Data Science Training | Data Science For Beginners | Data Science With Python...Simplilearn
This Data Science presentation will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist.
This Data Science presentation will cover the following topics:
1. What is Data Science?
2. Who is a Data Scientist?
3. What does a Data Scientist do?
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. A 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’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its largelibrary of mathematical functions.
Learn more at: https://www.simplilearn.com
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Data Science Training | Data Science For Beginners | Data Science With Python...Simplilearn
This Data Science presentation will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist.
This Data Science presentation will cover the following topics:
1. What is Data Science?
2. Who is a Data Scientist?
3. What does a Data Scientist do?
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. A 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’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its largelibrary of mathematical functions.
Learn more at: https://www.simplilearn.com
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
1. Introduction and how to get into Data
2. Data Engineering and skills needed
3. Comparison of Data Analytics for statistic and real time streaming data
4. Bayesian Reasoning for Data
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
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/
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Twitter: https://twitter.com/edurekain
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YouTube Link: https://youtu.be/aGu0fbkHhek
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Data Science Full Course" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science PPT will start with basics of Statistics and Probability and then moves to Machine Learning and Finally ends the journey with Deep Learning and AI. For Data-sets and Codes discussed in this PPT, drop a comment.
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/
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LinkedIn: https://www.linkedin.com/company/edureka
This PPT Programming for data science in python mainly focus on importance of Python programming language in Python it explains the characteristic features of the programming language, its pros and cons and its applications.
Statistics And Probability Tutorial | Statistics And Probability for Data Sci...Edureka!
YouTube Link: https://youtu.be/XcLO4f1i4Yo
** Data Science Certification using R: https://www.edureka.co/data-science **
This session on Statistics And Probability will cover all the fundamentals of stats and probability along with a practical demonstration in the R language.
Valencian Summer School 2015
Day 2
Lecture 11
The Future of Machine Learning
José David Martín-Guerrero (IDAL, UV)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Data Scientist Roles and Responsibilities | Data Scientist Career | Data Scie...Edureka!
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka "Data Scientist Roles and Responsibilities" PPT talks about the various Job Descriptions and specific skill sets for the different kinds of Data Scientists that are there. It explains why Data Science is the best career move, right now. Learn about various job roles and what they actually mean and the learning path to make a career in Data Science. Below are the topics covered in this module:
What is Data Science?
Who is a Data Scientist?
Types of Data Scientists
Skills Required to Become a Data Scientist
Data Science Masters Program @Edureka
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
Instagram: https://www.instagram.com/edureka_lea...
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This presentation introduces text analytics, its applications and various tools/algorithms used for this process. Given below are some of the important tools:
- Decision trees
- SVM
- Naive-Bayes
- K-nearest neighbours
- Artificial Neural Networks
- Fuzzy C-Means
- Latent Dirichlet Allocation
Building a performing Machine Learning model from A to ZCharles Vestur
A 1-hour read to become highly knowledgeable about Machine learning and the machinery underneath, from scratch!
A presentation introducing to all fundamental concepts of Machine Learning step by step, following a classical approach to build a performing model. Simple examples and illustrations are used all along the presentation to make the concepts easier to grasp.
Why is image analytics Important? What good can come of caption generation or image descriptions? And how does Data Science & Machine learning techniques work on Image Analytics and to what purpose? We see how it works for the retail industry and for the Healthcare industry. What more? Take a look...
Data Science for Business Managers - The bare minimum a manager should knowAkin Osman Kazakci
This module gives the basic and fundamental notions that a manager must comprehend in order to be able to work with technical data scientists.
After some terminology, differences between the notions of big data and data science is discussed. A basic prediction (classification) task is considered through an example. No technical background is assumed, since no math or coding is presented. The module concludes with a hands-on case study (bank direct marketing) to get the participants initiated with problem formulation for data science.
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science...Edureka!
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
6) Demo in R: Diabetes Prevention Use Case
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
1. Introduction and how to get into Data
2. Data Engineering and skills needed
3. Comparison of Data Analytics for statistic and real time streaming data
4. Bayesian Reasoning for Data
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
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
YouTube Link: https://youtu.be/aGu0fbkHhek
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Data Science Full Course" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science PPT will start with basics of Statistics and Probability and then moves to Machine Learning and Finally ends the journey with Deep Learning and AI. For Data-sets and Codes discussed in this PPT, drop a comment.
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
This PPT Programming for data science in python mainly focus on importance of Python programming language in Python it explains the characteristic features of the programming language, its pros and cons and its applications.
Statistics And Probability Tutorial | Statistics And Probability for Data Sci...Edureka!
YouTube Link: https://youtu.be/XcLO4f1i4Yo
** Data Science Certification using R: https://www.edureka.co/data-science **
This session on Statistics And Probability will cover all the fundamentals of stats and probability along with a practical demonstration in the R language.
Valencian Summer School 2015
Day 2
Lecture 11
The Future of Machine Learning
José David Martín-Guerrero (IDAL, UV)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Data Scientist Roles and Responsibilities | Data Scientist Career | Data Scie...Edureka!
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka "Data Scientist Roles and Responsibilities" PPT talks about the various Job Descriptions and specific skill sets for the different kinds of Data Scientists that are there. It explains why Data Science is the best career move, right now. Learn about various job roles and what they actually mean and the learning path to make a career in Data Science. Below are the topics covered in this module:
What is Data Science?
Who is a Data Scientist?
Types of Data Scientists
Skills Required to Become a Data Scientist
Data Science Masters Program @Edureka
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
Instagram: https://www.instagram.com/edureka_lea...
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This presentation introduces text analytics, its applications and various tools/algorithms used for this process. Given below are some of the important tools:
- Decision trees
- SVM
- Naive-Bayes
- K-nearest neighbours
- Artificial Neural Networks
- Fuzzy C-Means
- Latent Dirichlet Allocation
Building a performing Machine Learning model from A to ZCharles Vestur
A 1-hour read to become highly knowledgeable about Machine learning and the machinery underneath, from scratch!
A presentation introducing to all fundamental concepts of Machine Learning step by step, following a classical approach to build a performing model. Simple examples and illustrations are used all along the presentation to make the concepts easier to grasp.
Why is image analytics Important? What good can come of caption generation or image descriptions? And how does Data Science & Machine learning techniques work on Image Analytics and to what purpose? We see how it works for the retail industry and for the Healthcare industry. What more? Take a look...
Data Science for Business Managers - The bare minimum a manager should knowAkin Osman Kazakci
This module gives the basic and fundamental notions that a manager must comprehend in order to be able to work with technical data scientists.
After some terminology, differences between the notions of big data and data science is discussed. A basic prediction (classification) task is considered through an example. No technical background is assumed, since no math or coding is presented. The module concludes with a hands-on case study (bank direct marketing) to get the participants initiated with problem formulation for data science.
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science...Edureka!
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
6) Demo in R: Diabetes Prevention Use Case
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Machine learning for sensor Data AnalyticsMATLABISRAEL
במצגת זאת נראה כיצד עושים Machine Learning בסביבת MATLAB. נציג מספר יכולות ואפליקציות מובנות ההופכות את תהליך למידת המכונה ליעיל ומהיר יותר – כלים כמו ה-Classification Learner, ה-Regression Learner ו-Bayesian Optimization. בהסתמך על מידע המתקבל מחיישני סמארטפון, נבנה מערכת סיווג המזהה את הפעילות שמבצע המשתמש – הליכה, טיפוס במדרגות, שכיבה, וכו'
This is just to help my people who wants to pursue their career as a Data Scientist.
I strongly believe that 'We rise by lifting others'.
I made this for one of my project work thought to share it here. Hope you guys will like it. Please feel free to suggest changes for better.
Data Scientist has been regarded as the sexiest job of the twenty first century. As data in every industry keeps growing the need to organize, explore, analyze, predict and summarize is insatiable. Data Science is creating new paradigms in data driven business decisions. As the field is emerging out of its infancy a wide range of skill sets are becoming an integral part of being a Data Scientist. In this talk I will discuss the different driven roles and the expertise required to be successful in them. I will highlight some of the unique challenges and rewards of working in a young and dynamic field.
Slide presentasi ini dibawakan oleh Imron Zuhri dalam acara Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
Understanding Data Science: Unveiling the Basics
What is Data Science?
Data science is an interdisciplinary field that combines techniques from statistics, mathematics, computer science, and domain knowledge to extract insights and knowledge from data. It involves collecting, processing, analyzing, and interpreting large and complex datasets to solve real-world problems.
Importance of Data Science
In today's data-driven world, organizations are inundated with data from various sources. Data science allows them to convert this raw data into actionable insights, enabling informed decision-making, improved efficiency, and innovation.
Intersection of Data Science, Statistics, and Computer Science
Data science borrows heavily from statistics and computer science. Statistical methods help in understanding data patterns, while computer science provides the tools to process and analyze large datasets efficiently.
Key Components of Data Science
Data Collection and Storage
The first step in data science is gathering relevant data from various sources. This data is then stored in databases or data warehouses for further processing.
Data Cleaning and Preprocessing
Raw data is often messy and inconsistent. Data cleaning involves removing errors, duplicates, and irrelevant information. Preprocessing includes transforming data into a usable format.
Exploratory Data Analysis (EDA)
EDA involves visualizing and summarizing data to uncover patterns, trends, and anomalies. It helps in forming hypotheses and guiding further analysis.
Machine Learning and Predictive Modeling
Machine learning algorithms are used to build predictive models from data. These models can make predictions and decisions based on new, unseen data.
Data Visualization
Visual representations of data, such as graphs and charts, help in understanding complex information quickly. Data visualization aids in conveying insights effectively.
The Data Science Process
Problem Definition
The data science process begins with understanding the problem you want to solve and defining clear objectives.
Data Collection and Understanding
Collect relevant data and understand its context. This step is crucial as the quality of the analysis depends on the quality of the data.
Data Preparation
Clean, preprocess, and transform the data into a suitable format for analysis. This step ensures that the data is accurate and ready for modeling.
Model Building
Select appropriate algorithms and build predictive models using machine learning techniques. This step involves training and fine-tuning the models.
Model Evaluation and Deployment
Evaluate the model's performance using metrics and test datasets. If the model performs well, deploy it for making predictions on new data.
Technologies Driving Data Science
Programming Languages
Languages like Python and R are widely used in data science due to their extensive libraries and versatility.
Machine Learning Libraries
Libraries like Scikit-Learn and TensorFlow prov
Python for Data Analysis: A Comprehensive GuideAivada
In an era where data reigns supreme, the importance of data analysis for insightful decision-making cannot be overstated. Python, with its ease of learning and a plethora of libraries, stands as a preferred choice for data analysts.
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
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
2. ● Data Scientist at Mekari (Jun 2021-Present)
● Data Science Intern at Mekari (Jan 2021-Apr 2021)
● Part-time AI Researcher at Bisa AI (Aug 2020-Nov
2020)
● AI Engineer Intern at Bisa AI (Apr 2020-Aug 2020)
Ammar Chalifah
Teknik Biomedis ITB 2017 @ammarchalifah
ammarchalifah.com
2
5. 1. Artificial Intelligence Specialist (74% annual growth)
2. Robotics Engineer
3. Data Scientist (37% annual growth)
4. Full Stack Engineer
5. Site Reliability Engineer
6. Customer Success Specialist
7. Sales Development Representative
8. Data Engineer (33% annual growth)
9. Behavioral Health Technician
10. Cybersecurity Specialist
11. Back End Developer
12. Chief Revenue Officer
13. Cloud Engineer
14. JavaScript Developer
15. Product Owner
Top 15 Emerging Jobs in the US
LinkedIn Emerging Jobs Report (2020)
Linkedin 2020 Emerging Jobs Report.
https://business.linkedin.com/content/dam/me/business/en-
us/talent-solutions/emerging-jobs-
report/Emerging_Jobs_Report_U.S._FINAL.pdf
5
6. Demand for Data Science Skills
Between 2013 and 2015, demand
for data-related skills increased by
59%, 50%, 69%, and 88% for the ICT,
Media and Entertainment,
Professional Services, and Financial
Services industries.
However, Asia Pacific’s proficiency is
lagging behind other regions in key
data science skills.
High demand, low supply.
Demand is growing quickly with
big opportunity in Asia Pacific
World Economic Forum. (2019). Data Science in the New Economy, Insight Report.
http://www3.weforum.org/docs/WEF_Data_Science_In_the_New_Economy.pdf
6
7. Competition.
Company that uses data to make
data-driven decisions will win and
steal the laggards’ market share.
Every industry shows growing
demand for data-related skills. The
demand is expected to keep on
growing in the next several years.
Demand for data-related skills
is growing because it can be
used to extract values from
data.
What Drives Demand in Data-related Jobs?
Almost every
industry shows
growth in demand
for data-related
skills (WEF Report)
7
8. “Wait, what is data?”
It is just a collection of meaningless, raw facts.
8
9. DIKW Pyramid
Data
Raw facts, unprocessed,
unorganized
Information
Organized, processed
data, meaningful
Knowledge
Contextual, mix of values
and experiences
Wisdom
Evaluated understanding,
integrated knowledge
Data is only valuable if we can extract
values from it, by processing it to
create information, knowledge, and
wisdom.
“Yeah, ok. But this concept is too
abstract. What is data? What values can
we get from exploiting it? How can we
extract values from it?”
9
10. Types of Data
UNSW Sydney. (2020). Types of data and scales of measurement. https://studyonline.unsw.edu.au/blog/types-of-
data
Allen, Richard. What are the types of big data? https://www.selecthub.com/big-data-analytics/types-of-big-data-
analytics/
Structured vs.
unstructured
Quantitative vs. qualitative
Discrete vs. continuous
Nominal vs. ordinal
Binary vs. multi-class
Data is just random facts. To get
value, data must be processed.
Wait, but how do we get the
data that we need?
10
11. Data Science Hierarchy of Needs
https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007%5D
Top-of-the-pyramid
products (AI, Deep
Learning, A/B testing, etc)
can only be built on top of
a strong foundation.
11
12. Analytic Ascendancy Model
Data analytics have
different levels based
on difficulty and value:
descriptive, diagnostic,
predictive, and
prescriptive. These are
the values that we
want to get from data.
12
13. ● Data-related skills is growing in demand. Supply is inadequate. Opportunities everywhere!
● Demand is growing because data can be extracted to get values, giving upperhand to those who believe in
data-driven decision making.
● Data is just a collection of meaningless, raw facts. Data need to be processed to get useful information.
● Data have different types, which require different approaches to process them.
● Data science have a hierarchy of needs. Strong foundations in the data environment is needed before value
can be extracted.
● Value from data have different levels based on impact and difficulty.
For the sake of efficiency, we will jump directly to predictive analysis. Suppose we have a collected dataset, so there
are three steps left before we can extract predictive value from our data: (1) exploratory data analysis; (2) feature
engineering; and (3) modelling.
Recap
13
16. Goals of EDA
16
Look at data before making any assumptions.
Size, number of
columns, data
types
Understand
context
Look at data
distribution and
identify outliers
Have a
descriptive
understanding
(centrality,
variability)
Analyze
correlations
And many
more!
19. Unzip the downloaded data. Open Google Colaboratory, then upload the heart.csv file to session storage. Execute
the code snippet below to load the CSV file into a pandas DataFrame.
EDA tips number 1: Read relevant information from the data source (readme files, column descriptions)
and display your data. You can refer to UCI archive page to read the full documentation of the data
(https://archive.ics.uci.edu/ml/datasets/Heart+Disease ). The df.head() line was used to display the
first 5 rows of your data.
Load CSV to DataFrame
import pandas as pd
file_name = "heart.csv"
df = pd.read_csv(file_name)
df.head()
EDA 1
19
20. Observing Table and Reading Docs
Display the data. See the column names, see
the data types.
Browse the documentation. This heart
disease docs can be found on the
university’s archive page:
https://archive.ics.uci.edu/ml/dataset
s/Heart+Disease
After reading the docs and
seeing the table, you realized
that this dataset has 13
columns of features and 1
target. The objective of this
predictive analysis is to
predict the target value based
on features values.
20
21. Next, you want to know the size of your data, the exact data types of each column, existence of empty data points in
your dataset. Pandas provides you easy-to-use functions to do just that in few lines of codes. If you have null values,
you need an extra step to impute or manipulate them.
Data Shape, Types, and Non-null Count
EDA 2 Check data size, columns’
data types, and existence
of null values.
df.shape
df.info()
All your data are
numerical, with no null
values.
21
303 rows, 14 columns.
22. Next, descriptive statistics will help you understand the centrality and variability of each numerical
features.
Descriptive Statistics
EDA 3
df.describe()
22
23. Data Visualization
EDA 4
Freely explore the data. Use data visualization to help make
your exploration more intuitive.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(6, 6))
colors = {1:'red', 0:'blue'}
grouped = df.groupby('target')
for key, group in grouped:
group.plot(ax=ax, kind='scatter', x='age',
y='thalach', label=key, color=colors[key])
plt.show()
23
24. Besides visualizing our data, we also need
to check the correlation between features
and targets. There is possibility that a
feature is heavily correlated with the
target, making an ML approach inefficient.
There is also a possibility that several
features are heavily correlated with each
other, making the use of those features
together unnecessary.
Correlation Analysis
EDA 5
ax, fig = plt.subplots(nrows=1, ncols=1, figsize =
(10,10))
sns.heatmap(df.corr(), annot = True, ax = fig)
24
25. Check outliers. Outliers may cause biased models.
Outlier Checking
EDA 6
fig, ax = plt.subplots(nrows=1, ncols=len(df.columns), figsize =
(20,5))
for i,c in enumerate(df.columns):
sns.boxplot(data=df, y=c, ax = ax[i])
fig.tight_layout()
Box and whisker plot
25
26. Linearity check give us better understanding of the distribution of data points in each feature and
the skewness of the features.
Linearity and Distribution Check
EDA 7
fig, ax = plt.subplots(nrows=1, ncols=len(df.columns), figsize =
(20,5))
for i,c in enumerate(df.columns):
sns.histplot(data=df, y=c, ax = ax[i])
fig.tight_layout()
26
28. Goals of Feature Engineering
28
Clean and process the data to help analysis/modelling
Rescale
numeric value
Clean missing
values (by
dropping or
imputing data)
Combine
multiple
features
Decode data
(categorical to
numerical,
numerical to
ordinal, etc)
Handle outliers
And many
more!
29. The EDA we have done before only give descriptive of inferential statistics. To extract more values from data, the
higher level in analytics ascendancy model is predictive analysis. One popular way to do predictive analysis is by
using a machine learning approach i.e. letting the machine learn by providing inputs (features) and outputs, with the
goal of finding the underlying rules that transform inputs to outputs.
In our hands-on experience, we have 13 features (or inputs), where we want to know the output (whether the patient
has heart problem or not) based on those features. Most of the time, we need to process our input by using feature
engineering.
Predictive Machine Learning Model
ML model
Input Output
Input, or features Output, or labels
29
30. Why?
We are lucky to have a clean, non-null, and all numeric data. Sometimes, you will need to analyze data from not-so-
ideal datasets: which have null values, extreme outliers, or nominal data (e.g. string). We can’t directly pump the data
into our machine learning model, so feature engineering become an important part of data science process.
Besides missing values or nominal data, sometimes we also need to process our numerical data: standardize,
normalize, threshold, etc. Different machine learning models require different input characteristics.
Now, we will explore several important feature engineering techniques, and later on we will implement some of them
to our data.
30
31. FE 1
31
Drop
Numerical
Imputation
Categorical
Imputation
Drop rows or columns with
missing values. Easy to do, but
may cause significant data
loss.
Fill with another numerical
value, such as 0 or median
(depends on case)
Fill with another categorical
value, such as most frequent
value or new category (e.g.
’Others’)
Handling Missing Values
# Drop missing rows
df = df[df.isnull() == False]
# Drop missing columns
df = df[df.columns[df.isnull().mean() == 0]]
# Impute with 0
df = df.fillna(0)
# Impute with median
df = df.fillna(df.median())
# Impute with new categorical
df = df.fillna('Others')
# Impute with most frequent
df['column_name'].fillna(df['column_name'].value_counts().id
xmax(), inplace=True)
32. FE 2
32
Outlier
Detection
Standard deviation vs
percentile
Outliers can be handled by:
- Drop outliers
- Cap outliers
Handling Outliers
#Dropping the outlier rows with standard deviation
factor = 3
upper_lim = df['column'].mean () + df['column'].std () * factor
lower_lim = df['column'].mean () - df['column'].std () * factor
df = df[(df['column'] < upper_lim) & (df['column'] > lower_lim)]
#Dropping the outlier rows with Percentiles
upper_lim = df['column'].quantile(.95)
lower_lim = df['column'].quantile(.05)
df = df[(df['column'] < upper_lim) & (df['column'] > lower_lim)]
#Capping the outlier rows with Percentiles
upper_lim = df['column'].quantile(.95)
lower_lim = df['column'].quantile(.05)
df.loc[(df[column] > upper_lim),column] = upper_lim
df.loc[(df[column] < lower_lim),column] = lower_lim
33. FE 3
33
Binning make model
more robust by
sacrificing information to
create more general (or
regularized) categories. It
prevents overfitting, but
cost performance.
Binning
Rençberoğlu, Emre (2019). Fundamental Techniques of Feature Engineering for Machine Learning. https://towardsdatascience.com/feature-
engineering-for-machine-learning-3a5e293a5114
34. FE 4
34
One-hot encoding encodes categorical data into multi-columns
binary numerical data.
One-hot Encoding
User ID Major
1 Biomedical Engineering
2 Electrical Engineering
3 Electrical Engineering
User
ID
Biomedical
Engineering
Electrical
Engineering
1 1 0
2 0 1
3 0 1
35. FE 5
35
Rescales numerical data. Two most popular scaling methods are
min-max normalization and standardization. Min-max
normalization scales all values to a range between 0 and 1.
Standardization scales all values to a new distribution with 0
mean and 1 standard deviation.
Scaling
# Min-max normalization
df['normalized'] = (df['value'] -
df['value'].min()) / (df['value'].max() -
df['value'].min())
Min-max normalization
# Standardization
df['standardized'] = (df['value'] -
df['value'].mean()) / df['value'].std()
Standardization
38. Regression
Predicted value is a continuous
numerical value.
Performance measured by error.
38
Predicted value is a categorical data.
Performance measured by accuracy.
Classification
Generally, there are two kinds of prediction
https://www.javatpoint.com/regression-vs-classification-in-machine-learning
39. Machine learning model development is an iterative process, with successive trial-and-error. We may end up need to
try a bunch of different feature engineering methods, but we can make an educated guess for our first trial.
● First, we don’t need to process binary numerical data.
● Second, we know there are no outliers based on the histogram in linearity and distribution check.
● Third, there are several numerical value that is not normalized nor standardized. We may need to rescale these
columns.
● Lastly, there are no missing values nor categorical values in the data.
Choice of feature engineering is heavily dependent on which machine learning algorithm we’ll use. So, let’s jump to
the last phase of this workshop: picking our machine learning model!
39
Which feature engineering methods suit our need?
40. What is the function on the graph above?
40
Trivia 101
41. Regression Prediction
41
Regression maps input to a continuous output variable.
Main Idea: Given the regression function is hθ(x) = θ1x + θ0 ,
choose θ0 and θ1 so that hθ(x) is close to y of our training examples (x,y)
Questions that can be answered by regression:
● How expensive is this house?
● How many tonnes of product will be delivered next month?
Example of machine learning regression algorithms:
● Linear regression
Interestingly, an ordinal classification problem can be framed as a regression problem (for example, 3 class with
ordered severity can be seen as a regression).
Src: Machine Learning Andrew Ng, Stanford Edu
42. Classification Prediction
42
Classification maps input variables to probability of output classes. Classification may be binary or multi-class.
Questions that can be answered by classification:
● What animal is this?
● What kind of disease is this?
Example of machine learning regression algorithms:
● Logistic regression
● Naive Bayesian classification
● k-Nearest Neighbours
● Decision Tree
● Random Forest
Interestingly, a classification algorithm can be used to solve regression problems by framing it as a multi-class
classification problem with many classes!
Src: Machine Learning Andrew Ng, Stanford Edu
43. 43
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
logit = LogisticRegression(random_state = 17)
logit.fit(X_train, y_train)
print(accuracy_score(logit.predict(X_test), y_test))
importance = logit.coef_
# summarize feature importance
for x,v in zip(X_train.columns, importance[0]):
print('Feature: {}, Score: {:.5f}'.format(x,v))
# plot feature importance
plt.bar([x for x in range(len(importance[0]))],
importance[0])
plt.show()
Accuracy
Feature importance
44. So, how about our heart
disease data?
44
It’s up to you! Just do some
experiment to find the optimal
model. For now, let’s try to frame
it as a classification problem.
46. [1] Patil, Prasad (2018). What is Exploratory Data Analysis? https://towardsdatascience.com/exploratory-data-
analysis-8fc1cb20fd15
[2] Rençberoğlu, Emre (2019). Fundamental Techniques of Feature Engineering for Machine Learning.
https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114
References
46
47. ● Data Analyst Intern at Moving Walls (Apr - Jul 2021)
● Researcher Intern at NCIRI (Jul - Sep 2020)
● Backend Developer Intern at Bangunindo (Dec 2019-Jan 2020)
Ramadhita Umitaibatin
Teknik Biomedis ITB 2017
@ramadhitau
Ramadhita Umitaibatin
(LinkedIn)
47
Contributors
48. CREDITS: This presentation template was created by
Slidesgo, including icons by Flaticon, and infographics
& images by Freepik.
Thank you~
For further inquiries, please don’t
hesitate to contact me :)
48