# Data Science Training Online

Data Science Training Online course taught by ✓15+ years experienced working professional. **✓100% practical **course with real-time projects **✓Lifetime Access** to Digital library ✓Sample Resume. ✓fast-track (or 1 to 1 classes) with no extra charge. ✓Course Duration: **30+ hours.**

**Pre-requisites:** Any scripting languages such as Java, Perl, Python or R.

## Data Science Course Syllabus

- Limitations of Machine Learning

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

- 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

- 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?

- 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

- 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 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

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

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

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

- 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

**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**

- 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”

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

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

- 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

- 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

- 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 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

## Data Science Training Online

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- 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

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- IT professionals
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- Those new to the data analytics domain Pupils in UG/ PG Analytics Program

**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.

## CV, Interview Preparation

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.

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.

## Data Science Certification

We’ll guide you on how to get certified as Data Scientist. Our course isn’t aiming to get certified but covered real-time examples. Course helpful to get a job and fulfilling day to day activities in the office.

## Data Scientist Salary, Career

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.