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Data Science Masters Course Overview

Data Science aims to extract hidden patterns from unstructured data using a variety of algorithms, tools, and machine learning principles. It is used to make predictions and decisions using predictive causal analytics, machine learning, and prescriptive analytics. Data Science is becoming prominent over the years, and businesses have begun to use data science techniques to expand their operations and improve consumer satisfaction.

Our Data Science Masters Course equips candidates with a thorough understanding of:

  • Implementing different methodologies to find new trends and patterns
  • Creating quantitative algorithms to organise a massive amount of data
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Best Selling Courses in India

Data Science Masters Course Syllabus

Online Instructor-Led

Python Data Science Training

Python is beneficial for the organisation due to its ease of use and simple syntax, which makes it easy to adapt for individuals and quick prototyping. This Python Data Science Training provides learners with the core concepts of Python and various methodologies to use with data science for making informed business decisions.

  • Modules -5
  • Hours - 16
  • Skills - 4
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Course Content

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Module 1: Introduction of Python

What is Python?
What can be Done by Using Python Programming Language?
Why Python?

Module 2: Working with IPython

Launching IPython Shell and Jupyter Notebook Using Anaconda
Keyboard Shortcuts in the IPython Shell
Special Commands of Python
IPython’s In and Out Objects
IPython and Shell Commands
Errors and Debugging
Profiling and Timing Code

Module 3: Introduction to NumPy

Introduction to NumPy
Data Types in Python
NumPy Arrays
Universal Functions
Aggregations: Min, Max, and More
Computation on Arrays: Broadcasting
Comparison, Boolean Logic, and Masks
Fancy Indexing
Sorting Arrays
NumPy’s Structured Array

Module 4: Working with Pandas

Installing and Using Pandas
Pandas Objects
Data Indexing and Selection
Operating on Data in Pandas
Handling Missing Data
Hierarchical Indexing
Concat and Append
Merge and Join
Aggregations and Grouping
Pivot Tables
Vectorised String Operations
Working with Time Series
eval() and query()

Module 5: Visualisation with Matplotlib

Overview of Matplotlib
Object-Oriented Interface
Simple Line Plots and Scatter Plots
Visualising Errors
Contour Plots
Histograms, Binnings, and Density
Customising Plot Legends
Customising Colorbars
Multiple Subplots
Text Annotation
Three-Dimensional Plotting in Matplotlib
Visualisation with Seaborn
Online Instructor-Led

Python with Machine Learning Training

Python in Machine Learning makes data validation fast and easy, with different libraries and frameworks, significant code readability, and portable nature. This course provides learners with the Machine Learning and Python concepts that make the code easily understandable and encompass algorithms.

  • Modules - 10
  • Hours - 16
  • Skills - 7
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Course Content

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Module 1: Introduction to Python

What is Python?
Python Syntax
Control Flow Tools
Defining Functions
Modules
Input and Output

Module 2: Basics of Machine Learning

Introduction to Machine Learning
Benefits of Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Machine Learning

Module 3: Data Sets of Python

Structured Data Sets
Unstructured Data Sets
How to Manage the Missing Data?
Splitting Your Data
Training and Testing Your Data

Module 4: Supervised Learning with Regressions

Linear Regression
Cost Function
Using Weight Training with Gradient Descent
Polynomial Regression

Module 5: Regularisation

Types of Fitting with Predicted Prices
How to Detect Overfitting?
How to Fix Overfitting?

Module 6: Supervised Learning with Classification

Logistic Regression
Multiclass Classification

Module 7: Non-linear Classification Models

K-Nearest Neighbor
Decision Trees and Random Forests
Working with Support Vector Machines
Neural Networks

Module 8: Validation and Optimisation Techniques

Cross-Validation Techniques
Hyperparameter Optimisation
Grid and Random Search

Module 9: Unsupervised Machine Learning with Clustering

K-Means Clustering
Hierarchal Clustering
DBSCAN

Module 10: Reduction of Dimensionality

Principal Component Analysis
Linear Discriminant Analysis
Comparing PCA and LDA
Online Instructor-Led

Data Science with R Training

R for Data Science delivers extensive support for data wrangling, statistical modelling, and machine learning techniques to glean insights. This training session equips learners with R programming that deals with data science to help organisations analyse data and make more informed strategic decisions.

  • Modules - 8
  • Hours - 16
  • Skills - 6
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Course Content

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Module 1: Introduction to Data Mining

Data Science
Knowledge Discovery in Databases (KDD)
Model Types
Classification of Data Mining Methods
Applications
Challenges
R Programming Language
Basic Concepts, Definitions, and Notations
Tool Installation

Module 2: Introduction to R

Data Types
Basic Tasks
Control Structures
Functions
Scoping Rules
Iterated Functions
Console and Package Installation

Module 3: Types, Quality, and Data Pre-Processing

Categories and Types of Variables
Pre-Processing Processes
  • Data Cleansing
  • Data Unification
  • Data Transformation and Discretisation
  • Data Reduction
dplyr and tidyr Packages

Module 4: Summary Statistics and Visualisation

Measures of Position
Measures of Dispersion
Visualisation of Qualitative Data
Visualisation of Quantitative Data

Module 5: Classification and Prediction

Classification
Prediction
  • Classification Vs Prediction
  • Linear Regression
  • Learning Parameter
Overfitting and Regularisation
  • Overfitting
  • Model Regularisation
  • Linear Regression with Normalisation

Module 6: Clustering

Unsupervised Learning
Cluster
k-Means Algorithm
Hierarchical Clustering Algorithms
DBSCAN Algorithm

Module 7: Mining of Frequent Itemsets and Association Rules

Introduction
Apriori Algorithm
Frequent Itemsets Types
Positive and Negative Border of Frequent Itemsets
Association Rules Mining
Alternative Methods for Large Itemsets Generation
FP-Growth Algorithm
Arules Package

Module 8: Computational Methods for Big Data Analysis

Introduction to Hadoop
Advantages of Hadoop’s Distributed File System
Hadoop Users
Hadoop Architecture
Hadoop Cluster Architecture
Hadoop Java API
Lists Loops, Generic Classes, and Methods
Online Instructor-Led

Microsoft Power BI Masterclass

Power BI is essential for businesses as they can see their business performance more closely and get immediate results with real-time dashboards available for every device. This training helps learners get data from a wide range of systems in the cloud and create dashboards that will track the metrics they care about the most.

  • Modules - 5
  • Hours - 8
  • Skills - 4
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Course Content

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Module 1: Introduction to Power BI

What is Power BI?
Power BI Service
Power BI Report Server
Power BI Desktop
Reports and Dashboards
DataSets
Row-Level Security
Content Packs
Natural Language Queries

Module 2: Data Sources

Connecting to Files
Importing Excel Files
Publishing Power BI from Excel
Updating Files in Power BI
Data Refresh
Power BI Data Model
Managing Data Relationships
Optimising the Model for Reporting
Hierarchies
SQL Servers
Other Data Sources
R-Script Data Connector
Configuring Data for Q&A
Creating Content Packs
Creating a Group

Module 3: Shaping and Combining Data

Power BI Desktop Queries
Query Editor
Shaping Data and Applied Steps
Advanced Editor
Formatting Data
Transforming Data
Combining Data

Module 4: Modelling Data

What are the Relationships?
Viewing Relationships
Creating Relationships
Cardinality
Cross Filter Direction
What is DAX?
Syntax
Functions
Row Context
Calculated Columns
Calculated Tables
Measures

Module 5: Interactive Data Visualisations

Page Layout and Formatting
Multiple Visualisations
Creating Charts
Using Geographic Data
Histograms
Power BI Admin Portal
Service Settings
Desktop Settings
Dashboard and Report Settings
Self-Paced

Deep Learning with TensorFlow Training

TensorFlow is an open-source platform used for creating machine learning and deep learning applications. This course enables individuals to create powerful machine learning applications using various tools, libraries, and community resources.

  • Modules - 7
  • Hours - 8
  • Skills - 6
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Course Content

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Module 1: Getting Started with TensorFlow

Introduction to Tensors and TensorFlow
Installation of TensorFlow
Two Computation Phrases
Variables
Operations
Computational Graph with TensorBoard
Linear Regression

Module 2: Artificial Neural Network

Overview of Artificial Neural Network
Characteristics of Artificial Neural Network

Module 3: Activate Functions

Different Activation (Transfer) Functions
  • Unit Step (Threshold)
  • Sigmoid
  • Piecewise Linear
  • Gaussian
  • Linear

Module 4: Deep Learning Techniques

Introduction
Convolutional Neural Networks
Recurrent Neural Networks

Module 5: Deep Learning Applications

Application of Deep Learning
Automatic Game Playing

Module 6: Computing Gradients

Introduction
Different Steps to Display Code

Module 7: Single-Layer and Multi-Layer Perceptron

Introduction to Perceptron
Single-Layer Perceptron
Multi-Layer Perceptron (MLP)
  • Input Layer
  • Hidden Layer
  • Output Layer
Self-Paced

Data Analysis Training Using MS Excel

Data Analysis is the process of examining and evaluating a data set by using analytical and logical reasoning. By attending this training, learners will become proficient in using Excel functions and tools used for data analysis purposes.

  • Modules - 15
  • Hours - 8
  • Skills - 13
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Course Content

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Module 1: Overview of Data Analysis

What is Data Analysis?
Why Data Analysis?
Types of Data Analysis
Data Analysis Process

Module 2: Introduction to Data Analysis with MS Excel

Steps to Analyse the Data in MS Excel

Module 3: Work with Range Names

Steps to Create a Name Range
How to Rename Range Name?
How to Delete Range Name?
Use Name Range in Workbook

Module 4: Introduction to Tables

What is a Table?
What is the Purpose of Making Table?

Module 5: Cleaning Data with Text Functions

Removing Unwanted Characters from Text
Steps for Data Cleaning

Module 6: Working with Date Formats and Time Formats

Example to Change Date Format
Example to Change Time Format

Module 7: Conditional Formatting

What is Conditional Formatting and How to Use it?
Example to Apply Conditional Formatting on Text

Module 8: Sorting and Filtering

What is Sorting?
What is Filtering?
Sort a Particular Column
Applying Sorting on Two Columns
Example to Sort Dates
Example to Sort Columns by Colours
To Apply Filtering
Clear Filters
Apply Filter on Text

Module 9: Subtotals and Quick Analysis

Subtotals
Steps to Apply Subtotals
Quick Analysis
Steps to Use Quick Analysis

Module 10: Exploring Lookup Functions

VLookUp Functions
HLookUp Functions

Module 11: Working with PivotTables

PivotTable Overview
Features of PivotTable
Creating a Pivot Table
PivotTable Fields
Adding and Removing Data Fields in PivotTable
PivotTable Areas
Exploring Data
Sorting Data
Filtering Data
Manual Filters
Nesting
Report

Module 12: Data Visualisation and Validation

Data Validation
Editing Data Validation Rules
Data Visualisation

Module 13: Financial Analysis

Present Value of a Series of Future Payments

Module 14: Multiple Sheets

Steps to Insert New Worksheet
To Copy a Workbook
Rename a Worksheet
Steps to Group the Worksheets
Steps to Ungroup the Worksheets

Module 15: Formula Auditing

Show Formulas
Trace Precedents
Remove Arrows
Trace Dependents
Evaluate Formula
Self-Paced

Advanced SQL

SQL is advantageous due to its faster query processing, standardised language, and portability. This Advanced SQL Training will help in retrieving the large amount of records and data and receive answers to the complex questions in seconds.

  • Modules - 13
  • Hours - 16
  • Skills - 11
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Course Content

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Module 1: Creating Tables

Creating Tables in SQL
Inserting Data
Inserting Multiple Rows
View Table

Module 2: Stored Procedure Basics

Benefits of Stored Procedures
Creating Stored Procedures
Two Ways to Execute
System Stored Procedures

Module 3: Variables

Declaring Variables
SET Versus SELECT
Global Variables
Tricks with Variables

Module 4: Parameters and Return Values

Passing Parameters
Default Values and WHERE Clauses
Output Parameters
Using RETURN

Module 5: Scalar Functions

Introduction to Scalar Functions
Various Scalar Functions
Advantages

Module 6: Testing Conditions

IF/ELSE Statement
Using CASE Where Possible

Module 7: Looping

Syntax of WHILE
Breaking Out of a Loop
Basic Transactions
Using DELETE and UPDATE
Sys.Objects

Module 8: Temporary Tables and Table Variables

Using Temporary Tables
Creating Table Variables
Pros of Each Approach

Module 9: Table Valued Functions

In-line Table-Valued Functions
Multi-Statement Table-Valued Functions
User-Defined Functions

Module 10: Derived Tables and CTEs

Using Derived Tables
Common Table Expressions (CTEs)
Recursive CTEs

Module 11: Subqueries

Subquery
Using ALL, ANY, and IN
Correlated Subqueries
Using EXISTS

Module 12: Cursors

What is Cursor?
Life Cycle of Cursor
Types of Cursor
Syntax of Fetching Rows

Module 13: Error-Handling

Using TRY/CATCH
System Error Functions
Custom Error Messages
Obsolete @@Error Function
SQL Server Debugger
Self-Paced

Python Programming Training

Python is a programming language used to create websites, automated tasks, software, and conduct data analysis. This training will help learners to get detailed knowledge of Python language and its various concepts including object-oriented programming.

  • Modules - 13
  • Hours - 8
  • Skills - 9
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Course Content

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Module 1: Introduction to Python

Overview
Why Use Python?
Python
  • Platform
  • Flavours
  • Today
  • Installation
IDLE - Simple IDE
Writing a Program
Using Script Mode
PyDev Eclipse

Module 2: Python Basics

Statements
Blocks
Variables
Input () Function
Data Types
Numeric Operators
Strings
Tuple and List
Sets
Comparison
Logical Expression

Module 3: Flow Control and Functions

IF Statement
Functions
String Methods
Sorting Data
Range
Loops
Iterators
Generators

Module 4: Python Programming

Functions in Python
Namespaces
Scopes
Parameters
Map () Function
Modules and Packages
Random Numbers
Date and Time
Exception
Introducing the Handle It Program
Regular Expression

Module 5: Object-Oriented Programming

Creating Classes, Methods, and Objects
Introducing the Simple Critter Program
Defining a Class
Defining a Method
Using Constructors
Creating a Constructor
Instantiating an Object
Invoking a Method
Creating Multiple Objects
Access Object
Destroy Objects

Module 6: Class Attributes and Inheritance

Using Class Attributes and Static Methods
Creating a Class Attribute
Creating a Static Method
Class and Object Example
Class Vs Object Method
Class Built-in Attributes
Printing an Object (How?)
Class Inheritance

Module 7: Overloading and Overriding

Overloading and Overriding
Encapsulation: ‘setter’ and ‘getter’
What is the Output?

Module 8: File I/O Operations

Selected Binary File Access Modes
Text Files
Files and Directories
CSV
Path for Windows
JSON
Python PIP

Module 9: Database

SQL Language
Database Connection
NoSQL Database
Database Lab
Sqlite3 Lab

Module 10: Web Development

Django Tutorials
Creating a Project
Using PyCharm
Boot Up your Webserver
Browse your Website
Create your First App
TKAweb- urls.py
Create urls.py in Staff App
Modify Views.py
Database Setup

Module 11: Introduction to Django Framework

How to Create Django Superuser Password?
Modify models.py
Changing the setting.py
Migrate the Classes to the Database
Dealing with the Database
Working with admin.py
Add New App to Staff
Views.py
Publications Update
REST API Model
Change in setting.py
Models.py
Admin.py
Add the Stocks from Admin App

Module 12: Introduction to TCP/IP Networking

Socket Overview
Network Layering
Inter-Layer Relationships
TCP/IP Layering Model
TCP/IP Components
IP Characteristics
UDP Characteristics
TCP Characteristics

Module 13: Client/Server Concepts

Client/Server Concepts
Connectionless Services
Connection-Oriented Services
Socket Programming-1
Socket Programming-Telnet
The World’s Simplest Web Browser
Retrieving an Image over HTTP
Self-Paced

Tableau Training

Tableau is beneficial for individuals to create understandable information at any level in an organisation and helpful to make customised dashboards. This course enables individuals to fasten data analysis and removes unnecessary conditions.

  • Modules - 8
  • Hours - 8
  • Skills - 6
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Course Content

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Module 1: Introduction to Tableau

What is Tableau?
Environment Setup of Tableau
Get Started with Tableau
Tableau Design Flow
Tableau File and Data Types
Data Visualisation

Module 2: Tableau Architecture

Architectures

Module 3: Tableau Data Sources

Custom Data View
Extracting Data
Fields Operations
Editing Metadata
Data Joining and Blending

Module 4: Tableau Worksheets

Adding Worksheet
Renaming Worksheet
Save and Delete Worksheet
Paged Workbook

Module 5: Tableau Calculations

Operators
Functions
Calculations
  • Numeric
  • String
  • Date
  • Table
  • LOD

Module 6: Tableau Sort and Filters

Basic Sorting and Filters
Quick Filters
Context Filters
Condition Filters
Top Filters
Filter Operations

Module 7: Tableau Charts

Charts

Module 8: Advanced Features of Tableau

Tableau
  • Dashboard
  • Formatting
  • Forecasting
  • Trend Lines
Self-Paced

Probability and Statistics for Data Science Training

Probability theory is useful for making predictions on the basis of data in Data Science and statistics estimates the values for further analysis. This training session provides candidates with best practices to analyse any sort of data.

  • Modules - 12
  • Hours - 8
  • Skills - 10
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Course Content

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Module 1: Basic Probability Theory

Probability Spaces
Conditional Probability
Independence

Module 2: Random Variables

Definition
Discrete Random Variables
Continuous random variables
Conditioning on an Event
Functions of Random Variables
Generating Random Variables

Module 3: Multivariate Random Variables

Introduction to Multivariate Random Variables
Discrete Random Variables
Continuous Random Variables
Joint Distributions of Discrete and Continuous Variables
Independence
Functions of Several Random Variables
Generating Multivariate Random Variables
Rejection Sampling

Module 4: Expectation

Expectation Operator
Mean and Variance
Covariance
Conditional Expectation

Module 5: Random Processes

Definition
Mean and Autocovariance Functions
Independent Identically-Distributed Sequences Gaussian Process
Poisson Process
Random Walk

Module 6: The Convergence of Random Processes

Types of Convergence
Law of Large Numbers
Central Limit Theorem
Monte Carlo Simulation

Module 7: Markov Chains

Markov Property
Time-Homogeneous Discrete-Time Markov Chains
Recurrence
Periodicity
Convergence
Markov-Chain Monte Carlo

Module 8: Descriptive Statistics

Histogram
Sample mean and variance
Order statistics
Sample Covariance
Sample Covariance Matrix
Whitening

Module 9: Frequentist Statistics

Mean Square Error
Consistency
Confidence Intervals
Nonparametric Model Estimation
Parametric Model Estimation
Maximum Likelihood

Module 10: Bayesian Statistics

Bayesian Parametric Models
Conjugate Prior
Bayesian Estimators

Module 11: Hypothesis Testing

The Hypothesis-Testing Framework
Parametric Testing
Nonparametric testing: The Permutation Test
Multiple Testing

Module 12: Linear Regression

Meaning of Linear Regression
Linear Models
Least-Squares Estimation

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Why Data Science is Important for Businesses?

Business Intelligence for Making Smarter Decisions:

Business Intelligence has transformed itself to become a more dynamic field and quantify the quality of the data with the help of data science.

Making Better Products:

Businesses should be able to attract customers to their products. They must consider customer needs when creating products and guarantee to satisfy them.

Managing Businesses Efficiently:

Businesses may govern themselves more effectively with Data Science. Data Science helps businesses of all sizes expand, from established corporations to new start-ups.

Predictive Analytics to Predict Outcomes:

Predictive analytics is a statistical approach to data analysis that uses several machine learning algorithms to forecast future outcomes based on existing data.

Leveraging Data for Business Decisions:

Business decisions may be made with robust methodologies that handle data more quickly and produce reliable outcomes.

Assessing Business Decisions:

Businesses need to access their choices after using forecasts of future events to make decisions that affect their growth and performance.

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Skills To Master

  • Data Science
  • Artificial Intelligence (AI)
  • MLOps
  • GIT
  • Python
  • Data Analysis
  • Data Wrangling
  • Machine Learning
  • SQL
  • Prediction Algorithms
  • PySpark
  • NLP
  • Model
  • Data Visualisation
  • Story Telling
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Tools To Master

  • jupyter
  • r
  • scipy
  • pandas
  • seaborn
  • numpy
  • matplotlib
  • tensorflow
  • sql
  • tableau
  • excel
  • git
  • sparksql
  • scikitlearn
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Career Growth with Data Science Masters Course

Making a professional decision might take time and effort. To help individuals make decisions and choose the best course of action for immediate career progress, we help them analyse their abilities and provide information and guidance on the best training possibilities.

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lucrative

Lucrative Career

Data is an advantageous career choice due to a shortage of Data Scientists, which has created a big income bubble. Companies rely on their knowledge to make data-driven decisions.

jobs

Changing the World

Businesses are using data for social good. This will encourage you to learn Data Science to improve inhabitants' lives.

impression

Data Science is the Future

Data Science is the profession of the future. Technology is now a dynamic sector, and as more and more individuals use the internet to engage, more data is being produced.

55%

Faster Innovation Cycle

67%

Improved Business Efficiency

83%

More Effective R&D

91%

Better Product/Service

Top Industries Employ Data Science Experts

  • amazon

    Amazon

  • google

    Google

  • ibm

    IBM

  • microsoft

    Microsoft

Job Titles Include

  • Data Analyst
  • Big Data Engineer
  • Data Scientist
  • Business Analyst
  • Data Engineer
  • Machine Learning Engineer

Benefits of Data Science

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What Our Clients Are Saying

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Victoria Norris

Data Scientist

frank

Frank Clark

Research Analyst

ronald

Ronald Jones

Data Engineer

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Deborah Kyle

Data Analyst

raphael

Raphael Thompson

Database Administrator

cole

Cole Barker

Tableau Developer

I purchased the python data science training course from here. I am thankful to my trainer, who guided me on every step of my training. Peter was really knowledgeable, supportive, professional and gave us a quick overview of the study material with good examples. He was quick to respond to our queries during the course and clarified various concepts commendably.

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I enjoyed studying this deep learning with tensorflow training and would recommend doing this course from here and found it excellent in all counts. The provided course material was good and Thomas, a very experienced trainer, delivered the course. He skilfully managed the training session and provided us with a detailed understanding of topics and real-world examples. It was a great experience training with Thomas.

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I have completed the data science with R training course and found the course material very good. The tutor was also great and was spot on in delivering the course as he gave us many suitable examples. The training sessions were delightful to attend. As an introvert, this was a huge step in my career development and enhancement. The trainer was very helpful and answered most of the queries very clearly. I felt glad to choose this course from the knowledge academy.

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Excellent course with an amazing instructor. Michelle was my instructor for this microsoft power BI training course. This is the first time I did virtual learning. Although I was a bit nervous, but trainer ran the training session smoothly. The training material was nicely structured, pleasant to read and that allowed us to understand everything properly. Thank you, Michelle, and I suggested everyone does the course from here.

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Last week, I attended advanced SQL training from this training platform. The structure and material of this course were great. I would especially like to mention the trainer Simon as he was very engaging, knowledgeable, and gave great relevant examples, which helped me understand all the topics easily. I highly recommend The Knowledge Academy and this training.

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Recently, I have attended tableau training virtually from the knowledge academy. They provided me with an experienced trainer named Andy. He was flawless during training and explained each concept with helpful examples. The material in the course was also well explained and easy to grasp. I really enjoyed the training, and I would highly recommend the knowledge academy.

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Frequently Asked Questions

What is Data Science?

Data Science is the branch of study that integrates programming abilities, subject-matter expertise, and competence in statistics and mathematics to draw valuable insights from data.

What is the Data Science Expert Package?

The Data Science Expert Package is a learning roadmap designed by The Knowledge Academy for individuals to become Data Scientists per current business requirements.

What is included in the Data Science Masters Course?

This Data Science is a masters course that aids in attaining knowledge of the below-mentioned courses:

  • Python Data Science Training
  • Python with Machine leaning
  • Data Science with R Training
  • Microsoft Power BI Masterclass
  • Deep Learning with TensorFlow Training
  • Data Analysis Training Using MS Excel
  • Advanced SQL
  • Python Training
  • Tableau Training
  • Probability and Statistics for Data Science Training

Who is the Data Science Expert Package for?

This Data Science Expert Package is intended for everyone who wishes to enhance their skills and knowledge of Data Science, such as Data Analyst, Data Engineer, Software Developer, Tableau Developer, and Machine Learning Developer.

How will I benefit from the Data Science Masters Course?

Our Data Science Masters Course provides a knowledge base and helps to start a career in this field. You will learn the following:

  • Data Analysis Techniques
  • Machine Learning Algorithms
  • Analysis Segmentation Using Clustering
  • Deploying Machine Learning Model on Cloud
  • Microsoft Excel for Data Analysis and Data Transformation

What is the role of Data Analytics?

Data analytics assists organisations and individuals in making sense of data and typically mines raw data to get trends and insights. They use a variety of tools that enable organisations to make effective decisions and succeed.

What are the benefits of R analytics?

R analytics is beneficial for providing more profound and accurate insights, leveraging Big Data, democratising analytics across the organisation, and creating interactive data visualisation.

What is data pre-processing in data mining?

Data pre-processing is a process of mining data used to transform raw data into a valuable and efficient format by cleaning, changing, and reducing.

What is the use of Probability and Statistics in Data Science?

Probability is used to predict an outcome of an event about to occur and Statistics is helpful to estimate the values for further analysis which depends on probability theory. Both Probability and Statistics depend on the data.

What career opportunities can I explore after the Data Science Expert Package training?

After completing this Data Science expert package, you will be qualified for positions such as Data Scientist, Data Analyst, Software Developer, Database Administrator, Data Engineer, and Tableau Developer.

Is there a specific order in which I should take the courses?

No, we do not impose a course completion order. Our Masters Course offers the best path to becoming a Data Scientist. However, it is up to the student to finish the courses in any sequence they like.

Will these Data Science courses help me to get a better job with a high salary package?

Professionals with skills and knowledge in Data Science Training get higher ranks in companies and are generally paid more than any average data analyst and other professional.

Can we customise training and course material according to our company requirements?

We have subject matter experts who will work according to your company’s requirements.