Data Science

DATA ANALYTICS WITH PYTHON

  • Data Science Introduction & Use Cases
  • Python Basics: Basic Syntax, Data Structures
  • Python Basics: Loops, If-elif statements, Functions, Exception Handling
  • Statistics, Measures of central tendency, Population, Sample, Probability Distribution, Normal and Binomial Distribution, Random Variable, Pictorial Representations
  • Python Advanced: Numpy, Pandas
  • Python Advanced: Data Manipulation, Matplotlib

MACHINE LEARNING

  • ML Introduction & Use Cases
  • Statistics 2 – Inferential Statistics
  • Linear Regression
  • Logistic Regression
  • Decision Trees, Random Forest
  • Modelling Techniques (PCA, Feature Engineering)
  • KNN, Naive Bayes
  • Support Vector Machines(SVM)
  • Clustering, K-means

DEEP LEARNING WITH NLP

  • Introduction to NLP & Deep Learning
  • Word Embeddings
  • Word window classification
  • Introduction to Artificial Neural Networks
  • Introduction to Tensorflow
  • Recurrent Neural Networks for Language modelling
  • Gated Recurrent Units(GRUs), LSTMs
  • Recursive Neural network

ADVANCED MACHINE LEARNING

  • Market Basket Analysis & Apriori Algorithm
  • Recommendation System
  • Dimensionality Reduction
  • Anomaly Detection
  • XG Boost
  • Gradient Boosting Machine(GBM)
  • Stochastic Gradient Descent(SGD)
  • Ensemble Learning

DATA ANALYTICS WITH R

  • Data Science Introduction & Use Cases
  • R Basics: Basic Syntax, Variable assignment, Data Types-numeric, string, boolean
  • R Basics: Vectors, Matrices, Factors, Data Frames, ListsLoops, If-elif statements, Functions, Exception Handling
  • Statistics, Measures of central tendency, Population, Sample, Probability Distribution, Normal and Binomial Distribution, Random Variable, Pictorial Representations
  • R Advanced: Libraries
  • R Advanced: Data Manipulation, plots
  • Exploratory Data Analysis: Data Cleaning, Data Wrangling

MACHINE LEARNING

  • ML Introduction & Use Cases
  • Statistics 2 – Inferential Statistics
  • Linear Regression
  • Logistic Regression
  • Decision Trees, Random Forest
  • Modelling Techniques (PCA, Feature Engineering)
  • KNN, Naive Bayes
  • Support Vector Machines(SVM)
  • Clustering, K-means

DEEP LEARNING WITH NLP

  • Introduction to NLP & Deep Learning
  • Word Embeddings
  • Word window classification
  • Introduction to Artificial Neural Networks
  • Introduction to Tensorflow
  • Recurrent Neural Networks for Language modelling
  • Gated Recurrent Units(GRUs), LSTMs
  • Recursive Neural network

ADVANCED MACHINE LEARNING

  • Market Basket Analysis & Apriori Algorithm
  • Recommendation System
  • Dimensionality Reduction
  • Anomaly Detection
  • XG Boost
  • Gradient Boosting Machine(GBM)
  • Stochastic Gradient Descent(SGD)
  • Ensemble Learning

Introduction:

  • History
  • Feature
  • Setting up path
  • Working with Python
  • Basic Syntax
  • Variable and Data Types
  • Operator

Conditional Statements:

  • If
  • If- else
  • Nested if-else

Looping:

  • For
  • While
  • Nested loops

Control Statements:

  • Break
  • Continue
  • Pass

String Manipulation:

  • Accessing Strings
  • Basic Operations
  • String slices
  • Function and Methods

Lists:

  • Introduction
  • Accessing list
  • Operations
  • Working with lists
  • Function and Methods

Tuple:

  • Introduction
  • Accessing tuples
  • Operations
  • Working
  • Functions and Methods

Dictionaries:

  • Introduction
  • Accessing values in dictionaries
  • Working with dictionaries
  • Properties
  • Functions

Functions:

  • Defining a function
  • Calling a function
  • Types of functions
  • Function Arguments
  • Anonymous functions
  • Global and local variables

Modules:

  • Importing module
  • Math module
  • Random module
  • Packages
  • Composition

Input-Output:

  • Printing on screen
  • Reading data from keyboard
  • Opening and closing file
  • Reading and writing files
  • Functions

Exception Handling:

  • Exception
  • Exception Handling
  • Except clause
  • Try ? finally clause
  • User Defined Exceptions

OOPs concept:

  • Class and object
  • Attributes
  • Inheritance
  • Overloading
  • Overriding
  • Data hiding

Regular expressions:

  • Match function
  • Search function
  • Matching VS Searching
  • Modifiers
  • Patterns

CGI:

  • Introduction
  • Architecture
  • CGI environment variable
  • GET and POST methods
  • Cookies
  • File upload

Database:

  • Introduction
  • Connections
  • Executing queries
  • Transactions
  • Handling error

Networking:

  • Socket
  • Socket Module
  • Methods
  • Client and server
  • Internet modules

Multithreading:

  • Thread
  • Starting a thread
  • Threading module
  • Synchronizing threads
  • Multithreaded Priority Queue

GUI Programming:

  • Introduction
  • Tkinter programming
  • Tkinter widgets

Data Science with Python:

  • Setting up the environment for Data Science
  • Basic data types, loops, conditional statements
  • Lists, tuples and dictionaries
  • Introduction to Functional programming using Python
  • Jupyter notebook
  • NumPy arrays
  • Statistical and Linear algebra with NumPy
  • Exploring Pandas
  • Pandas Series and DataFrames
  • Statistics with Pandas DataFrames
  • Retrieving, Processing and Storing data
  • Data Visualization using Matplotlib and Seaborn

Student Splash(advantages):

  • 3classes for free.
  • Your complete resume will be prepared by us
  • Faq’s will be given which makes you to crack any interview
  • World class infrastructure class rooms.
  • Huge digital display for all the class rooms.
  • Latest study material and books(soft copy)
  • 100% placement assistance.
  • All 365 days our institute will be opened for any time clarifications
Student Reviews
DataBytes – Best Python Training Institute in Bangalore with Placement Assistance
A good platform for learning Python course and good teaching environment by Prem Sir……thank you DataBytes…..
Written by: Hemalatha H K
DataBytes BTM Bangalore Reviews
5 / 5 stars