String Operations in Python 3.
For beginners
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String Manipulation in Python 3.
For beginners
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https://youtu.be/6Ey9bQ-KJuk

#KeyNote #UnsupervisedMachineLearning #Clustering #k-means

Clustering is one of unsupervised machine learning algorithm. There are many models for clustering out there. Despite its simplicity, the K-means is vastly used for clustering in many data science applications, especially useful if you need to quickly discover insights from unlabeled data.

Some real-world applications of k-means:

- Customer segmentation
- Understand what the visitors of a website are trying to accomplish
- Pattern recognition
- Machine learning
- Data compression

#KeyNote #BusinessAnalytics #DataScience

Cross Industry Standard Process for Data Mining (CRISP-DM)
"A data mining process model that describes commonly used approaches that data mining experts use to tackle problems... it was the leading methodology used by industry data miners." -Wikipedia

CRISP-DM Steps

1. Business Issue Understanding
2. Data Understanding
3. Data Preparation
4. Analysis/Modeling
5. Validation
6. Presentation/Visualization

#KeyNote #DataAnanlysisMethodology #BusinessAnalytics

Type of data analysis methodology

Predictive

Predictive analytics uses existing data to predict a future outcome. For example, a company may use predictive analytics to forecast demand or whether a customer will respond to an advertising campaign.

Geospatial

This type of analysis uses location based data to help drive your conclusions. Some examples are:

Identifying customers by a geographic dimension such as zip code, state, or county, or
Calculating the distance between addresses and your stores, or
Creating a trade area based upon your customer locations for further analysis

Some types of Geospatial analysis require the use of special software - such as software that can convert an address to Latitude & Longitude, or can calculate the drive time between two geographic points on a map.

Segmentation

Segmentation is the process of grouping data together. Groups can be simple, such as customers who have purchased different items, to more complex segmentation techniques where you identify stores that are similar based upon the demographics of their customers.

Aggregation

This methodology simply means calculating a value across a group or dimension and is commonly used in data analysis. For example, you may want to aggregate sales data for a salesperson by month - adding all of the sales closed for each month. Then, you may want to aggregate across dimensions, such as sales by month per sales territory. In this scenario, you could calculate the sales per month for each salesperson, and then add the sales per month for all salespeople in each region.

Aggregation is often done in reporting to be able to “ slice and dice” information to help managers make decisions and view performance.

Descriptive

Descriptive statistics provides simple summaries of a data sample. Examples could be calculating average GPA for applicants to a school, or calculating the batting average of a professional baseball player. In our electricity supply scenario, we could use descriptive statistics to calculate the average temperature per hour, per day, or per date.

Some of the commonly used descriptive statistics are Mean, Median, Mode, Standard Deviation, and Interquartile range.

Pro Python 3: Features and Tools for Professional Development, Third Edition

#pythonbooks @epythonlab

MIT launched a New free Course on Machine learning : Click here
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Researchers release a huge dataset of 20 million #malware samples, which also contains metadata, labels, and features, aiming to help research for Machine Learning based malware detection.

Learn more about SOREL-20M here: https://thehackernews.com/2020/12/sorel-20m-huge-dataset-of-20-million.html

Introduction to Python for Science and Engineering

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#DataScience Vs #DataAnalytics Vs #Big Data
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#Tip about data science #datascience #python @epythonlab

#Discussion #Python

Why you would like to learn python? Explain your reasons?

Post to the discussion group. Let us discuss together. @pydiscussion

#Tip about Python
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Natural Language Processing with Spark NLP

@epythonlab #nlpbooks

The battle of Neighborhoods:
Investigate the similarity or
dissimilarity of New York and
Toronto City
https://www.linkedin.com/pulse/battle-neighborhoods-investigate-similarity-new-york-toronto-techane

Python for Bioinformatics

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Python for Data Science and AI

@epythonlab #datasciencebooks

#today_tip All 33 keywords in Python
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180 Data Science and Machine Learning Projects with Python

https://medium.com/coders-camp/180-data-science-and-machine-learning-projects-with-python-6191bc7b9db9

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