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Data Science with Python Training

$400.00 $250.00


What is Data Science?

Data science is a field of providing meaningful information based on large amounts of complex data. Data science, or data-driven science, combines different fields of work in statistics and computation in order to interpret data for the purpose of decision making.

Why Data Science?

The new-found love for data science in today’s computing world isn’t unjustified. Ranked as the hottest job on offer in the coming years by Harvard Business Review.

The fastest-growing roles are Data Scientists and Advanced Analysts, which are projected to see demand spike by 28% by 2020.

Who is a Data Scientist?

Data Scientists are “Part analyst, Part artist”.

The literal meaning for a data scientist is who practised and acquired a good amount of knowledge in data science course.  😊

Someone who gained knowledge and skills in analytics, computer science, mathematics, statistics, data visualisations and communication as well as business and strategy.

Why so much demand for Data Science?

The below viz explains all about how much demand and shortage of skills is increasing year on year.

DS with Python

About Data Science ?

Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using Python. Data Science Training encompasses a conceptual understanding of Statistics, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR.

The following topics will be covered as part of Data Science with Python Training.

1. Introduction to Python

Why Python

Introduction to Python

Download Python

Python 2 vs 3

Development Environment

Data Types

Python Statements


List Comprehension

Methods & Function

Python Libraries

Git & GitHub

Python : Hands-On

Extra Resources


2. Statistics

What is Statistics

Areas of Statistics

Statistical Jargons


Data Types in Statistics

Measures of Central tendency

Measures of Dispersion

Chebyshev Theorem





Hypothesis Testing

Extra Resources


3. Introduction to Data Science 

The need of Data Science

Data Science Domains

What is Data Science

Lifecycle of Data Science

Data Science Case Study

Types of DS Problems

Exploratory Data Analysis

Demo – EDA

Missing Value Analysis

Demo – Missing Value Analysis

Outlier Analysis

Demo – Outlier Analysis

Extra Resources


4. Exploratory Data Analysis

Feature Selection

Importance of Feature Selection

Feature Selection Methods

Feature Selection – Demo

Univariate Selection – Demo

Feature Importance – Demo

Correlation Matrix – Demo

Dimensionality Reduction Techniques

Dimensionality Reduction – Demo

Principal Component Analysis

PCA – Demo

Feature Scaling

Why Feature Scaling ?

How to scale features ?

When to scale features ?

Extra Resources


5. Introduction to Machine Learning

What is Machine Learning

How does it work

Types of Machine Learning

Supervised Machine Learning

Unsupervised Machine Learning

Supervised vs Unsupervised ML

Decision Trees

Build DT using ID3

Decision Trees Demo

Random Forests

Decision Trees vs Random Forests

Why Random Forests ?

Creating a Random Forests

Random Forests Demo

Extra Resources


6. Advanced Machine Learning 

Statistical Models

ML vs Statistical Models


Types of Regression

Linear Regression

Method of Least Squares

R-square Method – Goodness of Fit

Logistic Regression


Naïve Bayes

Evaluation Metrics

Classification Accuracy

Logarithmic Loss

Confusion Matrix

Area Under Curve

Precision and Recall

F1 Score

Mean Absolute Error & Mean Squared Error

Extra Resources


7. Natural Language Processing 

Introduction to NLP

Components of NLP

Benefits of NLP

NLP Libraries


Synonyms & Antonyms



Word Embeddings


Named Entity Recognition

Sentiment Analysis

Semantic Text Similarity

Language Identification

Text Summarisation

Dealing with Text Data

NLP – Demo

Extra Resources


8. Time Series 

Why Time Series Analysis ?

What is Time Series

Components of Time Series

When not to use Time Series ?

What is Stationarity ?


Demo : Forecast Future

Extra Resources


9. Deploying ML Models 

Deploy ML Models using Flask

Installing Libraries

ML Algorithm and Data

Code & Output

Pre-Processing the dataset

Fitting the Model



Flask Script

Run Flask Application

Predicting the Income Value

Folder Structure


Extra Resources


10. Introduction to Deep Learning 

Introduction to Deep Learning

Drawbacks of ML

Deep Learning Use Case



Activation Functions


Extra Resources


Pre-requisites :

Basic statistics knowledge and any computer programming language is preferred.

Duration & Timings :

Duration – 30 Hours.

Training Type: Online Live Interactive Session.

Faculty: Experienced.

Access to Class Recordings.

Weekday Session – Mon – Thu  8:30 PM to 10:30 PM (EST) – 4 Weeks. July 11, 2020.

Weekend Session – Sat & Sun 9:30 AM to 12:30 PM (EST) – 5 Weeks. July 25, 2020.

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