Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
Pandas data transformational data structure patterns and challenges finalRajesh M
The needs and requirements for Data Transformation technologies be it Big Data, Machine Learning, Deep Learning or Simple Search and Reporting is still maturing due to the fundamental focus loss on Data Structural Patterns that can enable it. This presentation is oriented towards it.
Understanding the Machine Learning AlgorithmsRupak Roy
includes distinguishable definitions from supervised vs unsupervised learning with their types and the workflow, algorithm map;
Let me know if anything is required. Happy to help, Talk soon! #bobrupakroy
Data preprocessing techniques are applied before mining. These can improve the overall quality of the patterns mined and the time required for the actual mining.
Some important data preprocessing that must be needed before applying the data mining algorithm to any data sets are completely described in these slides.
http://exelare.com/sourcing-vs-recruiting/ | Get to know a day in the life of a recruiter and sourcer and understand the main differences between the two using this easy-to-read infographic.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
Pandas data transformational data structure patterns and challenges finalRajesh M
The needs and requirements for Data Transformation technologies be it Big Data, Machine Learning, Deep Learning or Simple Search and Reporting is still maturing due to the fundamental focus loss on Data Structural Patterns that can enable it. This presentation is oriented towards it.
Understanding the Machine Learning AlgorithmsRupak Roy
includes distinguishable definitions from supervised vs unsupervised learning with their types and the workflow, algorithm map;
Let me know if anything is required. Happy to help, Talk soon! #bobrupakroy
Data preprocessing techniques are applied before mining. These can improve the overall quality of the patterns mined and the time required for the actual mining.
Some important data preprocessing that must be needed before applying the data mining algorithm to any data sets are completely described in these slides.
http://exelare.com/sourcing-vs-recruiting/ | Get to know a day in the life of a recruiter and sourcer and understand the main differences between the two using this easy-to-read infographic.
El amor no existe (Cuento). Historik. Revista virtual de investigación en historia, arte y humanidades. Año 2016. Vol. 5 - Nº 14. Junio-septiembre. ISSN 2027-7652. Disponible en: http://www.revistahistorik.com/eliterario14.html
Introduction to Data Science, Prerequisites (tidyverse), Import Data (readr), Data Tyding (tidyr),
pivot_longer(), pivot_wider(), separate(), unite(), Data Transformation (dplyr - Grammar of Manipulation): arrange(), filter(),
select(), mutate(), summarise()m
Data Visualization (ggplot - Grammar of Graphics): Column Chart, Stacked Column Graph, Bar Graph, Line Graph, Dual Axis Chart, Area Chart, Pie Chart, Heat Map, Scatter Chart, Bubble Chart
Start machine learning in 5 simple stepsRenjith M P
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
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 :
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#DataAnalyticstechnical Interview
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Frameworks provide structure. The core objective of the Big Data Framework is...RINUSATHYAN
Frameworks provide structure. The core objective of the Big Data Framework is to provide a structure for enterprise organisations that aim to benefit from the potential of Big Data
Several Python libraries offer solid execution of a range of machine learning algorithms. One of the best called is Scikit-Learn, a package that supports accurate versions of a large number of standard algorithms. A clean, uniform features and Scikit-Learn, and streamlined API, as well as by beneficial and complete online documentation.
Data pipelines are the heart and soul of data science. Are you a beginner looking to understand data pipelines? A glimpse into what they are and how they work.
Congrats ! You got your Data Science JobRohit Dubey
Congrats ! You got your Data Science Job after completion of this presentation course.
What can you find on this presentation course?
I aim to provide as many resources as possible for learning Data Science. These resources include:
Course to upskill yourself in analytics and data science
Real life industry problems being released in form of contests
This slide will help you get:
Jobs – Apply on data science jobs to start or improve your career
DSAT – Access your data science knowledge using our adaptive test
Tips and tricks related to Data Science, Machine Learning, Business Analytics and Business Intelligence tools
Case studies: Case studies of problems and their analytical solutions Interviews of Business Analytics & Business Intelligence leaders.
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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.
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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.
1. Intorduction:
Hi everyone, this session will be dealing with Data Analysis using R Language. Many would
have found difficult to get started with Data Analysis and R as well. I can assure this will be very
helpful for the beginners who really seeks help.
So what is Data Analysis? By definition, it is the process of evaluating data using analytical
and logical reasoning to examine each component of the data provided. This form of analysis is
just one of the many steps that must be completed when conducting a research experiment. Data
from various sources is gathered, reviewed, and then analyzed to form some sort of finding or
conclusion. There are a variety of specific data analysis method, some of which include data
mining, text analytics, business intelligence, and data visualizations. But in a very simple way,
we can say that FINDING PATTERNS OR DATA INSIGHTS which will help to get
concentrate business decisions/exceed customer experience.
And R is a statistical tool used for data analysis and data science as well. R has in-built
functions and provides a wide variety of statistical (linear and nonlinear modelling, classical
statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and
is highly extensible.
Basics:
To become a Data Analyst you should be strong in the following areas:
Statistics
Data Mining
Python/R
Distributed Computing
Let's start with Statistics, which is further classified into descriptive statistics( measure of central
tendency, measure of dispersion, shape of data ), inferential statistics( infer from the sample data what the
population might think ), explorative statistics( analysing to summarize their main characteristics ).
Next comes the Data Mining, which includes data pre-processing( data cleaning, data transformation
), modelling etc.
When comes to R i have already given a introduction about it, and Python, its again a wonderful
programming language for Data Analysis which has many packages namely pandas, scikit-learn,
matplotlib for visualization. R and Python are the 2 stars preferred by data analysts. Both are having their
own strength and weakness.
For Distributed Computing, i mean HADOOP technology, which is used mainly for storage and
processing time of big data. Since history, data volume and variety is getting increased distributed
computing been the limelight with Hadoop eco-system, which is simply called big data technology.
Nowadays Hadoop has become the synonym for big data.
2. Steps involved:
The actual session starts here.. Make sure that the environment is ready. I 've explained the
steps to be followed in detail...
Step-1: PROBLEM STATEMENT
You should be very clear about the problem statement given, what you are expected to do.
Ask yourselves, what problem you have, is the data given is sufficient to solve the given problem
statement.
Step-2: DATA PREPROCESSING
This is a very important process that a Data Analyst under goes. Initially you should
collect the required data. First set the working directory where the file is present using setwd().
you can use any of the code to read the file with respect to the file format.
read.csv()
read.table()
read.xlsx()
for XML do the following
library(XML)
doc <- xmlTreeParse(fileUrl, useInternal = TRUE)
And convert the loaded data to data frame to make the manipulation easy using
data.frame(). Next comes the data cleaning, to handle missing values you can make use of
is.na(), to remove missing values you can use na.omit() or na.exclude().
Next is data transformation, here we have type transformation which can be done by
as.numeric()/as.double()/as.factor() etc. Normalization and Standardization also comes under
data transformation.
Once the preprocessing process is over 60% of work is over.
Step-3: POPULATION AND SAMPLE
Before getting into this, load the necessary packages needed using library("package
name"), eg: library("caret"), library("class"). And dont forget to initialize the seed value, make
use of set.seed(). Coming to the point, it is very important to to split the given dataset to training
and testing data, since training data represents the population which is sample. Testing data
should only be used to test the model, unless you should not touch it. Model is built only using
the training data.
This can be done by many methods here i have used createDataPartition() which is the
function available in caret package.
index <- createDataPartition(y, times = 1, p = 0.5, list = TRUE, ...)
where, y - predictor variable
times - number of partitions
p - percentage of data that will be trained
list - logical - should the results be in a list (TRUE) or a matrix with the number of rows
3. equal to floor(p * length(y)) and times columns.
training_data <- dataframe[index,]
testing_data <- dataframe[-index,]
Now training and testing data are partitioned and the model is ready to train.
Step-4 : DATA MODELING
To train the model we can use the function train() available in the caret package.
model_trained <- train(y, x, method = "rf", preProcess = NULL, ...)
Here Y is the predictor variable and X(x1 to xn) is the control/independent variables.
There are many other methods like rf(random forest) such as glm(generic linear model).
Refer http://caret.r-forge.r-project.org/bytag.html to know more about the models. Each model
has its own restrictions.
Step-5 : PREDICTION
Once the model is been trained we can predict the model using the function available -
predict().
predicted_model <- predict(model_trained, testing_data)
You can also see whether your model is built and classified perfectly or not. Using
confusionMatrix() we can achieve this.
check <- confusionMatrix(y, predicted_model)
In other words, you can use this confusion matrix to check against the training model to
see how it will work for the training data.
Step-6 : PLOTS
The last step invloves plotting, you can make use of plot() which can be box plot or scatter
plot or histogram or as per the requirement. As per the saing "1 picture speaks more than 1000
words", you can make use of plots to describe your results.
Step-7 : REPORT
Finally for report submission you can use Rmarkdown, where the file should be saved
with the extension .rmd. To use Rmoarkdown check for the packages that are needed to be
installed.
--Thank You--