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.
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 .
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<a href="https://eduxfactor.com/selenium-online-training">Best Selenium certification course</a>
Look no further than our comprehensive Data Science Training program in Chandigarh. Designed to equip individuals with the skills and knowledge required to thrive in today's data-centric world, our course offers a unique blend of theoretical foundations and hands-on practical experience.
DATA SCIENCE IS CATALYZING BUSINESS AND INNOVATION Elvis Muyanja
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?
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 .
Join us for the Best Selenium certification course at Edux factor and enrich your carrier.
Dream for wonderful carrier we make to achieve your dreams come true Hurry up & enroll now.
<a href="https://eduxfactor.com/selenium-online-training">Best Selenium certification course</a>
Look no further than our comprehensive Data Science Training program in Chandigarh. Designed to equip individuals with the skills and knowledge required to thrive in today's data-centric world, our course offers a unique blend of theoretical foundations and hands-on practical experience.
DATA SCIENCE IS CATALYZING BUSINESS AND INNOVATION Elvis Muyanja
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?
Bridging the Gap Between Data Science & Engineer: Building High-Performance T...ryanorban
Data scientists, data engineers, and data businesspeople are critical to leveraging data in any organization. A common complaint from data science managers is that data scientists invest time prototyping algorithms, and throw them over a proverbial fence to engineers to implement, only to find the algorithms must be rebuilt from scratch to scale. This is a symptom of a broader ailment -- that data teams are often designed as functional silos without proper communication and planning.
This talk outlines a framework to build and organize a data team that produces better results, minimizes wasted effort among team members, and ships great data products.
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
There are ten areas in Data Science which are a key part of a project, and you need to master those to be able to work as a Data Scientist in much big organization.
Become a successful Data Scientist. Start Now!Edology
It is not rocket science; it is Data Science. What you need is proper guidance and a roadmap to become a successful data scientist. Here's how you can become a successful data scientist.
Overview Of Data Science Course
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.
Speaker: Venkatesh Umaashankar
LinkedIn: https://www.linkedin.com/in/venkateshumaashankar/
What will be discussed?
What is Data Science?
Types of data scientists
What makes a Data Science Team? Who are its members?
Why does a DS team need Full Stack Developer?
Who should lead the DS Team
Building a Data Science team in a Startup Vs Enterprise
Case studies on:
Evolution Of Airbnb’s DS Team
How Facebook on-boards DS team and trains them
Apple’s Acqui-hiring Strategy to build DS team
Spotify -‘Center of Excellence’ Model
Who should attend?
Managers
Technical Leaders who want to get started with Data Science
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
Patterns for Successful Data Science Projects (Spark AI Summit)Bill Chambers
Running data science workloads is challenge regardless of whether you are running them on your laptop, on an on-premises cluster, or in the cloud. While buying 100% managed service is an option, these tools can be expensive and lack extensibility. Therefore, many companies option for open source data science tools like scikit-learn and Apache Spark’s MLlib in order to balance both functionality and cost.
However, even if a project succeeds at a point in time with any set of tools, these projects become harder and harder to maintain as data volumes increase and a desire for real-time pushes technology to its limit. New projects also struggle as new challenges of scale invalidate previous assumptions.
This talk will discuss some patterns that we see at Databricks that companies leverage to succeed with their data science projects. Key takeaways will be:
– Striving for simplicity
– Removing cognitive load for you and your team
– Working with data, big and small
– Effectively leveraging the ecosystem of tools to be successful
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Bridging the Gap Between Data Science & Engineer: Building High-Performance T...ryanorban
Data scientists, data engineers, and data businesspeople are critical to leveraging data in any organization. A common complaint from data science managers is that data scientists invest time prototyping algorithms, and throw them over a proverbial fence to engineers to implement, only to find the algorithms must be rebuilt from scratch to scale. This is a symptom of a broader ailment -- that data teams are often designed as functional silos without proper communication and planning.
This talk outlines a framework to build and organize a data team that produces better results, minimizes wasted effort among team members, and ships great data products.
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
There are ten areas in Data Science which are a key part of a project, and you need to master those to be able to work as a Data Scientist in much big organization.
Become a successful Data Scientist. Start Now!Edology
It is not rocket science; it is Data Science. What you need is proper guidance and a roadmap to become a successful data scientist. Here's how you can become a successful data scientist.
Overview Of Data Science Course
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.
Speaker: Venkatesh Umaashankar
LinkedIn: https://www.linkedin.com/in/venkateshumaashankar/
What will be discussed?
What is Data Science?
Types of data scientists
What makes a Data Science Team? Who are its members?
Why does a DS team need Full Stack Developer?
Who should lead the DS Team
Building a Data Science team in a Startup Vs Enterprise
Case studies on:
Evolution Of Airbnb’s DS Team
How Facebook on-boards DS team and trains them
Apple’s Acqui-hiring Strategy to build DS team
Spotify -‘Center of Excellence’ Model
Who should attend?
Managers
Technical Leaders who want to get started with Data Science
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
Patterns for Successful Data Science Projects (Spark AI Summit)Bill Chambers
Running data science workloads is challenge regardless of whether you are running them on your laptop, on an on-premises cluster, or in the cloud. While buying 100% managed service is an option, these tools can be expensive and lack extensibility. Therefore, many companies option for open source data science tools like scikit-learn and Apache Spark’s MLlib in order to balance both functionality and cost.
However, even if a project succeeds at a point in time with any set of tools, these projects become harder and harder to maintain as data volumes increase and a desire for real-time pushes technology to its limit. New projects also struggle as new challenges of scale invalidate previous assumptions.
This talk will discuss some patterns that we see at Databricks that companies leverage to succeed with their data science projects. Key takeaways will be:
– Striving for simplicity
– Removing cognitive load for you and your team
– Working with data, big and small
– Effectively leveraging the ecosystem of tools to be successful
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
2. 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 Online Training in Hyderabad Course
Overview
3. • 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.
What You'll Learn
4. 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.
Data Science is an interdisciplinary field requiring statistics, data analysis, programming, and
business knowledge.
Listed below are some of the tasks of a typical data scientist.
• Ask the right set of questions to identify the data-based problems that hold the greatest
opportunity for the business.
• Collect large sets of relevant structured and unstructured data from diverse channels.
• Process and clean the data to ensure its accurate, complete, and uniform.
• Choose and apply appropriate data science models and algorithms to mine the big data stores.
• Perform analysis to identify patterns, trends, and relationships within data. Look for fitting
solutions and opportunities.
• Convert data-based insights into compelling visualizations and present that to stakeholders.
Make adjustments to the approach based on the received feedback.
What’s Data Science? What Does A Data Scientist Do?
5. To be able to look at various pieces of data and draw out conclusions is the most valuable skill you
can have, a skill that's often missing even amongst technically advanced employees.
Hailed as the "sexiest job of the 21st Century" (Harvard Business Review), here are a few solid
reasons to learn Data Science.
• Expand your problem-solving skills, a skill that's not useful for the professional world, but
also in everyday life as well.
• Data Science is a lucrative career option with an abundance of high paying job opportunities
($113k/yr base pay in the USA (Glassdoor), Rupees 8.15 lakhs in India (PayScale))
Generate side income with your data science skill set (Freelance, Start an informative
blog/YouTube channel, sell a data science course, or create something innovative with your
data knowledge)
• Get to make the world a better place with data science solutions
Why Should You Learn Data Science?
6. No matter what your background is, you can take this data science course provided you're
passionate about numbers, and love challenging problems.
But your journey to becoming a successful data scientist would be much easier if:
• You have a background in analytical disciplines such as mathematics, physics, computer
science, or engineering.
• You love coding and have a basic understanding of programming languages.
• You are patient enough to keep working on the project even when it seems to have hit a
roadblock.
Who Can Take Up This Course?
7. Most comprehensive and well-structured course covering basics to advanced topics, allowing
you to master the complete niche.
• Certified Trainers with extensive real-time experience in the Data Science domain and an
immense passion for teaching.
• Top-notch course with a perfect blend of theory, case studies, and capstone projects, along with
an assignment for every taught concept.
• 100% Job Placement assistance. Frequent mock interviews to evaluate and improve your
knowledge and expertise. Facilitation of interviews with various top companies. Help in building
a great resume, optimizing LinkedIn profile, and improving your marketability.
Why Should You Learn Data Science At EduXFactor?
8. Listed below are some of the leading data science careers you can break into after completing the
data science course.
Data Scientist
Data Analyst
Data Engineer
Business Intelligence Analyst
Marketing Analyst
Statistician
Database administrator
Database developer
Data Architect
Application Architect
Enterprise Architect
Infrastructure Architect
Machine Learning Engineer
Machine Learning Scientist
What Job Options Would Be Available To You After
Learning Data Science?
9. • Module 1 – Data Science Project Lifestyle
• Module 2 – Introduction To Basic Statistics Using R & Python
• Module 3 – Probability And Hypothesis Testing
• Module 4 – Exploratory Data Analysis – 1
• Module 5 – Linear Regression
• Module 6 – Logistic Regression
• Module 7 – Deployment
• Module 8 – Data Mining unsupervised Clustering
• Module 9 – Dimension Reduction Techniques
• Module 10 – Association Rules
• Module 11 – Recommender System
• Module 12 - Introduction to supervised Machine Learning
• Module 13 – Decision Tree
• Module 14 – Exploratory Data Analysis – 2
• Module 15 – Feature Engineering
Course Curriculum
11. Listed below are the five most popular algorithms that all data scientist should know (we
cover all of these):
• Logistic Regression
• Naive Bayes
• K-Nearest Neighbors
• Support Vector Machines
• Random Forest
Do I Need A Powerful Computer To Implement Data Science?
No! Just a basic laptop should be sufficient for most of your personal projects.
FAQ’s
12. Can You Explain Big Data, Data Analytics, And Data Science?
Big Data refers to the enormous amount of data with various formats (structured, unstructured,
semi-structured) generated from a variety of data sources or channels.
Data Analysis is the process of collecting and organizing raw data with the purpose to extract
helpful information from it.
Data Science is a blend of various tools, algorithms, and machine learning principles for gaining
useful insights from raw data. It involves designing and constructing data modelling and other
data-centered operations such as preprocessing, data cleaning, analysis, etc.
FAQ’s
13. Where Can I Get Datasets From For My Personal And Coursework Projects?
Here are a few datasets sources you can rely on:
• Kaggle
• Socrata
• Non-profit research group websites
FAQ’s
14. Is The Course Content Recently Developed, Or Just Randomly Repurposed?
This data science course is the most comprehensive, relevant, and contemporary, meeting all the
present demands of the Data Industry. Don’t expect it to be some repurposed or repackaged
content of redundant archaic course materials.
What’s more is that we continually upgrade the content of this course with the changes in
technology, trends, and demands to provide you the best learning resource.
FAQ’s
15. Master Data Science:
Learn the skills needed to solve complex data problems
• 10 - 20 weeks
• 102 Lectures
• 502 Student Enrolled
Data Science Training
4.5
16. Get In Touch With
us
Dwaraka One, Ground Floor,
Plot no. 6 & 7, Survey no. 85
Madhapur Near Raheja Mindspace,
Hyderabad, Telangana
500081,India.
Reach us through Google Maps
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