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Introduction of Data Science with Python and Machine Learning

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. There is no getting around the reality that nearly every industry releases a ton of data every day, which has to be managed effectively to extract useful data. Because of this, businesses search for qualified individuals who can effectively handle and convert data.

  • By the next year, almost 2 lakh jobs are anticipated.
  • By 2025, the big data industry is predicted to be worth $16 billion.
  • Nearly every IT sector is in need of qualified Data Science specialists.
  • The star players in the IT sector are data science, which pays an average of $106K annually.
Data Science with Python and Machine Learning Training in Noida
Why Apex T.G. India for Data Science Training?

With the aid of data science certification training from Apex T.G. India, you may quickly land a job in one of the world's top sectors and become ready for a variety of cloud certifications. If you wish to pursue a career in the field, you might join Data Science with Python and Machine Learning training course with Apex T.G. India.

  • Experts in the field assist you in providing thorough instruction that is appropriate for learning all the fundamentals.
  • We help you get ready to operate in a setting where you can make wise judgments.
  • Professionals train you how to use the tools so you can derive useful insights from them.
  • Get a thorough understanding of artificial intelligence, deep learning, big data, Hadoop, Tableau, etc.
  • Gain the confidence you need to handle challenging business challenges.
  • Learn about data analytics, data visualization, and other parts of computer data science.
  • Enhance your analytical, conversational, and intellectual abilities.

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 command window
  • Idle editor and idleshell
  • Understanding print() function
  • How to compile python program explicitly

Python Language Fundamentals

  • Character set
  • Keywords
  • Comments
  • Variables
  • Literals
  • Operators
  • Reading input from console
  • 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 accessing lists
  • Indexing and slicing lists
  • 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 from dictionary
  • Updating dictionary
  • Iterating dictionary
  • Dictionary comprehension

Python Functions

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

Modules & Packages

  • Why modules
  • Script v/s module
  • Importing module
  • Standard v/s third party modules
  • Why packages
  • Understanding pip utility

File I/O

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

Object Oriented Programming

  • Procedural v/s object oriented programming
  • OOP principles
  • Defining a class &object creation
  • Object attributes
  • Inheritance
  • Encapsulation
  • Polymorphism

Exception Handling

  • Difference between syntax errors and exceptions
  • Keywords used in exception handling
  • try, except, finally, raise,  assert
  • Types of except blocks

Regular Expressions(Regex)

  • Need of regular expressions
  • Re module
  • Functions /methods related to regex
  • Meta characters &special sequences

GUI Programming

  • Introduction to tkinter programming
  • Tkinter widgets
  • Tk, label, Entry, Textbox, Button
  • Frame, message box, file dialog etc
  • Layout managers
  • Event handling
  • Displaying image

Multi-Threading Programming

  • Multi-processing v/s Multi-threading
  • Need of threads
  • Creating child threads
  • Functions /methods related to threads
  • Thread synchronization and locking

Introduction to Database

  • Database Concepts
  • What is Database Package?
  • Understanding Data Storage
  • Relational Database (RDBMS) Concept

SQL (Structured Query Language)

  • SQL basics
  • DML, DDL & DQL
  • DDL: create, alter, drop
  • SQL constraints:
  • Not null, unique,
  • Primary & foreign key, composite key
  • Check, default
  • DML: insert, update, delete and merge
  • DQL : select
  • Select distinct
  • SQL where
  • SQL operators
  • SQL like
  • SQL order by
  • SQL aliases
  • SQL views
  • SQL joins
  • Inner join
  • Left (outer) join
  • Right (outer) join
  • Full (outer) join
  • Mysql functions
  • String functions
  • Char_length
  • Concat
  • Lower
  • Reverse
  • Upper
  • Numeric functions
  • Max, min, sum
  • Avg, count,abs
  • Date functions
  • Curdate
  • Curtime
  • Now

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
  • Cenltrallimittheorem

HypothlesisTesting

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

Numpy Package

  • Difference between list and numpy array
  • Vector and matrix operations
  • Array indexing and slicing

Panda Package

Introduction to pandas

  • Labeled and structured data
  • Series and data frame 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

Data Visualization using matplotlib and seaborn packages

  • Scatter plot, line plot, bar plot
  • Histogram, pie chart,
  • Joint plot, pair plot, heatmap
  • Outlier detection using box plot

Introduction To Machine Learning

  • Traditional v/s Machine Learning Programming
  • Real life examples based on ML
  • Steps of ML Programming
  • Data Pre-processing revised
  • Terminology related to ML

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 square derror
  • Least square error
  • Regularization

Logistic Regression for classification

  • Theory of logistic regression
  • Binary and multiclass classification
  • Implementing titanic dataset
  • Implementing iris dataset
  • Sigmoid and soft max functions

Support Vector Machines

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

Decision Tree Classification

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

Ensemble Learning

  • Random forest
  • Bagging and boosting
  • Voting classifier

Model Selection Techniques

  • Cross validation
  • Grid and random search for hyper parameter tuning

Recommendation System

  • Content based technique
  • Collaborative filtering technique
  • Evaluating similarity based on correlation
  • 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 component analysis(pca)

Open CV

  • Reading images
  • Understanding Gray scale image
  • Resizing image
  • Understanding haar classifiers
  • Face, eyes classification
  • How to use webcam in open cv
  • Building image dataset
  • Capturing video
  • Face classification in video
  • Creating model for gender prediction

Tableau – Home

  • Tableau -overview
  • Tableau – environment setup
  • Tableau – get started
  • Tableau -navigation
  • Tableau – design flow
  • Tableau - filetypes
  • Tableau - datatypes
  • Tableau – show me
  • Tableau – data terminology

Tableau - Data Sources

  • Tableau - custom data view
  • Tableau – data sources
  • Tableau – extracting data
  • Tableau - fields operations
  • Tableau – editing meta data
  • Tableau – data joining
  • Tableau – data blending

Tableau – Work Sheet

  • Tableau – add worksheets
  • Tableau – rename worksheet
  • Tableau - save &delete worksheet
  • Tableau – reorder worksheet
  • Tableau – paged workbook

Tableau – Calculation

  • Tableau -operators
  • Tableau -functions
  • Tableau – numeric calculations
  • Tableau – string calculations
  • Tableau – date calculations
  • Tableau – table calculations
  • Tableau – lod expressions

Tableau – Sorting & Filter

  • Tableau – basic sorting
  • Tableau – basic filters
  • Tableau – quick filters
  • Tableau – context filters
  • Tableau – condition filters
  • Tableau – top filters
  • Tableau - filter operations

Tableau – Charts

  • Tableau – bar chart
  • Tableau – line chart
  • Tableau – pie chart
  • Tableau -crosstab
  • Tableau - scatterplot
  • Tableau – bubble chart
  • Tableau – bullet graph
  • Tableau - boxplot
  • Tableau – tree map
  • Tableau – bump chart
  • Tableau – gantt chart
  • Tableau -histogram
  • Tableau – motion charts
  • Tableau – waterfall charts
  • Tableau –dashboard

Projects

One project using python &sql

  • One project using python &ml
  • One dashboard using tableau
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