DATA SCIENCE & ML USING PYTHON IN NOIDA

DATA SCIENCE & ML USING PYTHON TRAINING IN NOIDA

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Are you Looking for the Best Institute for Data Science ML using Python training in Noida? DUCAT offers Data Science ML using Python training classes with live projects by expert trainers in Noida. Our Data science machine learning with Python training program in Noida is specially designed for Under-Graduates (UG), Graduates, working professionals, and also for Freelancers. We provide end-to-end learning on Machine learning with Python Domain with deeper dives for creating a winning career for every profile.

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. Currently, Python is the most popular Language in IT. Python adopted as a language of choice for almost all the domains in IT including Web Development, Cloud Computing (AWS, OpenStack, VMware, Google Cloud, etc.. ), Infrastructure Automations, Software Testing, Mobile Testing, Big Data, and Hadoop, Data Science, etc. This course sets you on a journey in python by playing with data, creating your own application, and also testing the same.

Introduction To Python

  • Why Python
  • Application areas of python
  • Python implementations
  • Cpython
  • Jython
  • Ironpython
  • Pypy
  • Pythonversions
  • Installingpython
  • Python interpreter architecture
  • Python byte code compiler
  • Python virtual machine(pvm)

Writing and Executing First Python Program

  • Using interactive mode
  • Using script mode
  • General text editor and commandwindow
  • Idle editor and idleshell
  • Understanding print() function
  • How to compile python programexplicitly

Python Language Fundamentals

  • Character set
  • Keywords
  • Comments
  • Variables
  • Literals
  • Operators
  • Reading input fromconsole
  • Parsing string to int, float

Python Conditional Statements

  • If statement
  • If else statement
  • If elif statement
  • If elif else statement
  • Nested if statement

Looping Statements

  • While loop
  • For loop
  • Nested loops
  • Pass, break and continuekeywords

Standard Data Types

  • Int, float, complex, bool,nonetype
  • Str, list, tuple,range
  • Dict, set, frozenset

String Handling

  •  
  • What is string
  • String representations
  • Unicode string
  • String functions, methods
  • String indexing andslicing
  • String formatting

Python List

  • Creating and accessinglists
  • Indexing and slicinglists
  • List methods
  • Nested lists
  • List comprehension

Python Tuple

  • Creating tuple
  • Accessing tuple
  • Immutability of tuple

Python Set

  • How to create a set
  • Iteration over sets
  • Python set methods
  • Python frozenset

Python Dictionary

  • Creating a dictionary
  • Dictionary methods
  • Accessing values fromdictionary
  • Updating dictionary
  • Iterating dictionary
  • Dictionary comprehension

Python Functions

  • Defining a function
  • Calling a function
  • Types offunctions
  • Function arguments
  • Positional arguments, keywordarguments
  • Default arguments, non-defaultarguments
  • Arbitrary arguments, keyword arbitraryarguments
  • Function return statement
  • Nested function
  • Function as argument
  • Function as return statement
  • Decorator function
  • Closure
  • Map(), filter(), reduce(), any()functions
  • Anonymous or lambdafunction

Modules & Packages

  • Why modules
  • Script v/smodule
  • Importingmodule
  • Standard v/s third partymodules
  • Why packages
  • Understanding pip utility

File I/O

  • Introduction to filehandling
  • File modes
  • Functions and methods related to filehandling
  • Understanding with block

Object Oriented Programming

  • Procedural v/s object orientedprogramming
  • OOP principles
  • Defining a class &objectcreation
  • Object attributes
  • Inheritance
  • Encapsulation
  • Polymorphism

Exception Handling

  • Difference between syntax errors andexceptions
  • Keywords used in exceptionhandling
  • try, except, finally, raise,assert
  • Types of exceptblocks

Regular Expressions(Regex)

  • Need of regularexpressions
  • Re module
  • Functions /methods related toregex
  • Meta characters &specialsequences

GUI Programming

  • Introduction to tkinterprogramming
  • Tkinter widgets
  • Tk, label, Entry, Textbox,Button
  • Frame, messagebox, filedialogetc
  • Layout managers
  • Event handling
  • Displaying image

Multi-Threading Programming

  • Multi-processing v/s Multi-threading
  • Need of threads
  • Creating child threads
  • Functions /methods related tothreads
  • Thread synchronization andlocking
SQL

Introduction to Database

  • Database Concepts
  • What is DatabasePackage?
  • Understanding DataStorage
  • Relational Database (RDBMS)Concept

SQL (Structured Query Language)

  • SQLbasics
  • DML, DDL & DQL
  • DDL: create, alter, drop
  • SQLconstraints:
  • Not null, unique,
  • Primary & foreign key, compositekey
  • Check, default
  • DML: insert, update, delete andmerge
  • DQL : select
  • Select distinct
  • SQLwhere
  • SQLoperators
  • SQLlike
  • SQL orderby
  • SQLaliases
  • SQLviews
  • SQLjoins
  • Inner join
  • Left (outer) join
  • Right (outer) join
  • Full (outer) join
  • Mysql functions
  • Stringfunctions
  • Char_length
  • Concat
  • Lower
  • Reverse
  • Upper
  • Numericfunctions
  • Max, min, sum
  • Avg, count,abs
  • Date functions
  • Curdate
  • Curtime
  • Now

Statistics, Probability &Analytics:

Introduction to Statistics

  • Sample or population
  • Measures of central tendency
  • Arithmetic mean
  • Harmonic mean
  • Geometric mean
  • Mode
  • Quartile
  • First quartile
  • Second quartile(median)
  • Third quartile
  • Standard deviation

Probability Distributions

  • Introduction to probability
  • Conditional probability
  • Normal distribution
  • Uniform distribution
  • Exponential distribution
  • Right & left skeweddistribution
  • Random distribution
  • Centrallimittheorem

HypothesisTesting

  • Normality test
  • Mean test
  • T-test
  • Z-test
  • ANOVA test
  • Chi square test
  • Correlation and covariance

Numpy Package

  • Difference between list and numpyarray
  • Vector and matrixoperations
  • Array indexing andslicing

Panda Package

Introduction to pandas

  • Labeled and structureddata
  • Series and dataframe objects

How to load datasets

  • From excel
  • From csv
  • From html table

Accessing data from Data Frame

  • at &iat
  • loc&iloc
  • head() & tail()

Exploratory Data Analysis (EDA)

  • describe()
  • groupby()
  • crosstab()
  • boolean slicing /query()

Data Manipulation & Cleaning

  • Map(), apply()
  • Combining data frames
  • Adding/removing rows &columns
  • Sorting data
  • Handling missing values
  • Handling duplicacy
  • Handling data error

Handling Date and Time

Data Visualization using matplotlib and seaborn packages

  • Scatter plot, lineplot, barplot
  • Histogram, pie chart,
  • Jointplot, pairplot, heatmap
  • Outlier detection usingboxplot

Machine Learning:

Introduction To Machine Learning

  • Traditional v/s Machine LearningProgramming
  • Real life examples based onML
  • Steps of MLProgramming
  • Data Preprocessing revised
  • Terminology related toML

Supervised Learning

  • Classification
  • Regression

Unsupervised Learning

  • Clustering

KNN Classification

  • Math behind KNN
  • KNN implementation
  • Understanding hyperparameters

Performance metrics

  • Math behind KNN
  • KNN implementation
  • Understanding hyperparameters

Regression

  • Math behind regression
  • Simple linear regression
  • Multiple linear regression
  • Polynomial regression
  • Boston price prediction
  • Cost or loss functions
  • Mean absolute error
  • Mean squared error
  • Root mean squarederror
  • Least square error
  • Regularization

Logistic Regression for classification

  • Theory of logistic regression
  • Binary and multiclassclassification
  • Implementing titanic dataset
  • Implementing iris dataset
  • Sigmoid and softmaxfunctions

Support Vector Machines

  • Theory of SVM
  • SVM Implementation
  • kernel, gamma, alpha

Decision Tree Classification

  • Theory of decision tree
  • Node splitting
  • Implementation with iris dataset
  • Visualizingtree

Ensemble Learning

  • Random forest
  • Bagging and boosting
  • Voting classifier

Model Selection Techniques

  • Cross validation
  • Grid and random search for hyper parametertuning

Recommendation System

  • Content based technique
  • Collaborative filteringtechnique
  • Evaluating similarity based oncorrelation
  • Classification-based recommendations

Clustering

  • K-means clustering
  • Hierarchical clustering
  • Elbow technique
  • Silhouette coefficient
  • Dendogram

Text Analysis

  • Install nltk
  • Tokenize words
  • Tokenizing sentences
  • Stop words customization
  • Stemming and lemmatization
  • Feature extraction
  • Sentiment analysis
  • Count vectorizer
  • Tfidfvectorizer
  • Naive bayes algorithms

Dimensionality Reduction

  • Principal componentanalysis(pca)

Open CV

  • Reading images
  • Understanding gray scaleimage
  • Resizing image
  • Understanding haar classifiers
  • Face, eyes classification
  • How to use webcam in opencv
  • Building image dataset
  • Capturing video
  • Face classification invideo
  • Creating model for genderprediction

Tableau

Tableau - Home

  • Tableau -overview
  • Tableau - environmentsetup
  • Tableau - getstarted
  • Tableau -navigation
  • Tableau - designflow
  • Tableau - filetypes
  • Tableau - datatypes
  • Tableau - showme
  • Tableau - dataterminology

Tableau - Data Sources

  • Tableau - custom dataview
  • Tableau - datasources
  • Tableau - extractingdata
  • Tableau - fieldsoperations
  • Tableau - editingmetadata
  • Tableau - datajoining
  • Tableau - datablending

Tableau – Work Sheet

  • Tableau - addworksheets
  • Tableau - renameworksheet
  • Tableau - save &deleteworksheet
  • Tableau - reorderworksheet
  • Tableau - pagedworkbook

Tableau – Calculation

  • Tableau -operators
  • Tableau -functions
  • Tableau - numericcalculations
  • Tableau - stringcalculations
  • Tableau - datecalculations
  • Tableau - tablecalculations
  • Tableau - lodexpressions

Tableau – Sorting & Filter

  • Tableau - basicsorting
  • Tableau - basicfilters
  • Tableau - quickfilters
  • Tableau - contextfilters
  • Tableau - conditionfilters
  • Tableau - topfilters
  • Tableau - filteroperations

Tableau - Charts

  • Tableau - barchart
  • Tableau - linechart
  • Tableau - piechart
  • Tableau -crosstab
  • Tableau - scatterplot
  • Tableau - bubblechart
  • Tableau - bulletgraph
  • Tableau - boxplot
  • Tableau - treemap
  • Tableau - bumpchart
  • Tableau - ganttchart
  • Tableau -histogram
  • Tableau - motioncharts
  • Tableau - waterfallcharts
  • Tableau –dashboard

Projects

  • One project using python &sql
  • One project using python &ml
  • One dashboard usingtableau
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Sector 16, Noida

Sector 63, Noida

Ghaziabad

South Ex

Pitampura

Gurugram



Ducat: 70-70-90-50-90

Sector 16,Noida - Sector 63,Noida - Ghaziabad - SOUTH EX. - PITAMPURA - Gurugram

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