Data usually represents unprocessed numbers, pictures or statements; information is typically the result of analyzing or processing the data. Data are usually collected in a raw format and thus the inherent information is difficult to understand. Therefore, raw data need to be summarized, processed and analyzed. These days, data are often summarized, organized, and analyzed with statistical packages or graphics software. Data must be prepared in such a way they are properly recognized by the program being used.No matter how well manipulated, the information derived from the raw data should be presented in an effective format, otherwise, it would be a great loss for both authors and readers.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
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 visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
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.
Top 8 Different Types Of Charts In Statistics And Their UsesStat Analytica
Are you confused about various Types Of Charts In Statistics? In this blog, you will get to learn about the various Types Of Charts In Statistics in detail.
Paper presented in the research methodology workshop. The error if any is regretted and suggestions most welcome. Good for students and researchers alike, enjoy.
DATA VISUALIZATION FOR MANAGERS MODULE 3| Building Visualization| BUSINESS ANALYTICS PAPER 1 |MBA SEM 3| RTMNU NAGPUR UNIVERSITY| BY JAYANTI R PANDE
MBA Notes by Jayanti Pande
#JayantiPande
#MBA
#MBAnotes
#BusinessAnalyticsNotes
The role of fdi in india agriculture slide sharegentlemoro
After globalization, almost every country in Asia has welcome foreign direct investments (FDI) in many sectors and it is growing its limits steadily. India is not an exception, the country has also allowed FDI in various sectors including agriculture. This move has boosted the investment climate in the economy including the agriculture sector. This presentation presents both non-empirical and empirical analysis of the impact of Agricultural foreign direct investments (AFDI). The empirical results revealed that AFDI has a positive impact on Agriculture in India, thus increasing AFDI inflow improves fertilizer & pesticide consumption, irrigation adoption and production levels. Moreover, AFDI has increased the involvement of India in the international market (trade). However, AFDI does not has an effect on capital formation in Agriculture.
Questionnaire /Schedule design is a systematic approach/process of including relevant questions in a questionnaire in such a way that the best or accurate responses are obtained from respondent with very little / no discomfort on the part of the respondent as well as the enumerator.The most important part of the survey process is the creation of questions that accurately measure the opinions, experiences and behaviors of the target group. Questionnaire / Schedules design is one of the most critical stages in the survey research process and therefore has to be given the utmost attention. This power point presentation will guide you through schedules and questionnaire design.
The labour and capital of a country acting on its natural resources produce annually a certain net aggregate of commodities material and immaterial including services of all kinds- (Marshall)
To achieve success in marketing, effort must be made to have glimpse of the big picture and the activities that must be performed to achieve the set marketing objectives. These set of activities are called marketing functions
Co-ordination is the essence of management (because it permeates all levels of management). It is implicit and inherent in all functions of management.
As gender issues have become more mainstreamed in scientific research and media reports, confusion associated with the terms sex and gender has decreased. However, the discussion on sex and gender be integrated into our day to day conversations.
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).
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.
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/
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.
1. The use of Data visualization to
tell effective stories
By:
Moro Seidu (2018)
Email: gentlemoro@iwatchafrica.org
2.
3. Outline of Presentation
PART ONE: THEORY
Data Presentation & Visualization
Data
Forms of Data Presentation & Visualization
Text form;
tabular form;
graphical form.
Summary
PART TWO: PRACTICAL
Text Mining
Sentimental Analysis
Wordcloud Analysis
4. Data
Data usually represents unprocessed numbers,
text, pictures or statements
Data are usually collected in a raw format and
thus the inherent information is difficult to
understand
Therefore, raw data need to be summarized,
processed, and analysed.
Data set needs to be presented in a certain way
depending on what it is used for.
5. Data…
Planning how the data will be presented is essential
before appropriately processing raw data.
However, no matter how well the information derived
from the raw data is manipulated, it should be presented
in an effective format, otherwise, it would be a great loss
for both authors and readers.
These days, data are often summarized, organized, and
analysed with statistical packages or graphics software.
Data must be prepared in such a way that they are
properly recognized by the program being used.
6. Data…
This presentation does not discuss data preparation
process, which involves creating a data frame,
creating/changing rows and columns, changing the level of
a factor, categorical variable, coding, dummy variables,
variable transformation, data transformation, missing value,
outlier treatment, and noise removal.
7. Data Presentation/Visualisation
Methods of presentation must be determined according
to the
data format,
the method of analysis to be used, and
the information to be emphasized.
Even when the same information is being conveyed,
different methods of presentation must be employed
depending on what specific information is going to be
emphasized.
8. Data Presentation/Visualisation…
A method of presentation must be chosen after carefully
weighing the advantages and disadvantages of different
methods of presentation.
This conference shall focus on three different methods
of presentation:
Text form
tabular form
graphical form
One thing to always bear in mind regardless of what
method is used, however, is the simplicity of
presentation.
9. Text presentation
Text is the main method of conveying information as it
is used to explain results and trends, and provide
contextual information.
Data are fundamentally presented in paragraphs or
sentences.
Text can be used to provide interpretation or emphasize
certain data.
If quantitative information to be conveyed consists of
one or two numbers, it is more appropriate to use text
(written language) than tables or graphs.
10. Text presentation…
For instance, information about the occurrence of
accident in 2016–2017 can be presented with the use of
a few numbers:
“The occurrence of accident was 11% in 2016 and 15%
in 2017; no significant difference of occurrence was
found between the two years.”
If this information were to be presented in a graph or a
table, it would occupy an unnecessarily large space on
the page, without enhancing the readers'
understanding of the data.
11. Text presentation…
If more data are to be presented, or other
information such as that regarding data trends
are to be conveyed, a table or a graph would be
more appropriate.
By nature, data take longer to read when
presented as texts and when the main text
includes a long list of information, readers and
reviewers may have difficulties in understanding
the information.
12. Table presentation
Tables are the most appropriate for presenting
individual information, and can present both
quantitative and qualitative information.
Advantages of using tables
Tables can accurately present information that cannot
be presented with a graph. A number such as
“132.145852” can be accurately expressed in a table.
Information with different units can be presented
together on a table. For instance, atmospheric
temperature, speed of a vehicle, number of drivers,
travelling distance and time of travel can be presented
together in one table.
tables are useful for summarizing and comparing
quantitative information of different variables.
13. Table presentation…
Disadvantages of using tables
the interpretation of information takes longer in tables than in
graphs,
tables are not appropriate for studying data trends.
since all data are of equal importance in a table, it is not easy to
identify and selectively choose the information required.
14. Table presentation…
Heat maps help to improve visualization of information on tables.
Heat maps help to further visualize the information presented in a table
by applying colours to the background of cells.
By adjusting the colours or colour saturation, information is conveyed in
a more visible manner, and readers can quickly identify the information of
interest.
Software such as Excel (in Microsoft Office) have features that enable
easy creation of heat maps through the options available on the
“conditional formatting” menu.
15. Graph presentation
Whereas tables can be used for presenting all the
information, graphs simplify complex information by
using images and emphasizing data patterns or trends,
and are useful for summarizing, explaining, or exploring
quantitative data.
While graphs are effective for presenting large amounts
of data, they can be used in place of tables to present
small sets of data.
A graph format that best presents information must be
chosen so that readers and reviewers can easily
understand the information.
17. Scatter plot
Scatter plots present data on the x- and y-axes and are
used to investigate an association (correlation)
between two variables.
A point represents each individual or object, and an
association between two variables can be studied by
analysing patterns across multiple points.
A regression line is added to a graph to determine
whether the association between two variables can be
explained or not.
If multiple points exist at an identical location, the
correlation level may not be clear.
In this case, a correlation coefficient or regression line
can be added to further elucidate the correlation.
18. Bar graph and histogram
A bar graph is used to indicate and compare values
in a discrete category or group, and the frequency or
other measurement parameters (i.e. mean).
Depending on the number of categories, and the
size or complexity of each category, bars may be
created vertically or horizontally.
The height (or length) of a bar represents the
amount of information in a category.
Bar graphs are flexible, and can be used in a
grouped or subdivided bar format in cases of two or
more data sets in each category.
19. Bar graph and histogram…
By comparing the endpoints of bars, one
can identify the largest and the smallest
categories, and understand gradual
differences between each category.
One form of vertical bar graph is the
stacked vertical bar graph. A stack vertical
bar graph is used to compare the sum of
each category, and analyse parts of a
category.
20. Pie chart
A pie chart, which is used to represent nominal data
(in other words, data classified in different
categories), visually represents a distribution of
categories.
It is generally the most appropriate format for
representing information grouped into a small
number of categories.
It is also used for data that have no other way of
being represented aside from a table (i.e. frequency
table).
21. Line plot
A line plot is useful for representing time-series data such
as monthly electricity bills and yearly unemployment rates;
in other words, it is used to study variables that are
observed over time.
Line graphs are especially useful for studying patterns and
trends across data that include climatic influence, large
changes or turning points, and
are also appropriate for representing not only time-series
data, but also data measured over the progression of a
continuous variable such as distance.
It is also useful to represent multiple data sets on a single
line graph to compare and analyse patterns across
different data sets.
22. Box and whisker chart
A box and whisker chart does not make any assumptions
about the underlying statistical distribution, and
represents variations in samples of a population;
therefore, it is appropriate for representing
nonparametric data.
A box and whisker chart consists of boxes that represent
interquartile range (one to three), the median and the
mean of the data, and whiskers presented as lines
outside of the boxes.
Whiskers can be used to present the largest and smallest
values in a set of data or only a part of the data.
The spacing at both ends of the box indicates dispersion
in the data. The relative location of the median
demonstrated within the box indicates skewness
23. Summary
Text, tables, and graphs are effective communication media that present and convey data
and information.
They aid readers in understanding the content of research, sustain their interest, and
effectively present large quantities of complex information.
As readers will scan through these presentations before reading the entire text, their
importance cannot be disregarded.
For this reason, authors or publishers must pay as close attention to selecting appropriate
methods of data presentation as when they were collecting data of good quality and
analysing them.
Understanding of different methods of data presentation and their appropriate use will
enable one to develop the ability to recognize and interpret inappropriately presented
data or data presented in such a way that it deceives readers' eyes
24. PART II ( PRACTICALS)
Materials:
R, RStudio,
Library (tm, tidytext, janeaustenr, wordcloud, dplyr, stringr, gplot, SnowballC),
Exercise
Text mining
Sentimental Analysis
Wordcloud analysis