Data Science Using Python

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Become a Data Scientist

Data science is the new emerging field in today world with high growth and career perspective. It’s right time to move your career in data science by starting from basics of Data analytics and management to advanced topics like in Machine Learning with industrial case study. This course provides in-depth understanding of how data science integrates in various industrial verticals such as healthcare, banking, telecom, e-commerce, transportation and more.

Data Science with Machine Learning (using PYTHON)

( Professional Course )                                    Time : 40 hrs

Become a Data Scientist

Data science is the new emerging field in today world with high growth and career perspective. It’s right time to move your career in data science by starting from basics of Data analytics and management to advanced topics like in Machine Learning with industrial case study. This course provides in-depth understanding of how data science integrates in various industrial verticals such as healthcare, banking, telecom, e-commerce, transportation and more.

 

 
Introduction to Data Science

 

                                                                                                                Duration

Module 1                                                                                                2 hrs

Introduction to Data Science

Data Science Era

Data Science involvement in Industries

Business Intelligence vs Data Science

Data Science Life Cycle

Tools of Data Science

Introduction to Python

Introduction to Machine Learning

 

 

Module 2

Introduction to Python Programming
Introduction to Python
Basic Operations in Python
Variable Assignment
Functions: in-built functions, user defined functions
Condition: if, if-else, nested if-else, else-if

 

Module 3                                                                                                   2 hrs

Data Structure – Introduction
List: Different Data Types in a List, List in a List
Operations on a list: Slicing, Splicing, Sub-setting
Condition(true/false) on a List
Applying functions on a List
Dictionary: Index, Value
Operation on a Dictionary: Slicing, Splicing, Sub-setting
Condition(true/false) on a Dictionary

Applying functions on a Dictionary

Modules and Packages

Numpy Array: Data Types in an Array, Dimensions of an Array         2 hrs
Operations on Array: Indexing ,Slicing, Splicing, Sub-setting
Conditional(T/F) on an Array
Loops: For, While
Shorthand for For

Conditions in shorthand for For

Control statements

Shape Manipulation

Linear Algebra

Module 4                                                                                                         2 hrs
Python Pandas – Home
Python Pandas – Introduction
Python Pandas – Environment Setup
Introduction to Data Structures
Python Pandas – Series
Python Pandas – Data Frame
Python Pandas – Panel
Python Pandas – Basic Functionality
Function Application
Python Pandas – Reindexing
Python Pandas – Iteration
Python Pandas – Sorting

 

Module 5                                                                                          2 hrs

 

Intro to Statistics

Statistical Inference

Terminologies of Statistics

Descriptive statistics

Statistical functions

Measures of Centers

Mean

Median

Mode

Measures of Spread

Variance

Standard Deviation

Histogram

Probability

Normal Distribution

Binary Distribution

Poisson distribution

Skewness

Bell curve

Hypothesis Building and Testing

Chi-Square Test

Correlation Matrix

 

Module 6                                                                                                2 hrs

Scientific computing with Python

SciPy and its Characteristics

SciPy sub-packages

SciPy sub-packages –Integration

SciPy sub-packages – Optimize

Linear Algebra

SciPy sub-packages – Statistics

SciPy sub-packages – Weave

SciPy sub-packages – I O

 

Module 7                                                                                                2 hrs

 

Data Analysis Pipeline

What is Data Extraction

Types of Data

Raw and Processed Data

Data Wrangling

Exploratory Data Analysis

Visualization of Data

    MatplotLib
    Bar Plot
    Histogram Plot
    Box Plot
    Area Plot
    Scatter Plot

    Pie Plot

Seaborn

 

Module 8                                                                                                   2 hrs

Introduction to Machine Learning

Machine Learning Use-Cases

Machine Learning Process Flow

Machine Learning Categories

Module 9                                                                                                  
Data Preprocessing
Data preparation
Intro to Scikit Learn

 

Module 10                                                                                                  2 hrs                                                                        

 

Regression

Types

Algorithms

Linear Regression
Logistic Regression                                                                                   2 hrs

Importance of Dimensions                                                                     2 hrs

Introduction to Dimensionality

Why Dimensionality Reduction

PCA

Factor Analysis

Scaling dimensional model

Implementation with Case Studies

 

Intro to Kaggle and UCI repository

 

Module 11                                                                                                 2 hrs

Classification

K-nearest neighbours

Metrics

Confusion Matrix

Classification report

Support Vector Machines                                                                       2 hrs
Working of SVM

Naive Bayes

Hyperparameter Optimization

Decision Tree Classifier

Entropy                                                                                                       2 hrs

Gini Entropy

ROC

AUC

Random Forest classifier

Linear Discriminant Analysis

Cross –validation

Implementation with Case Studies

 

Module 12                                                                                                   2 hrs

Unsupervised learning

Clustering Algorithms

K-Means Clustering

Hierarchical Clustering

Implementation with Case Studies

 

Module 13                                                                                                  2 hrs   

NLTK Installation

Tokenize words

Tokenize sentences

Stop words in NLTK

Stemming words with NLTK

Speech tagging

Beautiful Soup                                                                                           2 hrs

Tf-idf Vectorise

Sentiment analysis

Implementation with Case Studies

 

Module 14                                                                                                     2 hrs

Association rule

Association Analysis

Association Rule Parameters

Apriori

Market Basket Analysis

Implementation with Case Study

 

At the End implementation of any one of the projects could be assigned.

Projects :                                                                                            4 hrs

1.      Customer churn prediction

2.      Bank fraud Loan Prediction

3.      Wine Type Prediction

4.      Titanic dataset

5.      Marketing channel sales prediction

 

 

Course Highlights :

 

Use Case Based study

Hands on Live Projects

Study material

Interview questions

Mock Interview

 

 
 

GET CERTIFICATION AFTER THE ASSESSMENT TEST CLEARED

Fees

 

Application Fees INR 0
Total Program Fees INR 30,000
The Program Fees can also be paid in installments
Registration  Fees INR 5000 (Non-Refundable)

First installment of the Program Fees

(Needs to Paid One Week Before the Batch Start Date)

INR 12,000

Second installment of the Program Fees

(Needs To Be Paid in the 6th Class)

INR 13,000

Discount on Program Fees: 10% Early Bird Discount valid till 10/02/19

10%  Extra discount on Lumsum Payment

Batch Closed:             27th Jan 2019

New Batch Starting:  23rd Feb 2019 (Few Seats Left)

2nd Batch:                   25th Feb 2019

Admission Open Till: 10th Feb 2019 ( If Required Seats Filled, the admissions will be closed even before the Closing Date)

 

 

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