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Data Science -Advanced
                 “Data Science” is an Inter-Disciplinary subject involving Mathematics, Statistics, Business acumen, Python, Machine Learning and Deep Learning etc.,  Using “Data Science”, we can analyse information which is collected or gathered from various sources of information and give an accurate solution to the problem in hand.  With the help of Data Science, useful insights can be attained, related to the problems of Business, Society, Science and Technologies etc.  Data Science helps Business Organisations or Companies to develop Business Strategies, leverage their Resource utilization, take accurate business decisions etc., Business Organisations can rein in their Cost of Production, increase Efficiencies, identity new Market Opportunities for their Products and achieve Competitive Advantage over their Competitors.

Python

                       Python is an Open-source and Object-Oriented Programming Language It is a simple and easy to read programming language and doesn’t require any license to utilize it.  New developments in Python Language happens very frequently as it is backed by various Communities which are diverse in programming skills. Python Language is extensively used in Web Development, Gaming, Scientific Applications, GUI Development, Web Scraping, Artificial Intelligence (Data Science), Machine Learning and Deep Learning


Course Content:

1. Python Installation

 

2. Data Structures in Python

 

· List

· Tuples

· Dictionaries

· Sets

 

3. Control Statements and functions

 

· Introduction

· If Elif Else

· Loops

· Comprehension

· Functions

· Map, Filter and Reduce

 

 

4. Python for Data Science

 

· Introduction to NumPy

· NumPy Basics

· Creating NumPy Arrays

· Structure and Content of Arrays

· Subset, Slice, Index and Iterates

   through Arrays

· Multidimensional Arrays

5. Operations on NumPy Arrays

 

· Introduction

· Basic Operators

· Operations on Arrays

· Basic Linear Algebra Operations

 

6. Introduction to Pandas Arrays

 

· Introduction

· Pandas Basics

· Indexing and Selecting Data

· Merge and Append

· Grouping and Summarizing Data Frames

· Lambda Functions and Pivot Tables

 

7. Getting and Cleaning data

 

· Introduction

· Reading Delaminated and Relational Database  

· Reading Data from Website

· Getting Data from API’s

· Cleaning Data Sets

 

8. Data Visualization in Python

 

· Necessity of Visualization

· Data Handling and Cleaning

· Sanity Checks

· Outliers

 

9. Data Visualization with Seaborn

 

· Introduction

· Distribution Plots

· Styling Options

· Pie Charts and Bar Charts

· Scatter Plots

· Pair Plots

· Heatmaps

· Line Charts

· Stacked Bar Charts

Statistics

1. Exploratory Data Analysis:

· Data Sourcing

· Data Cleaning

· Univariate Analysis

· Segmented Univariate

· BivariateAanalysis

· Derived Metrics

 

2. Introduction and Git

3. Inferential Stats

 

· Basics of Probability

· Discrete Probability Distribution

· Continuous Probability Distribution

· Central Limit Theorem

4. Hypothesis Testing

 

· Concepts of Demonstration

· Industry Demonstration

Machine Learning- I

· Linear Regression

· Logistic Regression

· Naïve Bayes

· Model Selection

 Machine Learning- II

· Advanced Regression

· Tree Models

· Boosting

· Unsupervised Learning

 Deep Learning

· Introduction to Neural Network-I

· Introduction to Neural Network-II

· ANN, CNN, RNN, Pooling Layers

Natural Language Processing

· Lexical Processing

· Syntactic Processing

· Semantic Processing- I

· Semantic Processing- II

 

Additional Concepts

v Application of Deep Learning in NLP-I

v Application of Deep Learning in NLP-II

v Introduction to MLOP’s

v MLOP’s + Deployment

v MLOP’s + Prerequisite

v Automating ML Workflow

v PCA, Time Series.

v AWS- Sage maker, Flask

v CV, Reinforcement Learning

v Building Continuous Learning Infrastructure

v Designing Machine Learning System

v Chat Bots