Programming**
- What are Python generators?
- How do you handle missing values in a dataset?
- Write a function to detect duplicate entries.
### **3.2 SQL**
- Find the second highest salary from an employee table.
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Data is no longer a luxuryits a competitive advantage. This presentation dives deep into how successful organizations build data-driven cultures and use analytics to outperform their competitors. From setting KPIs to measuring performance in real-time dashboards, we explore the frameworks companies use to make smarter, faster decisions based on reliable insights. Learn how giants like Amazon, Netflix, and Google have built scalable systems powered by data, and how small businesses can follow similar practices with tools like Power BI, Google Analytics, and Tableau. Youll walk away understanding how to integrate data into every business functionfrom marketing and sales to operations and product development.Clarkson Depot Business Report and Analysis
Clarkson Depot Business Report and AnalysisBlazing Perfection
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This business venture presents a highly lucrative opportunity, demonstrating robust market demand, scalable operations, and strong revenue potential. Positioned within a growing industry, it leverages competitive advantages such as innovative offerings, strategic partnerships, and a proven business model. With a clear path to profitability, favorable margins, and opportunities for expansion, this enterprise is poised for sustainable growth and high returns on investment. Market analysis indicates continued upward trends, reinforcing the long-term viability and attractiveness of this venture to stakeholders and investors alike.Salesforce Data Cloud with Xero 0425.pdf
Salesforce Data Cloud with Xero 0425.pdfRaksha Meanger
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Salesforce Wellington presentation on Data CloudStatistics for Management - standard deviation.pptx
Statistics for Management - standard deviation.pptxJeya Sree
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Steel rods are manufactured to be 3 inches in diameter, but they are acceptable if they are inside the limits of 2.99 inches and 3.01 inches. It is observed that 5% are rejected as oversize and 5% are rejected as undersize. Assuming that the diameters are normally distributed, how will you find the standard deviation of the distribution. Further how will you find the proportion of rejects would be, if the permissible limits were widened to 2.985 to 3.015 inches? Explain
Z-Table is used.
10 際際滷 Intro to ML
- 1. Introduction to Machine Learning NITESH SURANA INFORMATION TECHNOLOGY SECTION - A , ROLL NO. -55 DATE - 30.03.2017
- 2. What do you mean?
- 3. Formally A computer program is said to learn from experience (E) with some class of tasks (T) and a performance measure (P) if its performance at tasks in T as measured by P improves with E Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, it allows computers to find hidden insights without being explicitly programmed where to look.
- 4. Machine Learning Supervised Classification Regression Unsupervised Reinforcement Semi-Supervised
- 5. Supervised Learning Model preparation using training data If predictions are wrong, they are corrected The training process continues until the model achieves a desired level of accuracy on the training data. Some types Classification, Regression
- 6. Unsupervised Learning It is used against data that has no historical labels. The system is not told the "right answer." The algorithm must figure out what is being shown. A model is prepared by deducing structures present in the input data.
- 7. Where is ML?