Java Python R SQL Excel Compared Similarities For Data Science and Data Analytics – The Basics

Bydatapandasadmin June 9, 2017June 9, 2017

If you have ever worked with Java, Python, R, SQL, Excel and other Languages on a varied Data Science or Data Analytics projects, you will realise that all these languages have similar syntaxes, or at least, can achieve the same objective with very similar codes.

Below is a comparison and similarities of these various tools and programmes I put together for quick reference when I realised that there are some similarities between these languages languages and tools are.

Though it is not complete and exhaustive, I believe it will be of help to someone.

If you have any similarities amongst any of these languages, please leave it as a comment in the comment box I will update the table.

I would want this to help all new people who are into Data Science and Data Analytics as I wish I had something similar to this when I first started. Thank you for your contribution.

Navigating the table:

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Use the search box located directly on the top right hand corner of the table to search for terms
Use the scroll bar at the bottom of the table to see all columns
The table has 5 columns which are Java, Python, R, SQL, Excel, Example, Description

[table id=1 /]

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Coursera | Data Science | Python

Testing a Basic Linear Regression Model – Data Analysis and Intrepretation

Bydatapandasadmin February 14, 2016October 23, 2016

Testing a Basic Linear Regression Model Background My research work deals with Ghana, a country from the Gapminder dataset as has already been discussed from the beginning and progression through this course. 1) Program Code and Output

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# -*-

coding: utf-8 -*-

"""

Created on Sat Feb 13 12:28:07 2016

@author: Bernard

"""

import pandas

import matplotlib.pyplot as plt

import statsmodels.formula.api as smf

import seaborn

data = pandas.read_csv('gapminder_ghana_updated.csv')

data["incomeperperson"] =

data["incomeperperson"].convert_objects(convert_numeric=True)

data['lifeexpectancy'] = data['lifeexpectancy'].convert_objects(convert_numeric=True)

# listwise deletion of missing values

dataSub = data[['incomeperperson',

'lifeexpectancy']].dropna()

scat1 = seaborn.regplot(x="incomeperperson",

y="lifeexpectancy", scatter=True, data=dataSub)

plt.xlabel('Income Per Person')

plt.ylabel('Life Expectancy')

plt.title ('Scatterplot for the Association Between Income

Per Person and Life Expectancy of the People Of Ghana')

print(scat1)

# center quantitative Explanatory variable for regression

analysis

dataSub['incomeperperson_c'] = (dataSub['incomeperperson'] -

dataSub['incomeperperson'].mean())

print("Describe the centered quantitative Explanatory

variable")

ds0 = dataSub["incomeperperson_c"].describe()

print(ds0)

# printing mean

print("Mean for centered quantitative explanatory

variable: incomeperperson_c")

ds1 = dataSub.groupby('incomeperperson_c').mean()

print (ds1)

print("Standard deviation for centered quantitative

explanatory variable:incomeperperson_c")

sd1 = dataSub.groupby('incomeperperson_c').std()

print (sd1)

print("Mean for quantitative explanatory variable:

incomeperperson")

ds2 = dataSub.groupby('incomeperperson').mean()

print (ds2)

print("Checking values in incomeperperson_c")

print(dataSub["incomeperperson_c"])

#Value counts

print("Counts for incomeperperson_c")

inc_c_Count =

dataSub["incomeperperson_c"].value_counts(sort = False ,dropna=False)

#dropna displays missen values

print(inc_c_Count)

print ("OLS regression model for the association

between Income Per Person and Life Expectancy of the People of Ghana")

reg1 = smf.ols('lifeexpectancy ~ incomeperperson_c',

data=dataSub).fit()

print (reg1.summary())

##################### OUTPUT BEGIN ##################### Axes(0.125,0.125;0.775×0.775) Describe the centered…

Business Intelligence - BI | Data Science | Excel

Various Types of Basic Charts For Data Analysis and Exploration – Visualization and Meaning
Bydatapandasadmin October 30, 2016January 7, 2017

Sometimes, you need some charts to explore your data and many times when your analysis is done and you want to report, you would most likely want to convey your message using charts and graphs. This is a quick look at some charts which are available and their names: We use this raw data in…

Read More Various Types of Basic Charts For Data Analysis and Exploration – Visualization and Meaning
Python

Basic Plotting Using Bokeh Python Pandas Library – Scatter, Line Visualizations
Bydatapandasadmin September 26, 2016October 23, 2016

Bokeh is a powerful framework for data visualization in Python. Here I take a look at straightforward plotting and visualization using this powerful library. Credit to PythonHow. You can check him out on Youtube bokeh.models #It is a low level interface which involves a lot of work bokeh.plotting # It is a middle level interface bokeh.chart #…

Read More Basic Plotting Using Bokeh Python Pandas Library – Scatter, Line Visualizations

Data Science | Python

Quick Example and Walk-Through JSON with Python

Bydatapandasadmin November 13, 2016

This is a quick to create a dictionary object , write it to a JSON file and then read back the file and convert it to a dictionary and access the items in the dictionary. Credit to : codebasics. You can check him out on Youtube In [3]:

# lets create a dictionary of books

book = {}

book['Tom'] = {

'name':'Tom',

'address':'1 red street, NY',

'phone':98988988

}

book['Bob'] = {

'name':'Bob',

'address':'1 green street, NY',

'phone':252525885

}

In [6]:

#import json and pass the dictionary to it

import json

s = json.dumps(book)

Out[6]:

1	'{"Bob": {"phone": 252525885, "name": "Bob", "address": "1 green street, NY"}, "Tom": {"phone": 98988988, "name": "Tom", "address": "1 red street, NY"}}'

In [14]:…

Python

ImportError: No module named ‘scikits’ – Python Fix Jupyter Notebook
Bydatapandasadmin August 21, 2016November 14, 2016

If you have the above problem, to fix this, follow these guidelines as advised by Flowerowl at this link (https://github.com/muricoca/crab/issues/92): The steps are as follows: Don’t install crab in this way: pip install crab / easy_install crab You can find crab in pypi.python.org, this is not our scikits.crab source Solution: git clone https://github.com/muricoca/crab.git python setup.py…

Read More ImportError: No module named ‘scikits’ – Python Fix Jupyter Notebook
Coursera | Data Science

Choosing a Dataset – Data Management and Visualization
Bydatapandasadmin December 13, 2015October 23, 2016

I have gone through all the codebooks and having finished going through all of them I am particularly interested in the GapMinder dataset. From the GapMinder dataset my focus will be on the Life expectancy of a particular country. I have chosen to focus on the country, Ghana. The Life Expectancy of the people of…

Read More Choosing a Dataset – Data Management and Visualization

Java Python R SQL Excel Compared Similarities For Data Science and Data Analytics – The Basics

Testing a Basic Linear Regression Model – Data Analysis and Intrepretation

Various Types of Basic Charts For Data Analysis and Exploration – Visualization and Meaning

Basic Plotting Using Bokeh Python Pandas Library – Scatter, Line Visualizations

Quick Example and Walk-Through JSON with Python

ImportError: No module named ‘scikits’ – Python Fix Jupyter Notebook

Choosing a Dataset – Data Management and Visualization

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