Data Science Training Online

⭐⭐⭐⭐⭐ rated by 1320 learners

Enroll For Course

About Instructor

Any Queries?

Check with our instructor in the demo session if you want to customized the course topics.

Student Reviews

Trusted by 12k+ Learners

Jobs On Jboss

Top IT MNC such as Infosys, HCL, TCS, Wipro, etc.

Salary Range

Average salary for Data scientists would be Rs. 10.8 lakhs PA.

Pre-requisites

Any scripting languages such as Java, Perl, Python or R.

Data Science Training FAQ's!

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
  • Anonymization

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 value
  • 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 stud

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

  • Case study
  • Introduction to Predictive Modeling
  • Linear Regression Overview
  • Simple Linear Regression
  • Multiple Linear Regression

Logistic Regression

  • Case study
  • Logistic Regression Overview
  • Data Partitioning
  • Univariate Analysis
  • Bivariate Analysis
  • Multicollinearity Analysis
  • Model Building
  • Model Validation
  • Model Performance Assessment AUC & ROC curves
  • Scorecard

Support Vector Machines

  • Case Study
  • Introduction to SVMs
  • SVM History
  • Vectors Overview
  • Decision Surfaces
  • Linear SVMs
  • The Kernel Trick
  • Non-Linear SVMs
  • The Kernel SVM

Time Series Analysis

  • Describe Time Series data
  • Format your Time Series data
  • List the different components of Time Series data
  • Discuss different kind of Time Series scenarios
  • Choose the model according to the Time series scenario
  • Implement the model for forecasting
  • Explain working and implementation of ARIMA model
  • Illustrate the working and implementation of different ETS models
  • Forecast the data using the respective model
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective model for forecasting
  • Visualizing and formatting Time Series data
  • Plotting decomposed Time Series data plot
  • Applying ARIMA and ETS model for Time Series forecasting
  • Forecasting for given Time period
  • Case Study

Machine Learning Project

Machine learning algorithms Python

  • Various machine learning algorithms in Python
  • Apply machine learning algorithms in Python

Feature Selection and Pre-processing

  • How to select the right data
  • Which are the best features to use
  • Additional feature selection techniques
  • A feature selection case study
  • Preprocessing
  • Preprocessing Scaling Techniques
  • How to preprocess your data
  • How to scale your data
  • Feature Scaling Final Project

Which Algorithms perform best

  • Highly efficient machine learning algorithms
  • Bagging Decision Trees
  • The power of ensembles
  • Random Forest Ensemble technique
  • Boosting – Adaboost
  • Boosting ensemble stochastic gradient boosting
  • A final ensemble technique

Model selection cross validation score

  • Introduction Model Tuning
  • Parameter Tuning GridSearchCV
  • A second method to tune your algorithm
  • How to automate machine learning
  • Which ML algo should you choose
  • How to compare machine learning algorithms in practice

Text Mining& NLP

  • Sentimental Analysis
  • Case study

PySpark and MLLib

  • Introduction to Spark Core
  • Spark Architecture
  • Working with RDDs
  • Introduction to PySpark
  • Machine learning with PySpark – Mllib

DEEP LEARNING & AI USING PYTHON
Deep Learning & AI

  • Case Study
  • Deep Learning Overview
  • The Brain vs Neuron
  • Introduction to Deep Learning

Introduction to Artificial Neural Networks

  • The Detailed ANN
  • The Activation Functions
  • How do ANNs work & learn
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropogation
  • Understand limitations of a Single Perceptron
  • Understand Neural Networks in Detail
  • Illustrate Multi-Layer Perceptron
  • Backpropagation – Learning Algorithm
  • Understand Backpropagation – Using Neural Network Example
  • MLP Digit-Classifier using TensorFlow
  • Building a multi-layered perceptron for classification
  • Why Deep Networks
  • Why Deep Networks give better accuracy?
  • Use-Case Implementation
  • Understand How Deep Network Works?
  • How Backpropagation Works?
  • Illustrate Forward pass, Backward pass
  • Different variants of Gradient Descent

Convolutional Neural Networks

  • Convolutional Operation
  • Relu Layers
  • What is Pooling vs Flattening
  • Full Connection
  • Softmax vs Cross Entropy
  • ” Building a real world convolutional neural network  for image classification”

What are RNNs – Introduction to RNNs

  • Recurrent neural networks rnn
  • LSTMs understanding LSTMs
  • long short term memory neural networks lstm in python

Restricted Boltzmann Machine (RBM) and Autoencoders

  • Restricted Boltzmann Machine
  • Applications of RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Building an Autoencoder model

Tensorflow with Python

  • Introducing Tensorflow
  • Introducing Tensorflow
  • Why Tensorflow?
  • What is tensorflow?
  • Tensorflow as an Interface
  • Tensorflow as an environment
  • Tensors
  • Computation Graph
  • Installing Tensorflow
  • Tensorflow training
  • Prepare Data
  • Tensor types
  • Loss and Optimization
  • Running ensorflow programs

Building Neural Networks Using

Tensorflow

  • Tensors
  • Tensorflow data types
  • CPU vs GPU vs TPU
  • Tensorflow methods
  • Introduction to Neural Networks
  • Neural Network Architecture
  • Linear Regression example revisited
  • The Neuron
  • Neural Network Layers
  • The MNIST Dataset
  • Coding NIST NN

Deep Learning using Tensorflow

  • Deepening the network
  • Images and Pixels
  • How humans recognise images
  • Convolutional Neural Networks
  • ConvNet Architecture
  • Overfitting and Regularization
  • Max Pooling and ReLU activations
  • Dropout
  • Strides and Zero Padding
  • Coding Deep ConvNets demo
  • Debugging Neural Networks
  • Visualising NN using Tensorflow
  • Tensorboard

Transfer Learning using

Keras and TFLearn

  • Transfer Learning Introduction
  • Google Inception Model
  • Retraining Google Inception with our own data demo
  • Predicting new images
  • Transfer Learning Summary
  • Extending Tensorflow
  • Keras
  • TFLearn
  • Keras vs TFLearn Comparison
Yes, Faculty will help you at the end of the course. The instructor will share a sample resume before course completion. During course, you can seek the faculty’s help to make your CV. We’ll give you interview question answers.

We can provide you interview question answers. The course covered some real-time examples. These examples might help you.

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

What is Data Science?

The application grants access to high-quality simulation exams, eLearning content, and an association governed by authorities, and additional support that guarantee you comprehend the optimal way to your desired position of data scientist tutorial training online.

Data Science foundation delivers you a master in developing the applicability by leveraging skills of Data conversion utilizing Map Reduce, Integrating R, Random Forest Classifier,  beside Hadoop using R, Sub-setting data.

Data Science Training is an emerging discipline, with accelerated settings, necessary change, and inspiring occasions. Our education strives the first-ever benchmark of the data science online training society, examining how they communicate with their data, the devices all performance, their data, and whereby their standards progress data-driven problem-solving.

Confer an emerging capability way among company requirements and modern entrepreneur’s capabilities exemplified by the unique contributions data investigators can proceed to a business of data scientist training online and the full expectations of data science experts.

The Track runs with the purpose to know basic ideas about the fundamentals of Data Science Training, Machine learning, and Data retrieval, algorithms. In this foundation sessions of data analytics training for beginners, you spread the knowledge devices and methods of Experimentation, and Project, data mining and data collection.

Why Data Science Training Course?

The data expert is the spire rank in the analytics industry. Glassdoor has listed online data science training first in the 25 Best Jobs for 2016, and beneficial data specialists are limited and in immense interest Data Science Training. As a data scientist, you will be needed to know the industry obstacle, produce the investigation, accumulate and format the requested information, implement (Machine Learning Online Course) algorithms or methods employing the proper devices, and eventually make proposals supported by data.

Data Science Training Course Objectives

You will acquire an in-depth knowledge (salesforce training) of the big data concepts and the duties of a data scientist. This Data Science Training program design in such a way python data science training that it is genuine for you to gain and develop your data science abilities instantly. So, you can begin serving in this track once you finish the training favorably.

    • Hands-on Python
    • Linear Algebra Review
    • AI & Machine Learning an Introduction.
    • Skewness and Kurtosis
    • Elementary Statistics with Python+Tensorflow& R
    • Types of Central Tendency Measures of Dispersion
    • Data, Parametric and Non-Parametric Analyses

 

Representation and Population Express the Hypothesis Choose a Relevant Test Choose level of Significance Calculate Test Statistics

Who Should Do This Data Science Training Course?

The Data Science Training position lacks the ideal amalgam of expertise, data science familiarity, and employing suitable devices and technologies. It is an excellent profession option for both fresher and experienced experts. Enthusiastic specialists of any educational best data science training  credentials with an analytic structure of determination are most suited to attempt the Data Scientist Master’s Program, including

  • IT professionals
  • Business Analysts
  • Analytics Managers
  • Marketing Managers
  • Supply Chain Network Managers
  • Banking and Finance professionals
  • Those new to the data analytics domain Pupils in UG/ PG Analytics Program

Projects Included In The Data Science Training Course

Our trainer will give an in-depth insight into the data science scheme which concentrates on all the significant segments of Data Science Training. While as a decision, you can increase your clarity and develop data scientists online training your capability and draw actual associations among various elements of data science. You will further notice the entire material incorporating all the features of this scheme.

Scheme 1: Notice how Data Science uses in the area of engineering by delivering up the case above the study of MovieLens Dataset Analysis.

Domain: Engineering

Description: The GroupLens Research Project is a study club in the Department of Computer Science and Engineering at the University of Minnesota. The researchers of this club require multiple investigation schemes linked to the domains of data filtering, collaborative filtering, and recommended policies.
What is the Average Salary of Data Scientist?
Start-ups are paying average salaries of Rs. 10.8 lakhs to data scientists. This is 12.5 % higher than the average salaries paid by their larger counterparts. Indian companies are looking for a combination of analytics and Big data skills

What is the Scope of Data Science?

Gain admittance to a dedicated team of profession authorities authorized to supporting you succeed in your data science job hunt. Acquire personalized information to improve your answer rate for job appeals, and guarantee you ace your interviews.

The Data Scientist program is formulated with world-class industry associates to serve you for real jobs in Data Science Training and analytics. The abilities you master map straight to open opportunities, and you’ll rise from the program uniquely prepared to provide immediate value to any organization. We’ll support your career exploration entirely in the preparation and hunt process, and our purpose is to assist you in securing a rewarding role in your preferred career.

According to the Indian Times analysis Recently, the business report reveals that 50,000 jobs in Data Scientist and Machine learning. This shows how business analytics increased in India.

Scroll to Top