Data Science Training Online

You will learn Data Science Training With main topics i.e Need for Data Scientists, Big Data, Data Science Deep Dive, PYTHON, Operators, and Keywords for Sequences, Numpy & Pandas, Deep Dive – Functions & Classes & Oops, etc.

  • Duration: 40 hours
  • Fast-track / Weekend batches
  • Instant Doubt Clarification
  • 10+ y Experienced Faculty
  • 100% Hands on Classes
  • Real time Scenarios
  • Free Bundle Videos
  • Sample Resumes
  • Interview Q&A
online training

Data Science Training Details

You will complete this course within 40hours.

we will provide for the students fast track & Weekend Batches at your flexible timings.

Who Should Learn Data Science?

Generally, your journey to becoming a data scientist would be much easier if you possess the following: A solid background in an analytical discipline such as mathematics, physics, computer science, or engineering. A basic understanding of computer programming, e.g. Python and R.

Do the faculty have a real-time experience?
Yes, the faculty has more than 10+ years of experience in real-time.

How many batches does trainer complete for this course?
✓ Successfully trained more than 30 batches
✓ Trained more than 250 learners

Will the trainer concentrate on the practical sessions?
Absolutely, the trainer concentrates on 30% theoretical and 70% on practical.

Do the faculty clear all the doubts during the session?
Obviously, the faculty clears all the doubts during the session.

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 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 a 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

What you will get!

The trainer will provide the Environment/Server Access to the students and we ensure practical real-time experience and training by providing all the utilities required for the in-depth understanding of the course.

  • Sure, We will share all classes High-quality Pre-Recording videos.
  • You get Free Access for Bundle of videos. It will help you learn the next version of this technology.

A virtual classroom is an online learning environment that allows Instructor and students to communicate, interact, collaborate, and share knowledge.

In the Self-Paced Online Course, Students can take the time they need and set their suitable schedule. Self-paced courses do not follow a set schedule. Course materials are entirely available as soon as the course begins.

Self-paced learning has several advantages that will enhance the performance of the students.

  • No time pressure
  • No need for a schedule
  • Improves memory
  • Suitable for different learning styles

Choice of Training Mode

Instructor Led Training

Duration: 30-40 hours
20000
  • Experienced Faculty
  • Real time Scenarios
  • Free Bundle Life time Access
  • 100% Hands-on Classes
  • Sample CV/Resume
  • Interview Q&A
  • Instructor Led Live Online Classes
  • Instant Doubt Clarification

Data Science Training Videos

Duration: 35+ hours
999/-
  • Experienced Faculty
  • Real-time Scenarios
  • Free Bundle Access
  • Course Future Updates
  • Sample CV/Resume
  • Interview Q&A
  • Complimentary Materials
  • No Doubt Clarification
POPULAR

Data Science Course FAQ!

What if I miss a class?
We record each LIVE class session you undergo through and we will share the recordings of each session/class.,

If I cancel my enrollment, will I get the refund?
If you are enrolled in classes and/or have paid fees, but want to cancel the registration for certain reason, it can be attained within 48 hours of initial registration. Please make a note that refunds will be processed within 30 days of prior request.
Is this a job guarantee program?
Institute doesn’t provide you any guarantee to get a job but as a guide as a mentor, as a friend, we will always provide you a good opportunity.

Do you help me to get a job?
We will definitely help you in attending interviews and it’s your caliber, hard-work, and ability.

After getting job can I expect support from you?
Even after getting a job when you stuck in any problem we will help but not for sure.

How long they give support after the course?
We are ready to help always, however it should fit in our course curriculum.

Do you Provide any real-time projects in CV Preparation?
In CV Preparation for real-time projects provide template documentation for the implementation project. You can access practice that in your idea system. If you have any difficulty, you can approach the trainer.

Will you provide any sample CV’S?
Yes, we will provide sample C.V’s for experience 2-3 years.

Can I expect your helping hand in C.V Preparation?
Definitely, we help you during C.V Preparation.

In what way you help me in interview preparation?
Yeah, Even after the completion of course, we will provide you some interview questions where you can concentrate on them.

What will they provide for references for interview preparation?
We provide excellent study materials and customizable course curriculum to students for superior quality training and we also provide videos recordings which support throughout your career.

Can I crack the interview with 3 year’s experience?
At the completion of this course, with hands-on training definitely give you confidence that you go in an interview with leaving 3 years of experience.

Data Science is in full swing as of now. That implies there will be an enormous flood of supply as ‘everybody does it”. Much the same as entering the market when it’s at its high is definitely not a decent arrangement, it’s likewise difficult to join a field that is in design.

What is the certification procedure?
Yes during the course, we will guide you and give you a clear picture about certification procedure.

Can i attempt certification after this course?
All the topics will be covered during the course. We provide question banks which will be helpful for you to attempt for certification.

Do you support me to pass certification?
Even after completion, of course, you can approach a trainer if you have any doubt regarding certification.

Do they provide the certification sample queries and references?
Surely, We provide the certification sample queries and references.

Does this certification benefit my career?
The certification has multiple impacts and encouraging factors in the recruitment of experts. Organizations claim that hiring certified professionals has served them with greater deployment and yield on investment. Check Cloud Career Building Up Force for New-age IT Professionals to understand the career benefits.

A Data Scientist, as per Harvard Business Review, “is a high-positioning proficient with the preparation and interest to make revelations in the realm of Big Data”. Thusly it shocks no one that Data Scientists pine for experts in the Big Data Analytics and IT industry.

With specialists foreseeing that 40 zettabytes of information will be in presence by 2020 (Source), Data Science profession openings will just shoot through the rooftop! The deficiency of gifted experts in a world that is progressively going to information for basic leadership has likewise prompted the immense interest for Data Scientists in new companies just as entrenched organizations. A McKinsey Global Institute study expresses that by 2018, the only us will confront a deficiency of around 190,000 experts with profound scientific abilities. With the Big Data wave giving no indications of backing off, there’s a surge among worldwide organizations to enlist Data Scientists to tame their business-basic Big Data.

Data science is a “concept to unify statistics, data analysis, machine learning, and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
Data Science is about extraction of crude and quantitative information into composed and useful data with the goal that it tends to be examined, pictured, and kept up for records. It is a blend of some hard abilities (like learning Python and SQL) and delicate aptitudes (like business abilities or relational abilities) and so on.

Data Science Training

The Data Science Training will help you master abilities and devices like Hypothesis testing, Statistics, Decision trees, Clustering,  Linear and Logistic regression, Data Visualization, R Studio, Statistical methods, High-level analytics, Excel analytics functions, Matplotlib,  Hypothesis trial, Zookeeper, Kafka interfaces. Individual abilities of data scientist training will assist you to prepare for the position of a Data Scientist. (Data Science Course)

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 at 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 This 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.

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 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 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 (Digital Marketing). The researchers of this club require multiple investigation schemes linked to the domains of data filtering, collaborative filtering, and recommended policies.

About Exam & Certification

The certifications concerning data science provide by reputed companies similar to UCI, SAS, Harvard Extension School, Cloudera, Microsoft and Columbia University. In-depth knowledge (Java Training) of all the data science concepts is needed to get the certificate. The Data Science Training way and the scheme SVR proceeds up with will be specified in order including the certification plans which enables you to clear data science certification exams with excellent satisfaction and achieve a job in best MNCS.

To pass the Data Science – R Programming sessions, you must:

  • Complete 85% of the Data Science Training program
  • Complete any one project out of the four provided in the way. You will present the project deliverables in the LMS, which will be appraised by our lead instructor
  • Score a least of 60% in any one of the two simulation examinations
  • Pass the online exam with a minimum score of 80%.

Job Trends

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.

 

What is a live virtual classroom?
A virtual classroom is an online learning environment that allows Instructor and students to communicate, interact, collaborate, and share knowledge.

What are the benefits of online training?
Online training is a valuable and cost-effective way for Employees and Students. Online training courses are of two kinds – free and paid versions. Here are a few advantages of online training that showcase how it helps improve an employee’s professional life:

  • Convenience and flexibility
  • Comfortable learning environment
  • Career advancement
  • More choice of course topics
  • Improve your technical skills
  • Easy Accessibility
  • It speeds up career progression

Is online courses Good or bad?
Of course, It is good. One of the significant benefits of the online course is its much affordable cost of learning. Also, it saves a student’s energy & time.
What is online training for employees?
Live Virtual Classroom has several advantages. Online Training allows employees to learn at their own pace and a convenient time for them. In other words, they are suitable for self-paced Training also.

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