Data Science Training Online

Anyone can start Data Science Training course because there are no prior requirements necessary. We provide a 30-hour Data Science course taught by instructors with more than 10 years of real-time experience. The course includes real-world assignments and the faculty will direct you toward setting a work environment to practice assignments.

Assistance in CV preparation, interview questions answers, and materials are part of the training program. We do advise taking a one-hour session every day, From Monday through Friday, but one can also look into the weekend, fast-track, one-on-one, or customized programs.

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Data Science Career

✔️Data Scientist Pre-requisites

Basic Computer Knowledge.

✔️Jobs On Data Science

Top IT MNC such as Capgemini, Cognizant, IBM, Infosys, Accenture, etc.,

✔️Data Scientist Salary

The average salary for a Java Developer with 2 years experience in India is ₹4.5 Lakhs PA.

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INTRODUCTION TO DATA SCIENCE DEEP LEARNING & ARTIFICIALINTELLIGENCE

Introduction to Deep Learning & AI
Deep Learning: A revolution in Artificial Intelligence

  • Limitations of Machine Learning

What is Deep Learning?

  • Need for Data Scientists
  • Foundation of Data Science
  • What is Business Intelligence
  • What is Data Analysis
  • What is Data Mining

What is Machine Learning? Analytics vs. Data Science

  • Value Chain
  •  Types of Analytics
  • Lifecycle Probability
  • Analytics Project Lifecycle
  • Advantage of Deep Learning over Machine learning
  • Reasons for Deep Learning
  • Real-Life use cases of Deep Learning
  • Review of Machine Learning

Data

  • Basis of Data Categorization
  • Types of Data
  • Data Collection Types
  • Forms of Data & Sources
  • Data Quality & Changes
  • Data Quality Issues
  • Data Quality Story
  • What is Data Architecture
  • Components of Data Architecture
  • OLTP vs. OLAP
  • How is Data Stored?

Big Data

  • What is Big Data?
  • 5 Vs of Big Data
  • Big Data Architecture
  • Big Data Technologies
  • Big Data Challenge
  • Big Data Requirements
  • Big Data Distributed Computing & Complexity
  • Hadoop
  • Map Reduce Framework
  • Hadoop Ecosystem

Data Science Deep Dive

  • What Data Science is
  • Why Data Scientists are in demand
  • What is a Data Product
  • The growing need for Data Science
  • Large Scale Analysis Cost vs Storage
  • Data Science Skills
  • Data Science Use Cases
  • Data Science Project Life Cycle & Stages
  • Data Acquisition
  • Where to source data
  • Techniques
  • Evaluating input data
  • Data formats
  • Data Quantity
  • Data Quality
  • Resolution Techniques
  • Data Transformation
  • File format Conversions
  • Annonymization

PYTHON

  • Python Overview
  • About Interpreted Languages
  • Advantages/Disadvantages of Python pydoc.
  • Starting Python
  • Interpreter PATH
  • Using the Interpreter
  • Running a Python Script
  • Using Variables
  • Keywords
  • Built-in Functions
  • Strings Different Literals
  • Math Operators and Expressions
  • Writing to the Screen
  • String Formatting
  • Command Line Parameters and Flow Control.
  • Lists
  • Tuples
  • Indexing and Slicing
  • Iterating through a Sequence
  • Functions for all Sequences

Operators and Keywords for Sequences

  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets.

Numpy & Pandas

  • Learning NumPy
  • Introduction to Pandas
  • Creating Data Frames
  • Grouping Sorting
  • Plotting Data
  • Creating Functions
  • Slicing/Dicing Operations.

Deep Dive – Functions & Classes & Oops

  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values. Sorting
  • Alternate Keys
  • Lambda Functions
  • Sorting Collections of Collections
  • Classes & OOPs

STATISTICS

  • What is Statistics
  • Descriptive Statistics
  • Central Tendency Measures
  • The Story of Average
  • Dispersion Measures
  • Data Distributions
  • Central Limit Theorem
  • What is Sampling
  • Why Sampling
  • Sampling Methods
  • Inferential Statistics
  • What is Hypothesis testing
  • Confidence Level
  • Degrees of freedom
  • what is pValue
  • Chi-Square test
  • What is ANOVA
  • Correlation vs Regression
  • Uses of Correlation & Regression

MACHINE LEARNING, DEEP LEARNING & AI USING PYTHON

Introduction

  • ML Fundamentals
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques

Clustering

  • Similarity Metrics
  • Distance Measure Types: Euclidean, Cosine Measures
  • Creating predictive models
  • Understanding K-Means Clustering
  • Understanding TF-IDF, Cosine Similarity and their application to Vector Space
    Model
  • Case study

Implementing Association rule mining

  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Recommendation Use-case
  • Case study

Understanding Process flow of Supervised
Learning Techniques Decision Tree Classifier

  • How to build Decision trees
  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Decision Tree
  • Confusion Matrix
  • Case study

Random Forest Classifier

  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance
  • Case study

Naive Bayes Classifier.

  • Case study

Project Discussion

Problem Statement and Analysis

  • Various approaches to solve a Data Science Problem
  • Pros and Cons of different approaches and algorithms.

Linear Regression

Logistic Regression

Support Vector Machines

Time Series Analysis

Machine Learning Project

Machine learning algorithms Python

Feature Selection and Pre-processing

Which Algorithms perform best

Model selection cross validation score

Text Mining& NLP

PySpark and MLLib

Deep Learning & AI

Introduction to Artificial Neural Networks

Convolutional Neural Networks

What are RNNs – Introduction to RNNs

Restricted Boltzmann Machine (RBM) and Autoencoders

Tensorflow with Python

Building Neural Networks Using Tensorflow

Deep Learning using Tensorflow

Transfer Learning using Keras and TFLearn

Data Science Course FAQ's

01. can you help me in CV preparation?
Yes, We can help you preparing your resume.

02. will you help in interview preparation?
We can provide you interview question answers. The course covered many real-time examples. These examples might help you.

03. Is this course sufficient to get a job?
Our training covered as many real-time examples as we can. This course may equivalent to 2 to 3 years of work experience. You have to work hard if you are aimed at 4+ years of experience.

04. What’s the certification process?
Please come with an exam code. We’ll guide you further. We’ll guide you on how to get certified. Don’t worry, we’ll help you in certification process.

05. Can you provide Work Support?
We can provide job support for an additional fee. Contact the support team for fee details. You can choose either the hourly rate or monthly fee.

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