ProjectPro offers a hands-on approach to mastering machine learning and data science through 150+ solved end-to-end deployable machine learning and data science projects. They also provide 2000+ FREE data science code examples that can help one master the foundations of basic data science and machine learning concepts.
This 4-week course on "Python for Data Science" taught the basics of Python programming and libraries for data science. It covered topics like data types, sequence data, Pandas dataframes, data visualization with Matplotlib and Seaborn. Technologies taught included Spyder IDE, NumPy, Jupyter Notebook, Pandas and visualization libraries. The course aimed to equip participants with Python skills for solving data science problems. It examined applications of data science in domains like e-commerce, machine learning, medical diagnosis and more.
I am shubham sharma graduated from Acropolis Institute of technology in Computer Science and Engineering. I have spent around 2 years in field of Machine learning. I am currently working as Data Scientist in Reliance industries private limited Mumbai. Mainly focused on problems related to data handing, data analysis, modeling, forecasting, statistics and machine learning, Deep learning, Computer Vision, Natural language processing etc. Area of interests are Data Analytics, Machine Learning, Machine learning, Time Series Forecasting, web information retrieval, algorithms, Data structures, design patterns, OOAD.
Henry Harvin Analytics Academy offers 2-month courses in business analytics using Python and advanced Excel. The courses teach students to explore, analyze, and solve business problems using analytics tools and techniques. Students work on real-life case studies and complete an internship project. The goal is to empower students to become data-driven professionals who can determine organizational goals, mine and clean data, analyze trends, create clear reports, and maintain databases. Important Python libraries taught include NumPy, Pandas, Matplotlib, and Seaborn for tasks like statistical analysis, machine learning, and data visualization.
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
The document discusses connecting an AI model to Python using HTTP requests. It covers converting pandas dataframes to dictionaries, using the requests module to send HTTP requests to an AI, and interacting with a mood classification AI by copying code from its integration page. The JSON module is also discussed for converting response dictionaries to strings. An exercise is provided to calculate accuracy and confusion matrix by sending dictionary formatted mood data through the AI service.
This document provides an introduction to machine learning concepts and tools. It begins with an overview of what will be covered in the course, including machine learning types, algorithms, applications, and mathematics. It then discusses data science concepts like feature engineering and the typical steps in a machine learning project, including collecting and examining data, fitting models, evaluating performance, and deploying models. Finally, it reviews common machine learning tools and terminologies and where to find datasets.
1. The document discusses architecting data science platforms for a dating product using an event-driven architecture that stores all data as a stream of events.
2. Key aspects of the architecture include an event history repository that stores real-time event streams, a Solr search index for querying events, and using the event stream for both online and offline machine learning.
3. The architecture aims to enable fast experimentation cycles by using the same code and data for production, development, and training machine learning models.
Slides from Enterprise Search & Analytics Meetup @ Cisco Systems - http://www.meetup.com/Enterprise-Search-and-Analytics-Meetup/events/220742081/
Relevancy and Search Quality Analysis - By Mark David and Avi Rappoport
The Manifold Path to Search Quality
To achieve accurate search results, we must come to an understanding of the three pillars involved.
1. Understand your data
2. Understand your customers’ intent
3. Understand your search engine
The first path passes through Data Analysis and Text Processing.
The second passes through Query Processing, Log Analysis, and Result Presentation.
Everything learned from those explorations feeds into the final path of Relevancy Ranking.
Search quality is focused on end users finding what they want -- technical relevance is sometimes irrelevant! Working with the short head (very frequent queries) has the most return on investment for improving the search experience, tuning the results, for example, to emphasize recent documents or de-emphasize archive documents, near-duplicate detection, exposing diverse results in ambiguous situations, using synonyms, and guiding search via best bets and auto-suggest. Long-tail analysis can reveal user intent by detecting patterns, discovering related terms, and identifying the most fruitful results by aggregated behavior. all this feeds back into the regression testing, which provides reliable metrics to evaluate the changes.
By merging these insights, you can improve the quality of the search overall, in a scalable and maintainable fashion.
This 4-week course on "Python for Data Science" taught the basics of Python programming and libraries for data science. It covered topics like data types, sequence data, Pandas dataframes, data visualization with Matplotlib and Seaborn. Technologies taught included Spyder IDE, NumPy, Jupyter Notebook, Pandas and visualization libraries. The course aimed to equip participants with Python skills for solving data science problems. It examined applications of data science in domains like e-commerce, machine learning, medical diagnosis and more.
I am shubham sharma graduated from Acropolis Institute of technology in Computer Science and Engineering. I have spent around 2 years in field of Machine learning. I am currently working as Data Scientist in Reliance industries private limited Mumbai. Mainly focused on problems related to data handing, data analysis, modeling, forecasting, statistics and machine learning, Deep learning, Computer Vision, Natural language processing etc. Area of interests are Data Analytics, Machine Learning, Machine learning, Time Series Forecasting, web information retrieval, algorithms, Data structures, design patterns, OOAD.
Henry Harvin Analytics Academy offers 2-month courses in business analytics using Python and advanced Excel. The courses teach students to explore, analyze, and solve business problems using analytics tools and techniques. Students work on real-life case studies and complete an internship project. The goal is to empower students to become data-driven professionals who can determine organizational goals, mine and clean data, analyze trends, create clear reports, and maintain databases. Important Python libraries taught include NumPy, Pandas, Matplotlib, and Seaborn for tasks like statistical analysis, machine learning, and data visualization.
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
The document discusses connecting an AI model to Python using HTTP requests. It covers converting pandas dataframes to dictionaries, using the requests module to send HTTP requests to an AI, and interacting with a mood classification AI by copying code from its integration page. The JSON module is also discussed for converting response dictionaries to strings. An exercise is provided to calculate accuracy and confusion matrix by sending dictionary formatted mood data through the AI service.
This document provides an introduction to machine learning concepts and tools. It begins with an overview of what will be covered in the course, including machine learning types, algorithms, applications, and mathematics. It then discusses data science concepts like feature engineering and the typical steps in a machine learning project, including collecting and examining data, fitting models, evaluating performance, and deploying models. Finally, it reviews common machine learning tools and terminologies and where to find datasets.
1. The document discusses architecting data science platforms for a dating product using an event-driven architecture that stores all data as a stream of events.
2. Key aspects of the architecture include an event history repository that stores real-time event streams, a Solr search index for querying events, and using the event stream for both online and offline machine learning.
3. The architecture aims to enable fast experimentation cycles by using the same code and data for production, development, and training machine learning models.
Slides from Enterprise Search & Analytics Meetup @ Cisco Systems - http://www.meetup.com/Enterprise-Search-and-Analytics-Meetup/events/220742081/
Relevancy and Search Quality Analysis - By Mark David and Avi Rappoport
The Manifold Path to Search Quality
To achieve accurate search results, we must come to an understanding of the three pillars involved.
1. Understand your data
2. Understand your customers’ intent
3. Understand your search engine
The first path passes through Data Analysis and Text Processing.
The second passes through Query Processing, Log Analysis, and Result Presentation.
Everything learned from those explorations feeds into the final path of Relevancy Ranking.
Search quality is focused on end users finding what they want -- technical relevance is sometimes irrelevant! Working with the short head (very frequent queries) has the most return on investment for improving the search experience, tuning the results, for example, to emphasize recent documents or de-emphasize archive documents, near-duplicate detection, exposing diverse results in ambiguous situations, using synonyms, and guiding search via best bets and auto-suggest. Long-tail analysis can reveal user intent by detecting patterns, discovering related terms, and identifying the most fruitful results by aggregated behavior. all this feeds back into the regression testing, which provides reliable metrics to evaluate the changes.
By merging these insights, you can improve the quality of the search overall, in a scalable and maintainable fashion.
The document discusses artificial intelligence for text analytics and natural language processing. It provides an introduction to text analytics and NLP, explaining that text analytics extracts useful information from text sources while NLP makes natural language accessible to machines. It then discusses how AI can enable applications like competitive intelligence, human resource management, and market analysis by automatically analyzing large amounts of text data. The document also provides an overview of how natural language processing works using deep learning techniques.
The document provides a resume for Aakanksha Agnani. It summarizes her professional experience as a software engineer at Gap Inc. and Accenture, where she developed applications using technologies like Java, Python, Oracle and PostgreSQL. It also lists her education as a Master's in Computer Science from Ohio State University and Bachelor's from University of Mumbai.
This document provides information about a Machine Learning Specialist training program. The 100-hour program uses instructor-led training and hands-on projects to teach Python, machine learning algorithms, and their real-world applications. The goal is to equip students with in-demand skills for a career in applied machine learning. The curriculum covers topics like data analysis, predictive modeling, feature engineering, and deep learning architectures. Students will complete projects such as image classification and optica character recognition to build an employment portfolio.
This document summarizes a presentation about expanding the use of DITA (Darwin Information Typing Architecture) beyond technical publications. It discusses how organizations should focus on content strategy before implementing new technologies. The presentation will examine value of semantic markup for the enterprise and several non-traditional DITA projects. It also provides background on the presenter and their company which helps organizations improve information usability through content strategy, architecture, transformation and tools selection.
Machine Learning Vs. Deep Learning – An Example ImplementationSynerzip
A Deep Learning model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions. To achieve this, Deep Learning uses a layered structure of algorithms called an artificial neural network (ANN). The design of an ANN is inspired by the biological neural network of the human brain. This makes for machine intelligence that’s far more capable than that of standard Machine Learning models.
Deep learning is applied to fields such as:
- computer vision
- speech recognition
- natural language processing
- audio recognition
- social network filtering
- machine translation
- bioinformatics
- drug design
The results produced using Deep Learning are comparable to – and in sometimes superior to – human experts. Deep Learning is what powers the most human-like artificial intelligence.
Presenters: Vinayak Jogelekar & Krishna Bhavsar, Synerzip.
predictive analysis and usage in procurement ppt 2017Prashant Bhatmule
Predictive analytics can help reduce volatility and improve decision making in procurement processes. It allows understanding of future costs, demand, and supply to overcome challenges. Predictive models analyze past data and behaviors to forecast trends and outcomes. As data sources like IoT sensors expand, predictive analytics is increasingly used for applications like manufacturing process improvement, predictive maintenance of equipment, and optimizing building energy usage.
Dice.com Bay Area Search - Beyond Learning to Rank TalkSimon Hughes
This talk describes how to implement conceptual search (semantic search) within a modern search engine using the word2vec algorithm to learn concepts. We also cover how to auto-tune the search engine parameters using black box optimization techniques, and the problems of feedback loops encountered when building machine learning systems that modify the user behavior used to train the system.
Doing Analytics Right - Building the Analytics EnvironmentTasktop
Implementing analytics for development processes is challenging. As in discussed in the previous webinars, the right analytics are determined by the goals of the organization, not by the available data. So implementing your analytics solutions will require an efficient analytics and data architecture, including the ability to combine and stage data from heterogeneous sources. An architecture that excludes the ability to gain access to the necessary data will create a barrier to deploying your newly designed analytics program, and will force you back into the “light is brighter here” anti-pattern.
This webinar will describe the technical considerations of implementing the data architecture for your analytics program, and explain how Tasktop can help.
Data Science Job ready #DataScienceInterview Question and Answers 2022 | #Dat...Rohit Dubey
How Much Do Data Scientists Make?
The demand and salary for data scientists tend to be higher than most other ITES jobs. Experience is one of the key factors in determining the salary range of a data science professional.
According to Glassdoor, a Data Scientist in the United States earns an annual average of USD 117,212, and the same site reports that Data Scientists in India make a yearly average of ₹1,000,000.
Data Scientist Career Path
Data Science is currently considered one of the most lucrative careers available. Companies across all major industries/sectors have data scientist requirements to help them gain valuable insights from big data. There is a sharp growth in demand for highly skilled data science professionals who can straddle the business and IT worlds.
The career path to becoming a data scientist isn’t clearly defined since this is a relatively new profession. People from different backgrounds like mathematics, statistics, computer science or economics, end up in data science.
The major designations for data science professionals are:
Data Analyst
Data Scientist (entry-level)
Associate data scientist
Data Scientist (senior-level)
Product Manager
Lead data scientist
Director/VP/SVP
That was all about Data Scientist Job Description.
Become a Data Scientist Today!
In this write-up, we covered the Data Scientist job description in detail. Irrespective of which location you are in, there is no dearth of jobs for skillful data scientists. A career in data science is a rewarding journey to embark on, especially in the finance, retail, and e-commerce sectors. Jobs are also available with Government departments, universities and research institutes, telecoms, transports, the list goes on.
This video covers
Introductory Questions
Data Science Introduction
Data Science Technical Interview QnA :
#Excel
#SQL
#Python3
#MachineLearning
#DataAnalyticstechnical Interview
#DataScienceProjects
#coder #statistics #datamining #dataanalyst #code #engineering #linux #codinglife #cloudcomputing #businessintelligence #robotics #softwaredeveloper #automation #cloud #neuralnetworks #sql #science #softwareengineer #digitaltransformation #computer #daysofcode #coders #bigdataanalytics #programminglife #dataviz #html #digitalmarketing #devops #datasciencetraining #dataprotection
#rohitdubey
#teachtechtoe
#datascience #datasciencetraining #datasciencejobs #datasciencecourse #datasciencenigeria #datasciencebootcamp #datascienceworkshop #datasciencecareers #datasciencestudent #datascienceproject #datascienceforall #datasciencetraininginpatelnagar#datasciencetrainingindelhi
This document discusses connecting a mood classification AI to Python. It covers converting a Pandas dataframe to a dictionary, using the Requests module to send HTTP requests to an AI, and integrating a mood classification AI with Python code by copying the code from the AI interface. The document concludes with an exercise to convert dataset rows to dictionaries, trigger the AI service, calculate accuracy and a confusion matrix.
Pandas is useful for reading CSV files and describing the statistics of data columns. This helps understand the range of features in machine learning models. The Requests module allows sending HTTP requests in Python, including to interact with AI models. The document provides steps to connect a mood classification AI to Python code using the Requests module, including copying code from the AI integration page and calling the function to get predictions. Examples are given for building Python applications incorporating AI, such as a chatbot or games.
HacktoberFestPune - DSC MESCOE x DSC PVGCOETTanyaRaina3
HacktoberFestPune is a beginner-friendly, all-inclusive event that is absolutely free of cost. Certificates will be issued by DSC MESCOE and DSC PVGCOET for everyone who can complete 4 successful Pull Requests by 13th October 10 AM! An evening filled with speaker sessions, interactions with fellow developers, and mini-games, we think you'll have a great time with everyone!
The document describes a case study using soccer player data to perform data science analysis. It discusses acquiring the dataset, preparing the data by cleaning and selecting features, analyzing the data using statistical exploration, visualization, and clustering techniques in Python and scikit-learn. The analysis formed meaningful player groups and identified attributes that contribute to performance. Insights from the analysis can help coaches design training programs to improve player and team performance.
Driving Digital Transformation with Machine Learning in Oracle AnalyticsPerficient, Inc.
This document discusses driving digital transformation with machine learning in Oracle Analytics. It provides an overview of Perficient, an Oracle partner, and their Oracle Analytics practice. It then discusses disruptions in analytics towards augmented analytics. The document outlines typical steps to perform data discovery, preparation, analysis, and prediction using Oracle Analytics Cloud (OAC). It demonstrates the built-in machine learning models and typical workflow to perform machine learning using OAC data flows. Finally, it advertises an upcoming presentation and conference where attendees can learn more.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
The document discusses artificial intelligence for text analytics and natural language processing. It provides an introduction to text analytics and NLP, explaining that text analytics extracts useful information from text sources while NLP makes natural language accessible to machines. It then discusses how AI can enable applications like competitive intelligence, human resource management, and market analysis by automatically analyzing large amounts of text data. The document also provides an overview of how natural language processing works using deep learning techniques.
The document provides a resume for Aakanksha Agnani. It summarizes her professional experience as a software engineer at Gap Inc. and Accenture, where she developed applications using technologies like Java, Python, Oracle and PostgreSQL. It also lists her education as a Master's in Computer Science from Ohio State University and Bachelor's from University of Mumbai.
This document provides information about a Machine Learning Specialist training program. The 100-hour program uses instructor-led training and hands-on projects to teach Python, machine learning algorithms, and their real-world applications. The goal is to equip students with in-demand skills for a career in applied machine learning. The curriculum covers topics like data analysis, predictive modeling, feature engineering, and deep learning architectures. Students will complete projects such as image classification and optica character recognition to build an employment portfolio.
This document summarizes a presentation about expanding the use of DITA (Darwin Information Typing Architecture) beyond technical publications. It discusses how organizations should focus on content strategy before implementing new technologies. The presentation will examine value of semantic markup for the enterprise and several non-traditional DITA projects. It also provides background on the presenter and their company which helps organizations improve information usability through content strategy, architecture, transformation and tools selection.
Machine Learning Vs. Deep Learning – An Example ImplementationSynerzip
A Deep Learning model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions. To achieve this, Deep Learning uses a layered structure of algorithms called an artificial neural network (ANN). The design of an ANN is inspired by the biological neural network of the human brain. This makes for machine intelligence that’s far more capable than that of standard Machine Learning models.
Deep learning is applied to fields such as:
- computer vision
- speech recognition
- natural language processing
- audio recognition
- social network filtering
- machine translation
- bioinformatics
- drug design
The results produced using Deep Learning are comparable to – and in sometimes superior to – human experts. Deep Learning is what powers the most human-like artificial intelligence.
Presenters: Vinayak Jogelekar & Krishna Bhavsar, Synerzip.
predictive analysis and usage in procurement ppt 2017Prashant Bhatmule
Predictive analytics can help reduce volatility and improve decision making in procurement processes. It allows understanding of future costs, demand, and supply to overcome challenges. Predictive models analyze past data and behaviors to forecast trends and outcomes. As data sources like IoT sensors expand, predictive analytics is increasingly used for applications like manufacturing process improvement, predictive maintenance of equipment, and optimizing building energy usage.
Dice.com Bay Area Search - Beyond Learning to Rank TalkSimon Hughes
This talk describes how to implement conceptual search (semantic search) within a modern search engine using the word2vec algorithm to learn concepts. We also cover how to auto-tune the search engine parameters using black box optimization techniques, and the problems of feedback loops encountered when building machine learning systems that modify the user behavior used to train the system.
Doing Analytics Right - Building the Analytics EnvironmentTasktop
Implementing analytics for development processes is challenging. As in discussed in the previous webinars, the right analytics are determined by the goals of the organization, not by the available data. So implementing your analytics solutions will require an efficient analytics and data architecture, including the ability to combine and stage data from heterogeneous sources. An architecture that excludes the ability to gain access to the necessary data will create a barrier to deploying your newly designed analytics program, and will force you back into the “light is brighter here” anti-pattern.
This webinar will describe the technical considerations of implementing the data architecture for your analytics program, and explain how Tasktop can help.
Data Science Job ready #DataScienceInterview Question and Answers 2022 | #Dat...Rohit Dubey
How Much Do Data Scientists Make?
The demand and salary for data scientists tend to be higher than most other ITES jobs. Experience is one of the key factors in determining the salary range of a data science professional.
According to Glassdoor, a Data Scientist in the United States earns an annual average of USD 117,212, and the same site reports that Data Scientists in India make a yearly average of ₹1,000,000.
Data Scientist Career Path
Data Science is currently considered one of the most lucrative careers available. Companies across all major industries/sectors have data scientist requirements to help them gain valuable insights from big data. There is a sharp growth in demand for highly skilled data science professionals who can straddle the business and IT worlds.
The career path to becoming a data scientist isn’t clearly defined since this is a relatively new profession. People from different backgrounds like mathematics, statistics, computer science or economics, end up in data science.
The major designations for data science professionals are:
Data Analyst
Data Scientist (entry-level)
Associate data scientist
Data Scientist (senior-level)
Product Manager
Lead data scientist
Director/VP/SVP
That was all about Data Scientist Job Description.
Become a Data Scientist Today!
In this write-up, we covered the Data Scientist job description in detail. Irrespective of which location you are in, there is no dearth of jobs for skillful data scientists. A career in data science is a rewarding journey to embark on, especially in the finance, retail, and e-commerce sectors. Jobs are also available with Government departments, universities and research institutes, telecoms, transports, the list goes on.
This video covers
Introductory Questions
Data Science Introduction
Data Science Technical Interview QnA :
#Excel
#SQL
#Python3
#MachineLearning
#DataAnalyticstechnical Interview
#DataScienceProjects
#coder #statistics #datamining #dataanalyst #code #engineering #linux #codinglife #cloudcomputing #businessintelligence #robotics #softwaredeveloper #automation #cloud #neuralnetworks #sql #science #softwareengineer #digitaltransformation #computer #daysofcode #coders #bigdataanalytics #programminglife #dataviz #html #digitalmarketing #devops #datasciencetraining #dataprotection
#rohitdubey
#teachtechtoe
#datascience #datasciencetraining #datasciencejobs #datasciencecourse #datasciencenigeria #datasciencebootcamp #datascienceworkshop #datasciencecareers #datasciencestudent #datascienceproject #datascienceforall #datasciencetraininginpatelnagar#datasciencetrainingindelhi
This document discusses connecting a mood classification AI to Python. It covers converting a Pandas dataframe to a dictionary, using the Requests module to send HTTP requests to an AI, and integrating a mood classification AI with Python code by copying the code from the AI interface. The document concludes with an exercise to convert dataset rows to dictionaries, trigger the AI service, calculate accuracy and a confusion matrix.
Pandas is useful for reading CSV files and describing the statistics of data columns. This helps understand the range of features in machine learning models. The Requests module allows sending HTTP requests in Python, including to interact with AI models. The document provides steps to connect a mood classification AI to Python code using the Requests module, including copying code from the AI integration page and calling the function to get predictions. Examples are given for building Python applications incorporating AI, such as a chatbot or games.
HacktoberFestPune - DSC MESCOE x DSC PVGCOETTanyaRaina3
HacktoberFestPune is a beginner-friendly, all-inclusive event that is absolutely free of cost. Certificates will be issued by DSC MESCOE and DSC PVGCOET for everyone who can complete 4 successful Pull Requests by 13th October 10 AM! An evening filled with speaker sessions, interactions with fellow developers, and mini-games, we think you'll have a great time with everyone!
The document describes a case study using soccer player data to perform data science analysis. It discusses acquiring the dataset, preparing the data by cleaning and selecting features, analyzing the data using statistical exploration, visualization, and clustering techniques in Python and scikit-learn. The analysis formed meaningful player groups and identified attributes that contribute to performance. Insights from the analysis can help coaches design training programs to improve player and team performance.
Driving Digital Transformation with Machine Learning in Oracle AnalyticsPerficient, Inc.
This document discusses driving digital transformation with machine learning in Oracle Analytics. It provides an overview of Perficient, an Oracle partner, and their Oracle Analytics practice. It then discusses disruptions in analytics towards augmented analytics. The document outlines typical steps to perform data discovery, preparation, analysis, and prediction using Oracle Analytics Cloud (OAC). It demonstrates the built-in machine learning models and typical workflow to perform machine learning using OAC data flows. Finally, it advertises an upcoming presentation and conference where attendees can learn more.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
2. Table of Contents
Data Science Projects Big Data Projects
● Sales Forecasting
● Building a Chatbot Application
● Recommendation System
● Market Basket Analysis
● Resume Parsing Application
● Topic Identification
● Sentiment Analysis and Ranking
● Loan Eligibility Prediction
● Retail Price Optimisation
● Driver Availability Prediction
● Event Data Analysis
● Building a Big Data Pipeline
● Build an ETL Data Pipeline
● Bitcoin Data Mining
● Analysing NASA Log Files
● Movie Recommendation System
● Sentiment Analysis
● Big Data Project using COVID data
● Auto-Replying Twitter Handle
● Setting up a Redshift ETL Pipeline
5. Overview of the Project
• For any departmental store, it is important
to have a rough idea of their sales, so
that they can plan their inventory
accordingly.
• Determining the sale of high-selling and
low-selling products of each category is
also key for inventory planning.
Problem Statement:
• In this data science project, you will use R
programming language to predict the
sales of each department of a store using
the Walmart dataset.
6. Data Description
• Walmart Dataset has sales data of 45
stores based on store, department and
week.
• Size and type of each store has been
mentioned.
• Holidays weeks have been provided.
• Price markdown data (almost like
discount data) has been mentioned.
• A few macro-indicators like CPI,
Unemployment rate, Fuel price etc. are
also provided.
7. Learnings from the Project
• Exploratory Data Analysis (EDA)
techniques.
• Handling Missing values in a dataset.
• Using Univariate Analysis.
• Performing Bi-variate Analysis.
• Time Series ARIMA models
Implementation.
• Using multiple metrics to compare the
performance of different models.
Find the full solution of this project:
Walmart Sales Forecasting Data Science Project
9. Overview of the Project
• For many small businesses, it is difficult
to invest in human resources that can
interact with their customers instantly and
solve their queries.
• As an alternative to customer care team,
they can make a chatbot to resolve their
customers’ queries automatically.
Problem Statement:
• In this data science project, you will apply
Natural Language Processing techniques
in Python programming language to build
a Chatbot system.
10. Data Description
• The data is contained in a text file,
named, ’leaves.txt’
• It has three types of values: text,
category, answer.
• Text represents the input the user will
pass to interact with the bot.
• Category is label used to differentiate
different types of user queries.
• Answer represents the reply of the bot to
the users’ queries.
• The file has about 140 rows for each of
the three data values.
11. Learnings from the Project
• Introduction to NLTK library in Python
• NLP Techniques:
Tokenization
Lemmatization
Stemming
Removing Stopwords
Parts-of-Speech Tagging
• Bag-of-Words Model
• Decision Tree and Naive Bayes
Classifier.
• Building an NLP-based chatbot engine
that any UI can utilise.
Find the full solution of this project:
Natural language processing Chatbot application
using NLTK for text classification
13. Overview of the Project
• While visiting a shopping mall, many
salesmen often try to recommend the
customers exciting deals and offers that
might of interest them.
• Similarly, e-commerce sites use
recommender systems to suggest
products to their customers that they are
highly likely to buy.
Problem Statement:
• In this data science project, you will build
collaborative filtering algorithm based
recommender system.
14. Data Description
• The data is contained in a csv file, named
’ratings_beauty.csv’.
• It has four types of values: userid,
productid, ratings, and timestamp.
• UserId and Productid are used for
identification purposes.
• Ratings are the feedback provided by the
user corresponding to a product in that
row
• Timestamp represents the time at which
user submitted the rating.
• The file has about 2 million reviews and
ratings for beauty products available on
Amazon.
15. Learnings from the Project
• Introduction to Recommender Systems
• Popular Exploratory Data Analysis (EDA)
Techniques
• Data Visualization
• Data Encoding Methods
• Cosine Similarity and Centres Cosine
Similarity
• User-Item Matrix
• Ways to identify similar customers
Find the full solution of this project:
Build a Collaborative Filtering Recommender
System in Python
17. Overview of the Project
• Whenever customers purchase certain
products from a store, it is important for
the store to understand their buying
patterns. This can help stores in better
placement of specific products.
• The way to understand these patterns is
called Market Basket Analysis/
Problem Statement:
• In this data science project, you will use
Apriori and FP growth algorithms to
understand Market Basket Analysis.
18. Data Description
• The data is contained in 4 csv files,
• ‘customer.csv’ has the customer details
• ‘product.csv has the product information
• ‘product_class.csv’ has the details of
product department
• ‘region.csv’ has information about the
location where the store is located.
• ‘sales.csv’ contains the details of sales of
each product.
• ‘stores.csv’ has more information about
the stores.
• ‘time_by_day.csv’ has time of the day
when an item was purchased from the
store.
19. Learnings from the Project
• Introduction to Market Basket Analysis/
Product Association Analysis
• Apriori Algorithm
• Fpgrowth Algorithm
• Bivariate Analysis
• Feature Analysis
• One Hot Encoding
• User-Item Matrix
Find the full solution of this project:
Customer Market Basket Analysis using Apriori
and Fpgrowth algorithms
21. Overview of the Project
• Most companies these days wants to
efficiently utilise their human resources by
automating tasks wherever possible.
• The efficiency of HR Management team
members can be improved by providing
them a Resume Parsing system which
can shortlist resumes automatically.
Problem Statement:
• In this data science project, you will apply
Natural Language Processing techniques
in Python programming language to build
a Resume Parsing system.
22. Data Description
• The data for this project is available in
JSON file format.
• The file has resume content in the form of
text.
• The content from the resume has been
labelled (skills, location, etc. of the
applicant) for your convenience.
• This format is not what Spacy is served
with.
• You will learn how to make the given data
Spacy-friendly.
23. Learnings from the Project
• Introduction to Natural Language
Processing (NLP) and generic Machine
learning workflow
• Introduction to Spacy NLP Library in
Python
• Utilising Annotations and Entities in
Spacy
• Text Classification
• Optical Character Recognition
• Text extraction from PDFs
• TIKA OCR Procedure
Find the full solution of this project:
Resume parsing with Machine learning - NLP with
Python OCR and Spacy
25. Overview of the Project
• When exploring NLP machine learning
algorithms, an interesting application is
found in projects titled Topic
Identification.
• Here you are a given a document with a
certain set of words and the task is to
label that document with a title that best
describes its content.
Problem Statement:
• In this data science project, you will apply
Natural Language Processing techniques
in Python programming language to build
a Topic Modelling system.
26. Data Description
• This project will utilise tweets from twitter to
explain Topic Modeling in Python
• The data will be provided to you in csv format.
• It has four types of data values: username,
tweets, date, mentions.
• The username is the unique twitter handle of a
user whose tweet we will be using.
• Tweets has the content of a tweet by the
corresponding user.
• Date specifies the date of the tweet creation.
• Mentions has details of the other twitter handles
that have been referred to in a tweet.
27. Learnings from the Project
• Exploring various methods of Natural
Language Toolkit (NLTK) library in
Python
• Cleaning and Analysing Textual data
• Converting unstructured data to
structured data
• K-Means Clustering Machine Learning
Algorithm
• Clustering text from Twitter
• Tokenization of text into words
• Identifying topic of the given text
Find the full solution of this project:
Topic modelling using Kmeans clustering to group
customer reviews
29. Overview of the Project
• For businesses to grow and evolve, it is
crucial to analyse their customer views in
order to understand their needs.
• The analysis can help them in taking
decisions that can lead to an exponential
growth in their sales.
Problem Statement:
• In this data science project, you will apply
Natural Language Processing techniques
in Python programming language to build
a Customer Reviews Sentiment Analysis
and Ranking system.
30. Data Description
• The data for this project will be provided
to you in
• The ‘train.csv’ file has 1676 rows and 3
columns.
• The columns contain information about
specific products, their reviews and a
label for each review.
• There are about 8 different products
whose reviews have been listed.
• The label 0 is used to indicate non-
informative reviews and 1 indicates the
review are informative.
31. Learnings from the Project
• Implementing Data Preprocessing
techniques of on customer reviews data
• Using Markov Chain method to extract
gibberish
• Gathering text for features from reviews
• Performing Sentiment Analysis over the
reviews
• Implementation of TF-IDF Text Vectorizer
• Using Random Forest Machine Learning
Algorithm for pairwise ranking of reviews
Find the full solution of this project:
Ecommerce product reviews - Pairwise ranking
and sentiment analysis
33. Overview of the Project
• Various banks receive a large number of
loan applications everyday. Thus, a lot of
time is invested in analysing these
applications.
• The task of analysing applications can be
automated using Machine learning
algorithms.
Problem Statement:
• In this data science project, you will apply
machine learning algorithms in Python to
predict the eligibility of a loan applicant to
repay it.
34. Data Description
• The data is contained in a CSV file.
• It has 1111107 rows and 19 columns.
• It contains the following details:
Loan ID, Customer ID, Loan Status,
Current Loan Amount, Term, Credit
Score, Years in current job, Home
Ownership, Annual Income, Purpose,
Monthly Debt, Years of Credit History,
Months since last delinquency, Number of
open accounts, Number of credit
problem, Current Credit Balance,
Maximum Open Credit, Bankruptcies, Tax
Liens.
35. Learnings from the Project
• Utilising different libraries in Python and
their significance
• Building custom functions for utilising ML
algorithms
• Data Procurement
• Dealing with missing values in data
• Preprocessing data before the application
of ML algorithms
• Using Gradient Boosting and XGBoost
• Calculating various metrics to identify the
best model
Find the full solution of this project:
Loan Eligibility Prediction using Gradient Boosting
Classifier
37. Overview of the Project
• A significant amount of time is spent by
retail store owners in deciding the price of
an item. There are many aspects of the
items they have to keep in mind while
deciding the prices.
• A good analysis of various characteristics
of all the items can assist in optimizing
the retail prices.
Problem Statement:
• In this data science project, you will
analyse sales data of Cafe and predict
prices of their items using ML algorthims.
38. Data Description
• The data is contained in three CSV files.
• First is ‘Cafe - Sell MetaData.csv’. This file
has details about sales made by the cafe.
Columns: Sell ID, Sell Category, Item ID, Item Name
• Next is ‘Cafe - Transaction - Store.csv’. This
file contains information about transactions
and sale receipts of the cafe.
Columns: Calendar Date, Price, Quantity, Sell ID, Sell
Category
• And, the last is ‘Cafe - DateInfo.csv’. This
has date information corresponding to the
transactions performed.
Columns: Date, Year, Holiday, Weekend, School Break,
Temperature, Outdoor
39. Learnings from the Project
• Introduction to Retail Price Optimization
problem in Machine Learning
• Understanding price elasticity of demand
• Working with Jupyter Notebooks
• Making generic codes for price
optimisation for different items
• Methods of choosing the best prediction
model
• Predicting Price elasticity of demand for
all items
Find the full solution of this project:
Machine Learning project for Retail Price
Optimization
41. Overview of the Project
• Covid-19 forced people to stay indoors
and the food delivery apps thus noticed a
surge in orders.
• Estimating delivery charges is not an
easy task for the delivery apps
companies as it depends on complicated
features.
Problem Statement:
• In this data science project, you will
machine learning techniques in Python
programming language to efficiently
allocate drivers the orders for delivery.
42. Data Description
• The data is contained in three CSV files:
‘pings.csv’, ‘drivers.csv’, ‘test.csv’
• ‘pings.csv’ has information about when a
certain driver received a ping. So, it has
two columns driver_id and timestamp.
• ‘drivers.csv’ has biodata (driver ID,
gender, age, number of kids) of about
2500 drivers.
• ‘test.csv’ will be used for testing the
model that we will help you build in this
project.
43. Learnings from the Project
• Transforming a time series problem to a
supervised learning problem
• Introduction to Multi-step time series
forecasting
• Concept of Lead-Lag and Rolling Mean
• Autocorrelation Function (ACF) and Partial
Autocorrelation Function (PACF) in Time
Series
• Understanding recursive multi-step prediction
strategy
• Using Random Forest and XGBoost for
predicting online hours of a driver
Find the full solution of this project:
Demand prediction of driver availability using
multistep time series analysis
46. Overview of the Project
• When aiming for the role of a Big Data
Engineer, it is important to have
experience with real-time streaming data.
Problem Statement:
• In this Big Data project, you will learn how
to extract real time streaming event data
from the given dataset API. The dataset
that will be provided is about accidents in
New York City.
47. Data Description
• The Dataset that will be used for this Big
Data project is Motor Vehicles Collisions
Dataset that is updated regularly.
• The dataset is open source using the API
the host website provides.
• There are about 29 columns in the
dataset containing information about the
accidents like, location latitude and
longitude, time of the crash, etc.
• The dataset will be available to your in
the JSON format when you will use its
API.
48. Learnings from the Project
• Introduction to the following:
i.Apache Nifi
ii.Apache Spark
iii.AWS Elk Stack Elasticsearch
iv.Apache Kibana
v.Logstash
• Storing data in Apache Hadoop Distributed
File system (HDFS)
• Using PySpark for Data Exploration and Data
Analysis
Find the full solution of this project:
Event Data Analysis using AWS ELK Stack
50. Overview of the Project
• Decisions in the aviation industry are
highly data-driven. It is not only the flight
timings and locations that are relevant,
but also the load of passengers, the
weight of their luggage, weather, security,
time of the year, specifications of the
aircraft, etc.
Problem Statement:
• In this Big Data project, you will learn how
will work on an aviation dataset to create
a big data pipeline at scale on AWS.
51. Data Description
• In this Big data project you will work with
a dataset provided by an API from ICAO.
• The dataset you will be working with is
called Incidents.
• The data format will be JSON.
• It has 20+ features which include State of
Occurence, Time, Location, Operator,
Model, State of Registry, Flight Phase,
Fatalities, etc.
• After hitting the URL of the website, you
will be able to access this data.
52. Learnings from the Project
• Introduction to Big Data and Big Data
Pipeline
• Explore Data Architecture using Nifi, Kafka,
and Hive
• Apache Kafka Vs Apache Flume
• Optimisation Techniques in Apache Hive
• Understanding HDFS and Druid
• Transferring Data from NiFi to Kafka
• Using MySQL and AWS Quicksight
Find the full solution of this project:
Build a big data pipeline with AWS Quicksight,
Druid, and Hive
54. Overview of the Project
• This project is a follow up of the previous
project. However, it is not same. You will
be introduced to other technologies
widely used by Big Data Engineers, that
were not covered in the previous project.
Problem Statement:
• In this Big Data project, you will learn how
will work on a sales dataset to create a
big data pipeline at scale on AWS.
55. Data Description
• In this project, you will not retrieve data
from a website. Rather, the dataset will
be provided to you in a CSV format.
• The filename is ‘sales_data.csv’.
• There are 14 columns in the dataset
which include geographical information
about the purchase of products, item
type, mode of purchasing (online or
offline), unique IDs of the products,
shipping date of the order, quantity of the
sold products in a particular order, a label
for priority of the order, etc.
56. Learnings from the Project
• Implementing a big data pipeline on AWS
via Software As A Service (SAAS)
method
• Building scalable and reliable
architectures.
• Comparison of utilisation of IAAS, SAAS,
and PAAS wrt Big Data
• Extracting raw data of sales int AWS S3
• Using EMR Hive and Tableau Desktop
together
• Visualization of data using Tableau
Find the full solution of this project:
AWS Project - Build an ETL Data Pipeline on
AWS EMR Cluster
58. Overview of the Project
• Bitcoins are gradually becoming more
popular. So much that even of the world’s
richest person, Elon Musk, has started
tweeting about it.
• People are considering bitcoin as a
currency of the future.
Problem Statement:
• In this Big Data project, you will
implement Bitcoin Mining on Amazon
Web Services using exciting Big Data
tools.
59. Data Description
• In this Big Data project, you will work with
Bitcoin Dataset.
• You will use Python API to extract the
data from a URL.
• The dataset on the URL has information
about various cryptocurrencies available
like Dogecoin, Litecoin, Bitcoin, etc.
• For each cryptocurrency, you will be
having its ID, name, name_id, its price in
US Dollars, percentage change in its
price over the past hour and past 24
hours price in bitcoin, how much market it
is capturing, etc.
60. Learnings from the Project
• Introduction to Data Warehousing in Hive
and Spark
• Using AWS Quicksight for visualising
data
• Using Python API for extracting data
• Uploading data from Ec2 instance to
HDFS
• Understanding PySpark
• Creating Tables in Apache Hive
• Working with Apache Hadoop and
Apache Spark
Find the full solution of this project:
Bitcoin Data Mining on AWS Free Tier
62. Overview of the Project
• You have must have heard of logbooks
when you were in school. Teachers
usually maintain a logbook to keep a
track of daily events in the classroom.
• Similar to that, a computer also stores
information about events in log files.
Problem Statement:
• In this Big Data project, you will work with
practical data of logs obtained from NASA
Kennedy Space Center WWW Server.
techniques in Python programming
language to build a Resume Parsing
system. You will use Big Data tools to
perform scalable analytics over the data.
63. Data Description
• In this Big Data project, you will work on a
dataset that is in CSV file format.
• The file has information about NASA web
logs in 1995.
• It has remote host name, the time stamp
for access, then the request type, the
request path. In addition, it contains the
details of request status and the size of
the data that was requested.
• The dataset does not contain information
about user authentication name unlike
usual log files.
64. Learnings from the Project
• Using NiFi to extract dataset from servers
• Using Apache Kafka for preparing topics and
generating logs
• A detailed description of logs and their
analytics
• Introduction to Lambda Architecture
• Understanding Docker, Port Forwarding
• Working together with Plotly, Dash, and
Cassandra to display metrics live
• Loading Data in Cassandra
Find the full solution of this project:
Log Analytics Project with Spark Streaming and
Kafka
66. Overview of the Project
• Covid-19 has changed the world of
cinema. Netflix and Amazon Prime are
the popular OTT platforms that have
replaced cinema halls.
• While using these platforms, one doesn’t
need to search for movies to watch,
because the app recommends on its own.
Problem Statement:
• In this Big Data project, you will use Big
Data tools to perform analysis over a
movies dataset and build a
recommendation system using it.
67. Data Description
• In this Big Data project, you will work on a
dataset by Movielens.
• To implement the solution of this project,
you can work with any dataset from the
MovieLens.
• Their 25M dataset has movies reviews
from about 1,62,000 users for 60K+
movies.
• The dataset will be provided to you in zip
that contains several CSV files each
having information about movies, ratings,
tags, etc.
68. Learnings from the Project
• Implementing a big data pipeline on
Microsoft Azure and Databricks Spark
• Obtaining subscription of MS Azure
• Preparing Resource Group
• Introduction to Azure Data Factory, Azure
Databricks and Storage Account on
Azure
• Building and executing ADF Pipelines
• Using Spark SQL on Databricks
• Deploying models using Flask API
Find the full solution of this project:
Movielens dataset analysis for movie
recommendations using Spark in Azure
70. Overview of the Project
• As discussed before in this document,
understanding sentiments of the
customers is important if the businesses
intend to seek growth.
• Manually reading and analyzing the
sentiments is a difficult task, especially
when they are large in number.
Problem Statement:
• In this Big Data project, you will work with
a dataset and tweets to perform
sentiment analysis over customer
reviews.
71. Data Description
• The dataset you will be working on this
project has product reviews from Amazon
• The dataset has 100M+ product reviews
submitted from May 1996-July 2014
• You will learn how to work with dataset of
a particular category of products: Sports
and Outdoors.
• You will have following details of the
reviews:
whether the review is helpful or not,
overall rating out of 5, content of the
review, date and time on which the review
was submitted, etc.
72. Learnings from the Project
•Introduction to Containers and Sentiment
Analysis
•Understanding complete architecture of the
project
•Using Docker-composer
•Dividing data into buckets for labelling
•Data Ingestion using NiFi
•Using NiFi for producing tweets
•Using Kafka to read data
•Performing Sentiment Analysis using Spark
•Using MongoDB for storing data
Find the full solution of this project:
Real-Time Streaming of Twitter Sentiments AWS
EC2 NiFi
74. Overview of the Project
• During the mid-2020, most active users of
LinkedIn who followed Big Data and Data
Science projects, noticed that a large
number of project ideas related to
COVID-19 datasets were being
implemented and shared on the website.
Problem Statement:
• In this Big Data project, you will build a
Big data pipeline based on messaging.
You will explore exciting Big Data tools by
working on a COVID-19 dataset.
75. Data Description
• In this Big Data project, you will use
COVID-19 dataset obtained from an API.
• It is a summary dataset for COVID-19
patients worldwide.
• It contains information about active covid
cases, total number of deaths, total
number of people who have tested
positive for the virus so far, total number
of people who have recovered from the
virus. This data is available for all
countries and for the whole globe as well.
• Each country has a special variable
called country code for easier
representation.
76. Learnings from the Project
• Implementing a Big Data pipeline on
AWS
• Understanding how to use big data tools
to automate tasks
• Using NiFi to import real time streaming
data from an external API
• Transforming JSON file data into CSV
using NiFi and storing them in HDFS
• Creating tables using Hive
• Analysing Data using Tableau and AWS
Quicksight
Find the full solution of this project:
Create A Data Pipeline Based On Messaging
Using PySpark And Hive - Covid-19 Analysis
78. Overview of the Project
• Over the years, social media platforms
have evolved their position as a
communication tool. They not only
connect friends, but also businesses and
customers.
• Twitter is one such platforms that has
marked a distinct space in the domain of
social media.
Problem Statement:
• In this Big Data project, you will fetch data
from twitter and build an automatic
system for replying to tweets for a
business.
79. Data Description
• In this project, you will work with the
dataset of tweets of an airline.
• The dataset has relevant details like
airline names as tags, tweet content,
sentiment of the tweet (positive or
negative or neutral).
• It also has a topic for each tweet which
can be either of the following:
Baggage Issue, Customer Experience,
Delay and Customer Service, Extra
charges, Online Booking, Reschedule
and Refund, Reservation Issue, Seating
Preferences.
80. Learnings from the Project
• Using Tweepy for extracting data from
tweets and replying to a tweet
• Using Flask API for building an API that
will generate replies.
• Data Ingestion using Apache Kafka
• Utilizing Spacy for Named Entity
Recognition
• Exploring Python data science libraries
like Pandas, NumPy, and Matplotlib.
• Using Tensorflow and Keras frameworks
in Python
82. Overview of the Project
• ETL: Extract, Transform, and Load is a
method of collecting data from various
sources and transferring it to a data
warehouse.
• ETL is one of the most widely used
methods for data warehousing in various
top companies.
Problem Statement:
• In this Big Data project, you will prepare a
Redshift ETL Big data pipeline using
various Big Data tools by AWS.
83. Data Description
• You will work with a dataset of customer
reviews of certain products available on
Amazon.
• If you want to target a specific category of
products, then you can experiment with
small subsets as well.
• Each small subset contains the following
information:
whether the review is helpful or not,
overall rating out of 5, content of the
review, date and time on which the review
was submitted, etc.
84. Learnings from the Project
• Understanding the Architecture of the
whole project
• Building a Virtual Private Cloud (VPC)
• Creating a Redshift cluster and exploring
its usage.
• Utilising the AWS CLI tool for building S3
buckets
• Designing and running Glue jobs.
• Building an Amazon Simple Notification
Service (SNS).
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