• What you'll learn
    • Python
    • Stats
    • Machine learning
    • Deep learning
    • Computer vision
    • Natural language processing
    • Data analytics
    • Big data
    • Cloud
    • Data structure and algorithm
    • Architecture
    • Domain wise project
    • Databases
    • Negotiations skills
    • Interview preparation
    • Resume building
  • Requirements
    • Dedication
    • Computer with i3 and above configuration
  • Course Introduction
    • 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
  • Python Basic
    • 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
  • String Objects
    • basic data structure in python
    • String object basics
    • String inbuilt methods
    • Splitting and joining strings
    • String format functions
  • List Object Basics
    • List methods
    • List as stack and queues
    • List comprehension
  • Tuples, Sets, Dictionaries & its Function
    • Dictionary object methods
    • Dictionary comprehensions
    • Dictionary view objects
    • Functions basics, parameter passing, iterators
    • Generator functions
    • Lambda functions
    • Map, reduce, filter functions
  • Memory Management
    • Multithreading
    • Multiprocessing
  • OOPs Concepts
    • 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
  • Files
    • Working with files
    • Reading and writing files
    • Buffered read and write
    • Other file methods
    • Logging, debugger
    • Modules and import statements
  • Exception Handling and Difference between Exception and Error
    • Exceptions handling with try-except
    • Custom exception handling
    • List of general use exception
    • Best practice exception handling
  • GUI Framework
    • What is desktop and standalone application
    • Use of desktop app
    • Examples of desktop app
    • Tinker
  • Database
    • SQLite
    • MySQL
    • Mongo dB
    • NoSQL - Cassandra
  • Web API
    • What is web API
    • Difference b/w API and web API
    • Rest and soap architecture
    • Restful services
  • Django
    • Django introduction
    • Django project: weather app
    • Django project: memes generator
    • Django project: blog app
    • Django project in cloud
  • Flask
    • Flask introduction
    • Flask application
    • Open link flask
    • App routing flask
    • Url building flask
    • Http methods flask
    • Templates flask
    • Flask project: food app
    • Postman
    • Swagger
  • Stream Lit
    • Stream lit introduction
    • Stream lit project structure
    • Stream lit project in cloud
  • Pandas
    • Python pandas - series
    • Python pandas – data frame
    • 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
    • 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
  • Python Numpy
    • 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
  • Visualization
    • Matplotlib
    • Seaborn
    • Cufflinks
    • Plotly
  • Statistics Basic
    • 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
  • Statistics Advance
    • 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
  • Probability Distribution Function
    • 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
  • Linear Algebra
    • 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
  • Introduction to Machine Learning
    • Ai vs ml vs dl vs ds
    • Supervised, unsupervised, semi-supervised, reinforcement learning
    • Train, test, validation split
    • Performance
    • Overfitting, under fitting
    • Bias vs variance
Register