Best Data Science Using Python Institute in Delhi NCR

CURRICULUM OF CORE & ADVANCED PYTHON

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, twilio, smtp, pillow.

User Defined Modules

• Structure of Python Modules

FILE OPERATIONS

Text and Bytes files

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

JSON/PICKLE

FORMAT 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

EXCEPTION HANDLING

• Default Exception and 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
• Inheritance
• Polymorphism
• Generators
• Iterators

DATABASE

• SIntroduction to MySQL
• PYMYSQL Connections
• Executing queries
• Transaction Handling error

GUI PROGRAMMING

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

REVISITING PYTHON

• Revisiting Python
• List and dictionary comprehension
• Programming assignment

INTRODUCTION TO DATA ANALYTICS

• Why Analytics?
• Traditional Data Management
• Types of Analytics
• Dimensions and measures
• Why learn Python for data analysis?

LIBRARIES FOR DATA ANALYTICS

• Anaconda
• Numpy, Scipy, Pandas
• Matplotlib, Seaborn

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

JUPYTER NOTEBOOK

• Create Documentation
• Code mode
• Markdown mode
• Heading mode

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

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

MATPLOTLIB:

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

SEABORN:

• Style functions, Color palettes
• Distribution and 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, k-fold
• Overfitting/Underfitting
• Data split & hyper parameter

PROJECTS:

• Real time projects

NOTE: PREREQUISITES:

• Python programming

Machine Learning

• Introduction to ML
• What is ML? Why ML?
• Introduction to Supervised ML
• Introduction to Unsupervised ML
• Difference Between Al|DLIML

Tools required for development

Anaconda, Jupyter NB/Google Colab/Spyder

• ML libraries
• Numpy: Introduction to Numpy
• pandas:Introduction] DataFrame Loading
• datasets | Loading data from database | pandas Operation.
• Matplotlib: Introduction| Line Chart |Pie Chart
• Scatter Plot | Bar chart |Histogram
• Sklearn:: Introduction |Sklearn-API]
• Statsmodels.api
• ML Glossary
• Variable types, k-fold CV, AUC,
• F1 score,Overfitting/Underfitting,
• Generalization, ROC |Confusion matrix
• Mathematical Background for ML- Matrix ops
• Probability Theory (Bayes’ Theorem)
• Statistical knowledge for ML- Mean, Median,
• Mode, Z-scores, bias-variance dichotomy.
• Exploratory Data analysis using Visualisation
• Scikit-learn Library for ML
• Code Exercises

Steps of Machine Learning

• Data Collection. The quantity & quality of your data
• dictate how accurate our model is…..
• Data Preparation. Wrangle data and prepare it for
• training Data wrangling using Pandas|Preprocessing

data and feature engineering Data split

• Choose a Model.
• Train the Model….
• Evaluate the Model…..
• 6-Parameter Tuning] hyper parameter training
• Make Predictions.

Supervised Learning

• Introduction Maths behind Supervised Machine
• Learning and Algo.

Regression:

• Linear Regression
• Multi-Linear Regression
• Lasso/Rigde
• Decision Tree Regressor
• Support Vector Regressor

Classification

• Logistic Regression
• KNN-K Nearest Neighbors
• Support Vector Classifier(SVM-SVC)
• Decision Tree Classifier(DTC)
• Random Forest
• Naïve Bayes
• Ensemble Learning

Unsupervised Machine Learning

Clustering:

• Introduction:
• Mathematics behind Clustering

k-means clustering

• Implementation of K-mean Clustering

H-clustering

• Implementation of H-clustering
• Code Exercises

Assocation Rule:

• Apiori rule

Dimensionality Reduction

• Principle Component Analysis(PCA).

CAPSTONE PROJECT

• IBM ATTRITION RATE PREDICTION USING MACHINE LEARNING
• COVD-19 PATIENT OUTCOME PREDICTION USING ML
• ESTIMATE THE ONLINE SALES OF A E-COMMERCE RETAIL FIRM USING ML
• GUI BASED MACHINE LEARNING APPLICATION TO CLASSIFY THE PLANT SPECIES OF IRIS FLOWER
• PREDICT THE CHURN RATE IN A TELECOM COMPANY USING ML
• MALL CUSTOMER SEGMENTATION USING ML
• MARKET BASKET ANALYSIS AND ASSIT A SHOPPING MALL TO STACK PRODUCT
• PREDICT AND ESTIMATE CAR RE-SALE VALUE USING MACHINE LEARNING
• WORKING ON INBULIT DATASETS
• PREDICTION CLASSIFICATION OF HANDWRITTEN DIGITS