Data Science 360 Course

BY
Analytixlabs

Explore the field of data science by learning about the fundamental aspects of the advanced principles of the domain with the data science 360 course.

Mode

Online

Fees

₹ 40000

Quick Facts

particular details
Collaborators IBM
Medium of instructions English
Mode of learning Self study, Virtual Classroom +1 more
Mode of Delivery Video and Text Based
Frequency of Classes Weekdays, Weekends
Learning efforts 8-10 Hours Per Week

Course overview

The Data Science 360 Course enables the students to discover the significance of the data science field that transforms the data-driven industry and digital technologies. The course is offered by the online education provider platform, Analytixlab. The course consists of classroom learning, online pedagogy, and self-study options that will take five hundred hours in total to complete the course. 

The data science course study consists of forty-six classes with eighteen assignments and projects for the students to have an experiential learning process. The courses included are analytics edgedata science with python, and machine learning programs. The ‘Data Science 360 course’ training program equips the learners with skills in data visualization, descriptive analytics with predictive modeling with the knowledge of machine learning techniques for effective decision making in the business industry. The primary skills of data science and comprehensive understanding of the concepts allow the candidates to aspire for job opportunities in leading companies.

The highlights

  • Online mode
  • Interactive virtual sessions
  • Five hundred hours of study
  • Forty-six classes
  • Self- study program
  • Extensive curriculum
  • Demo session 
  • Hands-on projects
  • Data science certification
  • Classroom and bootcamps
  • Mentorship

Program offerings

  • Live sessions
  • Recorded lectures
  • Free demo
  • Independent study
  • Assignments
  • Capstone projects
  • Bootcamp
  • Certificate
  • Business profile
  • Job interview support
  • Referrals
  • Prolonged support
  • Testimonials

Course and certificate fees

Fees information
₹ 40,000

The Data Science 360 course online training is facilitated by Analytixlab for the students in three different modes and the course fee can be paid in three installments.

Data Science 360 course fee structure

Classroom and Bootcamp

Rs 48000 + Taxes

Self-Paced Learning

Rs 38000 + Taxes

Live online

Rs 48000 + Taxes

certificate availability

Yes

certificate providing authority

FutureSkills +1 more

Who it is for

The ‘Data Science 360 course’ online program benefits the candidates who qualify in engineering, finance, mathematics, and business management. The ‘Data Science 360 course’ helps the professionals gain training for business analytics and establish themselves as business analystsproject managers, and big data professionals in the domain of data science and analytics.

Eligibility criteria

Certificate qualifying details:

The course completion certificate for the Data science 360-course program is provided by Analytixlabs to the students who have completed the course assignments and submitted the projects without plagiarism within the one-year validity period. The candidates who are unable to submit by then will be required to take a mock interview or viva excluding the submission.

What you will learn

Business analytics knowledge Data science knowledge Knowledge of data visualization Knowledge of python R programming Machine learning Knowledge of deep learning Knowledge of excel Sql knowledge Tableau knowledge
  • The Data Science 360 course syllabus is designed for the students to gain job-oriented knowledge to implement in the database management systems. The curriculum includes concepts that enable the students to learn about fundamental concepts and programming in data science. 
  • Data visualization and analytics done using the software tools like ExcelSQL and Tableau describe data analysis, manipulation, reporting, and building dashboards to the students.
  • R for data science enables the students to explore R with databases along with the import and export of data.
  • In the python for data science module, tools like Python, NumPy, and Pandas are used to understand data analysis while the students also acquire skills based on predictive modeling and machine learning.
  • The Data Science 360 course classes equip the learners with the knowledge about data mining that is applied for natural language processing.
  • The training enhances the project management skills, risk, and operational analysis abilities, and capacity for evaluating digital and social networking.
  • This Data science 360-course training allows the learners to explore the field of banking and finance, retail and e-commerce, healthcare and networking, and the telecom industry.

The syllabus

Data Visualization and Analytics

Building blocks:
  • Introduction to Bridge Course & Analytics Software’s Basic Excel
  • Basic Programming Elements
  • Introduction to Basic Statistics
  • RDBMS & SQL (Basics)
  • Introduction to Analytics & Data Science
  • Introduction to Mathematical Foundation
Data analytics with Excel
  • Quick Recap of Basics of Excel
  • Data manipulation using functions         
  • Data analysis and reporting       
  • Data Visualization in Excel 
  • Overview of Dashboards 
  • Create dashboards in Excel - Using Pivot controls 
  • Business Dashboard Creation
Data analytics with SQL
  • Quick Recap of RDBMS & Basic SQL
  • Data based objects creation (DDL Commands)
  • Data manipulation (DML Commands)    
  • Accessing data from Multiple Tables using SELECT    
  • Advanced SQL    
  • Apply learning's on Business Case study    
Data visualization and analytics with Tableau
  • Getting Started
  • Data handling & summaries
  • Building Advanced Reports/ Maps
  • Calculated Fields
  • Table calculations
  • Parameters
  • Building Interactive Dashboards
  • Building Stories
  • Working with Data
  • Sharing work with others
Data Analytics using VBA
  • Introducing VBA
  • How VBA Works with Excel
  • Key Components of  Programming  language
  • A look at some commonly used code snippets
  • Programming constructs in VBA
  • Functions & Procedures in VBA – Modularizing your programs
  • Objects & Memory Management in VBA
  • Error Handling
  • Controlling accessibility of your code – Access specifiers
  • Code Reusability – Adding references and components to your code
  • Communicating with Your Users

R & Python for data science

R for data science
  • Data Importing/Exporting
  • Data Manipulation
  • Data Analysis 
  • Using R with Databases
  • Data Visualization with R (incl R Shiny)
  • Introduction to Statistics (e-learning)
  • Linear Regression: Solving regression problems (e-learning)
  • Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning (e-learning)
  • Supervised Learning I (e-learning)
  • Supervised Learning II (e-learning)
  • Unsupervised Learning (e-learning)
  • Dimensionality Reduction & Collaborative Filtering (e-learning)
Python for data science
  • Python Essentials (Core)
  • Operations with NumPy (Numerical Python)
  • Overview of Pandas
  • Cleansing Data with Python
  • Data Analysis using Python
  • Data Visualization with Python
  • Basic Visualization Tools
  • Advanced Visualization Tools
  • Visualizing Geospatial Data
  • Statistical Methods & Hypothesis Testing

Machine learning and text mining

Predictive Modeling for python
  • Introduction to predictive modeling and ML
  • Linear Regression: Solving regression problems
  • Logistic Regression: Solving classification problems
Machine learning with Python
  • Introduction to Machine Learning
  • Unsupervised Learning
  • Recommender Systems
  • Time Series Forecasting
Text Mining using NLP
  • Introduction to Text Mining
  • Text Processing with modules like NLTK, sklearn
  • Initial data processing and simple statistical tool
  • Advanced data processing and visualization
  • Text Mining – Predictive Modeling

AI and Cloud computing

Introduction to Google Colab
Introduction to Artificial Intelligence(AI)
  • The modern era of AI
  • Role of Machine learning & Deep Learning in AI
  • Hardware for AI (CPU vs. GPU vs. FPGA)
  • Software Frameworks for AI & Deep Learning
  • Key Industry applications of AI
Introduction to Deep Learning
  • What are the Limitations of Machine Learning?
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning
  • Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning
  • Overview of important python packages for Deep Learning
Artificial Neural Networks
  • Overview of Neural Networks
  • Activation Functions, hidden layers, hidden units
  • Illustrate & Training a Perceptron
  • Important Parameters of Perceptron
  • Understand limitations of A Single Layer Perceptron
  • Illustrate Multi-Layer Perceptron
  • Understand Backpropagation – Using Example
  • Implementation of ANN in Python- Keras
Introduction to Cloud computing
  • What is Cloud Computing? Why it matters?
  • Traditional IT Infrastructure vs. Cloud Infrastructure
  • Cloud Companies (Microsoft Azure, GCP, AWS ) & their Cloud Services (Compute, storage, networking, apps, cognitive, etc.)
  • Use Cases of Cloud computing
  • Overview of Cloud Segments: IaaS, PaaS, SaaS
  • Overview of Cloud Deployment Models
  • Overview of Cloud Security
  • AWS vs. Azure vs. GCP
  • Implementation of ML/DL model in Cloud
Industrial and functional session
  • Introduction to Data Sources for Various Industries
  • Introduction to Analytics Project Management
  • Marketing Analytics
  • Risk Analytics
  • Operation Analytics
  • Digital Analytics (Web Analytics)
  • Social Network Analytics
  • Banking & Financial Services, Insurance
  • Retail & E-Commerce
  • Pharma & Health Care
  • Telecom & Network

Admission details

The course admission for the data science 360 course online program is done through the Analytixlab website.

Step 1: Go to the course page on the official website using  the following link,

https://www.analytixlabs.co.in/data-science-specialization-course

Step 2: Choose the preferred method of learning and click on the enroll link

Step 3: Enter the relevant information and complete the enrollment.


Filling the form

For the enrollment for the ‘Data Science 360 course’ classes, the students have to sign up for the course by fill in the registration form with name, phone number, email address, course name, and city name.

How it helps

The Data Science 360 course training enables the students to improve their domain knowledge of data science and gain technical skills for MIS reporting analytics, data visualization, data mining and analysis, manipulation of data, statistical analysis, predictive modeling with supervised and unsupervised machine learning. The ‘Data Science 360' certification helps the learners understand the methods, modeling, and analysis of data science.

FAQs

Which online provider platform offers the ‘Data science 360 course’ for candidates looking for career development in data science and analytics?

The data science 360 courses are developed and provided by Analytixlab for the interested learners of data science.

Can I study on my own in the ‘Data science 360 course program’?

The candidates are provided with the option of self-study in this course.

What arein the eligibility criteria for the Data science 360 courses?

There is no mandatory prerequisite for the data science 360 course but the students with basic knowledge of engineering, finance, mathematics or business management will benefit from this course.

Do I get job referrals after the completion of the ‘Data Science 360 course’?

Analytixlab will help you get job opportunities from various firms.

Will I receive a certificate after the Data science 360 course completion?

Yes, after submission of weekly assignments, case studies, and projects you will receive the course completion certificate.

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