Data Science Training
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The Data Science Training enables you to gain knowledge of the entire Life Cycle of Data Science, analyzing and visualizing different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes.
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Following topics are included in the PPT:
Determine Organizational Goals
Mining Data
Data Cleaning
Analyzing Data
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Creating Reports with Clear Visualizations
Maintaining Databases and Data Systems
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This document provides information about data analysts, including their job trends, salary trends, required skills, job description, and resume tips. It notes that data analysts collect and analyze large amounts of data to find trends and conclusions, which they present in reports and visualizations. The document provides statistics on data analyst job openings and salaries in both the US and India for entry-level and experienced analysts. Required skills include analytical abilities, communication, critical thinking, attention to detail, mathematics, and technical skills like SQL, Python, and Hadoop.
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Email : fahimbd329@gmail.com
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References:
1) https://www.datacamp.com/community/tutorials/data-science-industry-infographic
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Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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YouTube: https://youtu.be/nhyomoZS30M
( ** Data Analyst Master's Program: https://www.edureka.co/masters-program/data-analyst-certification ** )
This Edureka tutorial on "Data Analyst Roles and Responsibilities" will explain, what are the Roles and Responsibilities of a Data Analyst in the Industry. It also explains who is a Data Analyst and what does it takes to become one.
Following topics are included in the PPT:
Determine Organizational Goals
Mining Data
Data Cleaning
Analyzing Data
Pinpointing Trends and Patterns
Creating Reports with Clear Visualizations
Maintaining Databases and Data Systems
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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This document provides information about data analysts, including their job trends, salary trends, required skills, job description, and resume tips. It notes that data analysts collect and analyze large amounts of data to find trends and conclusions, which they present in reports and visualizations. The document provides statistics on data analyst job openings and salaries in both the US and India for entry-level and experienced analysts. Required skills include analytical abilities, communication, critical thinking, attention to detail, mathematics, and technical skills like SQL, Python, and Hadoop.
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References:
1) https://www.datacamp.com/community/tutorials/data-science-industry-infographic
2) Analytics: The real-world use of big data, IBM, Executive Report
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1.What is statistics?
2.Application
3.Application of Statistics in Computer Science and Engineering
4.Machine learning’s Relation to statistics
5.Application of Statistics in Data mining
6.Data mining relation with Statistics
7.Outline of Applications
8.Some Outline of Application’s details are given below
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Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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URL: https://www.ijtsrd.com/papers/ijtsrd30062.pdf
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Queries based on SQL, a database programming language, are used to answer basic questions about data.
But, as the collection of data grows in a database, the amount of data can easily become overwhelming.
A data warehouse is a collection of databases that work together. A data warehouse makes it possible to integrate data from multiple databases, which can give new insights into the data.
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The overall goal of the data mining
process is to extract information from a
data set and transform it into an
understandable structure for further use.
Data mining is the computing process of
discovering patterns in large data sets
involving methods.
These patterns can be used for predictive
analytics.
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DATA SCIENCE IS CATALYZING BUSINESS AND INNOVATION
Today, data science is enabling companies, governments, research centres and other organisations to turn their volumes of big data into valuable and actionable insights. It is important to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. According to the McKinsey Global Institute, the U.S. alone could face a shortage of about 190,000 data scientists and 1.5 million managers and analysts who can understand and make decisions using big data by 2018. In coming years, data scientists will be vital to all sectors —from law and medicine to media and nonprofits. Has the African continent planned to train the next generation of data scientists required on the continent?
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Main points of this slide presentation:
1.What is statistics?
2.Application
3.Application of Statistics in Computer Science and Engineering
4.Machine learning’s Relation to statistics
5.Application of Statistics in Data mining
6.Data mining relation with Statistics
7.Outline of Applications
8.Some Outline of Application’s details are given below
Thank you
Application statistics in software engineering
This presentation introduces the application of statistics in software engineering. It begins with introducing the presenters and topic. Statistics is then defined as the science of collecting, organizing, analyzing and interpreting numerical data to make valid decisions. Software engineering is the application of engineering principles to software design, development, implementation, testing and maintenance. Some common elements between statistics and software engineering are discussed, such as data, variables, and information. Finally, the use of statistics in areas like machine learning, data science, data mining, informatics and big data is described. The presentation concludes with discussing some applications of statistics in software engineering and providing references.
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The document discusses the skills needed for the next generation of data scientists. It emphasizes that they will need a breadth of technical skills in areas like mathematics, statistics, machine learning, and coding. They also need strong soft skills, like leadership, communication, and time management. Next generation data scientists are advised to follow the scientific method, understand data properly, avoid irrelevant data, and consider the social impacts and ethics of their work. The document also provides examples of popular data science tools in areas like machine learning, querying, processing, and integrated development environments.
Introduction of data science
Data science combines fields like statistics, programming, and domain expertise to extract meaningful insights from data. It involves preparing, analyzing, and modeling data to discover useful information. Exploratory data analysis is the process of investigating data to understand its characteristics and check assumptions before modeling. There are four types of EDA: univariate non-graphical, univariate graphical, multivariate non-graphical, and multivariate graphical. Python and R are popular tools used for EDA due to their data analysis and visualization capabilities.
Big data
Business analytics is the process of examining large volumes of various types of data to discover hidden patterns and correlations. This analysis can provide competitive advantages by helping organizations make more effective marketing and pricing decisions, leading to higher revenues. The data comes from traditional and unstructured sources and is organized and analyzed using statistical tools to make real-time decisions. Descriptive analytics describes past trends while predictive and prescriptive analytics determine future outcomes and best actions. Most data is structured but unstructured and semi-structured data from sources like text is growing.
Welcome to CS310!
The first presentation from the series accompanying "Data Science Essentials in Python. Collect → Organize → Explore → Predict → Value."
Data Science Model Curricilum
The document outlines a proposed data science model curriculum. It discusses placing data science occupations into top-level hierarchies including managers, professionals, and technicians. It then proposes new third-level occupation groups for data science managers, professionals, and technology professionals. Finally, it describes five core competencies for data science: data analytics, data management, data science engineering, scientific/research methods, and data science domain knowledge.
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This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Data Analytics Features and Concepts
Data analytics is a qualitative and quantitative features and process used to enhance the productivity and business gain. Data is extracted and categories identity analyzes behavior data and patterns according to organization requirement. Analytics is an interpretation and communication of meaningful patterns or summary in data. Data integration is a precursor to data analysis, and data analysis is closed linked to data visualization and data. K. Balaji Kumar | S. Balamanoj | Ashik K. H ""Data Analytics Features and Concepts"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30062.pdf
Paper Url : https://www.ijtsrd.com/computer-science/data-processing/30062/data-analytics-features-and-concepts/k-balaji-kumar
Application of statistics in cse
In this slide presentation i have shown the basic application of statistics in computer science and engineering
Data mining course learning outcomes,Data Mining CMAP
The document outlines the learning outcomes of a data mining course. Students will learn about database technologies, data types, preprocessing methods, and data warehousing. They will apply various data mining techniques to discover patterns in large datasets, evaluate models, and complete a practical data mining project to solve a real-world problem. The goals are for students to understand data mining concepts, prepare and analyze data using algorithms, and design data mining applications.
A picture is worth a thousand words
A picture is worth a thousand words !
Data Analytics VS Web Analytics VS Business Intelligence VS Big Data. Why Data is important in Marketing ?
An overview of big data analytics
Big data analytics examines large volumes of data from both traditional and unstructured sources to discover hidden patterns and correlations that can provide competitive advantages. This analysis can be done with traditional tools and techniques like data mining and predictive analytics, but large, unstructured data may not fit traditional data stores. Integrating new big data infrastructures with existing systems and enabling conventional data warehouses to handle large volumes of data at scale are challenges. The big data analytics process involves acquiring data from various sources, organizing the information, performing advanced analysis, and making rapid decisions.
Data Mining Technniques
Presented by,
Mr. Blesson Joseph
Senior Software Engineer, Livares Technologies
What is data mining?
Data mining is the process of analyzing data and summarizing it to produce useful information.
Queries based on SQL, a database programming language, are used to answer basic questions about data.
But, as the collection of data grows in a database, the amount of data can easily become overwhelming.
A data warehouse is a collection of databases that work together. A data warehouse makes it possible to integrate data from multiple databases, which can give new insights into the data.
The ultimate goal of a database is not just to store data, but to help businesses make decisions based on that data.
Goal of Data Mining
The overall goal of the data mining
process is to extract information from a
data set and transform it into an
understandable structure for further use.
Data mining is the computing process of
discovering patterns in large data sets
involving methods.
These patterns can be used for predictive
analytics.
Caqdas
CAQDAS (Computer Assisted Qualitative Data Analysis Software) programs help organize, code, search, and analyze qualitative research data. They allow researchers to better organize their data, perform boolean searches, generate codebooks, and visualize relationships between codes and theoretical constructs. While CAQDAS cannot analyze data on its own, it can assist researchers with tasks like coding, indexing, sorting, and manipulating data to help increase organization and allow viewing data from different perspectives.
Prashant resume
Prashant Sharma is pursuing a Master's degree in Operations Research at North Carolina State University with a GPA of 3.63/4. He received a B.Tech in Civil Engineering from the National Institute of Technology Silchar in India with a GPA of 3.7/4. His technical skills include Python, SQL, machine learning algorithms and neural networks. He has experience as a technical analyst and supervisor at Saluja Tech and worked as a teaching assistant providing free math education. His relevant projects on GitHub involve building predictive models using machine learning algorithms to tag unsatisfied customers, predict customer purchases, and predict ACT scores.
Data Mining 101
This document provides an overview of data mining and the CRISP-DM methodology. It discusses key terminology, potential applications, and a Venn diagram comparing data mining, knowledge discovery, big data analytics, statistics, and data science. The CRISP-DM methodology is explained in six steps: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Various data exploration, cleaning, transformation, and dimensionality reduction techniques are covered. Common machine learning algorithms, model selection factors, and assessment metrics are also summarized.
Sample Paper.doc.doc
The document provides an overview of data mining and web mining techniques. It discusses how data mining uses statistical analysis, machine learning, and other techniques to extract patterns and correlations from large datasets. The document also presents results from a case study that analyzed web traffic statistics and visitor behavior on a website to gain insights on how to improve the user experience. Clustering algorithms were used to classify users and generate a web mining model. The case study demonstrated that data mining can efficiently analyze large amounts of web data and provide useful information for website optimization.
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Overview of what big data is, how it is different than other data, what data scientists are/do, and what the benefit of data science can be.
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Data Science Training and Placement
The world has witnessed explosive digital growth in the last two decades, which has led to a data deluge. This data may be
holding some key business insights or solutions to crucial problems. Data Science is the key that unlocks this possibility
to extract vital insights from the raw digital data. These findings can then be visualized, and communicated to the
decision-makers to be acted upon.Online Data Science Training is the best choice for the students to begin a new life. We
provide Data Science Training and Placement for the students .
Which institute is best for data science?
EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
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Data science training in hyd ppt (1)
Data Science Online Training In HA comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.hyderabad Data Science Online Training
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Data science training institute in hyderabad
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
Data science training in Hyderabad
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
Data science training Hyderabad
Eduxfactor is an online data science training institution based in Hyderabad. A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Data science online training in hyderabad
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
Data science training in hyd ppt (1)
Overview of Data Science Courses Online
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
What You'll Learn In Data Science Courses Online
Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
Comprehend the crucial steps required to solve real-world data problems and get familiar with the methodology to think and work like a Data Scientist.
Learn to collect, clean, and analyze big data with R. Understand how to employ appropriate modeling and methods of analytics to extract meaningful data for decision making.
Implement clustering methodology, an unsupervised learning method, and a deep neural network (a supervised learning method).
Build a data analysis pipeline, from collection to analysis to presenting data visually.
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data science training and placement
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge
online data science training
EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
Data science online training in hyderabad
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
data science online training in hyderabad
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge. Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
Best data science training in Hyderabad
Join us for the Best data science training in Hyderabad at Edux factor and enrich your carrier.
Dream for wonderful carrier we make to achieve your dreams come true Hurry up & enroll now.
Data science training Hyderabad
Eduxfactor is an online data science training institution based in Hyderabad. A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
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Data Science Training
- 1. Data Science Training Let’s Get Started!
- 2. Description Data science is a "concept to unify statistics, data analysis and their related methods" to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization. The Data Science Training enables you to gain knowledge of the entire Life Cycle of Data Science, analyzing and visualizing different data sets, different Machine Learning Algorithms like K- Means Clustering, Decision Trees, Random Forest, and Naive Bayes.
- 3. Objectives • Gain insight into the 'Roles' played by a Data Scientist • Analyze several types of data using R • Describe the Data Science Life Cycle • Work with different data formats like XML, CSV etc. • Learn tools and techniques for Data Transformation • Discuss Data Mining techniques and their implementation
- 4. Objectives • Analyze data using Machine Learning algorithms in R • Explain Time Series and it’s related concepts • Perform Text Mining and Sentimental analyses on text data • Gain insight into Data Visualization and Optimization techniques • Understand the concepts of Deep Learning
- 5. Why learn Data Science Data science incorporates tools from multi disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. The disciplinary areas that make up the data science field include mining, statistics, machine learning, analytics, and some programming. Data mining applies algorithms in the complex data set to reveal patterns which are then used to extract useable and relevant data from the set. Statistical measures like predictive analytics utilize this extracted data to gauge events that are likely to happen in the future based on what the data shows happened in the past.
- 6. This course is appropriate for: • Developers aspiring to be a 'Data Scientist‘ • Analytics Managers who are leading a team of analysts • Business Analysts who want to understand Machine Learning (ML) Techniques • Information Architects who want to gain expertise in Predictive Analytics • 'R' professionals who want to captivate and analyze Big Data • Analysts wanting to understand Data Science methodologies
- 7. Prerequistes There is no specific pre-requisite for this training program, however basic understanding of R can be beneficial.