Data Science Training Topics
 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
 RealLife 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
 Builtin 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
 ChiSquare 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 KMeans Clustering
 Understanding TFIDF, 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 Usecase
 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
 NonLinear 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 Preprocessing
 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 MultiLayer Perceptron
 Backpropagation – Learning Algorithm
 Understand Backpropagation – Using Neural Network Example
 MLP DigitClassifier using TensorFlow
 Building a multilayered perceptron for classification
 Why Deep Networks
 Why Deep Networks give better accuracy?
 UseCase 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
Our Happy Student Reviews!
About Data Science Training Course
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, Highlevel analytics.
The application grants access to highquality 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, Subsetting data.
Data Science Training is an emerging discipline, with accelerated settings, necessary change, and inspiring occasions. Our education strives the firstever 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 datadriven problemsolving.
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.
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.
You will acquire an indepth 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.

 Handson 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 NonParametric Analyses
Representation and Population Express the Hypothesis Choose a Relevant Test Choose level of Significance Calculate Test Statistics
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
Our trainer will give an indepth 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.
Data Science Training CV & Interview
Even after the completion Data science training course, we will provide you some interview questions where you can concentrate on them.
Yeah, Even after the completion of data science training course, we will provide you some interview questions where you can concentrate on them.
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.
At the completion of this Data science training course, with handson training definitely give you confidence that you go in an interview with leaving 3 years of experience.
Data Science Training Certification
we will guide you and give you a clear picture of the certification procedure during the data science training session.
The certifications concerning data science provide by reputed companies similar to UCI, SAS, Harvard Extension School, Cloudera, Microsoft and Columbia University. Indepth knowledge (Java Training) of all the data science concepts is needed to get the certificate.
The Data Science Training way and the scheme SVR proceed 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 the 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%.
All the topics will be covered in the data science training period. We provide question banks which will be helpful for you to attempt for certification.
Even after completion, of course, you can approach a trainer if you have any doubt regarding certification.
Surely, We provide the certification sample queries and references.
The certification in the data science training 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 Newage IT Professionals to understand the career benefits.
Data Science Training Salary & Career Growth
One of the top Indian Job seeking sites say more than 25,000+ jobs are there in India just for Data Scientist.
Startups 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
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 worldclass 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.
Data Science Training Job Support
We Provide job support by experienced and realtime working professionals. We can charge as per your convenience hourly or monthly. Feel free to ask initial interaction session with expert.