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100% JOB Assist

Full Stack Data Science

Overview

Complete stack of data science is covered in this unique program in live class along with this you will get doubt clearing session and you will be able to get 24/7 live support from iNeuron skype team. one-year internship is already included in this program and you will get one year of internship completion certificate and you will work along with iNeuron product development team in various domain according to your alignment and your time availability.

What you'll learn
  • Python
  • Stats
  • Machine learning
  • Deep learning
  • Computer vision
  • Natural language processing
  • Data analytics
  • Big data
  • Ml ops
  • Cloud
  • Data structure and algorithm
  • Architecture
  • Domain wise project
  • Databases
  • Negotiations skills
  • Mock interview
  • Interview preparation
  • Resume building after every module
Requirements
  • Dedication
  • Computer with i3 and above configuration
Course Features
  • Full stack Data Science master’s certification
  • Job guarantee otherwise refund
  • One year of internship Anytime
  • Online Instructor-led learning: Live teaching by instructors
  • 56 + hands-on industry real-time projects.
  • 500 hours live interactive classes.
  • Every week doubt clearing session after the live classes.
  • Lifetime Dashboard access
  • Doubt clearing one to one
  • Doubt clearing through mail and skype support team
  • Assignment in all the module
  • Quiz in every module
  • A live project with real-time implementation
  • Resume building Anytime
  • Career guidance Anytime
  • Interview Preparation Anytime
  • Regular assessment
  • Job Fair and Internal Hiring
  • Mock Interview Anytime
  • Course overview and dashboard description
  • Introduction of data science and its application in day to day life
  • Programming language overview
  • Installation (tools: sublime, vscode, pycharm, anaconda, atom,jupyter notebook, kite)
  • Virtual environment
  • Why python
  • Introduction of python and comparison with other programming language
  • Installation of anaconda distribution and other python ide
  • Python objects, number & Booleans, strings
  • Container objects, mutability of objects
  • Operators - arithmetic, bitwise, comparison and assignment operators, operator’s precedence and associativity
  • Conditions (if else, if-elif-else), loops (while, for)
  • Break and continue statement and range function
  • Basic data structure in python
  • String object basics
  • String inbuilt methods
  • Splitting and joining strings
  • String format functions
  • List methods
  • List as stack and queues
  • List comprehensions
  • Dictionary object methods
  • Dictionary comprehensions
  • Dictionary view objects
  • Functions basics, parameter passing, iterators
  • Generator functions
  • Lambda functions
  • Map, reduce, filter functions
  • Multithreading
  • Multiprocessing
  • oops basic concepts.
  • Creating classes
  • Pillars of oops
  • Inheritance
  • Polymorphism
  • Encapsulation
  • Abstraction
  • Decorator
  • Class methods and static methods
  • Special (magic/dunder) methods
  • Property decorators - getters, setters, and deletes
  • Working with files
  • Reading and writing files
  • Buffered read and write
  • Other file methods
  • Logging, debugger
  • Modules and import statements
  • Exceptions handling with try-except
  • Custom exception handling
  • List of general use exception
  • Best practice exception handling
  • SQLite
  • MySQL
  • Mongo dB
  • NoSQL - Cassandra
  • What is web API
  • Difference b/w API and web API
  • Rest and soap architecture
  • Restful services
  • Flask introduction
  • Flask application
  • Open link flask
  • App routing flask
  • Url building flask
  • Http methods flask
  • Templates flask
  • Flask project: food app
  • Postman
  • Swagger
  • Python pandas - series
  • Python pandas - series
  • Python pandas – panel
  • Python pandas - basic functionality
  • Reading data from different file system
  • Python pandas – re indexing python
  • Pandas – iteration
  • Python pandas – sorting
  • Working with text data options & customization
  • Indexing & selecting
  • Data statistical functions
  • Python pandas - window functions
  • Python pandas - date functionality
  • Python pandas –time delta
  • Python pandas - categorical data
  • Python pandas – visualization
  • Python pandas - iotools
  • Dask Array
  • Dask Bag
  • Dask DataFrame
  • Dask Delayed
  • Dask Futures
  • Dask API
  • Dask SCHEDULING
  • Dask Understanding Performance
  • Dask Visualize task graphs
  • Dask Diagnostics (local)
  • Dask Diagnostics (distributed)
  • Dask Debugging
  • Dask Ordering
  • Numpy - ND array object.
  • Numpy - data types.
  • Numpy - array attributes
  • Numpy - array creation routines.
  • Numpy - array from existing.
  • Data array from numerical ranges.
  • Numpy - indexing & slicing.
  • Numpy – advanced indexing.
  • Numpy – broadcasting.
  • Numpy - iterating over array.
  • Numpy - array manipulation.
  • Numpy - binary operators.
  • Numpy - string functions.
  • Numpy - mathematical functions.
  • Numpy - arithmetic operations.
  • Numpy - statistical functions.
  • Sort, search & counting functions.
  • Numpy - byte swapping.
  • Numpy - copies & views.
  • Numpy - matrix library.
  • Numpy - linear algebra
  • Matplotlib
  • Seaborn
  • Cufflinks
  • Plotly
  • Bokeh
  • Introduction to basic statistics terms
  • Types of statistics
  • Types of data
  • Levels of measurement
  • Measures of central tendency
  • Measures of dispersion
  • Random variables
  • Set
  • Skewness
  • Covariance and correlation
  • Probability density/distribution function
  • Types of the probability distribution
  • Binomial distribution
  • Poisson distribution
  • Normal distribution (Gaussian distribution)
  • Probability density function and mass function
  • Cumulative density function
  • Examples of normal distribution
  • Bernoulli distribution
  • Uniform distribution
  • Z stats
  • Central limit theorem
  • Estimation
  • a Hypothesis
  • Hypothesis testing’s mechanism
  • P-value
  • T-stats
  • Student t distribution
  • T-stats vs. Z-stats: overview
  • When to use a t-tests vs. Z-tests
  • Type 1 & type 2 error
  • Bayes statistics (Bayes theorem)
  • Confidence interval(ci)
  • Confidence intervals and the margin of error
  • Interpreting confidence levels and confidence intervals
  • Chi-square test
  • Chi-square distribution using python
  • Chi-square for goodness of fit test
  • When to use which statistical distribution?
  • Analysis of variance (anova)
  • Assumptions to use anova
  • Anova three type
  • Partitioning of variance in the anova
  • Calculating using python
  • F-distribution
  • F-test (variance ratio test)
  • Determining the values of F
  • F distribution using python
  • linear algebra
  • Vector
  • Scaler
  • Matrix
  • Matrix operations and manipulations
  • Dot product of two vectors
  • Transpose of a matrix
  • Linear independence of vectors
  • Rank of a matrix
  • Identity matrix or operator
  • Determinant of a matrix
  • Inverse of a matrix
  • Norm of a vector
  • Eigenvalues and eigenvectors
  • Calculus
  • Ai vs ml vs dl vs ds
  • Supervised, unsupervised, semi-supervised, reinforcement learning
  • Train, test, validation split
  • Performance
  • Overfitting, under fitting
  • Overfitting, under fitting
  • Handling missing data
  • Handling imbalanced data
  • Up-sampling
  • Down-sampling
  • Smote
  • Data interpolation
  • Handling outliers
  • Filter method
  • Wrapper method
  • Embedded methods
  • Feature scaling
  • Standardization
  • Mean normalization
  • Min-max scaling
  • Unit vector
  • Feature extraction
  • Pca (principle component analysis)
  • Data encoding
  • Nominal encoding
  • One hot encoding
  • One hot encoding with multiple categories
  • Mean encoding
  • Ordinal encoding
  • Label encoding
  • Target guided ordinal encoding
  • Covariance
  • Correlation check
  • Pearson correlation coefficient
  • Spearman’s rank correlation
  • Vif
  • Feature selection
  • Recursive feature elimination
  • Backward elimination
  • Forward elimination
  • Feature engineering and selection.
  • Analyzing bike sharing trends.
  • Analyzing movie reviews sentiment.
  • Customer segmentation and effective cross selling.
  • Analyzing wine types and quality.
  • Analyzing music trends and recommendations.
  • Forecasting stock and commodity prices
  • Linear regression
  • Gradient descent
  • Multiple linear regression
  • Polynomial regression
  • R square and adjusted r square
  • Rmse, mse, mae comparison
  • Regularized linear models
  • Ridge regression
  • Lasso regression
  • Elastic net
  • Complete end-to-end project with deployment on cloud and ui
  • Logistics regression in-depth intuition
  • In-depth mathematical intuition
  • In-depth geometrical intuition
  • Hyper parameter tuning
  • Grid search cv
  • Randomize search cv
  • Data leakage
  • Confusion matrix
  • Precision,recall,f1 score ,roc, auc
  • Best metric selection
  • Multiclass classification in lr
  • Complete end-to-end project with deployment in multi cloud platform
  • Decision tree classifier
  • In-depth mathematical intuition
  • In-depth geometrical intuition
  • Confusion matrix
  • Precision, recall,f1 score ,roc, auc
  • Best metric selection
  • Decision tree repressor
  • In-depth mathematical intuition
  • In-depth geometrical intuition
  • Performance metrics
  • Complete end-to-end project with deployment in multi cloud platform
  • Linear svm classification
  • In-depth mathematical intuition
  • In-depth geometrical intuition
  • Soft margin classification
  • Nonlinear svm classification
  • Polynomial kernel
  • Gaussian, rbf kernel
  • Data leakage
  • Confusion matrix
  • precision, recall,f1 score ,roc, auc
  • Best metric selection
  • Svm regression
  • In-depth mathematical intuition
  • In-depth geometrical intuition
  • Complete end-to-end project with deployment
  • Bayes theorem
  • Multinomial naïve Bayes
  • Gaussian naïve Bayes
  • Various type of Bayes theorem and its intuition
  • Confusion matrix
  • Precision ,recall,f1 score ,roc, auc
  • Best metric selection
  • Complete end-to-end project with deployment
  • Definition of ensemble techniques
  • Bagging technique
  • Bootstrap aggregation
  • Random forest (bagging technique)
  • Random forest repressor
  • Random forest classifier
  • Complete end-to-end project with deployment
  • Boosting technique
  • Ada boost
  • Gradient boost
  • Xgboost
  • Complete end-to-end project with deployment
  • Python
  • Python
  • Python
  • Python
  • Python
knn
  • Stacking technique
  • Complete end-to-end project with deployment
  • The curse of dimensionality
  • Dimensionality reduction technique
  • Pca (principle component analysis)
  • Mathematics behind pca
  • Scree plots
  • Eigen-decomposition approach
  • Clustering and their types
  • K-means clustering
  • K-means++
  • Batch k-means
  • Hierarchical clustering
  • Dbscan
  • Evaluation of clustering
  • Homogeneity, completeness and v-measure
  • Silhouette coefficient
  • Davies-bouldin index
  • Contingency matrix
  • Pair confusion matrix
  • Extrinsic measure
  • Intrinsic measure
  • Complete end-to-end project with deployment
  • Anomaly detection types
  • Anomaly detection applications
  • Isolation forest anomaly detection algorithm
  • Isolation forest anomaly detection algorithm
  • Support vector machine anomaly detection algorithm
  • Dbscan algorithm for anomaly detection
  • Complete end-to-end project with deployment
  • What is a time series?
  • Old techniques
  • Arima
  • Acf and pacf
  • Time-dependent seasonal components.
  • Autoregressive (ar),
  • Moving average (ma) and mixed arma- modeler.
  • The random walk model.
  • Box-jenkins methodology.
  • Forecasts with arima and var models.
  • Dynamic models with time-shifted explanatory variables.
  • The koyck transformation.
  • Partial adjustment and adaptive expectation models.
  • Granger's causality tests.
  • Stationarity, unit roots and integration
  • Time series model performance
  • Various approach to solve time series problem
  • Complete end-to-end project with deployment
  • Prediction of nifty stock price and deployment
  • Tokenization
  • Pos tags and chunking
  • Stop words
  • Stemming and lemmatization
  • Named entity recognition (ner)
  • Word vectorization (word embedding)
  • Tfidf
  • Complete end-to-end project with deployment
  • Aws segmaker
  • Aure ml studio
  • Ml flow
  • Kube flow
  • H2o
  • Pycaret
  • Auto sklearn
  • Auto time series
  • Auto viml
  • Auto gluon
  • Auto viz
  • Tpot
  • Auto neuro
  • Detail mathematical explanation
  • Neural network overview and its use case.
  • Various neural network architect overview.
  • Use case of neural network in nlp and computer vision.
  • Activation function -all name
  • Multilayer network.
  • Loss functions. - all 10
  • The learning mechanism.
  • Optimizers. - all 10
  • Forward and backward propagation.
  • Weight initialization technique
  • Vanishing gradient problem
  • Exploding gradient problem
  • Visualization of nn
SQL
  • Introduction
  • ER Daigram
  • Schema Design
  • Normalization
  • SQL SELECT Statement
  • SQL SELECT Using common functions
  • SQL JOIN Overview
  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL JOIN
  • SQL Best Practice
  • INNER JOIN - Advanced
  • INNER JOIN & LEFT JOIN Combo
  • SELF JOIN
  • Joins & Aggregation - Subqueries
  • Sorting
  • Independent Subqueries
  • Correlated Subqueries
  • Analytic Function
  • Set Operations
  • SQL Views
  • Create a view
  • Create a view using DDL
  • SQL Insert - Advanced Technique
  • INSERT to create a table
  • INSERT new data to an existing table-1
  • INSERT new data to an existing table-2
  • INSERT new data to an existing table-3
  • INSERT new data to an existing table-4
  • SQL Update - Advanced Technique and TCL
  • SQL DELETE and TCL
  • SQL Constraints
  • SQL Aggregations
  • SQL Programmability
  • SQL Query Performance
  • SQL Xtras
  • Talking about Business Intelligence
  • Tools and Methodlogies used in BI
  • Why Visualization is getting more popular
  • Why Tableau?
  • Gartner Magic Quadrant of Market Leaders
  • Future buisness impact of BI
  • Tableau Products
  • Tableau Architecture
  • BI Project Excecution
  • Tableau Installation in local system
  • Introduction to Tableau Prep
  • Tableau Prep Builder User Interface
  • Data Preparation techniques using Tableau Prep Builder tool
  • How to connect Tableau with different data source
  • Visual Segments
  • Visual Analytics in depth
  • Filters, Parameters & Sets
  • Tableau Calculations using functions
  • Tableau Joins
  • Working with multiple data source (Data Blending)
  • Building Predictive Models
  • Dynamic Dashboards and Stories
  • Sharing your Reports
  • Tableau Server
  • User Security
  • Scheduling
  • PDF File
  • JSON File
  • Spatial File
  • Statistical File
  • Microsoft SQL Server
  • Salesforce
  • AWS
  • Azure
  • Google Analytics
  • R
  • Python
  • Hadoop
  • OneDrive
  • Microsoft Access
  • SAP HANA
  • SharePoint
  • Snowflake
  • Subject
  • Planning
  • Pen & Paper approach
  • Tools
  • Color theme
  • Shapes
  • Fonts
  • Image Selection
  • text position
  • visual placing
  • Story layout & design
  • Dashboard planning

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