Data Science Courses Online in Bangalore
Data Science Courses online in Bangalore are providing better education from others. This is the best coaching institute for achieving your excellence. With the growing number of institutes offering Data Science courses online but you have to choose the right one. We bring here the best data science institute for you. Online IT Class is giving a short term course with 100% Job Placement. We have great infrastructure, campus, placement, reviews, experience trainers, live projects, real-time classes, etc.
Data Science is basically dealing with structured and unstructured data. Data Science is comprising of everything which is related to data cleansing, preparation, and data analysis. A Data Science is a professional who possesses the ability to transform raw data into useful insights to make better business decision. Data Science will provide you with extensive expertise in the booming data analytics and data science fields. Data Science is the process of using data to find solutions / to predict outcomes for a problem statement.
• Business Requirements
• Data collection
• Data Cleaning
• Data Exploration & Analysis
• Data Modelling
• Data validation
• Deployment & optimization
It is an E-commerce platform like Amazon & Flipkart. It is also the logic behind Netflix’s recommendation system now. It has made remarkable changes in today’s market. A data scientist is the most promising job role in India.
A data scientist must be in statistics expertise programming languages like R and python. You’re required to have a good understanding of processes like data extraction processing wrangling and exploration. You must also be well-versed with the different types of machine learning algorithms and how they work. Advanced machine learning concepts like deep learning is also needed, you must also possess a good understanding of different big data processing Frameworks, like Hadoop and spark and finally you must know how to visualize the data by using tools like tableau and power bi. So now its time to buckle up and kick start your career as a data scientist. A data scientist should have the following 7 skills:
• Database knowledge
• Statistics
• Programming tools
• Data Wrangling
• Data Visualization
• Big Data
• Machine Learning
At least a master’s degree in a quantitative discipline, or a bachelors degree and several online data science courses. Many data scientists will be heavily specialized in business, often specific segments of the economy (such as automotive or insurance) or business-related fields like marketing or pricing. These degrees will help you to learn data science include:
• Computer science
• Statistics
• Physics
• Social science
• Mathematics
• Applied math
• Economics
Yes it is possible to learn data science online. Online instructor come with practical knowledge and may be from any location across the globe. This allows students to be exposed to knowledge that can‘t be learned in books and see how class concepts are applied in real business situation. Using internet to attend class. Information & communication teaches skills in using technologies. Students can interact with their peers in virtual break-out rooms, take notes and screenshots easily, and access previous recordings of lectures. Online environment makes instructors more approachable. Online course development allows for a broad spectrum of content.







Assured Seamless Connectivity During the Training Period. No Disturbance Training Experience Gauranteed
Latest Course Material Created and Maintained by Industry Veterans. Full Value for the Money You Spend
Trainers and Teaching Staff – All have more than 6+ years experience – Real-Time as well as Teaching



DATA Science Course Content
Online it class Data Science with Python Syllabus
SECTION I — Python Basics
Lesson 1: Overview
Why do we need Python?
Program structure
Environment Setup
Python Installation
Execution Types
What is an interpreter?
Interpreters vs Compilers
Using the Python Interpreter
Interactive Mode
Running python files
Working with Python shell
Integrated Development Environments (IDES)
Interactive Mode Programming
Script Mode Programming
Lesson 2 : Basic Concepts
Basic Operators
Types of Operator
Python Arithmetic Operators
Python Comparison Operators
Python Assignment Operators
Python Bitwise Operators
Python Logical Operators
Python Membership Operators (in, not in)
Python Identity Operators (is, is not)
Python Operators Precedence
Data Types
Variables
Assigning Values to Variables
Multiple Assignment
Python Numbers
Python Strings
Accessing Values in Strings
String Special Operators
String Formatting Operator
Triple Quotes
Built-in String Operations
Python Lists
Accessing Values in Lists
Updating Lists
Delete List Elements
Basic List Operations
Indexing, Slicing, and Matrixes
Built-in List Functions & Methods
Python Tuples
Accessing Values in Tuples
Updating Tuples
Delete Tuple Elements
Basic Tuples Operations
Indexing, Slicing, and Matrixes
No Enclosing Delimiters
Built-in Tuple Functions
Python Dictionary
Accessing Values in Dictionary
Updating Dictionary
Delete Dictionary Elements
Properties of Dictionary Keys
Built-in Dictionary Functions & Methods
Lesson 3:Loops and Decision Making
if statements
if…else statements
nested if statements
while loop
for loop
nested loops
Loop Control Statements
1) break statement
2) continue statement
3) pass statement
Lesson 4 :Functions
Defining a Function
Syntax
Calling a Function
Pass by reference vs value
Function Arguments
Required arguments
Keyword arguments
Default arguments
Variable-length arguments
The return Statement
Scope of Variables
Global vs. Local variables
Lesson 5: Basic OOPs Concept
Creating class in Python
Documented String
Private Identifier
Constructor
Inheritance
Polymorphism
Lesson 6 : Python Modules and Packages
Framework vs Packages
Folium Introduction
Why are modules used?
Creating modules
The import Statement
The from…import Statement
The from…import * Statement
Locating Modules
The PYTHONPATH Variable
Namespaces and Scoping
The dir( ) Function
The globals() and locals() Functions
The reload() Function
Packages in Python
Lesson 7: Advance Python
Decorator, Iterator and Generator
Anonymous Function
Lambda
Map
Filter
Reduce
Errors and Exception Handling
Standard exceptions
Assertions in Python
The assert Statement
What is Exception?
Handling an exception
Section II — Statistics and Data Science Overview
Lesson8: Data Science Overview
Data Science Disciplines
Data Science and Business Buzzwords Why are there so many
What is the difference between Analysis and Analytics
An Introduction–Business Analytics, Data Analytics, and Data Science
Data Science Diagram
Introduction — BI, ML and AI
Careers in Data Science Fields
Data Overview
What is Data
Measuring Data
Measurement of Central Tendency
Measurements Dispersion
Measurement Quartile
Bi-variate Data and Co-variance
Pearson Correlation Coefficient
Lesson 9 : Probability
What is Probability
Permutations
Combinations
Intersections Unions and Complements
Independent and Dependent Events
Conditional Probability
Addition and Multiplication Rules
Bayes Theorem
Lesson 10: Distributions
Introduction to Distributions
Uniform Distribution
Binomial Distribution
Poisson Distribution
Normal Distribution
Lesson 11:Statistics
What is Statistics
Sampling
Central Limit Theorem
Standard Error
Hypothesis Testing
Hypothesis Testing Example Exercise
Type 1 and Type 2 Errors
Students T Distribution
Practical Example Descriptive Statistics Exercise
What are Confidence Intervals
Correlation Matrix
Lesson 12:Anova
Introduction to ANOVA
Two Way ANOVA Overview
F- Distribution
Lesson 13:Chi Square Analysis
Chi-Square Analysis
Chi Squared Analysis – Exercise Example
Section III — Python for Data Analysis
Lesson 14 : Python: Environment Setup and Essentials
Introduction to Anaconda
Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
Jupyter Notebook Installation
Jupyter Notebook Introduction
Lesson 15:Data Analysis- Numpy
Introduction to Numpy
Numpy Array
Numpy Indexing
Numpy Operations
Broadcasting Numpy Array
Lesson 16:Data Analysis — Pandas
Introduction to Pandas
Series
Data Frames
Missing Data
Groupby
Operations
Merging, Joining and concatenating
Missing Data
Data Input and Output
Lesson 17:Pandas Exercise
Salaries Exercise
Ecommerce Purchases Exercise
Lesson 18:Numpy Exercise
Solving Linear System
Problem Set
Section IV — Python for Data Visualization
Lesson 19:Matplotlib
Introduction
Matplotlib Drawing Graph — Histogram, Plotting, Box Plot etc
Exercise
Lesson 20:Seaborn
Introduction
Distribution
Categorical Plots
Matrix Plots
Regression Plots
Grids
Style and Colors
Exercise
Lesson 21:Data Visualization with Pandas
Pandas Built-in Data Visualization
Pandas Data Visualization Exercise
Lesson 22:Data Visualization – Geographical Plotting
Introduction to Geographical Plotting
Choropleth Maps – Part 1 – USA
Choropleth Maps – Part 2 – World
Choropleth Exercises
Capstone Project I
Calls Data Capstone Project
Finance Project
Lesson 23:Time Series Analysis
Pandas for Time Series
Introduction to Time Series with Pandas
Date time Index
Time Re-sampling
Time Shifts
Pandas Rolling and Expanding
Time Series Analysis
Introduction to Time Series
Time Series Basics
Introduction to Statsmodel
Capstone Project II
Stock Market Analysis Project
Lesson 24:Scientific computing with Python (Scipy)
SciPy and its Characteristics
SciPy sub-packages
SciPy sub-packages –Integration
SciPy sub-packages – Optimize
Linear Algebra
SciPy sub-packages – Statistics
SciPy sub-packages – Weave
SciPy sub-packages – I O
Lesson 25: Data Science with Python Web Scraping
Web Scraping
Common Data/Page Formats on The Web
The Parser
Importance of Objects
Understanding the Tree
Searching the Tree
Navigating options
Modifying the Tree
Parsing Only Part of the Document
Printing and Formatting
Encoding
Section V — Machine Learning
Lesson 26: Machine Learning with Python (Scikit–Learn)
Introduction to Machine Learning
Machine Learning Approach
How Supervised and Unsupervised Learning Models Work
Scikit-Learn
Supervised Learning Models – Linear Regression
Supervised Learning Models: Logistic Regression
K Nearest Neighbors (K-NN) Model
K Means Algorithm
SVMs
Unsupervised Learning Models: Clustering
Unsupervised Learning Models: Dimensionality Reduction
Pipeline
Model Persistence
Model Evaluation – Metric Functions
Lesson 27: Natural Language Processing with Scikit-Learn
NLP Overview
NLP Approach for Text Data
NLP Environment Setup
NLP Sentence analysis
NLP Applications
Major NLP Libraries
Scikit-Learn Approach
Scikit – Learn Approach Built – in Modules
Scikit – Learn Approach Feature Extraction
Section VI: Project Works
Project 1– Board Game Review Prediction— To perform a Linear Regression Analysis by predicting the average reviews in a board game
Project 2 — Credit Card Fraud Detection — To focus on Anomaly Detection by using probability densities to detect credit card fraud
Project 3 – Stock Market Clustering – Learn how to use the K-means clustering algorithm to find related companies by finding correlations among stock market movements over a given time span
Project 4 – Getting Started with Natural Language Processing in Python – This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking.