Question 1

UNIT 1: Orientation (8 Hours)

Accepted Answer

This unit serves as a primer for data science, introducing key tools and concepts. It's designed to equip non-programmers with foundational Python skills, facilitating a deeper understanding and practical application throughout the course.

Module 1: Introduction To Data Analytics, Predictive Analytics & Business Intelligence

Introduction to tools, key concepts, and definitions
Real-time project applications in different domains
Practical applications of data science in various industries

Module 2: Fundamentals of Programming

Introduction to Python tools
Installation of Python
Python Fundamentals

Tools Covered: Python, Anaconda, Jupyter, Google Colab

Module 3: Fundamentals of Statistics

Importance and Use of Statistics in Data Science
Descriptive Statistics & Predictive Statistics
Learn how predictive Statistics connects with Machine Learning

Note:

Module 2 and Module 3 of Unit 1 are specially designed for non-programmers to understand the basics of computer programming and math.

Question 2

UNIT 2: Portfolio Building (6 hours)

Accepted Answer

This unit provides an extensive roadmap for building a robust portfolio in data science. You'll master GitHub, a version control system, for efficient collaboration and project management. Additionally, you'll harness LinkedIn's power for networking and career advancement.

Module 1: Git & GitHub (VCS)

Introduction to Version Control Systems
Installing and Configuring Git
Git Essentials

Branching and Merging
GitHub Essentials

Collaborating on GitHub
Forking repositories
Creating pull requests

Best Practices and Workflows

Application: Initiate, collaborate, and work on a real-time project

Tools Covered: Git, GitHub

Module 2: LinkedIn Profile building

Introduction to LinkedIn as a Professional Networking Platform
Crafting a Compelling LinkedIn Profile

Leveraging LinkedIn Features for Engagement

Growing Your Network on LinkedIn

Increasing Followers and Engagement

Enhancing Professional Branding on LinkedIn

Leveraging LinkedIn for Career Advancement

Question 3

UNIT 3: Python for Data Science & AI (42 Hours)

Accepted Answer

This Python course introduces fundamental to advanced concepts tailored for data science and AI applications. Learn Python step by step from basics to advanced. Learn all libraries, functions, and modules to perform data science projects by analyzing and building ML & AI models using Python.

Module 1: Core Python Programming

Python Environment
Data Types & Operators
Operators & Loop controls

Project: Build a simple calculator

Module 2: Advanced Python Programming

Functions & Modules
Regular Expressions (RegEx)
File Handling & Exception Handling
Generators & Decorators

Class Hands-on:

25+ programs/coding exercises on data types, loops, operators, functions, generators, file I/O, reg-ex, and exception handling

Module 3: Web Scraping using Python

Introduction to Web Scraping
Web Requests & HTTP
Parsing HTML with Beautiful Soup

Project: Scrape and Analyze Data from a Website (2-3 Projects)

Module 4: OOPs in Python

Classes and Objects
Encapsulation, Inheritance, and Polymorphism
Abstraction and Interfaces
Method Overriding and Overloading
Class Variables and Instance Variables

Module 5: Python For Data Analytics

Data Analysis using NumPy (Array Operations)
Data Analysis using Pandas (On Dataframes)
Data Visualization using Matplotlib
Data Visualization using Seaborn

Tools Covered: NumPy, Pandas, MatplotLib, Seaborn, Beautiful Soup

EDA Project (Create Insights using Data Analytics)

2 Full-Length Projects on Data Analytics using Pandas, MatplotLib & Seaborn to analyze Data to Gain Insights and Identify Patterns.

Question 4

UNIT 4: Data Analytics Tools (84 Hours)

Accepted Answer

Learn practically data mining, using SQL database. Data visualization and analytics using Power BI & Tableau and data handling using Excel.

Module 1: SQL - Structured Query Language

Introduction to SQL
SQL & RDBMS
SQL Syantax and data types
CRUD operations in SQL
Retrieving Data with SQL
Filtering, sorting & formatting query results
Advanced SQL Queries
Database Design and Normalization
Advanced Database Concepts
Stored Procedures
Integrating SQL with Python for Data

Hands-on practice:

Joins, Sub-queries, Aggregation query
Views, Filtering, Sorting
Group By and Having clause

Module 2: Power BI

Introduction to Power BI
Data Preparation and Modeling
Clean, transform & load data in Power BI
Data Visualization Techniques
Advanced Analytics in Power BI
Designing Interactive Dashboards

Power Query
Design Power BI Reports
Connecting Power BI to SQL
Create, Share, and Collaborate on Power BI Dashboards

Class Project & Assignments:

Project 1: Education Institute’s student data analysis

Project 2: Sales Data Analysis

- Learn to visualize data to find patterns & insights using interactive charts

Module 3: Tableau

Introduction to Tableau
Connecting Tableau to data sources
Data Types in Tableau
Data Preparation and Transformation
Building Visualizations in Tableau

Advanced Analytics in Tableau
Tableau Dashboards and Storytelling
Connecting Tableau to SQL
Tableau Online to collaborate, share & publish dashboards

Class Project & Assignments:

Project 1: Supermarket data analysis

Project 2: Covid Data Analysis

- Learn to visualize data to find patterns & insights using interactive charts

- Deployment of Predictive model in Tableau

Tools Covered: Power BI, Tableau, Excel

Module 4: Excel for Analytics

Introduction to Excel for Analytics
Basic Formulas & Function
Data Preparation and Cleaning
Charts & Graphs in Excel
Data Analysis Techniques in Excel
PivotTables and PivotCharts for data summarization
Data visualization techniques in Excel
Excel's data analysis add-ins

Question 5

UNIT 5: Math & Statistical Approach for Data Analytics (24 Hours)

Accepted Answer

This course provides a comprehensive overview of statistical concepts for analytics & machine learning techniques with their practical applications.

Module 1: Statistics & Probability

Fundamentals of Math, Probability & Statistics
Descriptive vs inferential statistics
Types of data, Sample and Population
Descriptive Statistics
Handling outliers & missing values in data
Discrete and continuous probability distributions
Normal distribution and central limit theorem
Linear Algebra, Sampling and Estimation
Hypothesis Testing Workflow
Confusion Matrix, Performance Metrics
P-values, Z Scores, Confidence Level
Significance Level, Sampling Techniques
Parametric Tests: T-test, Z-test, F-test, ANOVA test
Non-Parametric Tests: Chi-square test, Man Whitney U Test & Wilcoxon Rank Sum Test
Regression & Classification Analysis

Class Hands-on:

Problem-solving for central tendency, ANOVA, central limit theorem & hypothesis testing Case study

Question 6

UNIT 6: Predictive Analytics using Machine Learning (24 Hours)

Accepted Answer

You will learn forecasting from data and model building from scratch with various case studies to practice on real-world applications.

Module 1: Machine Learning

Set Theory
Data Preprocessing
Traditional coding vs Machine learning
Supervised and unsupervised learning
Model evaluation
Exploratory Data Analysis
Data Analysis & Visualisation
Feature Engineering
Machine learning model building & evaluation
- Linear Regression Model & Evaluation
- L1 & L2 Regularization (Lasso and Ridge Regression)
- Logistic Regression Model & Evaluation
- K Nearest Neighbours (KNN) & Evaluation
- Decision Tree Classifier & Regressor
- Random Forest Classifier & Regressor
- Naive Bayes Classifier
Overfitting, bias-variance tradeoff
Cross-validation

Project:

EDA for Weight Prediction task from Height (Regression task)

1 project each for Regression & Classification

Module 2: Time-Series Data Analysis

Introduction to time series data
Linear Regression Vs ARIMA model
Time series visualization and exploration
Time series decomposition
Stationarity and its tests
Autoregressive (AR) Models
Moving Average (MA) Models
Autoregressive Integrated Moving Average (ARIMA) Models
Seasonal ARIMA (SARIMA) models
Exponential smoothing methods

Class Projects:

Project to predict the number of customers of an Airline organization using Time Series Model ARIMA & SARIMAX
Financial Market Stock Price analysis and forecasting
Sales data forecasting to understand trend and seasonality

Tools Covered: SciKit Learn, Pandas, Matplotlib

Note: Once this unit is completed, the Capstone Project will be released for the batch.

Question 7

UNIT 7: Prompt Engineering (10 Hours)

Accepted Answer

Expertise with prompt engineering with generative AI tools to learn efficient prompt building to guide AI models for specific tasks.

Module 1: Prompt Engineering

Exploring prompt tools
Understanding prompt tools & their architecture
Future advancement in AI and Large Language tools
Overview of tools like (GPT, Dall E, Midjourney Etc.)

ChatGPT: Prompt for text Generation (Natural Language Processing)

Introduction to NLP concept and role in GPT tools
ChatGPT and its architecture
Hands-on with ChatGPT / Microsoft Copilot prompt for Text Generation
Tuning ChatGPT for desired output and application

Dall E / Midjourney: Prompt for image Generation

Introduction to image generation using prompt
Exploring Midjourney / Dall E 2 & 3 / Gencraft prompt for Image generation
Tuning prompt for the desired output
Ethical consideration for AI-generated images

Synthesia for Video Generation & Slides AI for PPT creation

Learning prompt with Slides AI (from Google) / Simplified.com for PPT generation
Using prompt on Synthesia / Invideo AI for Video Generation

Tools Covered: ChatGPT, Midjourney, Dall E, MS Copilot, Synthesia, Invideo AI, Slides AI

Question 8

UNIT 8: Cloud Deployment of ML Models (32 Hours)

Accepted Answer

In this cloud deployment unit, you will learn to deploy machine learning models using AWS and Azure, two leading cloud platforms. You'll gain proficiency in deploying, scaling, and managing models in the cloud environments through practical exercises.

Module 1: AWS

Introduction to Cloud Deployment for ML and AI Models
AWS cloud platform and its services for model deployment
Understanding deployment architectures and best practices
AWS IAM (Identity and Access Management)
Elastic Compute Cloud (Amazon EC2)
Elastic Block Storage (EBS) and Elastic File System (EFS)
Model Deployment with AWS
Model Deployment using Python on AWS using Flask
Model Deployment using Python on AWS using Django

Module 2: Azure

Azure cloud platform and its services for model deployment
Understanding deployment architectures and best practices
Fundamental Principles of Machine Learning on Azure
Model Deployment on Azure
Model Deployment using Python on Azure using Flask
Model Deployment using Python on Azure using Django

Tools Covered: AWS, EC2, S3, ECS, Sagemaker, Lambda, Azure, Azure ML, Flask, Django

Question 9

UNIT 1: Orientation (8 Hours)

Accepted Answer

This unit serves as a primer for data science, introducing key tools and concepts. It's designed to equip non-programmers with foundational Python and math skills.

Module 1: Introduction To Data Science, Analytics & AI

Introduction to tools & key concepts
Real-time project applications
Practical applications of data science in various industries

Module 2: Fundamentals of Programming

Introduction to Python tools
Installation of Python
Python Fundamentals

Tools Covered: Python, Anaconda, Jupyter, Google Colab

Module 3: Fundamentals of Statistics

Importance and Use of Statistics in Data Science
Descriptive Statistics & Predictive Statistics
Learn how predictive Statistics connects with Machine Learning

Note:

Module 2 and 3 of Unit 1 are specially designed for non-programmers to understand the basics of computer programming and math.

Question 10

UNIT 2: Portfolio Building (6 hours)

Accepted Answer

This unit provides an extensive roadmap for building a robust portfolio in data science. You'll master GitHub for efficient collaboration and LinkedIn for networking and career advancement.

Module 1: Git & GitHub (VCS)

Introduction to VCS
Installing and Configuring Git
Git Essentials

Branching and Merging
GitHub Essentials

Collaborating on GitHub
Forking repositories
Creating pull requests

Best Practices and Workflows

Class Hands-On:

Initiate, collaborate, and work on a real-time project

Tools Covered: Git, GitHub

Module 2: LinkedIn Profile building

Introduction to LinkedIn as a Professional Networking Platform
Crafting a Compelling LinkedIn Profile

Leveraging LinkedIn for Engagement

Growing Your Network on LinkedIn

Increasing Followers and Engagement

Enhancing Professional Branding

Leveraging LinkedIn for Career Advancement

Question 11

UNIT 3: Python for Data Science & AI (42 Hours)

Accepted Answer

This Python course is built to learn Python programming from fundamental to advanced concepts tailored for data science and AI applications.

Module 1: Core Python Programming

Python Environment
Data types & Operators
Operators & Loop controls

Project: Build a simple calculator

Module 2: Advanced Python Programming

Functions & Modules
Regular Expressions (RegEx)
File Handling & Exception Handling
Generators & Decorators

Class Hands-on:

25+ programs/coding exercises on data types, loops, operators, functions, generators, file I/O, reg-ex, and exception handling

Module 3: Web Scraping using Python

Introduction to Web Scraping
Web Requests & HTTP
Parsing HTML with Beautiful Soup

Project: Scrape and Analyze Data from a Website (2-3 Projects)

Module 4: OOPs in Python

Classes and Objects
Encapsulation, Inheritance, and Polymorphism
Abstraction and Interfaces
Method Overriding and Overloading
Class Variables and Instance Variables

Module 5: Python For Data Analytics

Data Analysis using NumPy (Array Operations)
Data Analysis using Pandas (On Dataframes)
Data Visualization using Matplotlib
Data Visualization using Seaborn

Tools Covered: NumPy, Pandas, MatplotLib, Seaborn, Beautiful Soup

EDA Project (Create Insights using Data Analytics)

2 Full-Length Projects on Data Analytics using Pandas, MatplotLib & Seaborn to analyze Data to Gain Insights and Identify Patterns.

Question 12

UNIT 4: Data Analytics Tools (84 Hours)

Accepted Answer

Learn practically data mining, using SQL database. Data visualization and analytics using Power BI & Tableau and data handling using Excel.

Module 1: SQL - Structured Query Language

Introduction to SQL
SQL & RDBMS
SQL Syantax and data types
CRUD operations in SQL
Retrieving Data with SQL
Filtering, sorting & formatting query results
Advanced SQL Queries
Database Design and Normalization
Advanced Database Concepts
Stored Procedures
Integrating SQL with Python for Data

Hands-on practice:

Joins, Sub-queries, Aggregation query
Views, Filtering, Sorting
Group By and Having clause

Module 3: Power BI

Introduction to Power BI
Data Preparation and Modeling
Clean, transform & load data in Power BI
Data Visualization Techniques
Advanced Analytics in Power BI
Designing Interactive Dashboards

Power Query

Design Power BI Reports
Connecting Power BI to SQL
Create, Share, and Collaborate on Power BI Dashboards

Class Project & Assignments:

Project 1: Education Institute’s student data analysis

Project 2: Sales Data Analysis

- Learn to visualize data to find patterns & insights using interactive charts

Module 4: Tableau

Introduction to Tableau
Connecting Tableau to data sources
Data Types in Tableau
Data Preparation and Transformation
Building Visualizations in Tableau

Advanced Analytics in Tableau
Tableau Dashboards and Storytelling
Connecting Tableau to SQL
Tableau Online to collaborate, share & publish dashboards

Class Project & Assignments:

Project 1: Supermarket data analysis

Project 2: Covid Data Analysis

- Learn to visualize data to find patterns & insights using interactive charts

- Deployment of Predictive model in Tableau

Tools Covered: Power BI, Tableau, Excel

Module 5: Excel for Analytics

Introduction to Excel for Analytics
Basic Formulas & Function
Data Preparation and Cleaning
Charts & Graphs in Excel
Data Analysis Techniques in Excel
PivotTables and PivotCharts for data summarization
Data visualization techniques in Excel
Excel's data analysis add-ins

Question 13

UNIT 5: Math & Statistical Approach for Data Analytics (24 Hours)

Accepted Answer

This course provides a comprehensive overview of statistical concepts for analytics & machine learning techniques with their practical applications.

Module 1: Statistics & Probability

Fundamentals of Math, Probability & Statistics
Descriptive vs inferential statistics
Types of data, Sample and Population
Descriptive Statistics
Handling outliers & missing values in data
Discrete and continuous probability distributions
Normal distribution and central limit theorem
Linear Algebra, Sampling and Estimation
Hypothesis Testing Workflow
Confusion Matrix, Performance Metrics
P-values, Z Scores, Confidence Level
Significance Level, Sampling Techniques
Parametric Tests: T-test, Z-test, F-test, ANOVA test
Non-Parametric Tests: Chi-square test, Man Whitney U Test & Wilcoxon Rank Sum Test
Regression & Classification Analysis

Class Hands-on:

Problem-solving for central tendency, ANOVA, central limit theorem & hypothesis testing Case study

Question 14

UNIT 6: Predictive Analytics using Machine Learning (24 Hours)

Accepted Answer

You will learn machine learning algorithms, and model building from scratch to advance exploring various case studies to practice on real-world applications to reinforce your learning.

Module 1: Machine Learning

Set Theory
Data Preprocessing
Traditional coding vs Machine learning
Supervised and unsupervised learning
Model evaluation
Exploratory Data Analysis
Data Analysis & Visualisation
Feature Engineering
Machine learning model building & evaluation
- Linear Regression Model & Evaluation
- L1 & L2 Regularization (Lasso and Ridge Regression)
- Logistic Regression Model & Evaluation
- K Nearest Neighbours (KNN) & Evaluation
- Decision Tree Classifier & Regressor
- Random Forest Classifier & Regressor
- Naive Bayes Classifier
Overfitting, bias-variance tradeoff
Cross-validation

Project:

EDA for Weight Prediction task from Height (Regression task)

1 project each for Regression & Classification

Module 2: Time-Series Data Analysis

Introduction to time series data
Linear Regression Vs ARIMA model
Time series visualization and exploration
Time series decomposition
Stationarity and its tests
Autoregressive (AR) Models
Moving Average (MA) Models
Autoregressive Integrated Moving Average (ARIMA) Models
Seasonal ARIMA (SARIMA) models
Exponential smoothing methods

Class Projects:

Project to predict the number of customers of an Airline organization using Time Series Model ARIMA & SARIMAX
Financial Market Stock Price analysis and forecasting
Sales data forecasting to understand trend and seasonality

Tools Covered: SciKit Learn, Pandas, Matplotlib

Note: All Machine Learning Algorithms will be covered in depth with real-time projects & case studies for each algorithm. Once this module is completed, the Capstone Project will be released for the batch.

Question 15

UNIT 7: Prompt Engineering (10 Hours)

Accepted Answer

Expertise with prompt engineering with generative AI tools to learn efficient prompt building to guide AI models for specific tasks.

Module 1: Prompt Engineering

Exploring prompt tools
Understanding prompt tools & their architecture
Future advancement in AI and Large Language tools
Overview of tools like (GPT, Dall E, Midjourney Etc.)

ChatGPT: Prompt for text Generation (Natural Language Processing)
Dall E / Midjourney: Prompt for image Generation
Synthesia for Video Generation & Slides AI for PPT creation

Tools Covered: ChatGPT, Midjourney, Dall E, MS Copilot, Synthesia, Invideo AI, Slides AI

Question 16

UNIT 8: Cloud Deployment of ML Models (32 Hours)

Accepted Answer

In this cloud deployment unit, you will learn to deploy machine learning and AI models using AWS and Azure, two leading cloud platforms.

Module 1: AWS

Introduction to Cloud Deployment for ML and AI Models
AWS cloud platform and its services for model deployment
Understanding deployment architectures and best practices
AWS IAM (Identity and Access Management)
Elastic Compute Cloud (Amazon EC2)
Elastic Block Storage (EBS) and Elastic File System (EFS)
Model Deployment with AWS
Model Deployment using Python on AWS using Flask
Model Deployment using Python on AWS using Django

Module 2: Azure

Azure cloud platform and its services for model deployment
Understanding deployment architectures and best practices
Fundamental Principles of Machine Learning on Azure
Model Deployment on Azure
Model Deployment using Python on Azure using Flask
Model Deployment using Python on Azure using Django

Tools Covered: AWS, EC2, S3, ECS, Sagemaker, Lambda, Azure, Azure ML, Flask, Django

Question 17

What are the prerequisites for the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

The Advanced Data Analytics Course with Predictive Analytics includes a comprehensive Job Assistance program. This program is designed to support students in their job search and career advancement.

Our job assistance program is a four step program:

Github & LinkedIn Profile building
Resume Preparation
Mock Interviews
Job Referrals

Question 18

What will I be preparing for in the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

This advanced data analytics program with predictive analytics focuses on developing the necessary skills for descriptive and predictive analytics using machine learning concepts. The program includes:

Python Programming
Web Scraping
GitHub
Statistics for data analytics
Machine learning
SQL
Power BI
Tableau
Hadoop
Excel for Data Analytics

Question 19

Am I eligible for this program if I belong to a non-technical background with no programming background?

Accepted Answer

Yes, this program is specifically designed for professionals looking for career transition in data analytics and are non-programmers. The advanced data analytics with predective analytics is recommended for candidates with little to no prior knowledge of statistics and Python/R programming.

Question 20

How many students are there in one batch?

Accepted Answer

The Advanced Data Analytics Course with Predictive Analytics includes a comprehensive Job Assistance program. This program is designed to support students in their job search and career advancement.

Our job assistance program is a four step program:

Github & LinkedIn Profile building
Resume Preparation
Mock Interviews
Job Referrals

Question 21

What are the benefits of a 2-year subscription to the program?

Accepted Answer

Students undertaking our Advanced+- Data Analytics Course with Predictive Analytics are granted a 2-year subscription. This provides them with continuous access to live class support, mentorship from the institute, and job referrals throughout the subscription period.

Question 22

How does the online training program benefit students?

Accepted Answer

The online training program for the Advanced Data Analytics Course with Predictive Analytics offers students a range of benefits:

Prompt resolution of queries during live sessions.
Access to recorded classes for reviewing previous sessions and addressing doubts.
Availability of recorded discussions on assignments and projects.
Access to session recordings and comprehensive course materials for future reference.

Question 23

What is the duration of the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

The duration of the Advanced Data Analytics Course with Predictive Analytics is approximately 6 months (180 hours). It includes live training sessions, hands-on training on live projects, and interview preparations. Classes are conducted on both weekdays and weekends. The weekday batch spans 5 months, with classes from Monday to Friday for 2 hours per day. The weekend batch lasts for 6 months, with classes on Saturdays and Sundays for 3.5 hours per day.

Question 24

What does instructor-led online training entail in the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

The Advanced Data Analytics Course with Predictive Analytics includes a comprehensive Job Assistance program. This program is designed to support students in their job search and career advancement.

Our job assistance program is a four step program:

Github & LinkedIn Profile building
Resume Preparation
Mock Interviews
Job Referrals

Question 25

What happens if I miss a live session in the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

In the Advanced Data Analytics Course with Predictive Analytics, if you are unable to attend a live session, you can still access the recorded session. The instructor-led online training format ensures that you have the flexibility to catch up on missed sessions and review the material. This ensures that you can continue your learning journey smoothly and benefit from the comprehensive course content.

Question 26

How does a smaller batch size contribute to better learning in the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

The Advanced Data Analytics Course with Predictive Analytics includes a comprehensive Job Assistance program. This program is designed to support students in their job search and career advancement.

Our job assistance program is a four step program:

Github & LinkedIn Profile building
Resume Preparation
Mock Interviews
Job Referrals

Question 27

Can students interact and ask questions during the live training sessions in the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

Absolutely! We highly encourage student interaction and questions during the live training sessions in the Advanced Data Analytics Course with Predictive Analytics. Our aim is to provide a supportive learning environment where students can engage with the trainer and address any queries they may have. To ensure effective interaction, we limit the class size to a maximum of 15 students per batch.

Question 28

How will this course help me in my career?

Accepted Answer

The Advanced Data Analytics Course unleashes the power of data analytics. Learn advanced techniques, statistical models, and predictive analytics methodologies. Apply data visualization, machine learning algorithms, and predictive modeling using tools like data visualization and machine learning to drive strategic decision-making.

Question 29

Are there any assessments or exams during the course?

Accepted Answer

Yes, to assess your progress and understanding of the concepts taught, there will be periodic assessments and exams throughout the Advanced Data Analytics Course with Predictive Analytics. These evaluations are designed to ensure that you have a strong grasp of the topics covered and to help you identify areas that may require additional focus.

Question 30

Will I have access to the course materials even after completing the program?

Accepted Answer

Yes, you will receive lifetime access to the course materials. This includes recordings of the live sessions, class notes, assignments, and other learning resources. This ensures that you can refer back to the content whenever you need to revise or revisit any topic covered during the course.

Question 31

Can I access the learning materials on my mobile device?

Accepted Answer

Yes, the learning materials for the Advanced Data Analytics Course with Predictive Analytics, including recorded sessions, assignments, and course materials, are accessible through our online learning platform. This allows you to access the content on your mobile device, giving you the flexibility to learn on the go.

Question 32

Can I switch from the weekday batch to the weekend batch or vice versa?

Accepted Answer

We understand that you may need to switch between batches due to office workload or personal reasons. If such a situation arises during the Advanced Data Analytics Course with Predictive Analytics, you can contact our support team, and they will assist you in making the necessary batch transfer arrangements, depending on the availability of seats in the desired batch.

Question 33

What kind of support can I expect during the course?

Accepted Answer

During the Advanced Data Analytics Course with Predictive Analytics, you can expect comprehensive support from our team. This includes live class support, doubt-solving sessions, discussion forums, mentorship, and access to the learning materials. Our aim is to ensure that you have a smooth learning journey and that all your queries and concerns are addressed promptly.

Question 34

Will I gain practical training in the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

Practical training is a key component of the Advanced Data Analytics Course with Predictive Analytics. You'll engage in hands-on exercises and real-world projects, applying advanced analytics techniques and predictive modeling. This practical experience will strengthen your analytical skills and empower you to derive actionable insights from data.

Question 35

What is the role of real-time projects in the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

Real-time projects play a crucial role in the Advanced Data Analytics Course with Predictive Analytics. These projects utilize industry datasets (with confidential information modified) and enable participants to apply data analytics concepts and predictive algorithms to real-world scenarios. By working on 11 industry projects covering diverse scenarios, students can strengthen their practical skills, gain confidence, and become proficient in data analytics with predictive capabilities.

Question 36

Can you elaborate on the importance of domain specializations in the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

Domain specializations play a crucial role in the Advanced Data Analytics Course with Predictive Analytics. Through capstone projects and mentorship, these specializations provide industry-specific training and practical exposure to predictive analytics techniques in different domains. By working on real-world projects sourced from diverse industries, participants can enhance their skills, gain domain-specific knowledge, and become proficient in applying advanced data analytics and predictive techniques in specific business contexts.

Question 37

How many Capstone projects are included in the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

The Advanced Data Analytics Course with Predictive Analytics includes a comprehensive Job Assistance program. This program is designed to support students in their job search and career advancement.

Our job assistance program is a four step program:

Github & LinkedIn Profile building
Resume Preparation
Mock Interviews
Job Referrals

Question 38

What is project experience and how do I get certified for it?

Accepted Answer

Project experience in our Advanced Data Analytics Course with Predictive Analytics involves engaging in industry projects relevant to domain specializations. Students work in groups with assigned mentors. Following successful completion, the project is evaluated by our institute and partner company. If it meets the required standards, we issue a project experience certificate, recognizing your practical experience in advance data analytics.

Question 39

Can I receive guidance from industry experts or mentors during the Advanced Data Analytics Course?

Accepted Answer

Yes, the Advanced Data Analytics Course with Predictive Analytics allows you to connect with industry experts and mentors. These experienced professionals will be available to guide you, share their expertise, and provide mentorship as you explore advanced data analytics techniques and delve into the realm of predictive analytics.

Question 40

Will I have access to a community or forum for interaction and collaboration in the Advanced Data Analytics Course?

Accepted Answer

Yes, the Advanced Data Analytics Course with Predictive Analytics provides a community forum for students to interact and collaborate. This forum enables you to connect with peers, discuss course topics, share insights, and seek feedback on your predictive analytics projects. It fosters a supportive community where you can engage in meaningful discussions and leverage collective knowledge.

Question 41

How can I resolve my queries outside the class for the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

For participants of the Advanced Data Analytics Course with Predictive Analytics, we offer a dedicated student forum where you can seek resolution for your queries outside the class. If you have any doubts or encounter challenges during your practice, simply post your queries on the forum. Our trainers and fellow students actively engage on the forum, providing assistance and valuable insights.

Question 42

What is the approach for conducting doubt-solving sessions in the Advanced Data Analytics Course?

Accepted Answer

In the Advanced Data Analytics Course with Predictive Analytics, we understand the significance of addressing doubts and ensuring a comprehensive learning experience. As part of the course structure, we conduct regular doubt-solving sessions within the class, allowing participants to raise questions and seek clarification. These sessions contribute to a deeper understanding of the course topics and help participants overcome challenges.

Question 43

What is the Fee for the Advanced Data Analytics Program with Predictive Analytics?

Accepted Answer

The total fees for advanced data analytics program with predictive analytics is INR 59,000/- + 18% GST

Question 44

Can I pay in instalments for the Advanced Data Analytics Program with Predictive Analytics?

Accepted Answer

Yes, you can pay the fees in instalments by taking a no-cost EMI option for INR 3868/month for a 12-month EMI. You can choose an interest free loan by submitting Aadhar, PAN, 3-month salary slip and other required documents to our banking partner.

Question 45

What are the different modes of payments available?

Accepted Answer

The different payment methods accepted by us are:

Unified Payments Interface (UPI)
Net Banking
Bank Transfer
Debit Card
Credit Card
Visa
*Zero-cost EMI

Question 46

Is there any scholarship/discount available?

Accepted Answer

1stepGrow offers 15 - 20% scholarship on early-birds. Our counselors will inform you if an early bird discount is available for the course.

Question 47

What is Group Discount?

Accepted Answer

Group discounts are available to promote ease in program fees. The discount applies to all members of a group who join the course together. For a group of 2, there is a 5% extra discount, and for a group of 3 or more, there is a 10% extra discount.

Question 48

What does the Job Assistance program in the Advanced Data Analytics Course with Predictive Analytics offer?

Accepted Answer

The Advanced Data Analytics Course with Predictive Analytics includes a comprehensive Job Assistance program. This program is designed to support students in their job search and career advancement.

Our job assistance program is a four step program:

Github & LinkedIn Profile building
Resume Preparation
Mock Interviews
Job Referrals

Question 49

How will the mock interview be conducted and how can I understand where to improve?

Accepted Answer

Mock interviews for the Advanced Data Analytics Course with Predictive Analytics are conducted online using video mode. Feedback on your performance will be provided within a week. You will receive a recorded video of the interview, enabling you to identify areas for improvement in both soft skills and technical skills. This course includes up to 2 mock interviews.

Question 50

Is job assistance available after completing the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

Absolutely! We offer comprehensive job assistance to students upon the completion of the Advanced Data Analytics Course with Predictive Analytics. Our placement cell supports students in resume building, interview preparation, and connects them with relevant job opportunities in the field of data analytics and predictive analytics. We strive to empower our students to excel in their careers.

Question 51

How many job referrals will be provided?

Accepted Answer

Our Advanced Data Analytics Course with Predictive Analytics offers job referral assistance to our students. We have established partnerships with various companies and consultancies in the data analytics field, and we refer our qualified students to these organizations for job opportunities.

Question 52

What’s the eligibility for a job assistance program at 1stepGrow?

Accepted Answer

To be eligible for job assistance from 1stepGrow, you need to meet specific criteria. These include achieving a score of 70% or higher in all assessment tests, submitting assignments in a timely manner, completing real-time projects, and successfully completing Capstone project.

Question 53

Will I get a Course Completion Certificate from 1stepGrow?

Accepted Answer

Yes, upon successfully completing the Advanced Data Analytics Course with Predictive Analytics, you will receive a Course Completion Certificate from 1stepGrow. This certificate validates your proficiency in advanced data analytics techniques and predictive analytics.

Question 54

Are there academic certifications provided in the course?

Accepted Answer

Yes, we provide academic certifications as part of the Advanced Data Analytics Course with Predictive Analytics. These certifications recognize your knowledge and skills in the field of data analytics and highlight your expertise in applying predictive analytics techniques. We are partnered with Microsoft. On successful completion of the assessment you will be awarded with a globally recognized Data certificate by Microsoft.

Question 55

Will I get project experience certification from a company?

Accepted Answer

Yes! Our course offers you the opportunity to work on real-world projects in collaboration with our partner companies. Upon successful completion of these projects, you will receive a Project Experience Certificate, demonstrating your practical skills and experience in data analytics and predictive analytics.

Question 56

As a college student or fresher, what kind of recognition should I expect from the course?

Accepted Answer

As a college student or fresher, this course provides you with valuable recognition. You can expect a Course Completion Certificate that showcases your proficiency in Advanced Data Analytics with Predictive analytics. The project experience gained during the course will also enhance your resume and increase your chances of securing internships and entry-level positions in the data analytics field.

Question 57

As an on-job professional, what kind of recognition will help me progress in my career?

Accepted Answer

As an on-job professional, this course offers recognition that can accelerate your career growth. You will receive a Course Completion Certificate, validating your expertise in Advanced Data Analytics with Predictive Analytics. The project experience and practical skills gained through the course will enhance your professional profile, enabling you to take on more challenging data analytics roles and responsibilities.

Question 58

How valuable is a project experience certification by a company?

Accepted Answer

A project experience certification by a company holds significant value in the industry. It demonstrates your ability to apply advanced data analytics and predictive analytics techniques to real-world projects, showcasing your practical skills and problem-solving abilities. This certification enhances your credibility and can significantly impact your career advancement in the data analytics field.

Question 59

What are the prerequisites for the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

The Advanced Data Analytics Course with Predictive Analytics includes a comprehensive Job Assistance program. This program is designed to support students in their job search and career advancement.

Our job assistance program is a four step program:

Github & LinkedIn Profile building
Resume Preparation
Mock Interviews
Job Referrals

Question 60

What will I be preparing for in the Advanced Data Analytics Course with Predictive Analytics?

Accepted Answer

This advanced data analytics program with predictive analytics focuses on developing the necessary skills for descriptive and predictive analytics using machine learning concepts. The program includes:

Python Programming
Web Scraping
GitHub
Statistics for data analytics
Machine learning
SQL
Power BI
Tableau
Hadoop
Excel for Data Analytics

Question 61

Am I eligible for this program if I belong to a non-technical background with no programming background?

Accepted Answer

Yes, this program is specifically designed for professionals looking for career transition in data analytics and are non-programmers. The advanced data analytics with predective analytics is recommended for candidates with little to no prior knowledge of statistics and Python/R programming.

Question 62

How many students are there in one batch?

Accepted Answer

The Advanced Data Analytics Course with Predictive Analytics includes a comprehensive Job Assistance program. This program is designed to support students in their job search and career advancement.

Our job assistance program is a four step program:

Github & LinkedIn Profile building
Resume Preparation
Mock Interviews
Job Referrals

Question 63

What are the benefits of a 2-year subscription to the program?

Accepted Answer

Students undertaking our Advanced+- Data Analytics Course with Predictive Analytics are granted a 2-year subscription. This provides them with continuous access to live class support, mentorship from the institute, and job referrals throughout the subscription period.

Question 64

How does the online training program benefit students?

Accepted Answer

The online training program for the Advanced Data Analytics Course with Predictive Analytics offers students a range of benefits:

Prompt resolution of queries during live sessions.
Access to recorded classes for reviewing previous sessions and addressing doubts.
Availability of recorded discussions on assignments and projects.
Access to session recordings and comprehensive course materials for future reference.

Live 1:1 Training by Market Experts

Live 1:1 Training by Market Experts

Live 1:1 Training by Market Experts

Live 1:1 Training by Market Experts

Our Flagship Program

Advanced Data Analytics Course With Predictive Analytics

partnered with AI Companies and

In Collaboration with

1:1 Live Interactive Classes

280+ Hiring Partners

100% Guaranteed Job Referrals

76% Avg. Salary Hike

Advanced Data Analytics Course With Predictive Analytics

Partnered with AI Companies and Microsoft

1:1 Live Interactive Classes

280+ Hiring Partners

100% Guaranteed Job Referrals

76% Avg. Salary Hike

Advanced Data Analytics Course Overview

Advanced Data Analytics Course Overview

Advanced Data Analytics Program Key Features

Skills Covered

100% Live Interactive Sessions

100% Live Interactive Sessions

Skills Covered

Skills Covered

Grow your Data Analytics skills to be Future-Ready

Grow your Data Analytics skills to be Future-Ready

Microsoft Certification

Microsoft Certification

Real Work Experience Certificate

Real Work Experience Certificate

Education

Work experience

Career stage

Aspirations

Education

Work experience

No Barrier

Aspirations

Partnered With 280+ Companies

Partnered With 280+ Companies

Harness our influential industry network

Program Highlights

UNIT 1: Orientation (8 Hours)

UNIT 2: Portfolio Building (6 hours)

UNIT 3: Python for Data Science & AI (42 Hours)

UNIT 4: Data Analytics Tools (84 Hours)

UNIT 5: Math & Statistical Approach for Data Analytics (24 Hours)

UNIT 6: Predictive Analytics using Machine Learning (24 Hours)

UNIT 7: Prompt Engineering (10 Hours)

UNIT 8: Cloud Deployment of ML Models (32 Hours)

UNIT 1: Orientation (8 Hours)

UNIT 2: Portfolio Building (6 hours)

UNIT 3: Python for Data Science & AI (42 Hours)

UNIT 4: Data Analytics Tools (84 Hours)

UNIT 5: Math & Statistical Approach for Data Analytics (24 Hours)

UNIT 6: Predictive Analytics using Machine Learning (24 Hours)

UNIT 7: Prompt Engineering (10 Hours)

UNIT 8: Cloud Deployment of ML Models (32 Hours)

Program Highlights

Project 1 : Banking

Detect fraudulent activities in financial transactions

Project 2 : Pharma

Drug Demand Forecasting

Project 3 : Call Center

Call Volume forecasting

Project 4 : Fitness

Evidence-based training program

Project 5: Education

Student Performance Prediction

Project 6: Banking

Credit Risk Analytics

Project 7 : Transportation

Taxi Fare Estimation using Machine Learning

Project 8 : Construction materials

Cement strength prediction

What makes us Unique?

Products in the markets are

Irrelevant Curriculum

Our Flagship
Program