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IQ Stream Technologies is one of the top Data Science with Python and Machine Learning Training institutes in Bangalore with highly experienced and skilled trainers. IQ Stream Technologies Bangalore also offers placement assistance for students who enrolled in Advanced Data Science Training Courses. We offer advanced Data Science with Python and Machine Learning training and advanced tools for better learning, understanding and experience. IQ Stream Technologies also offering Data Science with Python and Machine Learning online training for Bangalore, Delhi, Mumbai, and Chennai students.

Also in: Marathahalli, Hebbal, KR Puram, Whitefield

Course Name | Data Science Training |

Venue |
IQ Stream Technologies |

Official URL | Data Science Training |

Demo Classes | On Demand |

Training Methodology: | 25% Theory & 75% Practical |

Course Duration | 40-45 Hours |

Class Availability | Weekdays & Weekends |

Demo Training | Email ID : info@iqstreamtech.com |

Reviewed By: **Tharani Dharan**

Reviewed Date: **March, 2018**

I took Fusion HCM training at IQ Stream Technologies.They have good teaching staff like Mr.Sachin and Bhaskar. The management is very professional and helpful, Affordable fees and quality courses offered.

Reviewed By: **Zaki**

Reviewed Date: **March, 2018**

I attended the Oracle Cloud Fusion session in IQ Stream Technologies. Sachin is the Trainer and the Training was good. The trainer explained the fundamental concepts very clearly and showed the roadmap of how to independently explore deeper into details.

Reviewed By: **Naresh Bogavaram**

Reviewed Date: **March, 2018**

The best thing i like at IQ stream is the personalized care that the trainers take to make me understand concepts well. The experienced trainers make it easy to learn.

Structure of Data Science team

Role of a data scientist

Problem Definition

Data Collection

Cleansing Data

Data Exploration

Introduction to SQL

Data Tools

Decision trees

Naive Bayes Classifier

K Means Clustering Modelling errors and resolutions

This module will be based on the expertize of the attendees on Python. Below are some topics:

Overview of PythonRelevance of Python and Data Scince and Machine Learning

Programming style in Python

Objects

List

Tuple

Dictionary

Functions

Lamda Programming

Pandas

Numpy

Datavisualization

Mean, Median, Mode

Using Mean, Median and Mode in Python

Variation and Standard Deviation

Probability

Data Distributions

Percentiles

Moments

Covariance and Correlation

Conditional Probability

Bayes' Theorem

Patterns in data, what does it mean?

Representing reality in models

What is Machine Learning?

Requisites for Machine Learning

Supervised Learning

Unsupervised Learning

Identify data items

Identify sources of data

Data cleansing

Preparing data for Machine Learning

Supervised machine learning

Naive Bayesian classifiers Unsupervised Learning Methods

Unsupervised machine learning

Design Flow

File and Data Types

Terminology

Data Sources

Extracting Data

Custom Data View and Data Joining

Metadata and Data Blending

Tableau Woksheets

Tableau Calucations

Tableau Sorting & Filtering

Tableau Various Charts

Data Compression

Visualization

Reconstruction from Compressed Representation

Inverse and Transpose

Matrices and Vectors

Linear Regression - Multiple variables

Normal distribution and its applications

Confidence levels and confidence interval

Measure of significance, area of acceptance, area of rejection

P Value, Z scores

Sample, sampling and population

Logistic Regression

Hypothesis and hypothesis testing

Decision Boundary

Infer about population from sample

Regularized Linear Regression

Regularized Logistic Regression

Interpreting R Square analysis

Selecting attributes for dimensions, independent dimensions

Correlation filters

Low variance filters

Mutual information filters

Principal component analysis

Neurons and Brain

Model Representation

Multiclass Classification

Discrete Numerical Data: This is Integer based.

Example: Population of cities

Continuous Numerical Data: Infinite number of possible values ie., it may contain fractions.

Example: Height of a person

Categorical Data: Doesn't have inherent numeric meaning.

Example: Gender, Product category

Ordinal Data: Mixure of Numerical and Categorical

Example: Movie Ratings on a scale of 1-5

Mean: This is average of all the sample values. Sum / Number of samples

Example: 2, 5, 3, 7, 4 , 8, 4, 1, 9

Mean => (2+5+3+7+4+8+4+1+9)/9 = 4.89

Median: The mid value of all the samples sorted

Example: 2, 5, 3, 7, 4 , 8, 4, 1, 9

Sorted: 1,2,3,4,4,5,7,8,9

Median is 4

If the number of sample values are even, then take the average of the two mid values

Median is considered better than Mean when there are outliers in the samples as Mean would be skewed.

Mode: The sample value that has most number of occurances

Example: 2, 5, 3, 7, 4 , 8, 4, 1, 9

Mode: 4 as it is appearing two times where as all other sample values are appearing only once.

import numpy as np

from scipy import stats as st

age = [20, 40, 30, 60, 85, 64, 23, 56, 78, 56, 34, 56, 78, 34, 67, 65]

print ("Mean:",np.mean(age))

print ("Median:", np.median(age))

print ("Mode:", st.mode(age))

Median: 56.0

Mode: ModeResult(mode=array([56]), count=array([3]))

In a set of values, identify the frequency of occuring of each of the values and plot them as a bar graph by each bar representing frequency of each value.

Ex: Below is the list of age of the people attending an event

25, 30, 45, 60, 25, 45, 34, 56, 25, 30, 30, 25, 45, 60, 25

You can make a table of ranges of ages and number of values in each range

Age Range and Frequency

20-29: 5

30-39: 4

40-49: 3

50-59: 1

60-69: 2

If you plot these value on a bar graph, taking age range on X and Frquency on Y, that gives a histogram.

import matplotlib.pyplot as plt

#Generate ages instead of hard coding, we will get more meaningful values

#40 is centered values

#5 standard deviation

#1000 number of values

ages = np.random.normal(40, 5, 1000)

plt.hist(ages, 50)

plt.show()

According to wikipedia , It measures how far a set of (random) numbers are spread out from their average value. In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its mean.

According to wikipedia ,
In statistics, the standard deviation (SD, also represented by the lower case Greek letter sigma σ or the Latin letter s)
is a measure that is used to quantify the amount of variation or dispersion of a set of data values.

A low standard deviation indicates that the data points tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the data points are spread out over a wider range of values.

SD can also be treated as square root of Variance.

import matplotlib.pyplot as plt

#Generate ages instead of hard coding, we will get more meaningful values

#40 is centered value

#5 standard deviation

#1000 number of values

ages = np.random.normal(40, 5, 1000)

#Now ages is a list. How to get Standard deviation and variance of the list

print("Standard Deviation:", ages.std())

print("Variance:", ages.var())

Standard Deviation: 5.24306013408

Variance: 27.4896795696

Referring to wikipedia:
Probability of random variable for a speific value in continuous data is almost '0'. However, there will be a +ve value for probability of the random variabe falling within a particular range of values.

Probability Density Function is used to specify the probability of the random variable falling within a particular range of values, as opposed to taking any one value.

Referring to wikipedia: This is used for set of discrete values. Probability mass function gives probability that a discrete random variable is exactly to some value.

#Draw a uniform distribution

import numpy as np

import matplotlib.pyplot as plt

#Get a list of random values and use uniform function

#start value, end value and number of points

values = np.random.uniform(-10.0, 20.0, 100000)

#Plot a histogram with 50 bars

plt.hist(values, 50)

#Show the histogram

plt.show()

from scipy.stats import norm

import matplotlib.pyplot as plt

import numpy as np

#Get random values between -5 and 5 with interval of 0.1

x = np.arange(-5, 5, 0.1)

#Use normal probability density function to get the histogram

plt.plot(x, norm.pdf(x))

#Show the histogram

plt.show()

#Binomial Distribution

from scipy.stats import binom

import matplotlib.pyplot as plt

import numpy as np

n, p = 10, 0.5

x = np.arange(0, 10, 0.001)

plt.plot(x, binom.pmf(x, n, p))

plt.show()

#A Restaurant gets 200 guests on average per day.

#What is the probabiity of getting 220 on a day

from scipy.stats import poisson

import matplotlib.pyplot as plt

import numpy as np

mu = 200

x = np.arange(140, 270, 0.5)

plt.plot(x, poisson.pmf(x, mu))

plt.show()

A percentile (or a centile) is a measure used in statistics to indicate how much % is below a value.

For example: A student got 80th peercentile score in an exam means, 80% of the students got score below that sutdent.

50th percentile is equalent to Median. That is the mid value among all.

import matplotlib.pyplot as plt

import numpy as np

vals = np.random.normal(50, 4, 10000)

print ("50th percentile:", np.percentile(vals,50))

print ("10th percentile:", np.percentile(vals,10))

print ("90th percentile:", np.percentile(vals,90))

50th percentile: 50.0183934715

10th percentile: 44.8104131231

90th percentile: 55.2289663083

1st Moment is same as Mean

2nd Momemn is Variance

3rd Moment is Skew

4th Moment is Kurtosis

Skew and Kurtosis indicate shape and sharpness of the curve of a histogram.

Skew may be -ve or +ve.

Higher the kurtosis, sharper the curve

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