Introduction to Data Analytics
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This document introduces data science, big data, and data analytics. It discusses the roles of data scientists, big data professionals, and data analysts. Data scientists use machine learning and AI to find patterns in data from multiple sources to make predictions. Big data professionals build large-scale data processing systems and use big data tools. Data analysts acquire, analyze, and process data to find insights and create reports. The document also provides examples of how Netflix uses data analytics, data science, and big data professionals to optimize content caching, quality, and create personalized streaming experiences based on quality of experience and user behavior analysis.
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Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka! Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
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Untitled document.pdfMuhammadTahiriqbal13 Understanding Data Science: Unveiling the Basics
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At Digicrome, we offer a top-notch online Data Science course that covers a wide range of essential concepts and tools. From Python programming to advanced Python concepts, from data visualization to statistics, from machine learning to SQL, our course encompasses all the key areas you need to master. Our logical and structured approach makes it easy to comprehend complex topics, ensuring a solid foundation in data science.
Our course features 250 hours of intensive live training, where you'll receive hands-on learning experiences. We believe in practical training, even for students with little or no technical background. You'll work on 25+ projects and 4+ capstone projects, allowing you to apply your knowledge to real-world scenarios. This practical exposure will make you job-ready and equip you with the necessary skills to tackle the challenges of the industry.
We understand the importance of placements, which is why we provide 100% job guarantee to our students. Upon completing our course, you'll have the opportunity to be placed in renowned multinational companies such as Nykaa, Myntra, Cred, Meesho, Razorpay, Wipro, Infosys, TCS, Microsoft, Hungama, PharmEasy, and many more. Our industry connections and partnerships ensure that you have ample opportunities for career growth and development.
To further enhance your learning experience, we offer a 3-month paid internship, allowing you to gain practical experience in a professional setting. You'll also have access to mock interviews conducted by hiring managers, helping you refine your interview skills and boost your confidence.
Our course is backed by seven types of certifications, validating your expertise in various data science domains. These certifications will add significant value to your resume and demonstrate your commitment to professional growth.
To ensure individual attention and guidance, we provide 1:1 sessions with industry mentors. These experienced professionals will guide you through your learning journey, offering personalized support and insights. You'll have the opportunity to learn from their experiences and gain valuable industry insights.
We understand that financial constraints can sometimes hinder educational pursuits. That's why we offer a no-cost EMI option, allowing you to manage your payments conveniently. We also provide discounts of up to 60% off academic fees, making our course more accessible and affordable.
Don't miss the chance to embark on an exciting career as a data scientist. Enroll in the Digicrome Python Data Science course, prepare yourself for the future, and become a professional data scientist. Unlock your potential with Digicrome today!
Data+Science : A First Course
Data+Science : A First CourseArnab Majumdar Students will be able to work as
research assistants in academia and industry.
Entrepreneurship: Students can start their own
data science consulting firms or startups.
Higher Education: Students will be well
prepared for advanced degrees in Data Science,
Computer Science, Statistics or related fields.
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What is the difference between Data Science and Data Analytics.pdf
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Model validation
Model validationUtkarsh Sharma This document discusses various machine learning model validation techniques and ensemble methods such as bagging and boosting. It defines key concepts like overfitting, underfitting, bias-variance tradeoff, and different validation metrics. Cross validation techniques like k-fold and bootstrap are explained as ways to estimate model performance on unseen data. Bagging creates multiple models on resampled data and averages their predictions to reduce variance. Boosting iteratively adjusts weights of misclassified observations to build strong models, but risks overfitting. Gradient boosting and XGBoost are powerful ensemble methods.
Introduction to statistics
Introduction to statisticsUtkarsh Sharma This document provides an introduction to statistics and discusses various statistical concepts. It begins by outlining the course instructor and topics to be covered, including statistics in business, data measurement, and variables and data. It then discusses levels of data measurement, describing nominal, ordinal, interval, and ratio levels. Finally, it explains key statistical terms such as population, sample, parameters, and statistics.
Web mining: Concepts and applications
Web mining: Concepts and applicationsUtkarsh Sharma The document discusses various techniques for web mining including web content mining and web structure mining. Web content mining involves extracting useful information from web documents and can include techniques like classification, clustering, and association rule mining. Web structure mining analyzes the hyperlink structure of the web and can be used to determine authoritative pages using algorithms like PageRank and HITS. The document provides examples of how these algorithms like PageRank and HITS work.
Time series analysis
Time series analysisUtkarsh Sharma The document provides an overview of time series analysis. It discusses key concepts like components of a time series, stationarity, autocorrelation functions, and various forecasting models including AR, MA, ARMA, and ARIMA. It also covers exponential smoothing and how to decompose, validate, and test the accuracy of forecasting models. Examples are given of different time series patterns and how to make non-stationary data stationary.
Text analytics
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Evaluating classification algorithms
Evaluating classification algorithmsUtkarsh Sharma This document discusses various metrics for evaluating classification algorithms, including accuracy, precision, recall, F1-score, and specificity. It defines each metric using equations and confusion matrix terms. Accuracy alone is not sufficient for evaluation, as it can be misleading when applied to imbalanced datasets. The document also provides examples to demonstrate how to calculate and interpret these important evaluation metrics.
Principle Component Analysis
Principle Component AnalysisUtkarsh Sharma This document discusses principal component analysis (PCA), a dimensionality reduction technique. It explains that PCA calculates the covariance matrix of the data, finds the eigenvectors and eigenvalues, and uses the top eigenvectors as principal components to represent the data in a lower dimensional space while preserving as much information as possible. The original data can then be recovered from the lower dimensional representation.
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Density based Clustering Algorithms(DB SCAN, Mean shift )Utkarsh Sharma The document discusses several clustering algorithms: Mean shift clustering works by iteratively shifting a sliding window to areas of higher point density until convergence; DBSCAN begins with a starting point and identifies neighboring points within a distance epsilon, forming clusters if sufficient neighbors are found and labeling outliers; Agglomerative hierarchical clustering treats each data point as an initial cluster and successively merges the closest pairs of clusters using a distance metric like average linkage until all points are in one cluster.
Association rule mining
Association rule miningUtkarsh Sharma The document discusses association rule mining and the Apriori and FP-Growth algorithms. It provides the following information:
- Association rule mining discovers interesting relationships between variables in large databases. It expresses relationships between frequently co-occurring items as association rules.
- The Apriori algorithm uses frequent itemsets to generate association rules. It employs an iterative approach and pruning to reduce candidate sets.
- FP-Growth improves upon Apriori by compressing transaction data into a frequent-pattern tree structure and scanning the database twice instead of multiple times. This improves mining efficiency over Apriori.
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Introduction to Data Analytics
- 1. INTRODUCTION TO DATA ANALYTICS Utkarsh Sharma Asst. Prof (CSE Dept.) Jaypee University of Engineering & Technology
- 2. CONTENTS What is Data Science, Big Data, Data Analytics? Roles and Responsibilities of Data Scientist, Big Data Professional and Data Analyst Required Skill set. Understanding how data science, big data, and data analytics is used to drive the success of Netflix.
- 3. ROLE OF STATISTICAL LEARNING Here are some examples of learning problems: Predict whether a patient, hospitalized due to a heart attack, will have a second heart attack. The prediction is to be based on demo-graphic, diet and clinical measurements for that patient. Predict the price of a stock in 6 months from now, on the basis of company performance measures and economic data. Identify the numbers in a handwritten ZIP code, from a digitized image. Estimate the amount of glucose in the blood of a diabetic person, from the infrared absorption spectrum of that person’s blood. Identify the risk factors for prostate cancer, based on clinical and demographic variables.
- 4. WHAT IS DATA SCIENCE? Combination of mathematics, statistics and programming. Context of problem being solved. Ingenious way of capturing data which is not captured. Ability to look at the things differently
- 5. WHAT IS BIG DATA? Large amount of data from various source. Traditional data processing system are incapable to deal. In terms of Volume, Variety, Veracity, Velocity and value.
- 6. WHAT IS DATA ANALYTICS? Discovering useful information from data. Supports decision making. Involves inspecting, cleansing, transforming and modelling data. Uses qualitative and quantitative techniques.
- 7. WHAT DO DATA SCIENTISTS DO? Predicts future based on past patterns using AI and machine learning. Examines data from multiple sources. Finding co-relations and hidden patterns from data.
- 8. WHAT DOES A BIG DATA PROFESSIONAL DO? Architect distributed systems. Build large scale data processing system. Process the data using various big data tools.
- 9. WHAT DOES A DATA ANALYST DO? Acquire, analyse and process the data. Finding insights of captured data. Create data report using various reporting tools.
- 10. SKILLS REQUIRED
- 11. SALARIES TO EXPECT Data Scientist ₹610,811 Big Data Professional ₹520,811 Data Analyst ₹390,811
- 12. How Data analytics, Data science and Big data professional used in Netflix…..
- 13. AN EXAMPLE SCENARIO Big Data Professional
- 14. AN EXAMPLE SCENARIO Data Scientist Understanding of the impact of QoE on User Behavior Creating personalized streaming experience Optimizing content caching Improving content quality
- 16. For any queries kindly reach me at [email protected].