Detailed course with timings & practicals.

**GETTING STARTED**

History & need of

python

Advantages of python

Disadvantages of python

Features

Setting up Path

Working with Python

Basic Syntax

Variable and Data

Types

Operator**DATA TYPES**

Numbers

Strings

Lists

Tuples

Dictionary

Set

Frozenset

Bool

Mutable and immutable**LISTS MANIPULATION**

Accessing list

Operations

Working with lists

Function and Methods**TUPLES**

Introduction

Creating tuples

Accessing tuples

Joining tuples

Replicating tuples

Tuples Slicing**DICTIONARIES**Arithmetic Operator

Relational Operator

Logical Operator

Membership Operator

Identity Operator

Bitwise Operator

Assignment Operator

Type Casting

**
CONDITIONAL STATEMENT**

If

If-Else

Elif(Nested if-else)**LOOPING**

For

While

Nested loops**CONTROL STATEMENT**

Break

Continue

Pass**FUNCTIONS**

Defining a function

Calling a function

Types of functions

Structure of python Functions

Anonymous functions

Global and local variables

Lambda Functions**MODULES**

Importing module

Math module

Random module

Packages

Composition**EXCEPTION HANDLING**

Default Exception and Errors

Catching Exceptions

Raise an Exception

User defined Exception**INPUT - OUTPUT**Printing on screen

Reading data from keywords

Opening and Closing file

Reading and Writing file

This part of course includes multiple programs and projects.

**OOPS CONCEPT
**

Class and objects

Attributes

Inheritance

Overloading

Overriding

Polymorphism

**GUI PROGRAMMING
**

Introduction

Tkinter Programming

Tkinter Widgets

Frame

Button

Label

Entry

Messagebox

Labelframe

**REGULAR EXPRESSIONS
**

Match

Function

Search

Function

Grouping

Matching at

Beginning or End

Match Object

Flags

**MULTI THREADING
**

Thread and Process

Starting a Thread

Threading

Modules

Synchronizing

Threads

Multi Threaded

Priority Queue

**CGI
**

Architecture

CGI

Environment variables

GET and POST methods

Cookies, FileUpload

**DATABASE
**

MYSQL/MONGODB

PYMYSQL Connections

Executing

Queries

Transactions

Handling

Error

**Libraries in Python
**

**MULTI THREADING
**

Thread and Process

Starting a Thread

Threading

Modules

Synchronizing

Threads

Multi Threaded

Priority Queue

**NUMPY
**

Setup

Numpy Array

Numby Append

Numpy

Reshape

Numpy SUM

Numpy Random

Numpy Log

Numpy Degree

**PANDAS
**

Environment

Setup

Series

Data Frame

Sorting

Basic

Functionality

Working with Text Data

This part of course includes multiple programs and projects.

The program concludes with a capstone project designed to reinforce the learning by building a real industry product encompassing all the key aspects learned throughout the program. The skills focused on in this program will help prepare you for the role of a Data Scientist.

Tools Covered..**Flume, NumPy, pandas, SciPy, Spark, IBM Watson, Apache HBASE, hive, Pig, Sqoop,Hadoop Hdfs, Hadoop Map Reduce, Python, R, Scala****Detailed Program**

1. Data science overview

2. Data Analytics Overview

3. Statistical Analysis & Business Application

4. Python Environment Setup & Essentials

5. Mathematical Computing with Python (NumPy)

6. Scientific Computing with Python (Scipy)

7. Data Manipulation with Pandas

8. Machine Learning with Scikit-Learn

9. Data Visulation in Python using matplotlib

10. Web Scraping with BeautifulSoup

11. Python integration with Hadoop MapReduce & Spark

In-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling.**Eligibility**

Course is well-suited for participants at the intermediate level including, analytics managers, business analysts, information architects, developers looking to become data scientists, and graduates seeking a career in Data Science and Machine Learning.

Requires an understanding of basic statistics and mathematics at the college level, Familiarity with Python programming is also beneficial.

You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science**Detailed Program**

1. Introduction to AL & Machine Learning

2. Data Pre-processing

3. Supervised Learning

4. Feature Engineering

5. Supervised Learning Classification

6. Unsupervised Learning

7. Time Series Modeling

8. Ensemble Learning

9. Recommender Systems

10. Text Mining

**Course 1: Introduction to Python**

Why Python
Programming

Data Types and
Operators

Control Flow

Functions

Scripting

Classes**Course 2: Anaconda, Jupyter Notebook,
NumPy, Pandas, and Matplotlib**

Anaconda

Jupyter Notebooks

NumPy Basics

Pandas Basics

Matplotlib Basics**Course 3: Linear Algebra Essentials**Vectors

Linear Combination

Linear Transformation and Matrices

Linear Algebra in Neural Networks

Labs

Derivatives Through Geometry

More on Derivatives

Limits

Integration

Calculus in Neural Networks

Introduction to Neural Networks

Training Neural Networks

Deep Learning with PyTorch