Learn Data Science Online

You will learn data science 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

Learn Data Science Online 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

Learn Data Science Online 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.

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.

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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