1593888900

Data Science, Machine Learning and Deep Learning are the recent hot topics of research and development. These are not new but have currently gained high importance and high attention due to advancements in tools, technology, and computing power of systems. But the main part is the **data. **Most of the time goes in data wrangling, feature extraction, data cleansing, etc. You may have heard,

About 80 percent time goes in finding, cleaning, and reorganizing huge amounts of data and rest 20 percent for the actual data analysis or other purposes.

Hence the time spent with data is important. As more time is given to build something, it yields better results. To know more about data and to reach insightful data analysis, we can divide the case study into the following parts,

- Gathering DataUnderstanding the type of featureHandling the problems in featuresModel requirementsTrain-Test data processingMachine Learning / Deep Learning task

Gathering of data can be done from online resources such as Kaggle, UCI, google dataset search, etc. The above concept can be understood better with the real-world dataset (not really though! 😋 the real-world data is way more untidy 😥) for which I have chosen House Price Data provided by Kaggle,

Predict sales prices and practice feature engineering, RFs, and gradient boosting

Features are the data points or the measurable property of the phenomenon being observed. We use the standard libraries for data analysis(pandas, NumPy, matplotlib, plotly and seaborn) and import them as,

import pandas as pd

import matplotlib.pyplot as plt

import plotly.express as px

import numpy as np

import seaborn as sns

We define a function for extracting the variable types,

```
def extract_var(self):
'''
Function to extract types of variables.
Input : Dataframe
Output : Column list of Numerical, Categorical, Continuous, Discrete, Date-time
'''
self.numerical = [var for var in self.data.columns if self.data[var].dtype != 'O']
self.categorical = [var for var in self.data.columns if self.data[var].dtype == 'O']
self.year_vars = [var for var in self.numerical if 'Yr' in var or 'Year' in var]
self.discrete = []
for var in self.numerical:
if len(self.data[var].unique()) < 20 and var not in self.year_vars:
self.discrete.append(var)
self.continuous = [var for var in self.numerical if var not in self.discrete and \
var not in ['Id', 'SalePrice'] and var not in self.year_vars]
return self.numerical, self.categorical, self.continuous, self.discrete, self.year_vars
view raw
extract-type-variable.py hosted with ❤ by GitHub
```

Extracting the variable types

The data types are broadly classified as follows,

Data Type Classification

The above function yields following output to the training data,

```
Numerical : 38, Categorical 43, Continuous: 18, Discrete: 14
```

The numerical data is information that is measurable, and it is, data represented as numbers. Our data consists of about 38 numerical data types. Further Numerical is subdivided into:**(a) Discrete **arevariables whose values are whole numbers (counts), example,

- Number of children in a householdNumber of languages a person speaksNumber of people sleeping in stats class

**(b) Continuous **represents measurements and have certain values in a particular range, for example,

- Height of peopleWeight of peopleTime to wake up in the morningSpeed of the train

Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational level. Our data contains about 43 categorical values. Categorical data is subdivided into :**(a) Binary data **is discrete data that can be in only one of two categories either yes or no, 1 or 0, off or on, etc.**(b) Ordinal data **arecategorical variables in which categories can be meaningfully ordered, example,

- Student’s grade in an exam (A, B, C or Fail)Days of the week (Monday = 1 and Sunday = 7)

**© Nominal data **show no intrinsic order of the labels, for example,

- Country of birth (Argentina, England, Germany)Postcode

Date and time, or DateTime variables they take dates and/or time

as values. Example,

- Date of birth (‘19–01–1867’, ‘01–10–2010’)Date of application (‘2020-May’, ‘2018-March’)Time of accident (01:20)

Our data contains features such as,

```
['YearBuilt', 'YearRemodAdd', 'GarageYrBlt', 'YrSold']
```

#machine-learning #data-preprocessing #linear-regression #data-science #data-processing #data analysis

1620466520

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

1620629020

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

*This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.*

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

1624546800

As data mesh advocates come to suggest that the data mesh should replace the monolithic, centralized data lake, I wanted to check in with Dipti Borkar, co-founder and Chief Product Officer at Ahana. Dipti has been a tremendous resource for me over the years as she has held leadership positions at Couchbase, Kinetica, and Alluxio.

- A data lake is a concept consisting of a collection of storage instances of various data assets. These assets are stored in a near-exact, or even exact, copy of the resource format and in addition to the originating data stores.
- A data mesh is a type of data platform architecture that embraces the ubiquity of data in the enterprise by leveraging a domain-oriented, self-serve design. Mesh is an abstraction layer that sits atop data sources and provides access.

According to Dipti, while data lakes and data mesh both have use cases they work well for, data mesh can’t replace the data lake unless all data sources are created equal — and for many, that’s not the case.

All data sources are not equal. There are different dimensions of data:

- Amount of data being stored
- Importance of the data
- Type of data
- Type of analysis to be supported
- Longevity of the data being stored
- Cost of managing and processing the data

Each data source has its purpose. Some are built for fast access for small amounts of data, some are meant for real transactions, some are meant for data that applications need, and some are meant for getting insights on large amounts of data.

Things changed when AWS commoditized the storage layer with the AWS S3 object-store 15 years ago. Given the ubiquity and affordability of S3 and other cloud storage, companies are moving most of this data to cloud object stores and building data lakes, where it can be analyzed in many different ways.

Because of the low cost, enterprises can store all of their data — enterprise, third-party, IoT, and streaming — into an S3 data lake. However, the data cannot be processed there. You need engines on top like Hive, Presto, and Spark to process it. Hadoop tried to do this with limited success. Presto and Spark have solved the SQL in S3 query problem.

#big data #big data analytics #data lake #data lake and data mesh #data lake #data mesh

1593888900

Data Science, Machine Learning and Deep Learning are the recent hot topics of research and development. These are not new but have currently gained high importance and high attention due to advancements in tools, technology, and computing power of systems. But the main part is the **data. **Most of the time goes in data wrangling, feature extraction, data cleansing, etc. You may have heard,

About 80 percent time goes in finding, cleaning, and reorganizing huge amounts of data and rest 20 percent for the actual data analysis or other purposes.

Hence the time spent with data is important. As more time is given to build something, it yields better results. To know more about data and to reach insightful data analysis, we can divide the case study into the following parts,

- Gathering DataUnderstanding the type of featureHandling the problems in featuresModel requirementsTrain-Test data processingMachine Learning / Deep Learning task

Gathering of data can be done from online resources such as Kaggle, UCI, google dataset search, etc. The above concept can be understood better with the real-world dataset (not really though! 😋 the real-world data is way more untidy 😥) for which I have chosen House Price Data provided by Kaggle,

Predict sales prices and practice feature engineering, RFs, and gradient boosting

Features are the data points or the measurable property of the phenomenon being observed. We use the standard libraries for data analysis(pandas, NumPy, matplotlib, plotly and seaborn) and import them as,

import pandas as pd

import matplotlib.pyplot as plt

import plotly.express as px

import numpy as np

import seaborn as sns

We define a function for extracting the variable types,

```
def extract_var(self):
'''
Function to extract types of variables.
Input : Dataframe
Output : Column list of Numerical, Categorical, Continuous, Discrete, Date-time
'''
self.numerical = [var for var in self.data.columns if self.data[var].dtype != 'O']
self.categorical = [var for var in self.data.columns if self.data[var].dtype == 'O']
self.year_vars = [var for var in self.numerical if 'Yr' in var or 'Year' in var]
self.discrete = []
for var in self.numerical:
if len(self.data[var].unique()) < 20 and var not in self.year_vars:
self.discrete.append(var)
self.continuous = [var for var in self.numerical if var not in self.discrete and \
var not in ['Id', 'SalePrice'] and var not in self.year_vars]
return self.numerical, self.categorical, self.continuous, self.discrete, self.year_vars
view raw
extract-type-variable.py hosted with ❤ by GitHub
```

Extracting the variable types

The data types are broadly classified as follows,

Data Type Classification

The above function yields following output to the training data,

```
Numerical : 38, Categorical 43, Continuous: 18, Discrete: 14
```

The numerical data is information that is measurable, and it is, data represented as numbers. Our data consists of about 38 numerical data types. Further Numerical is subdivided into:**(a) Discrete **arevariables whose values are whole numbers (counts), example,

- Number of children in a householdNumber of languages a person speaksNumber of people sleeping in stats class

**(b) Continuous **represents measurements and have certain values in a particular range, for example,

- Height of peopleWeight of peopleTime to wake up in the morningSpeed of the train

Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational level. Our data contains about 43 categorical values. Categorical data is subdivided into :**(a) Binary data **is discrete data that can be in only one of two categories either yes or no, 1 or 0, off or on, etc.**(b) Ordinal data **arecategorical variables in which categories can be meaningfully ordered, example,

- Student’s grade in an exam (A, B, C or Fail)Days of the week (Monday = 1 and Sunday = 7)

**© Nominal data **show no intrinsic order of the labels, for example,

- Country of birth (Argentina, England, Germany)Postcode

Date and time, or DateTime variables they take dates and/or time

as values. Example,

- Date of birth (‘19–01–1867’, ‘01–10–2010’)Date of application (‘2020-May’, ‘2018-March’)Time of accident (01:20)

Our data contains features such as,

```
['YearBuilt', 'YearRemodAdd', 'GarageYrBlt', 'YrSold']
```

#machine-learning #data-preprocessing #linear-regression #data-science #data-processing #data analysis

1618039260

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt