This document discusses why Python is a popular programming language for data science. It notes that Python has a clean syntax, expansive library, and large user base. Additionally, major companies like Google use Python for various applications. The document also provides examples of what businesses use Python for, including building data pipelines, descriptive analytics, machine learning, and data science tasks like clustering and prediction. Finally, it outlines some common tools and processes used in working as a data analyst or scientist, such as cleaning, reshaping, and analyzing data in Python.
Want to pursue career in Data Science? Have knowledge of limited opportunities? Don't worry!
This e- book helps readers to know about top career opportunities one can pursue in Data Science. Further info.- https://www.henryharvin.com/business-analytics-course-with-python
From Lab to Factory: Or how to turn data into valuePeadar Coyle
We've all heard of 'big data' or data science, but how do we convert these trends into actual business value. I share case studies, and technology tips and talk about the challenges of the data science process. This is all based on two years of in-the-field research of deploying models, and going from prototypes to production.
These are slides from my talk at PyCon Ireland 2015
Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
Want to pursue career in Data Science? Have knowledge of limited opportunities? Don't worry!
This e- book helps readers to know about top career opportunities one can pursue in Data Science. Further info.- https://www.henryharvin.com/business-analytics-course-with-python
From Lab to Factory: Or how to turn data into valuePeadar Coyle
We've all heard of 'big data' or data science, but how do we convert these trends into actual business value. I share case studies, and technology tips and talk about the challenges of the data science process. This is all based on two years of in-the-field research of deploying models, and going from prototypes to production.
These are slides from my talk at PyCon Ireland 2015
Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
Data science researchers use their data and systematic capability to find and understand wealthy data sources; handle considerable quantities of information despite components, software, and data transfer useage constraints; combine data sources; make sure reliability of datasets; make visualizations to aid in knowing data; develop statistical designs using the data; and current and connect the information insights/findings. They are often predicted to generate alternatives in days rather than months, execute by exploratory research and fast version, and to generate and current results with dashboards (displays of current values) rather than papers/reports, as statisticians normally do
The deck describes:
The importance of Project Management in context of Data Science
The Spotle SMART (Specify-Measure-Analyse-Roll-out-Test) model for managing Data Science projects
The CRISP-DM standard for data mining and analytics projects
Stakeholders
Tools and Methodologies used in Data Science projects
Common risks in Data Science Projects
Data science skills and development for the energy sectorDr. Cher Han Lau
I gave a talk in conjunction with Big Data Week 2018 in Malaysia to talk about data science skills and development for the energy sector. The conference by DEJ is 7 years running in Malaysia. They are collaborating with ADAX as we gear to drive 4IR (digital transformation). I covered how talent harnessing, embracing the wealth of skills & technology is of paramount importance and demands radical change across all sectors. And the pathway to become a data scientist in the Oil and Gas industry.
Data is growing exponentially. What should business managers do to make better business decisions? I explain three key things step by step. Just start today!
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
Data Science Salon: Culture, Data Engineering and Hamburger Stands: Thoughts ...Formulatedby
Presented by Becky Tucker, Data Scientist at Netflix
Next DSS NYC Event 👉 https://datascience.salon/newyork/
Next DSS LA Event 👉 https://datascience.salon/la/
Becky Tucker is speaking about how Netflix culture uniquely interacts with data science, the importance of data engineering to our data science teams, how their teams are structured to do data science "at scale," and what "data science at scale" looks like for her.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
Data analytics with managerial application ass 2Nishant Kumar
This presentation depicts insights of the article "Data Scientist: The Sexiest Job of the 21st Century", and also how these insight are relevant to a manager in india.
Data Science Applications | Data Science For Beginners | Data Science Trainin...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science Applications" PPT takes you through the various domains in which data science is being deployed today, along with some potential applications of this technology. The world today runs on data and this PPT shows exactly that.
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
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 scientist the sexiest job of the 21st centuryFrank Kienle
Invited talk, describing the exciting work at Blue Yonder (www.blue-yonder.com),
'congress smart services - new business models' in Aachen, Germany 2015
What do PLI, MetOpera, ASCO, and PLOS have in common? Content management and content discovery needed major improvements. User were not getting the results they needed. The content production team including editorials, managing editorials – the whole team – could no longer cope with the volume and variety. Content quality was suffering. Brief discussions of each organization’s challenges set the stage for AI-based, human curated solutions. What worked, what didn’t, and the how and the why will be presented.
Licensed to Analyze? Strata Data NY 2019 IADSS Session - Usama Fayyad, Hamit ...IADSS
The latest insight into IADSS Research was shared with analytics community at Strata Data NY 2019 by O'reilly. IADSS Co-founders Usama Fayyad and Hamit Hamutcu talked about the current status of data science job market, the wasted cost of data science recruitment and role definitions & required skill-sets for most common roles in data science.
Please check out IADSSglobal on Twitter and visit www.iadss.org for more information.
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
Data science researchers use their data and systematic capability to find and understand wealthy data sources; handle considerable quantities of information despite components, software, and data transfer useage constraints; combine data sources; make sure reliability of datasets; make visualizations to aid in knowing data; develop statistical designs using the data; and current and connect the information insights/findings. They are often predicted to generate alternatives in days rather than months, execute by exploratory research and fast version, and to generate and current results with dashboards (displays of current values) rather than papers/reports, as statisticians normally do
The deck describes:
The importance of Project Management in context of Data Science
The Spotle SMART (Specify-Measure-Analyse-Roll-out-Test) model for managing Data Science projects
The CRISP-DM standard for data mining and analytics projects
Stakeholders
Tools and Methodologies used in Data Science projects
Common risks in Data Science Projects
Data science skills and development for the energy sectorDr. Cher Han Lau
I gave a talk in conjunction with Big Data Week 2018 in Malaysia to talk about data science skills and development for the energy sector. The conference by DEJ is 7 years running in Malaysia. They are collaborating with ADAX as we gear to drive 4IR (digital transformation). I covered how talent harnessing, embracing the wealth of skills & technology is of paramount importance and demands radical change across all sectors. And the pathway to become a data scientist in the Oil and Gas industry.
Data is growing exponentially. What should business managers do to make better business decisions? I explain three key things step by step. Just start today!
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
Data Science Salon: Culture, Data Engineering and Hamburger Stands: Thoughts ...Formulatedby
Presented by Becky Tucker, Data Scientist at Netflix
Next DSS NYC Event 👉 https://datascience.salon/newyork/
Next DSS LA Event 👉 https://datascience.salon/la/
Becky Tucker is speaking about how Netflix culture uniquely interacts with data science, the importance of data engineering to our data science teams, how their teams are structured to do data science "at scale," and what "data science at scale" looks like for her.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
Data analytics with managerial application ass 2Nishant Kumar
This presentation depicts insights of the article "Data Scientist: The Sexiest Job of the 21st Century", and also how these insight are relevant to a manager in india.
Data Science Applications | Data Science For Beginners | Data Science Trainin...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science Applications" PPT takes you through the various domains in which data science is being deployed today, along with some potential applications of this technology. The world today runs on data and this PPT shows exactly that.
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
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 scientist the sexiest job of the 21st centuryFrank Kienle
Invited talk, describing the exciting work at Blue Yonder (www.blue-yonder.com),
'congress smart services - new business models' in Aachen, Germany 2015
What do PLI, MetOpera, ASCO, and PLOS have in common? Content management and content discovery needed major improvements. User were not getting the results they needed. The content production team including editorials, managing editorials – the whole team – could no longer cope with the volume and variety. Content quality was suffering. Brief discussions of each organization’s challenges set the stage for AI-based, human curated solutions. What worked, what didn’t, and the how and the why will be presented.
Licensed to Analyze? Strata Data NY 2019 IADSS Session - Usama Fayyad, Hamit ...IADSS
The latest insight into IADSS Research was shared with analytics community at Strata Data NY 2019 by O'reilly. IADSS Co-founders Usama Fayyad and Hamit Hamutcu talked about the current status of data science job market, the wasted cost of data science recruitment and role definitions & required skill-sets for most common roles in data science.
Please check out IADSSglobal on Twitter and visit www.iadss.org for more information.
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
Algorithm Marketplace and the new "Algorithm Economy"Diego Oppenheimer
Talk by Diego Oppenheimer CEO of Algorithmia.com at Data Day Texas 2016.
Peter Sondergaard VP of Research for Gartner recently said the next digital gold rush is "How we do something with data not just what you do with it". During this talk we will cover a brief history of the different algorithmic advances in computer vision, natural language processing, machine learning and general AI and how they are being applied to Big Data today. From there we will talk about how algorithms are playing a crucial part in the next Big Data revolution, new opportunities that are opening up for startups and large companies alike as well as a first look into the role Algorithm Marketplaces will play in this space.
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
This presentation was given in one of the DSATL Mettups in March 2018 in partnership with Southern Data Science Conference 2018 (www.southerndatascience.com)
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.
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
2. Why should you think career as Data
Scientist?
• Data Scientist is the best job of the 21st century -
Harvard Business Review
• Global Data science market to reach $122B in revenue
by 2025 – Frost & Sullivan
• The US alone could face a shortage of 1.4 -1.9 million
Data Analysts by 2019 – Mckinsey
There is a serious shortage of Data Scientists and this is a
major concern for Top MNCs around the world. All this
means the major corporations are ready to pay top dollar
salaries for professionals with the right Data Science
skills.
5. What is Python?
• An all-purpose, general language that works
on multiple platforms
• High level and easy to learn.
• More commonly used for machine learning
and predictive modeling (particularly good for
academics and data scientists)
• Open source and free to learn and use more
commonly by developers.
7. Why Is Python So Popular?
• Java
public class Main { public static void
main(String[] args) {
System.out.println("hello world"); } }
• Python
print(‘hello world’)
Minimal setup is another of Python’s perks.
8. Why Python is so Popular?
• The language continued to rank highly on various
lists of the world’s most popular programming
languages.
• Many programmers view Python as a language
with a clean syntax and an expansive library.
• Python’s massive user base has created
something of a positive feedback loop
• In Python’s case, it’s Google, which uses the
programming language in a number of
applications (a corporate sponsor).
9.
10.
11. What do businesses use python for?
• Building “data pipelines”:
•New data is coming in all the time
•Needs to be extracted, transformed and loaded
•Needs to be fast
• Descriptive Analytics
• These skills are in demand.
• Businesses want to know about their historical data.
• They also want to know what is happening right now.
• New marketing opportunities? Save time and money in
current processes?
• Machine learning and data science?
• Can our customers be divided into clusters?
• Can we predict what a customer is likely to buy and make
recommendations?
• Can we detect fraud? Can we predict risk?
12. Working as an analyst/scientist
• You may be familiar with some tools already, depending
where you’ve come from:
• Excel and Office tools
• SPSS, MATLAB
• SQL
• BI and analytics are a bit of a continuous process:
• Cleaning data –missing values? Bad data?
• Reshape data –is the data in the right format?
• Loading –how much is there?
• Find patterns –do these patterns add value?
• Presentation –can you tell a story?
14. Data Science with Python
Python Environment
Setup and Essentials
Data Science with
Python
Advanced Data
Science Concepts
Job Readiness
Pro-Degree Program
Contents