The Basics of Statistics for Data Science By StatisticiansStat Analytica
Want to learn data science, but don't know how to start learn data science from scratch? Here in this presentation you will going to learn the basics of statistics for data science. Start learn these basic statistics to get the good command over data science.
Statistics For Data Science | Statistics Using R Programming Language | Hypot...Edureka!
( ** Data Science Certification Using R: https://www.edureka.co/data-science ** )
This Edureka tutorial on "Statistics for Data Science" talks about the basic concepts of Statistics, which is primarily an applied branch of mathematics, that attempts to make sense of observations in the real world. Statistics is generally regarded as one of the most crucial aspects of data science.
Introduction to statistics
Basic Terminology
Categories in Statistics
Descriptive Statistics
Reasons for moving to R
Descriptive Statistics in R Studio
Inferential Statistics
Inferential Statistics using R Studio
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
The Basics of Statistics for Data Science By StatisticiansStat Analytica
Want to learn data science, but don't know how to start learn data science from scratch? Here in this presentation you will going to learn the basics of statistics for data science. Start learn these basic statistics to get the good command over data science.
Statistics For Data Science | Statistics Using R Programming Language | Hypot...Edureka!
( ** Data Science Certification Using R: https://www.edureka.co/data-science ** )
This Edureka tutorial on "Statistics for Data Science" talks about the basic concepts of Statistics, which is primarily an applied branch of mathematics, that attempts to make sense of observations in the real world. Statistics is generally regarded as one of the most crucial aspects of data science.
Introduction to statistics
Basic Terminology
Categories in Statistics
Descriptive Statistics
Reasons for moving to R
Descriptive Statistics in R Studio
Inferential Statistics
Inferential Statistics using R Studio
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
Introduction to Statistical Machine Learningmahutte
This course provides a broad introduction to the methods and practice
of statistical machine learning, which is concerned with the development
of algorithms and techniques that learn from observed data by
constructing stochastic models that can be used for making predictions
and decisions. Topics covered include Bayesian inference and maximum
likelihood modeling; regression, classi¯cation, density estimation,
clustering, principal component analysis; parametric, semi-parametric,
and non-parametric models; basis functions, neural networks, kernel
methods, and graphical models; deterministic and stochastic
optimization; over¯tting, regularization, and validation.
Missing data handling is typically done in an ad-hoc way. Without understanding the repurcussions of a missing data handling technique, approaches that only let you get to the "next step" in your analytics pipeline leads to terrible outputs, conclusions that aren't robust and biased estimates. Handling missing data in data sets requires a structured approach. In this workshop, we will cover the key tenets of handling missing data in a structured way
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
Data Analytics PowerPoint Presentation SlidesSlideTeam
This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Analytics PowerPoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
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
Linear Regression vs Logistic Regression | EdurekaEdureka!
YouTube: https://youtu.be/OCwZyYH14uw
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka PPT on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session:
Types of Machine Learning
Regression Vs Classification
What is Linear Regression?
What is Logistic Regression?
Linear Regression Use Case
Logistic Regression Use Case
Linear Regression Vs Logistic Regression
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
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Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Introduction to Statistical Machine Learningmahutte
This course provides a broad introduction to the methods and practice
of statistical machine learning, which is concerned with the development
of algorithms and techniques that learn from observed data by
constructing stochastic models that can be used for making predictions
and decisions. Topics covered include Bayesian inference and maximum
likelihood modeling; regression, classi¯cation, density estimation,
clustering, principal component analysis; parametric, semi-parametric,
and non-parametric models; basis functions, neural networks, kernel
methods, and graphical models; deterministic and stochastic
optimization; over¯tting, regularization, and validation.
Missing data handling is typically done in an ad-hoc way. Without understanding the repurcussions of a missing data handling technique, approaches that only let you get to the "next step" in your analytics pipeline leads to terrible outputs, conclusions that aren't robust and biased estimates. Handling missing data in data sets requires a structured approach. In this workshop, we will cover the key tenets of handling missing data in a structured way
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
Data Analytics PowerPoint Presentation SlidesSlideTeam
This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Analytics PowerPoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
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
Linear Regression vs Logistic Regression | EdurekaEdureka!
YouTube: https://youtu.be/OCwZyYH14uw
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka PPT on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session:
Types of Machine Learning
Regression Vs Classification
What is Linear Regression?
What is Logistic Regression?
Linear Regression Use Case
Logistic Regression Use Case
Linear Regression Vs Logistic Regression
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
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
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Data Mining StepsProblem Definition Market AnalysisCsharondabriggs
Data Mining Steps
Problem Definition
Market Analysis
Customer Profiling, Identifying Customer Requirements, Cross Market Analysis, Target Marketing, Determining Customer purchasing pattern
Corporate Analysis and Risk Management
Finance Planning and Asset Evaluation, Resource Planning, Competition
Fraud Detection
Customer Retention
Production Control
Science Exploration
> Data Preparation
Data preparation is about constructing a dataset from one or more data sources to be used for exploration and modeling. It is a solid practice to start with an initial dataset to get familiar with the data, to discover first insights into the data and have a good understanding of any possible data quality issues. The Datasets you are provided in these projects were obtained from kaggle.com.
Variable selection and description
Numerical – Ratio, Interval
Categorical – Ordinal, Nominal
Simplifying variables: From continuous to discrete
Formatting the data
Basic data integrity checks: missing data, outliers
> Data Exploration
Data Exploration is about describing the data by means of statistical and visualization techniques.
· Data Visualization:
o
Univariate
analysis explores variables (attributes) one by one. Variables could be either categorical or numerical.
Univariate Analysis - Categorical
Statistics
Visualization
Description
Count
Bar Chart
The number of values of the specified variable.
Count%
Pie Chart
The percentage of values of the specified variable
Univariate Analysis - Numerical
Statistics
Visualization
Equation
Description
Count
Histogram
N
The number of values (observations) of the variable.
Minimum
Box Plot
Min
The smallest value of the variable.
Maximum
Box Plot
Max
The largest value of the variable.
Mean
Box Plot
The sum of the values divided by the count.
Median
Box Plot
The middle value. Below and above median lies an equal number of values.
Mode
Histogram
The most frequent value. There can be more than one mode.
Quantile
Box Plot
A set of 'cut points' that divide a set of data into groups containing equal numbers of values (Quartile, Quintile, Percentile, ...).
Range
Box Plot
Max-Min
The difference between maximum and minimum.
Variance
Histogram
A measure of data dispersion.
Standard Deviation
Histogram
The square root of variance.
Coefficient of Deviation
Histogram
A measure of data dispersion divided by mean.
Skewness
Histogram
A measure of symmetry or asymmetry in the distribution of data.
Kurtosis
Histogram
A measure of whether the data are peaked or flat relative to a normal distribution.
Note: There are two types of numerical variables, interval and ratio. An interval variable has values whose differences are interpretable, but it does not have a true zero. A good example is temperature in Centigrade degrees. Data on an int ...
UNIT - 5 : 20ACS04 – PROBLEM SOLVING AND PROGRAMMING USING PYTHONNandakumar P
UNIT-V INTRODUCTION TO NUMPY, PANDAS, MATPLOTLIB
Exploratory Data Analysis (EDA), Data Science life cycle, Descriptive Statistics, Basic tools (plots, graphs and summary statistics) of EDA, Philosophy of EDA. Data Visualization: Scatter plot, bar chart, histogram, boxplot, heat maps, etc.
Data reduction: breaking down large sets of data into more-manageable groups or segments that provide better insight.
- Data sampling
- Data cleaning
- Data transformation
- Data segmentation
- Dimension reduction
Can you teach coding to kids in a mobile game app in local languages. Do you need to be good in English to learn coding in R or Python?
How young can we train people in coding-
something we worked on for six months but now we are giving up due to lack of funds is this idea.
Feel free to use it, it is licensed cc-by-sa
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).
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.
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.
3. Basics of Data Science
http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
the culture of academia, which does not reward researchers for understanding technology.
DANGER ZONE- this overlap of skills gives people the ability to create what appears to be
a legitimate analysis without any understanding of how they got there or
what they have created
Being able to manipulate text files at the command-line,
understanding vectorized operations, thinking algorithmically;
these are the hacking skills that make for a successful data hacker.
data plus math and statistics only gets you machine learning,
which is great if that is what you are interested in, but not if you are doing data science
4. What is Business Analytics
Definition – study of business data using statistical techniques and
programming for creating decision support and insights for achieving
business goals
Predictive- To predict the future.
Descriptive- To describe the past.
5. Data
Data is a set of values of qualitative or quantitative variables. An example of qualitative
data would be an anthropologist's handwritten notes about her interviews. data is
collected by a huge range of organizations and institutions, including businesses (e.g.,
sales data, revenue, profits, stock price), governments (e.g., crime rates, unemployment
rates, literacy rates) and non-governmental organizations (e.g., censuses of the number
of homeless people by non-profit organizations). Data is measured, collected and
reported, and analyzed, whereupon it can be visualized using graphs, images or other
analysis tools.
https://en.wikipedia.org/wiki/Data
Data is distinct pieces of information, usually formatted in a special way. All software is
divided into two general categories: data and programs . Programs are collections of
instructions for manipulating data.Data can exist in a variety of forms -- as numbers or
text on pieces of paper, as bits and bytes stored in electronic memory, or as facts stored
in a person's mind.
http://www.webopedia.com/TERM/D/data.html
6. Data
https://en.oxforddictionaries.com/definition/data Definition of data in English:
data
noun
[mass noun] Facts and statistics collected together for reference or analysis:
‘there is very little data available’
The quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted
in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media.
Philosophy Things known or assumed as facts, making the basis of reasoning or calculation.
8. Variable
Ordinal variables are variables that have two or more categories just like nominal variables only the categories can also be ordered or
ranked eg Excellent- Horrible. Dichotomous variables are nominal variables which have only two categories or levels. Nominal
variables are variables that have two or more categories, but which do not have an intrinsic order.
Interval variables are variables for which their central characteristic is that they can be measured along a continuum and they have a
numerical value (for example, temperature measured in degrees Celsius or Fahrenheit).
Ratio variables are interval variables, but with the added condition that 0 (zero) of the measurement indicates that there is none of that
variable. a distance of ten metres is twice the distance of 5 metres.
https://statistics.laerd.com/statistical-guides/types-of-variable.php
.
9. Central Tendency
Mean
Arithmetic Mean- the sum of the values divided by the number of values.
The geometric mean is an average that is useful for sets of positive numbers that are interpreted according to their product and
not their sum (as is the case with the arithmetic mean) e.g. rates of growth.
Median
the median is the number separating the higher half of a data sample, a population, or a probability distribution, from the lower
hal
Mode-
The "mode" is the value that occurs most often.
10. Dispersion
Range
the range of a set of data is the difference between the largest and smallest values.
Variance
mean of squares of differences of values from mean
Standard Deviation
square root of its variance
Frequency
a frequency distribution is a table that displays the frequency of various outcomes in a sample.
11. Distribution
The distribution of a statistical data set (or a population) is a listing or function showing all the possible values (or intervals) of
the data and how often they occur. When a distribution of categorical data is organized, you see the number or percentage of
individuals in each group.
http://www.dummies.com/education/math/statistics/what-the-distribution-tells-you-about-a-statistical-data-set/
14. Skewed Distribution
skewness is a measure of
the asymmetry of the
probability distribution of a
real-valued random variable
about its mean. The
skewness value can be
positive or negative, or even
undefined.
Image
https://en.wikipedia.org/wiki/F
ile:Negative_and_positive_sk
ew_diagrams_(English).svg
15. Skewed Distribution
kurtosis is a measure of the
"tailedness" of the probability distribution
of a real-valued random variable. kurtosis
is a descriptor of the shape of a probability
distribution
Image
http://www.itl.nist.gov/div898/handbook/eda/
section3/eda35b.htm
16. Skewed Distribution
skewness
returns value of
skewness,
kurtosis
returns value of kurtosis,
https://cran.r-project.org/
web/packages/moments
/moments.pdf
Image
http://www.janzengroup.
net/stats/lessons/descrip
tive.html
17. Distributions
Bernoulli
Distribution of a random variable which takes value 1 with success probability and value 0 with failure probability. It
can be used, for example, to represent the toss of a coin
19. Distributions
Poisson
a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time
and/or space if these events occur with a known average rate and independently of the time since the last event
20. Probability
Probability Distribution
The probability density function (pdf) of the normal distribution, also called Gaussian or "bell curve", the most important
continuous random distribution. As notated on the figure, the probabilities of intervals of values correspond to the area
under the curve.
26. Central Limit Theorem
Central Limit Theorem -
In probability theory, the central limit theorem (CLT) states that, given certain conditions, the arithmetic mean of a sufficiently
large number of iterates of independentrandom variables, each with a well-defined expected value and well-defined variance, will
be approximately normally distributed, regardless of the underlying distribution.
27. Hypothesis testing
Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true. The
usual process of hypothesis testing consists of four steps.
1. Formulate the null hypothesis (commonly, that the observations are the result of pure chance) and the
alternative hypothesis (commonly, that the observations show a real effect combined with a component of
chance variation).
2. Identify a test statistic that can be used to assess the truth of the null hypothesis.
3. Compute the P-value, which is the probability that a test statistic at least as significant as the one observed
would be obtained assuming that the null hypothesis were true. The smaller the -value, the stronger the
evidence against the null hypothesis.
4. Compare the -value to an acceptable significance value (sometimes called an alpha value). If , that the
observed effect is statistically significant, the null hypothesis is ruled out, and the alternative hypothesis is
valid.
http://mathworld.wolfram.com/HypothesisTesting.html
34. Chi Square Distribution
Problem
Find the 95th
percentile of the Chi-Squared distribution with 7 degrees of freedom.
Solution
We apply the quantile function qchisq of the Chi-Squared distribution against the decimal values 0.95.
> qchisq(.95, df=7) # 7 degrees of freedom
[1] 14.067
http://www.r-tutor.com/elementary-statistics/probability-distributions/chi-squared-distribution
35. Normal Distribution
we are looking for the percentage of students scoring
higher than 84 , we apply the function pnorm of the normal
distribution with mean 72 and standard deviation 15.2. We
are interested in the upper tail of the normal distribution.
> pnorm(84, mean=72, sd=15.2, lower.tail=FALSE)
[1] 0.21492
36. Student T Distribution
Problem
Find the 2.5th
and 97.5th
percentiles of the Student t distribution with 5 degrees of freedom.
Solution
We apply the quantile function qt of the Student t distribution against the decimal values 0.025 and 0.975.
> qt(c(.025, .975), df=5) # 5 degrees of freedom
[1] -2.5706 2.5706