This document outlines the details of an introductory data science course, including its mission, vision, core values, and schedule. It introduces data science and related fields such as data mining and analytics. It discusses the data science process and common job roles. Finally, it provides an overview of data science skills in high demand and lists several resources for data, tools, and learning.
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
Data Mining: Concepts and Techniques (3rd ed.) - Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
Overview of the course. Introduction to image sciences, image processing and computer vision. Basics of machine learning, terminologies, paradigms. No-free lunch theorem. Supervised versus unsupervised learning. Clustering and K-Means. Classification and regression. Linear least squares and polynomial curve fitting. Model complexity and overfitting. Curse of dimensionality. Dimensionality reduction and principal component analysis. Image representation, semantic gap, image features, and classical computer vision pipelines.
The process of finding, loading and cleaning data in the real world. This is the pre-cursor to data analysis and data science.
This presentation explains sources where you can find data, the various formats in which data is usually available or stored, and a checklist of activities to perform when cleaning the data.
One of the determinants for a good anomaly detector is finding smart data representations that can easily evince deviations from the normal distribution. Traditional supervised approaches would require a strong assumption about what is normal and what not plus a non negligible effort in labeling the training dataset. Deep auto-encoders work very well in learning high-level abstractions and non-linear relationships of the data without requiring data labels. In this talk we will review a few popular techniques used in shallow machine learning and propose two semi-supervised approaches for novelty detection: one based on reconstruction error and another based on lower-dimensional feature compression.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
K-Folds cross-validation is one method that attempts to maximize the use of the available data for training and then testing a model. It is particularly useful for assessing model performance, as it provides a range of accuracy scores across (somewhat) different data sets.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
We are living in a world, where a vast amount of digital data which is called big data. Plus as the world becomes more and more connected via the Internet of Things (IoT). The IoT has been a major influence on the Big Data landscape. The analysis of such big data brings ahead business competition to the next level of innovation and productivity.
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
Data Mining: Concepts and Techniques (3rd ed.) - Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
Overview of the course. Introduction to image sciences, image processing and computer vision. Basics of machine learning, terminologies, paradigms. No-free lunch theorem. Supervised versus unsupervised learning. Clustering and K-Means. Classification and regression. Linear least squares and polynomial curve fitting. Model complexity and overfitting. Curse of dimensionality. Dimensionality reduction and principal component analysis. Image representation, semantic gap, image features, and classical computer vision pipelines.
The process of finding, loading and cleaning data in the real world. This is the pre-cursor to data analysis and data science.
This presentation explains sources where you can find data, the various formats in which data is usually available or stored, and a checklist of activities to perform when cleaning the data.
One of the determinants for a good anomaly detector is finding smart data representations that can easily evince deviations from the normal distribution. Traditional supervised approaches would require a strong assumption about what is normal and what not plus a non negligible effort in labeling the training dataset. Deep auto-encoders work very well in learning high-level abstractions and non-linear relationships of the data without requiring data labels. In this talk we will review a few popular techniques used in shallow machine learning and propose two semi-supervised approaches for novelty detection: one based on reconstruction error and another based on lower-dimensional feature compression.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
K-Folds cross-validation is one method that attempts to maximize the use of the available data for training and then testing a model. It is particularly useful for assessing model performance, as it provides a range of accuracy scores across (somewhat) different data sets.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
We are living in a world, where a vast amount of digital data which is called big data. Plus as the world becomes more and more connected via the Internet of Things (IoT). The IoT has been a major influence on the Big Data landscape. The analysis of such big data brings ahead business competition to the next level of innovation and productivity.
Who Owns Faculty Data?: Fairness and transparency in UCLA's new academic HR s...chloejreynolds
Abstract: Beginning in 2015, Opus will be the information system of record for faculty activities at the University of California, Los Angeles (UCLA). Opus will serve as both a profile system, storing data about faculty work, and as a workflow and approval engine for the promotion and tenure process. Opus leverages institutional master data wherever possible to collect data about faculty activity. However, re-purposing institutional data collected for purposes not related to academic review necessitates allowing data subjects (UCLA faculty), to contextualize and reframe the data for the review process. Collecting, displaying and storing these augmented records (master data with manually added metadata from faculty) has forced the project team to grapple with questions regarding fairness and transparency to both data subjects and to data consumers. How can we hold to “good design” and usability practices, while faithfully representing the inherent “messiness” of the data? How does the context in which the data was collected impact re-purposing the data for academic review? What does it mean to “own” faculty data? This paper outlines our attempts to address these questions, noting the trade-offs and limitations of the selected solutions.
This topic was presented at the 2015 iConference on March 26, 2015 in Newport Beach, CA. Since 2005, the iConference series has provided forums in which information scholars, researchers and professionals share their insights on critical information issues in contemporary society. An openness to new ideas and research fields in information science is a primary characteristics of the event.
Recommendation of Data Mining Technique in Higher Education Prof. Priya Thaka...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
In the dynamic landscape of the 21st century, data science has emerged as a pivotal discipline, driving innovation, decision-making, and insights across industries. As we step into 2023, the field of data science continues to evolve at a rapid pace, presenting exciting opportunities for those aspiring to embark on a career that blends mathematics, statistics, programming, and domain expertise.
Experience unparalleled data-driven success with our cutting-edge Data Scienc...proitbridgePvtLtd
This presentation will equip you with the knowledge and skills to navigate the Data Science Institute at Proitbridge For More info Call Us:9740230130 Visit Our Website:www.proitbridge.com
#datasciencecoursesinbangalore #dataanalystcourseinbangalore #datascienceinstituteinbangalore #bestinstitutefordatascienceinbangalore #datascienceinbangalore#proitbridge
Study and Analysis of K-Means Clustering Algorithm Using RapidminerIJERA Editor
Institution is a place where teacher explains and student just understands and learns the lesson. Every student has his own definition for toughness and easiness and there isn’t any absolute scale for measuring knowledge but examination score indicate the performance of student. In this case study, knowledge of data mining is combined with educational strategies to improve students’ performance. Generally, data mining (sometimes called data or knowledge discovery) is the process of analysing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for data. It allows users to analyse data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).This project describes the use of clustering data mining technique to improve the efficiency of academic performance in the educational institutions .In this project, a live experiment was conducted on students .By conducting an exam on students of computer science major using MOODLE(LMS) and analysing that data generated using RapidMiner(Datamining Software) and later by performing clustering on the data. This method helps to identify the students who need special advising or counselling by the teacher to give high quality of education.
Python is a Simple, Easy to learn and most demanded high level general purpose programming language across the world. And is meant for everyone. Anybody can learn Python.
Role of digital technology in autism a case studyUmmeSalmaM1
Autism Spectrum Disorder (ASD) is neurological disorder found majorly among the children, however it has no age or gender restriction. ASD is not a disease & doesn’t have any cure. This work is a case study which highlights the contribution of digital technologies such as ICT, IoT, Robotics and others in shaping the lives of autistic patients.
This PPT Programming for data science in python mainly focus on importance of Python programming language in Python it explains the characteristic features of the programming language, its pros and cons and its applications.
A recommender system(RS) is an information filtering system that recommends the related suggestions as per the end users requirement. Applications of RS include recommendation of movies, music, serials, books, documents, websites, tourist places etc.
Benefits of RS: RSs are beneficial to both service providers and to the users. RSs reduce transaction costs of finding and selecting items.& RSs help in decision making. The proposed work DEMOGRAPHY BASED HYBRID SYSTEM FOR MOVIE RECOMMENDATIONS highlights the combination of collaborative, content based & demographic filtering to recommend movies to the end user. The model uses SVD++ technique available in Surprise Python library and achieves the MSE of 0.92 which is comparatively less than the other techniques.
Systematic study of data is called data science. Data science is a multi-disciplinary field which uses scientific methods, processes, algorithms and systems to extract the hidden knowledge from data. This knowledge helps in decision making.
Among various programming languages available, Python is best suited for data science and is most popular among the data scientists as it provides wide range of libraries such as to accomplish the desired tasks.1. Data exploration and analysis : Pandas; NumPy; SciPy;
2. Data visualization: Matplotlib; Seaborn; Datashader;
3. Classical machine learning: Scikit-Learn, StatsModels
4. Deep learning: Keras, TensorFlow
5. Data storage and big data frameworks: Apache Spark; Apache Hadoop; HDFS etc. In this PPT we will be learning about introduction to data science and Python, their applications and use cases.
A fun and learn Visual quiz with key answers on some of the core concepts of machine learning with key answers. The machine learning concepts covered are supervised learning, unsupervised learning, parametric approach, non-parametric approach, clustering, classification, regression, association rule mining, dimensionality reduction, feature selection, feature extraction, wrapper methods, PCA, LDA, overfitting, underfitting etc.
Lecture1 introduction to machine learningUmmeSalmaM1
Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.
Entrepreneurs are Born, Not Made!
This PPT discuss the roadmap of an entrepreneurial journey. Which includes details related to startup opportunities , funding agencies, stories of successful entrepreneurs etc.
Impact of Learning Functions on Prediction of Stock DataUmmeSalmaM1
The impact of various learning functions to predict the stock data is discussed in this PPT.
Here back propagation neural network is used to predict the closing value of Nifty stock data. The neural network model uses four different learning functions namely – Unipolar, sigmoid, bi-polar sigmoid, tanh, and radial basis function to train and test the model. It was observed that tanh() function outperformed the prediction task.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. MISSION
CHRIST is a nurturing ground for an individual’s
holistic development to make effective contribution to
the society in a dynamic environment
VISION
Excellence and Service
CORE VALUES
Faith in God | Moral Uprightness
Love of Fellow Beings
Social Responsibility | Pursuit of Excellence
MTH341C - PRINCIPLES OF DATA SCIENCE
Week1: 18 to 23 July 2022
Department of Data Science and Statistics, CHRIST (DEEMED TO BE
UNIVERSITY)
BANGALORE, KARNATAKA, INDIA
Introduction to Data Science
Dr. UMME SALMA M
Assistant Professor
Ummesalma.m@christuniversity.in
2. Excellence and Service
CHRIST
Deemed to be University
Class Details
● Programme
○ MSC Mathematics
● Course
○ MTH341C
○ PRINCIPLES OF DATA SCIENCE
● Unit 1
○ Introduction To Data Science and Big Data
● Topic 1
○ Data Science Market
● Material
○ Online resources
2
8. Excellence and Service
CHRIST
Deemed to be University
Data Science Family
8
● Data Science is a field of science than mere data
● Data Mining is mainly about
finding useful information in a dataset and utilizing that information to
uncover hidden patterns.
● Data Analytics involves tools and techniques
○ [information resulting from the systematic analysis of data or
statistics]
& Data Mining
13. Excellence and Service
CHRIST
Deemed to be University
Data Science Steps
13
Step 1: The first step of this process is setting a research goal. The main purpose here is making sure all the
stakeholders understand the what, how, and why of the project.
Step 2: The second phase is data retrieval. You want to have data available for analysis, so this step
includes finding suitable data and getting access to the data from the data owner. The result is data in its
raw form, which probably needs polishing and transformation beforeit becomes usable.
Step 3: Data transformation converts a raw form into directly usable form. To achieve this, you’ll detectand
correctdifferentkinds of errors in the data, combine data from differentdata sources,and transform it. If you
have successfullycompletedthis step, you can progress to data visualization and modeling.
Step 4: Data Exploration helps to gain a deep understanding of the data. You’ll look for patterns, correlations,
and deviations based on visual and descriptive techniques.The insights you gain from this phase will enable
you to start modeling.
Step 5: Data modelling is the phase to attempt to gain the insights or make the predictions stated in your
projectcharter. Now is the time to bring out the heavy guns, but rememberresearchhas taught us that often
(but not always) a combinationof simple models tends to outperform one complicatedmodel.
Step 6:Presentation and automation is all about presenting your results and automating the analysis, if needed.
14. Excellence and Service
CHRIST
Deemed to be University
Data Science Steps Outcome
14
Step1 Outcome:Clear Understanding of the goals of research and its context.
A projectcharter requires teamwork, and your input covers at least the following:
■ A clear researchgoal
■ The projectmissionand context
■ How you’re going to perform your analysis
■ What resources you expectto use
■ Proof that it’s an achievable project,or proof of concepts
■ Deliverables and a measure of success
■ A timeline
Step 2 Outcome:Sometimesyou need to go into the field and designa data collectionprocess
yourself,but most of the time you won’t be involved in this step.
Step 3 Outcome:Getting access to data is another difficulttask. Organizations understand the
value and sensitivity of data and oftenhave policies in place so everyone has access to what
they need and nothing more. Don’t be afraid to shop around.
15. Excellence and Service
CHRIST
Deemed to be University
15
Step 4 Outcome:Cleansing data
Data cleansing is a subprocess of the data science processthat focuses on
removing rrors in your data so your data becomesa true and consistent
representationof the processes itoriginates from.
Combiningdata from differentdata sources
Step 5 Outcome:Working Model based upon the
requirement
Step 6: Deployed Model
23. Excellence and Service
CHRIST
Deemed to be University
23
Source: https://analyticsindiamag.com/why-you-may-not-be-getting-a-call-back-for-that-data-science-job/
25. Excellence and Service
CHRIST
Deemed to be University
25
Souce: https://www.gartner.com/smarterwithgartner/gartner-top-10-data-and-analytics-trends-for-2021/
26. Excellence and Service
CHRIST
Deemed to be University
Data Repositories
26
•Google DatasetSearch.
•Kaggle.
•Data.Gov.
•Datahub.io.
•UCI Machine Learning Repository.
•Earth Data.
•CERN Open Data Portal.
•Global Health ObservatoryData Repository.
•NCBI
•CERT
•NCRB
•Indiastat
27. Excellence and Service
CHRIST
Deemed to be University
Resources
27
● https://www.kdnuggets.com
● https://www.kaggle.com/
● https://www.analyticsvidhya.com/
● https://towardsdatascience.com
● https://machinelearningmastery.com/
● https://pydata.org/
● https://www.meetup.com/topics/data-science/
arXiv ; GitHub; MOOCS
28. Excellence and Service
CHRIST
Deemed to be University
THANKYOU
Next Topic: Unit 1: Chapter 1
Data Science in a Big Data World
Next session: Monday 12.00 PM
28
