Data Science with Python and Machine Learning Training in Noida

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