Steps for avoiding pitfalls
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This document outlines common mistakes made when implementing an Enterprise Resource Planning (ERP) system. Key errors include poor planning, not properly vetting ERP vendors, underestimating time and resource needs, and not prioritizing or having the right team from the start to ensure a successful ERP implementation.
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CDULA: 8-465-654
FECHA: JUEVES 02 DE OCTUBRE DEL 2014
ASIGNATURA: INFORMTICA EDUCATIVA 3
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Financial institutions provide various financial services and fall under government regulation. They include banks, insurance companies, investment companies, and other non-banking financial institutions. Banks accept deposits and make loans. Central banks are responsible for a country's monetary policy. Commercial banks accept deposits and make loans exceeding deposits through fractional-reserve banking. Investment banks help companies raise money through issuing securities. Other financial institutions include savings banks, credit unions, and non-banking financial companies.Swot analysis
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This document discusses women's empowerment in India. It provides background on important historical figures who advocated for women's rights. Women's empowerment means creating an environment where women can make independent decisions and have equal rights in society. The main problems facing women include lack of education, financial constraints, and family responsibilities. Crimes against women include domestic violence, dowry, acid attacks, female feticide, and rape. Women have rights to work, equal employment opportunities, social security, and paid leave. Empowering women benefits families and societies by sharing financial burdens and strengthening family units. True potential cannot be reached until women have equal opportunities as men.Media pembelajaran unsur instrinsik
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PLANEAMIENTO DE UNA CLASE ACTIVA # 1
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CDULA: 8-465-654
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ASIGNATURA: INFORMTICA EDUCATIVA 3
EJE DE INTERS: INFORMTICA EDUCATIVA 油
AULA: 806 LAB
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油
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- It involves specifying objectives and identifying internal and external factors that help or hinder achieving objectives.
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A disaster is defined as a serious disruption exceeding a community's ability to cope that causes widespread human, material, economic, or environmental losses. Man-made disasters include hazardous materials, power outages, nuclear incidents, explosions, and cyber attacks. The 2014 GAIL pipeline explosion in Andhra Pradesh, India was caused when a spark ignited leaking gas and condensate from the pipeline. It killed 22 people and injured 17 others. Industrial accidents can seriously harm human life and the environment through air, water, and soil pollution. The 2011 Fukushima Daiichi nuclear disaster in Japan was triggered by an earthquake and tsunami that caused nuclear meltdowns and released radiation, contaminating the area and increasing cancer risks.Disaster
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- Reporting the results, including conclusions, observations, and recommendations for improvement.
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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.
- Use w
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- 2. Poor planning Not properly vetting ERP vendors. Not understanding or using key features Underestimating the time and resources required Not having the right people on the team from the start Not setting priorities Not investing in training and change management
- 3. Underestimating the importance of accurate data Taking the kitchen sink approach Not decommissioning legacy applications Not having an active load testing environment Ignoring third-party support alternatives Not having a maintenance strategy