**Master Data Science Course Program**

Data Science Course prepares you for the Data Science Certification exam and for the role of Data Scientist by making you an expert in Statistics, Analytics, Data Science, Big Data, AI, Machine Learning and Deep Learning. The Data science master program Hyderabad has been designed by the industry expert to help you master data mining, management, exploration, and carry out several industry-relevant projects.

- Data Science with SAS Training
- Data Science Certification Training – R Programming
- Big Data Hadoop and Spark Developer
- Data Science with Python
- Business Analytics with Excel
- Machine Learning
- Deep Learning with TensorFlow

### 7 Days

**For Classroom & Online Training**

**Reviews **

## Master Data Science Online Training

## GET IN TOUCH

## DESCRIPTION

## DESCRIPTION

### Data Science Course Training

Data science is an interdisciplinary field of scientific methods, processes, algorithms & systems to extract knowledge or insights from data in various forms, structured or unstructured, similar to data mining. Data science Training Hyderabad is an interdisciplinary field of scientific methods, processes, algorithms, and systems to extract knowledge or insights from data in various forms, structured or unstructured, similar to data mining.

### What is Data Science?

Data science is a “concept to unify statistics, data analysis, machine learning & their related methods” in order to “understand & analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, data mining, databases, and visualization.

Data Science Turing award winner Jim Gray imagined data science as a ” fourth paradigm” of science (empirical, theoretical, computational and now data-driven) and asserted that ” everything about science is changing because of the impact of information technology” and the data deluge.

### Learning Objectives?

Data Scientist Master’s Program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures,Advanced analytics, Matplotlib, Excel analytics functions, Hypothesis testing, Zookeeper, Kafka interfaces.

You will master TensorFlow, Machine Learning, and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems. These skills will help you prepare for the role of a Data Scientist.

The program provides access to high-quality eLearning content, simulation exams, a community moderated by experts, and other resources that ensure you follow the optimal path to your dream role of data scientist.

### Why Choose to be a Data Scientist?

The data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data scientist, you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.

## CURRICULUM

## Data Science Course Training

## CURRICULUM

## Data Science Course Training

###### Data Science with SAS

**Course Content**

**Analytics Overview**

- Introduction
- Introduction to Business Analytics
- Types of Analytics
- Areas of Analytics
- Analytical Tools
- Analytical Techniques

**Introduction to SAS**

- Introduction
- What is SAS
- Navigating in the SAS Console
- SAS Language Input Files
- DATA Step
- PROC Step and DATA Step
- DATA Step Processing
- SAS Libraries
- Importing Data
- Exporting Data

**Combining and Modifying Datasets**

- Introduction
- Why Combine or Modify Data
- Concatenating Datasets
- Interleaving Method
- One – to – one Reading
- One – to – one Merging
- Data Manipulation
- Modifying Variable Attributes

**PROC SQL**

- Introduction
- What is PROC SQL
- Retrieving Data from a Table
- Selecting Columns in a Table
- Retrieving Data from Multiple Tables
- Selecting Data from Multiple Tables
- Concatenating Query Results
- Activity

**SAS Macros**

- Introduction
- Need for SAS Macros
- Macro Functions
- Macro Functions Examples
- SQL Clauses for Macros
- The % Macro Statement
- The Conditional Statement

**Basics of Statistics**

- Introduction to Statistics
- Statistical Terms
- Procedures in SAS for Descriptive Statistics
- Descriptive Statistics
- Hypothesis Testing
- Variable Types
- Hypothesis Testing
- Process
- Parametric and Non – parametric Tests
- Parametric Tests
- Non – parametric Tests
- Parametric Tests – Advantages and Disadvantages

**Statistical Procedures**

- Introduction o Statistical Procedures
- PROC Means
- PROC FREQ
- PROC UNIVARIATE
- PROC CORR
- PROC CORR Options
- PROC REG
- PROC REG Options
- PROC ANOVA

**Data Exploration**

- Introduction
- Data Preparation
- General Comments and Observations on Data Cleaning
- Data Type Conversion
- Character Functions
- SCAN Function
- Date/Time Functions
- Missing Value Treatment
- Various Functions to Handle Missing Value
- Data Summarization

**Advanced Statistics**

- Introduction
- Introduction to Cluster
- Clustering Methodologies
- K Means Clustering
- Decision Tree
- Regression
- Logistic Regression

**Working with Time Series Data**

- Introduction
- Need for Time Series Analysis
- Time Series Analysis — Options
- Reading Date and DDateTimeValues
- White Noise Process
- Stationarity of a Time Series
- Stages of ARIMA Modelling
- Transform Transpose and Interpolating Time Series Data

**Designing Optimization Models**

- Introduction
- Need for Optimization
- Optimization Problems
- PROC OPTMODEL

###### Data Science Certification Training - R Programming

**COURSE CONTENT**

**Introduction to Business Analytics**

- Introduction
- Objectives
- Need of Business Analytics
- Business Decisions
- Introduction to Business Analytics
- Features of Business Analytics
- Types of Business Analytics
- Descriptive Analytics
- Predictive Analytics
- Prescriptive Analytics
- Supply Chain Analytics
- Health Care Analytics
- Marketing Analytics
- Human Resource Analytics
- Web Analytics
- Application of Business Analytics
- Business Decisions
- Business Intelligence (BI)
- Data Science
- Importance of Data Science
- Data Science as a Strategic Asset
- Big Data
- Analytical Tools

**Introduction to R**

- Introduction
- Objectives
- An Introduction to R
- Comprehensive R Archive Network (CRAN)
- Cons of R
- Companies Using R
- Understanding R
- Installing R on Various Operating Systems
- Installing R on Windows from CRAN Website
- Install R
- IDEs for R
- Installing RStudio on Various Operating Systems
- Install R-Studio
- Steps in R Initiation
- Benefits of R Workspace
- Setting the Workplace
- Functions and Help in R
- Access the Help Document
- R Packages o Installing an R Package
- Install and Load a Package

**R Data Structure**

- Introduction
- Objectives
- Types of Data Structures in R
- Vectors
- Create a Vector
- Scalars
- Colon Operator
- Accessing Vector Elements
- Matrices
- Accessing Matrix Elements
- Create a Matrix
- Arrays
- Accessing Array Elements
- Create an Array
- Data Frames
- Elements of Data Frames
- Create a Data Frame
- Factors
- Create a Factor
- Lists
- Create a List
- Importing Files in R
- Importing an Excel File
- Importing a Minitab File
- Importing a Table File
- Importing a CSV File
- Read Data from a File
- Read Data from a File
- Exporting Files from R

**Apply Functions**

- Introduction
- Objectives
- Types of Apply Functions
- Apply() Function
- Lapply() Function
- Sapply() Function
- Tapply() Function
- Vapply() Function
- Mapply() Function
- Dplyr Package
- Installing the Dplyr Package
- Functions of the Dplyr Package
- Functions of the Dplyr Package – Select()
- Use the Select() Function
- Functions of Dplyr-Package – Filter()
- Use the Filter() Function
- Use Select Function
- Functions of Dplyr Package – Arrange()
- Use Arrange Function
- Functions of Dplyr Package – Mutate()
- Functions of Dply Package – Summarise()
- Use Summarise Function

**Data Visualization**

- Introduction
- Objectives
- Graphics in R
- Types of Graphics
- Bar Charts
- Creating Simple Bar Charts
- Editing a Simple Bar Chart
- Create a Stacked Bar Plot and Grouped Bar Plot
- Pie Charts
- Editing a Pie Chart
- Create a Pie Chart
- Histograms
- Creating a Histogram
- Kernel Density Plots
- Creating a Kernel Density Plot
- Create Histograms and a Density Plot
- Line Charts
- Creating a Line Chart
- Box Plots
- Creating a Box Plot
- Create Line Graphs and a Box Plot
- Heat Maps o Creating a Heat Map
- Create a Heatmap
- Word Clouds
- Creating a Word Cloud
- File Formats for Graphics Outputs
- Saving a Graphic Output as a File
- Save Graphics to a File
- Exporting Graphs in RStudio
- Exporting Graphs as PDFs in RStudio
- Save Graphics Using RStudio

**Introduction to Statistics**

- Introduction
- Objectives
- Basics of Statistics
- Types of Data
- Qualitative vs. Quantitative Analysis
- Types of Measurements in Order
- Nominal Measurement
- Ordinal Measurement
- Interval Measurement
- Ratio Measurement
- Statistical Investigation
- Normal Distribution
- Example of Normal Distribution
- Importance of Normal Distribution in Statistics
- Use of the Symmetry Property of Normal Distribution
- Standard Normal Distribution
- Use Probability Distribution Functions
- Distance Measures
- Distance Measures – A Comparison
- Euclidean Distance
- Example of Euclidean Distance
- Manhattan Distance
- Minkowski Distance
- Mahalanobis Distance
- Cosine Similarity
- Correlation
- Correlation Measures Explained
- Pearson Product Moment Correlation (PPMC)
- Pearson Correlation
- Dist() Function in R
- Perform the Distance Matrix Computations

**Hypothesis Testing I**

- Introduction
- Objectives
- Hypothesis
- Need of Hypothesis Testing in Businesses
- Null Hypothesis
- Alternate Hypothesis
- Null vs. Alternate Hypothesis
- Chances of Errors in Sampling
- Types of Errors
- Contingency Table
- Decision Making
- Critical Region
- Level of Significance
- Confidence Coefficient
- Bita Risk
- Power of Test
- Factors Affecting the Power of Test
- Types of Statistical Hypothesis Tests
- Upper Tail Test
- Test Statistic
- Factors Affecting Test Statistic
- Critical Value Using Normal Probability Table

**Hypothesis Testing II**

- Introduction
- Objectives
- Parametric Tests
- Z-Test
- Z-Test in R
- T-Test
- T-Test in R
- Use Normal and Student Probability Distribution Functions
- Testing Null Hypothesis
- Objectives of Null Hypothesis Test
- Three Types of Hypothesis Tests
- Hypothesis Tests About Population Means
- Decision Rules
- Hypothesis Tests About Population Means
- Hypothesis Tests About Population Proportions
- Chi-Square Test
- Steps of Chi-Square Test
- Degree of Freedom
- Chi-Square Test for Independence
- Chi-Square Test for Goodness of Fit
- Chi-Square Test for Independence
- Chi-Square Test in R
- Use Chi-Squared Test Statistics
- Introduction to ANOVA Test
- One-Way ANOVA Test
- The F-Distribution and F-Ratio
- F-Ratio Test
- F-Ratio Test in R
- One-Way ANOVA Test
- One-Way ANOVA Test in R
- Perform ANOVA

**Regression Analysis**

- Introduction
- Objectives
- Introduction to Regression Analysis
- Use of Regression Analysis
- Types Regression Analysis
- Simple Regression Analysis
- Multiple Regression Models
- Simple Linear Regression Model
- Perform Simple Linear Regression
- Correlation
- Correlation Between X and Y
- Find Correlation
- Method of Least Squares Regression Model
- Coefficient of Multiple Determination Regression Model
- Standard Error of the Estimate Regression Model
- Dummy Variable Regression Model
- Interaction Regression Model
- Non-Linear Regression
- Non-Linear Regression Models
- Perform Regression Analysis with Multiple Variables
- Non-Linear Models to Linear Models
- Algorithms for Complex Non-Linear Models

**Classification**

- Objectives
- Introduction to Classification
- Examples of Classification
- Classification vs. Prediction
- Classification System
- Classification Process
- Classification Process – Model Construction
- Classification Process – Model Usage in Prediction
- Issues Regarding Classification and Prediction
- Data Preparation Issues
- Evaluating Classification Methods Issues
- Decision Tree
- Decision Tree – Dataset
- Classification Rules of Trees
- Overfitting in Classification
- Tips to Find the Final Tree Size
- Basic Algorithm for a Decision Tree
- Statistical Measure – Information Gain
- Calculating Information Gain for Continuous-Value Attributes
- Enhancing a Basic Tree
- Decision Trees in Data Mining
- Model a Decision Tree
- Naive Bayes Classifier Model
- Features of Naive Bayes Classifier Model
- Bayesian Theorem
- Naive Bayes Classifier
- Applying Naive Bayes Classifier
- Naive Bayes Classifier – Advantages and Disadvantages
- Perform Classification Using the Naive Bayes Method
- Nearest Neighbor Classifiers
- Computing Distance and Determining Class
- Choosing the Value of K
- Scaling Issues in Nearest Neighbor Classification
- Support Vector Machines
- Advantages of Support Vector Machines
- Geometric Margin in SVMs
- Linear SVMs
- Non-Linear SVMs
- Support a Vector Machine

**Clustering**

- Introduction
- Objectives
- Introduction to Clustering
- Clustering vs. Classification
- Use Cases of Clustering
- Clustering Models
- K-means Clustering
- K-means Clustering Algorithm
- Pseudo Code of K-means
- K-means Clustering Using R
- K-means Clustering
- Perform Clustering Using K-means
- Hierarchical Clustering
- Hierarchical Clustering Algorithms
- Requirements of Hierarchical Clustering Algorithms
- Agglomerative Clustering Process
- Perform Hierarchical Clustering
- DBSCAN Clustering
- Concepts of DBSCAN
- DBSCAN Clustering Algorithm
- DBSCAN in R
- DBSCAN Clustering

**Association**

- Introduction
- Objectives
- Association Rule Mining
- Application Areas of Association Rule Mining
- Parameters of Interesting Relationships
- Association Rules
- Association Rule Strength Measures
- Limitations of Support and Confidence
- Apriori Algorithm
- Applying Apriori Algorithm
- Step 1 – Mine All Frequent Item Sets
- Algorithm to Find Frequent Item Set
- Ordering Items
- Candidate Generation
- Step 2 – Generate Rules from Frequent Item Sets
- Perform Association Using the Apriori Algorithm
- Perform Visualization on Associated Rules
- Problems with Association Mining

###### Big Data Hadoop And Spark Developer

**Course Content**

**Introduction to Big data and Hadoop Ecosystem**

- Introduction
- Overview to Big Data and Hadoop
- Hadoop Ecosystem

**HDFS and YARN**

- Introduction
- HDFS Architecture and Components
- Block Replication Architecture
- YARN Introduction

**MapReduce and Sqoop**

- Introduction
- Why Mapreduce
- Small Data and Big Data
- Data Types in Hadoop
- Joins in MapReduce
- What is Sqoop

**Basics of Hive and Impala**

- Introduction
- Interacting with Hive and Impala
- Working with Hive and Impala
- Data Types in Hive
- Validation of Data
- What is Catalog and Its Uses

**Types of Data Formats**

- Introduction
- Types of File Format
- Data Serialization
- Importing MySql and Creating hive to
- Parquet With Sqoop

**Advanced Hive Concept and Data File Partitioning**

- Introduction
- Overview of the Hive Query Language

**Apache Flume and HBase**

- Introduction
- Introduction to HBase

**Pig**

- Introduction
- Getting Datasets for Pig Development

**Basics of Apache Spark**

- Introduction
- Spark – Architecture, Execution, and Related Concepts
- RDD Operations
- Functional Programming in Spark

**RDDs in Spark**

- Introduction
- RDD Data Types and RDD Creation
- Operations in RDDs

**Implementation of Spark Applications**

- Introduction
- Running Spark on YARN
- Running a Spark Application
- Dynamic Resource Allocation
- Configuring Your Spark Application

**Spark Parallel Processing**

- Introduction
- Parallel Operations on Partitions

**Spark RDD Optimization Techniques**

- Introduction
- RDD Persistence

**Spark RDD Optimization Techniques**

- Spark Algorithm
- Introduction
- Spark: An Iterative Algorithm
- Introduction To Graph Parallel System
- Introduction To Machine Learning
- Introduction To Three C’s

**Spark SQL**

- Introduction
- Interoperating with RDDs

**Apache Kafka**

**Core Java**

###### Data Science With Python

**Course Overview**

**Data Science**

- Introduction to Data Science
- Different Sectors Using Data Science
- Purpose and Components of Python

**Data Analytics**

- Data Analytics Process
- Exploratory Data Analysis(EDA)
- EDA-Quantitative Technique
- EDA – Graphical Technique
- Data Analytics Conclusion or Predictions
- Data Analytics Communication
- Data Types for Plotting
- Data Types and Plotting

**Statistical Analysis and Business Applications**

- Introduction to Statistics
- Statistical and Non-statistical Analysis
- Major Categories of Statistics
- Statistical Analysis Considerations
- Population and Sample
- Statistical Analysis Process
- Data Distribution
- Dispersion o Histogram
- Testing

**Python Environment Setup and Essentials**

- Anaconda
- Installation of Anaconda Python Distribution (contd.)
- Data Types with Python
- Basic Operators and Functions

**Mathematical Computing with Python (NumPy)**

- Introduction to Numpy
- Activity-Sequence it Right
- Creating and Printing an array
- Class and Attributes of array
- Basic Operations
- Activity-Slice It
- Copy and Views
- Mathematical Functions of Numpy

**Scientific computing with Python (Scipy)**

- Introduction to SciPy
- SciPy Sub Package – Integration and Optimization
- SciPy sub package
- Calculate Eigenvalues and Eigenvector
- SciPy Sub Package – Statistics, Weave and IO

**Data Manipulation with Pandas**

- Introduction to Pandas
- Understanding DataFrame
- View and Select Data
- Missing Values
- Data Operations
- File Read and Write Support
- Pandas Sql Operation

**Machine Learning with Scikit–Learn**

- Machine Learning Approach
- How it Works
- Supervised Learning Model Considerations
- Scikit-Learn
- Supervised Learning Models – Linear Regression
- Supervised Learning Models – Logistic Regression
- Unsupervised Learning Models
- Pipeline
- Model Persistence and Evaluation

**Natural Language Processing with Scikit Learn**

- NLP Overview
- NLP Applications
- NLP Libraries-Scikit
- Extraction Considerations
- Scikit Learn-Model Training and Grid Search

**Data Visualization in Python using matplotlib**

- Introduction to Data Visualization
- Line Properties
- (x,y) Plot and Subplots
- Types of Plots

**Web Scraping with BeautifulSoup**

- Web Scraping and Parsing
- Understanding and Searching the Tree
- Navigating options
- Navigating a Tree
- Modifying the Tree
- Parsing and Printing the Document

**Python integration with Hadoop MapReduce and Spark**

- Why Big Data Solutions are Provided for Python
- Hadoop Core Components
- Python Integration with HDFS using Hadoop Streaming
- Using Hadoop Streaming for Calculating Word Count
- Python Integration with Spark using PySpark
- Using PySpark to Determine Word Count

**Python Basics**

###### Business Analytics With Excel

COURSE CONTENT

**Introduction to Business Analytics**

- Introduction
- What Is in It for Me
- Types of Analytics
- Areas of Analytics

**Formatting Conditional Formatting and Important Functions**

- Introduction
- What Is in It for Me
- Custom Formatting Introduction
- Conditional Formatting Introduction
- Logical Functions
- Lookup and Reference Functions
- VLOOKUP Function
- HLOOKUP Function
- MATCH Function
- INDEX and OFFSET Function
- Statistical Function
- SUMIFS Function
- COUNTIFS Function
- PERCENTILE and QUARTILE
- STDEV, MEDIAN and RANK Function

**Analyzing Data with Pivot Tables**

- Introduction
- What Is in It for Me
- Pivot Table Introduction
- Concept Video of Creating a Pivot Table
- Grouping in Pivot Table Introduction
- Custom Calculation
- Calculated Field and Calculated Item
- Slicer Intro
- Creating a Slice
- Dashboarding
- Introduction
- What Is in It for Me
- What is a Dashboard
- Principles of Great Dashboard Design
- How to Create Chart in Excel
- Chart Formatting
- Thermometer Chart
- Pareto Chart
- Form Controls in Excel
- Interactive Dashboard with Form Controls
- Chart with Checkbox
- Interactive Chart

**Business Analytics With Excel**

- Introduction
- What Is in It for Me
- Concept Video Histogram
- Concept Video Solver Addin
- Concept Video Goal Seek
- Concept Video Scenario Manager
- Concept Video Data Table
- Concept Video Descriptive Statistics

**Data Analysis Using Statistics**

- Introduction
- Moving Average
- Hypothesis Testing
- ANOVA
- Covariance
- Correlation
- Regression
- Normal Distribution

**Power BI**

- Introduction
- Power Pivot
- Power View
- Power Query
- Power Map

**Microsoft Power BI Desktop**

**Microsoft Power BI Recipes**

###### Machine Learning

**COURSE CONTENT**

**Machine Learning Introduction**

- Techniques of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning and Reinforcement Learning
- Some Important Considerations in Machine Learning

**Data Preprocessing**

- Data Preparation
- Feature engineering
- Feature scaling
- Datasets
- Dimensionality reduction

**Math Refresher**

- Eigenvalues, Eigenvectors, and Eigendecomposition
- Concepts of Linear Algebra
- Introduction to Calculus
- Probability and Statistics

**Regression**

- Regression and Its Types
- Linear Regression: Equations and Algorithms

**Classification**

- Logistic regression
- K-nearest neighbours
- Support Vector Machines
- Kernel SVM
- Naive Bayes
- Decision tree classifier
- Random forest classifier

**Unsupervised learning – Clustering**

- K-Means Clustering
- Clustering Algorithms

**Introduction to Deep Learning**

- Meaning and importance of deep learning
- Artificial Neural networks
- TensorFlow

**Introduction to Artificial Intelligence and Machine Learning**

- Artificial Intelligence
- Machine Learning o Machine Learning algorithms o Applications of Machine Learning

**Python Programming for Beginners**

**Python Django From Scratch**

###### Deep Learning With TensorFlow

**COURSE CONTENT**

**Introduction to Deep Learning with TensorFlow**

- Introduction to TensorFlow
- Intro to TensorFlow
- Computational Graph
- Key highlights
- Creating a Graph
- Regression example
- Gradient Descent
- TensorBoard
- Modularity
- Sharing Variables
- Keras

**Perceptrons**

- What is a Perceptron
- XOR Gate

**Activation Functions**

- Sigmoid
- ReLU
- Hyperbolic Fns
- Softmax

** ****Artificial Neural Networks**

- Introduction
- Perceptron Training Rule
- Gradient Descent Rule

**Gradient Descent and Backpropagation**

- Gradient Descent
- Stochastic Gradient Descent
- Backpropagation
- Some problems in ANN

** ****Optimization and Regularization**

- Overfitting and Capacity
- Cross-Validation
- Feature Selection
- Regularization
- Hyperparameters

**Intro to Convolutional Neural Networks**

- Intro to CNNs
- Kernel filter
- Principles behind CNNs
- Multiple Filters
- CNN applications

** ****Intro to Recurrent Neural Networks**

- Intro to RNNs
- Unfolded RNNs
- Seq2Seq RNNs
- LSTM
- RNN

** ****Deep Learning applications**

- Image Processing
- Natural Language Processing
- Speech Recognition
- Video Analytics

#### Certified Master Data Science Course

## Online/Classroom

From **October 27, 2018 09:00** until **November 4 2018 18:00**

**At:**: Bangalore | Hyderabad | Noida | Delhi(NCR)**Categories**: Master Data Science Course**Tags**: Classroom Training, Duration 7 Days**View Details and Book**

## FAQ | Data Science Online Training Course

##### What are the prerequisites for learning Data science Course Training?

In order to learn Data science master program quickly, you should have knowledge of AI Programming Langage:

- Fundamentals of Python programming
- Basic knowledge of statistics

If you don’t know about python, statistics, machine learning etc. don’t worry we will provide training for that to you so, that you can analyze and statistical techniques, it will take hardly 15-20 hours.

##### Who should pursue this Data science Online Course?

With the huge demand for AI in industries, Mildaintrainings’s AI course is well suited for a variety of roles and disciplines, including:

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

##### What is the Data science Online course duration?

The duration of Data Science Online training course will be 7-8 Days. At Mildaintraings we will cover basic of all courses, and if you want to gain in-depth knowledge than you will have to learn below-mentioned courses separately:

##### Why should I take bootcamp/training from Mildaintraining?

One must take ** Data Science** training from

__Mildaintrainings__because our trainers are having more than 10 years of industry practical training experience & also we at Mildaintrainings providing six (6) months technical support, 100% Job assistance and try to solve all the queries.

##### Who are the instructors and how are they selected?

Our highly qualified trainers are industry experts with several years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us.

##### If I need to cancel my enrollment, can I get a refund?

Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy

##### How do I enroll for the Data science Online course ?

You can enroll for this training on our website or you can fill the form on our website and make an online payment using any of the following options:

- Visa Credit or Debit Card
- MasterCard
- PayUMoney
- National Electronic Funds Transfer (NEFT)
- PayPal

Once payment is received you will automatically receive a payment receipt and access information via email.

##### What are the Data science Online course and what can I expect?

- Access to exclusive forums, moderated by expert faculty and industry thought leaders
- 15+ In-Demand Skills & Tools
- 10+ Real-Life Projects
- Mildaintrainings Job Assistance/Guidance

##### Who will provide the certificate?

At ** Mildaintrainings** you will be provided with participation certificate after successful completion (*course, test and projects) of Data Science course from Mildaintrainings.

##### When will the classes be held?

Classes will be held in weekend & weekdays accordingly, for one to one sessions you have to tell your dates prior, in order to book next dates.

##### What if I miss the class?

Don’t worry at all, if you’ll miss the class, in that case, we will accommodate you for backup classes by adjusting you in next live session of the same course.

##### How can I learn more about this training program?

Contact us using the chat option on the bottom-right of any page on the Mildaintraining website, or you can fill the Contact Us form and post your queries. Our customer service representatives can provide you with more details. We can also arrange one to one call or meeting with the trainer if you need.

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If you have any questions, please call us at +91-8447121833 between 9:00 am – 6:00 pm IST.