Best Data Analytics with python in Delhi NCR

GETTING STARTED

• History & need of Python
• Application of Python
• Advantages of Python
• Disadvantages of Python
• Installing Python
• Program structure
• Interactive Shell
• Executable or script files.
• User Interface or IDE

PYTHON FUNDAMENTALS

• Working with Interactive mode
• Working with Script mode
• Python Character Set
• Python Tokens, Keywords, Identifiers, Literals, Operators
• Variables and Assignments
• Input and Output in Python

DATA HANDLING

Data Types

• Numbers
• Strings
• Lists
• Tuples
• Dictionary
• Set
• Frozenset
• Bool
• Mutable and Immutable

STRING MANIPULATION

• Introduction to Python String
• Accessing Individual Elements
• String Operators
• String Slices
• String Functions and Methods

LIST MANIPULATION

• Introduction to Python List
• Creating List
• Accessing List
• Joining List
• Replicating List
• List Slicing

TUPLES

• Introduction to Tuple
• Creating Tuples
• Accessing Tuples
• Joining Tuples
• Replicating Tuples
• Tuple Slicing

DICTIONARIES

• Introduction to Dictionary
• Accessing values in dictionaries
• Working with dictionaries
• Properties

SET AND FROZENSET

• Introduction to Set and Frozenset
• Creating Set and Frozenset
• Accessing and Joining
• Replicating and Slicing

OPERATORS

• Arithmetic Operators
• Relational Operators
• Logical Operators
• Membership Operators
• Identity Operators
• Bitwise Operators
• Assignment Operators
• Operators Precedence
• Evaluating Expression
• Type Casting

PROGRAM CONTROL FLOW

Conditional Statements

• The if Statement
• The if-else Statement
• The if-elif Statement
• Nested if Statements
• Python Indentation

Looping and Iteration

• The For Loop
• The While Loop
• Loop else Statement
• Nested Loops
• Break and Continue

The Range Function

• Introduction to range()
• Types of range() function
• Use of range() function

INTRODUCTION TO FUNCTIONS

Built-In Functions

• Introduction to Functions
• Using a Functions
• Python Function Types
• Structure of Python Functions
• E.g. – map, zip, reduce, filter, any, chr, ord, sorted, globals, locals, all, etc.

User Defined Functions

• Structure of a Python Program w.r.t. UDF
• Types of Functions
• Invoking UDF
• Flow of Execution
• Arguments and Parameters
• Default Arguments, Named Arguments
• Scope of Variables
• Lambda function

Recursion Function

• Use of recursion function

MODULES AND PACKAGES

Built-in Modules

• Importing Modules in Python Programs
• Working with Random Modules
• E.g. – builtins, os, time, datetime, calendar, sys, etc.

User Defined Functions

• Structure of Python Modules

FILE OPERATIONS

Text and Bytes files

• Opening a file
• Reading and Writing Files
• Other File tools

MS Excel files

• Introduction to MS Excel files

CLASSES AND OBJECTS

• Classes as User Defined Data Type
• Objects as Instances of Classes
• Creating Class and Objects
• Creating Objects By Passing Values
• Variables & Methods in a Class

EXCEPTION HANDLING

• Default Exception and Errors
• Catching Exceptions
• Raise an exception
• Try…. except statement
• Raise, Assert, Finally blocks
• User defined exception Errors
• Catching Exceptions
• Raise an exception
• Try…. except statement
• Raise, Assert, Finally blocks
• User defined exception

INTRODUCTION TO OOPS

• Procedural Vs Modular Programming
• The Object Oriented Programming
• Data Abstraction
• Data Hiding
• Encapsulation
• Modularity
• Inheritance
• Polymorphism

DATABASE

• Introduction to MySQL
• PYMYSQL Connections
• Executing queries
• Transactions
• Handling error

GUI PROGRAMMING

• Introduction
• Tkinter programming
• Tkinter widgets
• Frame
• Button
• Label
• Entry

TURTLE PROGRAMMING

• Introduction to Turtle
• Controlling Turtle
• Animation Programming

MULTITHREADING

• Thread and Process
• Starting a thread
• Threading module
• Synchronizing threads
• Multithreaded Priority Queue

NETWORKING

• Socket Module
• Server-client-socket
• Connecting client server
• Client-server chatting program

REGULAR EXPRESSIONS

• Match function
• Search function
• Grouping
• Matching at Beginning or End
• Match Objects
• Flags

ADVANCED CONCEPTS*

• Decorators
• Generators
• Iterators
• Co-routines

CGI*

• Architecture
• CGI environment variable
• GET and POST methods
• Cookies
• File upload

Projects:

• Console Project
• GUI Project

Data Analytics Using Python

REVISITING PYTHON

• List and dictionary comprehension
• Programming assignment

INTRODUCTION TO DATA ANALYTICS

• Why Analytics?
• Traditional Data Management
• Analytical tools
• Types of Analytics
• Hind sight, ore sight and insight
• Dimensions and measures
• Why learn Python for data
  analysis?
• Using the IPython notebook

LIBRARIES FOR DATA ANALYTICS

• Anaconda
• Numpy
• Scipy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn

JUPYTER NOTEBOOK

• Create Documentation
• Code mode
• Markdown mode

STATISTICS:

• Mean, Median, Mode
• Z-scores
• Bias -variance dichotomy
• Sampling and t-tests
• Sample vs Population statistics
• Random Variables
• Probability distribution function
• Expected value
• Binomial Distributions
• Normal Distributions
• Central limit Theorem
• Hypothesis testing
• Z-Stats vs T-stats
• Type 1 type 2 error
• Chi Square test
• ANOVA test and F-stats

NUMPY:

• Creating NumPy arrays
• Indexing and slicing in NumPy
• Downloading and parsing data
• Creating multidimensional arrays
• NumPy Data types
• Array tributes
• Indexing and Slicing
• Creating array views copies
• Manipulating array shapes I/O

SCIPY:

• Introduction to SciPy
• Create function
• Modules of SciPy

MATPLOTLIB:

• Scatter plot
• Bar charts, histogram
• Stack charts
• Legend title Style
• Figures and subplots
• Plotting function in pandas
• Labelling and arranging figures
• Save plots

PANDAS:

• Using multilevel series
• Series and Data Frames
• Grouping, aggregating
• Merge DataFrames
• Generate summary tables
• Group data into logical pieces
• Manipulate dates
• Creating metrics for analysis
• Data wrangling
• Merging and joining
• Analytics Vidhya dataset- Loan Prediction Problem
• Data Mugging using Pandas
• Building a Predictive Model

SEABORN:

• Style functions
• Color palettes
• Distribution plots
• Categorical plots
• Regression plots
• Axis grid objects

WEB SCRAPING:

• Scraping Webpages
• Beautifulsoup package
• Real time project

INTRODUCTION TO ML

• What is ML? And Why ML?
• Introduction to Supervised ML
• Introduction to Unsupervised ML
• Mathematical Background for ML- Matrix ops Probability Theory (Bayes’ Theorem)
• ML Glossary- Variable types, kfold
• CV, AUC, F1 score,
• Overfitting / Underfitting
• Data split & hyper parameter

SCIKIT-LEARN

Supervised learning –

Regression

• Different types of Regression-
• Linear Regression
• Logistic Regression
• Decision tree Algorithms

Classification

• Naive- Bayes’
• KNN Classification
• Curse of Dimensionality-PCA
• SVM Classification

Unsupervised learning –

Clustering

• k-means clustering Random Forest

INTRODUCTION TO BIG DATA

• What is Hadoop?
• MapReduce
• File handling with Hadoopy
• Pig
• Pyspark

Projects:

• Real time projects