Data Science and Machine Learning Course For Non-Programmers

Especially Designed For Working Professionals

Generative AI Integrated Advanced Data Science & AI Course

Generative-AI Integrated
Curriculum

In Collaboration with

Microsoft Partnered Advanced Data Science and AI Course

&

IBM

Certification

Learn From IIT, NIT and Top MNC Professionals

Data Science and Machine Learning Course

1:1

Live Interactive Classes

510+

Hiring Partners

100%

Guaranteed Job Referrals

79%

Avg. Salary Hike

Data Science and Machine Learning Course For
Non-Programmers

Especially Designed For Working Professionals

Generative AI Integrated Advanced Data Science & AI Course

Generative AI-Integrated Curriculum

In Collaboration with

Microsoft Partnered Advanced Data Science and AI Course

&

IBM Cerification

1stepGrow NASSCOM Certified - ADS
1stepGrow Silicon India Certified - ADS
1stepGrow Business Connect Certified - ADS
1 (3)
2 (3)
3 (3)

Data Science & Machine Learning Course Overview

Data Science & Machine Learning Course Overview

Our Data Science and Machine Learning course provides extensive training in Python programming, covering Data Analytics, Web Scraping, Machine Learning, NLP, and Deep Learning. You’ll also learn Database Management System, Data Visualization with Power BI & Tableau, and version control with GitHub. Gain comprehensive knowledge and expertise in essential Data Science tools and techniques using Python through this course.

Our Data Science and Machine Learning course provides extensive training in Python programming, covering Data Analytics, Web Scraping, Machine Learning, NLP, and Deep Learning. You’ll also learn Database Management System, Data Visualization with Power BI & Tableau, and version control with GitHub. Gain comprehensive knowledge and expertise in essential Data Science tools and techniques using Python through this course.

Data Science & Machine Learning Program Key Skills and Features

Data Science & Machine Learning Program Key Skills and Features

Features Built on Industry Insights for Unmatched Success!

Key Program Features

Key Skills Covered

Key Program Features

Key Skills Covered

Skills Covered

Who This Advanced Data Science and AI Program Is For?

Education

Graduates from computer science, mathematics, or a related field

Advanced Data Science Course Work Experience Qualification

Work experience

Professionals with experience in technology background

Advanced Data Science Course Career Stage Qualification

Career stage

Early to mid-career professionals seeking career leap

Aspirations

Ambitious individuals aiming for hands-on experience

Dual Certified Data Science and ML Program

DSML- IBM - Python for DS

IBM Certification

Same size AI900

Microsoft AI Certification

Data Science & ML

Project Experience Certification

Who This Data Science and Machine Learning Program Is For?

Dual Certified Data Science and ML Program

DSML- IBM - Python for DS

IBM Certification

Same size AI900

Microsoft AI Certification

Project Experience Certification

Get Your Dream Job With Highest Possible Pay

Harness Extensive Industry Network of 510+ Companies with Relevant Skills

Get Your Dream Job With Highest
Possible Pay

Harness Extensive Industry Network of 510+ Companies with Relevant Skills

Access to job openings and referrals from leading firms

Unlimited job support with resume
building

Upgrade profile with industry relevant
projects

Network with professionals and experts
in the field

Access to job openings and referrals from leading firms

Unlimited job support with resume building

Upgrade profile with industry relevant projects

Network with professionals and experts in the field

Syllabus | Data Science and Machine Learning Course

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Syllabus | Data Science and Machine Learning Course

Explore a meticulously designed syllabus that blends fundamental knowledge with cutting-edge techniques. Our Advanced Certification in Data Science and AI Course offers an extensive curriculum that immerses you in practical applications.

Explore a meticulously designed syllabus that blends fundamental knowledge with cutting-edge techniques. Our Advanced Certification in Data Science and AI Course offers an extensive curriculum that immerses you in practical applications.

UNIT 1: Orientation (8 Hours)

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.

UNIT 2: Portfolio Building (6 hours)

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

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

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.

UNIT 4: Data Science Tools (94 Hours)

Learn practically data mining, using SQL and NoSQL databases like MongoDB. Data visualization and analytics using Power BI & Tableau and big data analytics using Hadoop preparing students for diverse data roles.

 

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: MongoDB 

  • Introduction to MongoDB
  • MongoDB essentials
  • Structure of MongoDB
  • Advanced MongoDB Queries
  • Integrating MongoDB with Python for Data

 

Tools Covered: MySQL, SQL Server, MongoDB

 

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

 

Module 6: Apache Hadoop – Big Data Analytics

  • Overview of Big Data and Distributed Computing
  • The Hadoop ecosystem and its components
  • Architecture: HDFS and MapReduce
  • Setting Up Hadoop Environment
  • Managing files and directories in HDFS
  • Performing HDFS operations
  • MapReduce paradigm: mapper, reducer, and shuffle phases
  • Running and monitoring MapReduce jobs on Hadoop clusters
  • YARN and Hadoop Ecosystem
  • Hadoop ecosystem projects: Hive, Pig, HBase, etc.
  • SQOOP (SQL in HADOOP)
  • Integrating Hadoop with other Big Data technologies

 

Tools Covered: Hadoop, Pig, Hive, HBase, Yarn MapReduce

UNIT 5: Statistical Approach for Data
Science (24 Hours)

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

UNIT 6: Predictive Analytics using
Machine Learning (40 Hours)

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: Advanced Machine Learning

  • Clustering & K-means
  • K-Means Clustering Model
  • Ensemble approach
  • Bootstrapping + Aggregation = Bagging
  • Bagging vs Boosting
  • Hyperparameter Tuning for GridSearchCV
  • XGBoost Explanatory Model Building
  • Boosting Ensemble Models
  • Adaptive Boosting (AdaBoost)
  • Handling Imbalanced Dataset
    • Resampling (Oversampling & Undersampling)
    • Oversampling Technique (SMOTE)
  • Gradient Boosting
  • CatBoost
  • LightGBM
  • Support Vector Classifier (SVC) & Support Vector Machines (SVM)
  • Principal Component Analysis (PCA)
    • Use of Dimensionality Reduction Technique
    • Difference with Feature Selection Techniques
  • Density-based Spatial Clustering of Applications with Noise (DBSCAN)
  • Hyperparameter Tuning

 

Tools Covered: Pandas, Matplotlib, Sk Learn, LightGBM 

 

Class Projects:

  • Project with practical application of Regression, Classification, and Clustering algorithms using Machine Learning concepts.
  • Case studies in various domains (e.g., healthcare, finance, marketing, supply chain, etc.) like:
  • Spam Mail Classifier using Naive Bayes Algorithm
  • Detect car Insurance Fraud Claims
  • Heart disease detection using ML

 

Module 3: 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.

UNIT 7: NLP - Natural Language Processing
(16 Hours)

NLP Specialization will help you on projects like sentiment analysis and other text based projects. 

 

Module 1: NLP

  • Introduction to Natural Language Processing
  • Text Preprocessing
  • Text Embedding Techniques
  • Word2Vec Text Embedding
  • Topic modeling (LDA, LSA)
  • Named Entity Recognition (NER)
  • Part-of-Speech Tagging (POS Tagging)
  • Transformer architecture and BERT model
  • Text classification models

 

Class Projects:

  • To classify an email as spam or not spam
  • Social media sentiment analysis
  • Translation & summarization of News
  • Generate optimized title/headline
  • Case Study on Recommendation Engine

 

Tools Covered: NLTK, Spacy, BERT

UNIT 8: Intro to Deep Learning & Prompt Engineering (14 Hours)

Deep Learning, focuses on training neural networks to build a model by studying hierarchical patterns and features from the input data.

 

Module 1: Artificial Neural Network 

Module 1: Introduction to Deep Learning 

  • Introduction to Deep Learning
  • Forward Propagation in ANN
  • Backpropagation in ANN
  • Artificial Neural Network (ANN)

 

Class Projects

  • Diabetes detection using Artificial Neural Network (ANN)

 

Tools Covered: Tensorflow, Keras, PyTorch

 

Module 2: 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.)
  1. ChatGPT: Prompt for text Generation (Natural Language Processing)
  2. Dall E / Midjourney: Prompt for image Generation
  3. Synthesia for Video Generation & Slides AI for PPT creation

 

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

UNIT 9: Cloud Deployment of ML Models
(32 Hours)

In 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

Program Highlights

UNIT 1: Orientation (8 Hours)

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 Science, Analytics & Machine Learning

  • 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.

UNIT 2: Portfolio Building (6 hours)

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

 

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 Features for Engagement
  • Growing Your Network on LinkedIn
  • Increasing Followers and Engagement
  • Enhancing Professional Branding on LinkedIn
  • Leveraging LinkedIn for Career Advancement

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

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.

UNIT 4: Data Science Tools (94 Hours)

Learn practically data mining, using SQL and NoSQL databases like MongoDB. Data visualization and analytics using Power BI & Tableau and big data analytics using Hadoop preparing students for diverse data roles.

 

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: MongoDB 

  • Introduction to MongoDB
  • MongoDB essentials
  • Structure of MongoDB
  • Advanced MongoDB Queries
  • Integrating MongoDB with Python for Data

 

Tools Covered: MySQL, SQL Server, MongoDB

 

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

 

Module 6: Apache Hadoop – Big Data Analytics

  • Overview of Big Data and Distributed Computing
  • The Hadoop ecosystem and its components
  • Architecture: HDFS and MapReduce
  • Setting Up Hadoop Environment
  • Managing files and directories in HDFS
  • Performing HDFS operations
  • MapReduce paradigm: mapper, reducer, and shuffle phases
  • Running and monitoring MapReduce jobs on Hadoop clusters
  • YARN and Hadoop Ecosystem
  • Hadoop ecosystem projects: Hive, Pig, HBase, etc.
  • SQOOP (SQL in HADOOP)
  • Integrating Hadoop with other Big Data technologies

 

Tools Covered: Hadoop, Pig, Hive, HBase, Yarn MapReduce

UNIT 5: Statistical Approach for Data Science (24 Hours)

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

UNIT 6: Predictive Analytics using Machine Learning (40 Hours)

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: Advanced Machine Learning

  • Clustering & K-means
  • K-Means Clustering Model
  • Ensemble approach
  • Bootstrapping + Aggregation = Bagging
  • Bagging vs Boosting
  • Hyperparameter Tuning for GridSearchCV
  • XGBoost Explanatory Model Building
  • Boosting Ensemble Models
  • Adaptive Boosting (AdaBoost)
  • Handling Imbalanced Dataset
    • Resampling (Oversampling & Undersampling)
    • Oversampling Technique (SMOTE)
  • Gradient Boosting
  • CatBoost
  • LightGBM
  • Support Vector Classifier (SVC) & Support Vector Machines (SVM)
  • Principal Component Analysis (PCA)
    • Use of Dimensionality Reduction Technique
    • Difference with Feature Selection Techniques
  • Density-based Spatial Clustering of Applications with Noise (DBSCAN)
  • Hyperparameter Tuning

 

Tools Covered: Pandas, Matplotlib, Sk Learn, LightGBM

 

Class Projects:

  • Project with practical application of Regression, Classification, and Clustering algorithms using Machine Learning concepts.
  • Case studies in various domains (e.g., healthcare, finance, marketing, supply chain, etc.) like:
  • Spam Mail Classifier using Naive Bayes Algorithm
  • Detect car Insurance Fraud Claims
  • Heart disease detection using ML

 

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

 

Module 3: 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

UNIT 7: NLP - Natural Language Processing (16 Hours)

The NLP specialization will help you gain experience in techniques such as; text preprocessing, sentiment analysis, and building text-based models. These concepts will further help us build Machine Learning & AI models like Grammarly, ChatGPT, and Alexa.

 

Module 1: NLP

  • Introduction to Natural Language Processing
  • Text Preprocessing
  • Text Embedding Techniques
  • Word2Vec Text Embedding
  • Topic modeling (LDA, LSA)
  • Named Entity Recognition (NER)
  • Part-of-Speech Tagging (POS Tagging)
  • Transformer architecture and BERT model
  • Text classification models

 

Class Projects:

  • To classify an email as spam or not spam
  • Social media sentiment analysis
  • Translation & summarization of News
  • Generate optimized title/headline
  • Case Study on Recommendation Engine

 

Tools Covered: NLTK, Spacy, BERT

UNIT 8: Intro to Deep Learning & Prompt Engineering (14 Hours)

Deep Learning, focuses on training neural networks to build a model by studying hierarchical patterns and features from the input data. On the other hand, Prompt engineering is an act of engineering a suitable output with an efficient prompt on Generative AI tools like ChatGPT.

 

Module 1: Introduction to Deep Learning 

  • Introduction to Deep Learning
  • Forward Propagation in ANN
  • Backpropagation in ANN
  • Artificial Neural Network (ANN)

 

Class Projects

  • Diabetes detection using Artificial Neural Network (ANN)

 

Tools Covered: Tensorflow, Keras, PyTorch

 

Module 2: 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

UNIT 9: Cloud Deployment of ML Models (32 Hours)

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

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      What are the prerequisites for the Data Science and Machine Learning Course?

      The Data Science and Machine Learning Course is specifically designed for non-programmers and individuals from non-technical backgrounds. The course is beginner-friendly, starting from the basics, and is open to students with diverse backgrounds.

      What will I be preparing for in the Data Science and Machine Learning course for non-programmers?

      We offer two courses in the Data Science program:

      1. Foundation Data Science and Machine Learning program, which includes:
      • Python programming
      • Statistics for data science
      • Machine learning
      • Time-Series Analysis
      • SQL
      1. Foundation Plus Data Science and Machine Learning program, which includes all the components of the Foundation Data Science and Machine Learning program, plus:
      • Web Scraping
      • NLP (Natural Language Processing)
      • Artificial Neural Network
      • MongoDB
      • Power BI for Data Visualization
      • Tableau for Data Visualization
      • Hadoop for Big Data
      • AWS Cloud Deployment

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

      Yes, this program is tailored for non-programmers looking to excel in the field of data science and machine learning. It is recommended for candidates with little to no prior knowledge of statistics and Python/R programming.

      How many students are there in one batch?

      Our Data Science and Machine Learning Course focuses on providing high-quality training with a personalized touch. To ensure an optimal learning experience and encourage regular doubt-solving sessions, we maintain small batch sizes of up to 15 students. This enables mentors to provide individualized support and fosters interactive learning among participants.

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

      Students enrolled in our Data Science and Machine Learning Course receive a 2-year subscription. This entitles them to ongoing access to live class support, mentorship from the institute, and job referrals for the duration of the subscription.

      What advantages does the online training program provide for students?

      The online training program for the Data Science and Machine Learning Course provides students with several advantages:

      • Swift resolution of queries during live sessions.
      • Access to recorded classes for reviewing previous sessions and clarifying doubts.
      • Availability of recorded discussions on assignments and projects.
      • Access to session recordings and extensive course materials for future reference.

      How long is the duration of the Data Science and Machine Learning Course?

      The Data Science and Machine Learning Course has two options. The foundation data science and machine learning course has a duration of 4 months (120 hours), while the foundation PLUS data science and machine learning program lasts for 7 months (240 hours). Both options include live training sessions, hands-on training on live projects, and interview preparations. Classes are conducted on both weekdays and weekends. The weekday batch is held from Monday to Friday for 2 hours per day, and the weekend batch takes place on Saturdays and Sundays for 3.5 hours per day.

      How is instructor-led online training structured in the Data Science and Machine Learning Course?

      In the Data Science and Machine Learning Course, the instructor-led online training follows a structured format. Students engage in live sessions conducted by experienced trainers, allowing them to actively participate and interact with the instructor and peers. This training methodology facilitates the learning of foundational data science and machine learning concepts through practical exercises and real-world applications.

      What if I miss a live session in the Data Science and Machine Learning Course?

      If you missed a live session in the Data Science and Machine Learning Course, there’s no need to worry. The instructor-led online training offers recorded sessions that you can access afterwards. This allows you to catch up on the missed content and stay on track with the course curriculum, ensuring that you don’t miss out on any valuable learning opportunities.

      How does a smaller batch size contribute to better learning in the Data Science and Machine Learning Course?

      In the Data Science and Machine Learning Course, a smaller batch size plays a crucial role in improving the learning process. With a limited number of students, each individual can actively participate and have their questions addressed during the session. The trainer can devote ample time to explain concepts and ensure a thorough understanding of the course material.

      Can students interact and ask questions during the live training sessions in the Data Science and Machine Learning Course?

      Yes, students are encouraged to actively participate and ask questions during the live training sessions in the Data Science and Machine Learning Course. Our goal is to foster a collaborative learning environment, enabling students to engage with the trainer and seek clarification on any uncertainties. To facilitate effective interaction, we maintain small class sizes with a maximum of 15 students per batch.

      How will this course help me in my career?

      The Data Science and Machine Learning Course builds a solid career foundation. Master concepts, statistical techniques, and programming skills in Python and SQL. Gain proficiency in data science libraries and frameworks. Unlock career prospects in finance, healthcare, e-commerce, and more by making data-driven decisions.

      Are there any assessments or exams during the course?

      Yes, to assess your progress and understanding of the concepts taught, there will be periodic assessments and exams throughout the Data Science and Machine Learning Course. 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.

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

      Yes, upon completing the Data Science and Machine Learning Course, 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.

      Can I access the learning materials on my mobile device?

      Answer: Yes, the learning materials for the Data Science and Machine Learning Course for Managers, including recorded sessions, assignments, and course materials, are accessible through our online learning platform (LMS). This allows you to access the content on your mobile device, giving you the flexibility to learn on the go.

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

      We understand that you may need to switch between batches if you miss an entire module or with any work commitment. If such a situation arises during the Data Science and Machine Learning Course, 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.

      What kind of support can I expect during the course?

      During the Data Science and Machine Learning Course, 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.

      Does the Data Science and Machine Learning Course provide practical training?

      Absolutely! The Data Science and Machine Learning Course emphasizes practical training to complement theoretical learning. Through practical exercises and real-world applications, you’ll develop the skills to extract insights from data and build predictive models. This hands-on experience will equip you with the necessary expertise to tackle data-driven challenges effectively.

      How do real-time projects benefit students in the Data Science and Machine Learning Course?

      Real-time projects in the Data Science and Machine Learning Course are based on industry data (with confidentiality protected) and offer students the opportunity to apply concepts and algorithms to actual datasets. These projects enhance the learning experience by providing practical exposure and allowing students to develop their skills in data science and machine learning. With 12 industry projects included in the course, students can gain hands-on experience and practice the techniques learned.

      How do domain specializations benefit students in the Data Science and Machine Learning Course?

      Domain specializations is part of Data Science and Machine Learning Course that provide industry-specific training through capstone projects and mentorship. These projects are sourced from diverse domains, allowing students to gain practical experience and apply data science and machine learning concepts in real-world scenarios. Domain specializations benefit students by deepening their understanding of data science in specific industries, preparing them for domain-specific challenges, and increasing their employability in the field of data science and machine learning.

      What is the count of Capstone projects included in the Data Science and Machine Learning Course?

      The Data Science and Machine Learning Course includes 1 end-to-end Capstone projects. These projects provide students with the opportunity to apply their knowledge and gain practical experience by working on real-world scenarios in the domain of data science and machine learning.

      Will I have the opportunity to interact with industry experts or mentors during the Data Science and Machine Learning Course?

      Certainly! The Data Science and Machine Learning Course ensures that you have access to industry experts and mentors. These knowledgeable professionals will be there to support you, offer valuable insights, and provide mentorship as you dive into the fundamentals of data science and machine learning.

      Can I join a community or forum to interact and collaborate with other students in the Data Science and Machine Learning Course?

      Yes! The Data Science and Machine Learning Course offers a community forum designed for students to interact and collaborate. Within this forum, you can engage in discussions, seek clarification on concepts, and work together on projects. It serves as a platform to connect with fellow learners, exchange ideas, and enhance your understanding of data science and machine learning foundations.

      How can I seek resolution for my queries outside the class in the Data Science and Machine Learning Course?

      To address your queries outside the class, we provide a student forum exclusively for participants of the Data Science and Machine Learning Course. If you have any doubts or encounter difficulties while practicing, you can post your queries on the forum. Our trainers and fellow students actively participate on the forum, offering their expertise and insights to help you find solutions.

      What is the approach for conducting doubt-solving sessions in the Data Science and Machine Learning Course?

      In the Data Science and Machine Learning Course, we understand the importance of resolving doubts and ensuring a strong foundation. Therefore, we conduct doubt-solving sessions within the class to address any questions or uncertainties that arise during the course. These sessions provide an opportunity for participants to seek clarification and gain a deeper understanding of the concepts covered.

      What is the fees of Data Science and Machine Learning Course

      The fees for the Data Science and Machine Learning course is INR 79,000/- + 18% GST.

      What are the different modes of payments available?

      The different payment methods accepted by us are:

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

      Can I pay in instalments for the Data Science and Machine Learning Program?

      Yes, you can pay the fees in instalments by taking a no-cost EMI option for INR 5179/month for a 12-month EMI (Foundation Data Science and Machine Learning Course) and INR 5,890/month for a 12-month EMI ( Data Science and Machine Learning Course). You can choose an interest free loan by submitting Aadhar, PAN, 3-month salary slip and other required documents to our banking parner.

      Are there any installment options available for course fee payment in the Data Science and Machine Learning Course?

      Yes! We provide installment options for course fee payment in the Data Science and Machine Learning Course. We understand that managing course fees is important, and we aim to support our students financially. You can connect with our admissions team to discuss the available payment plans and select the one that aligns with your financial needs.

      Is there any scholarship/discount available?

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

      What is Group Discount?

      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.

      What does the Job Assistance program in the Data Science and Machine Learning Course offer?

      The Job Assistance program in the Data Science and Machine Learning Course is designed to help participants kick-start their careers in the field of data science and machine learning. Through this program, we provide job search support, resume building guidance, interview preparation, and networking opportunities. Our goal is to equip participants with the necessary skills and resources to secure job positions in the dynamic and rapidly growing field of data science and machine learning.

      Our job assistance program is a four step program:

      • Github & LinkedIn Profile building
      • Resume Preparation
      • Mock Interviews
      • Job Referrals

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

      The mock interviews in the  Data Science and Machine Learning Course are conducted online via video mode. Within a week, you will receive feedback on your performance. By reviewing the recorded video of the interview, you can identify areas for improvement in both soft skills and technical skills. This course offers up to 2 mock interviews.

      Will I receive job assistance after completing the Data Science and Machine Learning Course?

      Yes, we provide job assistance to students who have successfully completed the Data Science and Machine Learning Course. Our dedicated placement cell assists students in crafting effective resumes, preparing for interviews, and connects them with suitable job opportunities in the data science and machine learning domain. We are committed to helping our students achieve their career goals in this dynamic field.

      How many job referrals will be provided?

      Our Data Science and Machine Learning Course offers job referral assistance to our students until the end of the subscription period. We connect you with our network of partner companies and consultancies, increasing your chances of finding suitable job opportunities in the data science and machine learning field.

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

      To be eligible for job assistance from 1stepGrow, you need to fulfill certain requirements. These include successfully completing all assessment tests with a score of 70% or higher, submitting assignments on time, completing real-time projects, and Capstone project.

      Will I get a Course Completion Certificate from 1stepGrow?

      Yes, upon successfully completing the Data Science and Machine Learning Course, you will receive a Course Completion Certificate from 1stepGrow. This certificate validates your proficiency in data science and machine learning, showcasing your skills to potential employers.

      Are there academic certifications provided in the course?

      Yes, we provide academic certifications as part of the Data Science and Machine Learning Course. These certifications validate your knowledge and skills in the field of data science and machine learning, giving you a competitive edge in the job market.  We are partnered with Microsoft. On successful completion of the assessment you will be awarded with a globally recognized Data certificate by Microsoft.

      Will I get project experience certification from a company?

      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, highlighting your practical skills and project-based learning.

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

      As a college student or fresher, this course provides you with valuable recognition. You can expect a Course Completion Certificate that demonstrates your knowledge in data science and machine learning. On completion of course and real-time projects.  The project experience gained during the course will also enhance your resume and increase your chances of securing internships and entry-level positions.

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

      As an on-job professional, this course offers recognition that can propel your career forward. You will receive a Course Completion Certificate, validating your expertise in Data Science and Machine Learning. You will also be certified for the project experience and practical skills gained through completion of the capstone project. This will enhance your professional profile, enabling you to take on more challenging roles and responsibilities.

      How valuable is a project experience certification by a company?

      A project experience certification by a company holds significant value in the industry. It showcases your ability to apply Data Science and Machine Learning techniques to real-world projects, demonstrating your practical skills and problem-solving capabilities. This certification enhances your credibility and can make a positive impact on your career growth.

      Will I get project experience certification / internship from a company?

      For working professionals we help our students specialize in domain by working on end-to-end industry projects. The projects will require implementation of concepts and tools you will be trained in the class. On successful completion of the project you will be awarded with a Project experience certificate by our collaborated company.

       

      For college students / freshers, we help you work on an internship program for upto 6 months and get certified for the same.

      Advanced Data Science & AI Course Book Counselling

      Have any questions in mind?

      Talk to our team directly

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      in touch with you shortly

      Know More About Your Learning Options

      All Answers To Your Future Career

      What are the prerequisites for the Data Science and Machine Learning Course?

      The Data Science and Machine Learning Course is specifically designed for non-programmers and individuals from non-technical backgrounds. The course is beginner-friendly, starting from the basics, and is open to students with diverse backgrounds.

      What will I be preparing for in the Data Science and Machine Learning course for non-programmers?

      We offer two courses in the Data Science program:

      1. Foundation Data Science and Machine Learning program, which includes:
      • Python programming
      • Statistics for data science
      • Machine learning
      • Time-Series Analysis
      • SQL
      1. Foundation Plus Data Science and Machine Learning program, which includes all the components of the Foundation Data Science and Machine Learning program, plus:
      • Web Scraping
      • NLP (Natural Language Processing)
      • Artificial Neural Network
      • MongoDB
      • Power BI for Data Visualization
      • Tableau for Data Visualization
      • Hadoop for Big Data
      • AWS Cloud Deployment

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

      Yes, this program is tailored for non-programmers looking to excel in the field of data science and machine learning. It is recommended for candidates with little to no prior knowledge of statistics and Python/R programming.

      How many students are there in one batch?

      Our Data Science and Machine Learning Course focuses on providing high-quality training with a personalized touch. To ensure an optimal learning experience and encourage regular doubt-solving sessions, we maintain small batch sizes of up to 15 students. This enables mentors to provide individualized support and fosters interactive learning among participants.

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

      Students enrolled in our Data Science and Machine Learning Course receive a 2-year subscription. This entitles them to ongoing access to live class support, mentorship from the institute, and job referrals for the duration of the subscription.

      What advantages does the online training program provide for students?

      The online training program for the Data Science and Machine Learning Course provides students with several advantages:

      • Swift resolution of queries during live sessions.
      • Access to recorded classes for reviewing previous sessions and clarifying doubts.
      • Availability of recorded discussions on assignments and projects.
      • Access to session recordings and extensive course materials for future reference.

      How long is the duration of the Data Science and Machine Learning Course?

      The Data Science and Machine Learning Course has two options. The foundation data science and machine learning course has a duration of 4 months (120 hours), while the foundation PLUS data science and machine learning program lasts for 7 months (240 hours). Both options include live training sessions, hands-on training on live projects, and interview preparations. Classes are conducted on both weekdays and weekends. The weekday batch is held from Monday to Friday for 2 hours per day, and the weekend batch takes place on Saturdays and Sundays for 3.5 hours per day.

      How is instructor-led online training structured in the Data Science and Machine Learning Course?

      In the Data Science and Machine Learning Course, the instructor-led online training follows a structured format. Students engage in live sessions conducted by experienced trainers, allowing them to actively participate and interact with the instructor and peers. This training methodology facilitates the learning of foundational data science and machine learning concepts through practical exercises and real-world applications.

      What if I miss a live session in the Data Science and Machine Learning Course?

      If you missed a live session in the Data Science and Machine Learning Course, there’s no need to worry. The instructor-led online training offers recorded sessions that you can access afterwards. This allows you to catch up on the missed content and stay on track with the course curriculum, ensuring that you don’t miss out on any valuable learning opportunities.

      How does a smaller batch size contribute to better learning in the Data Science and Machine Learning Course?

      In the Data Science and Machine Learning Course, a smaller batch size plays a crucial role in improving the learning process. With a limited number of students, each individual can actively participate and have their questions addressed during the session. The trainer can devote ample time to explain concepts and ensure a thorough understanding of the course material.

      Can students interact and ask questions during the live training sessions in the Data Science and Machine Learning Course?

      Yes, students are encouraged to actively participate and ask questions during the live training sessions in the Data Science and Machine Learning Course. Our goal is to foster a collaborative learning environment, enabling students to engage with the trainer and seek clarification on any uncertainties. To facilitate effective interaction, we maintain small class sizes with a maximum of 15 students per batch.

      How will this course help me in my career?

      The Data Science and Machine Learning Course builds a solid career foundation. Master concepts, statistical techniques, and programming skills in Python and SQL. Gain proficiency in data science libraries and frameworks. Unlock career prospects in finance, healthcare, e-commerce, and more by making data-driven decisions.

      Are there any assessments or exams during the course?

      Yes, to assess your progress and understanding of the concepts taught, there will be periodic assessments and exams throughout the Data Science and Machine Learning Course. 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.

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

      Yes, upon completing the Data Science and Machine Learning Course, 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.

      Can I access the learning materials on my mobile device?

      Answer: Yes, the learning materials for the Data Science and Machine Learning Course for Managers, including recorded sessions, assignments, and course materials, are accessible through our online learning platform (LMS). This allows you to access the content on your mobile device, giving you the flexibility to learn on the go.

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

      We understand that you may need to switch between batches if you miss an entire module or with any work commitment. If such a situation arises during the Data Science and Machine Learning Course, 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.

      What kind of support can I expect during the course?

      During the Data Science and Machine Learning Course, 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.

      Does the Data Science and Machine Learning Course provide practical training?

      Absolutely! The Data Science and Machine Learning Course emphasizes practical training to complement theoretical learning. Through practical exercises and real-world applications, you’ll develop the skills to extract insights from data and build predictive models. This hands-on experience will equip you with the necessary expertise to tackle data-driven challenges effectively.

      How do real-time projects benefit students in the Data Science and Machine Learning Course?

      Real-time projects in the Data Science and Machine Learning Course are based on industry data (with confidentiality protected) and offer students the opportunity to apply concepts and algorithms to actual datasets. These projects enhance the learning experience by providing practical exposure and allowing students to develop their skills in data science and machine learning. With 12 industry projects included in the course, students can gain hands-on experience and practice the techniques learned.

      How do domain specializations benefit students in the Data Science and Machine Learning Course?

      Domain specializations is part of Data Science and Machine Learning Course that provide industry-specific training through capstone projects and mentorship. These projects are sourced from diverse domains, allowing students to gain practical experience and apply data science and machine learning concepts in real-world scenarios. Domain specializations benefit students by deepening their understanding of data science in specific industries, preparing them for domain-specific challenges, and increasing their employability in the field of data science and machine learning.

      What is the count of Capstone projects included in the Data Science and Machine Learning Course?

      The Data Science and Machine Learning Course includes 1 end-to-end Capstone projects. These projects provide students with the opportunity to apply their knowledge and gain practical experience by working on real-world scenarios in the domain of data science and machine learning.

      Will I have the opportunity to interact with industry experts or mentors during the Data Science and Machine Learning Course?

      Certainly! The Data Science and Machine Learning Course ensures that you have access to industry experts and mentors. These knowledgeable professionals will be there to support you, offer valuable insights, and provide mentorship as you dive into the fundamentals of data science and machine learning.

      Can I join a community or forum to interact and collaborate with other students in the Data Science and Machine Learning Course?

      Yes! The Data Science and Machine Learning Course offers a community forum designed for students to interact and collaborate. Within this forum, you can engage in discussions, seek clarification on concepts, and work together on projects. It serves as a platform to connect with fellow learners, exchange ideas, and enhance your understanding of data science and machine learning foundations.

      How can I seek resolution for my queries outside the class in the Data Science and Machine Learning Course?

      To address your queries outside the class, we provide a student forum exclusively for participants of the Data Science and Machine Learning Course. If you have any doubts or encounter difficulties while practicing, you can post your queries on the forum. Our trainers and fellow students actively participate on the forum, offering their expertise and insights to help you find solutions.

      What is the approach for conducting doubt-solving sessions in the Data Science and Machine Learning Course?

      In the Data Science and Machine Learning Course, we understand the importance of resolving doubts and ensuring a strong foundation. Therefore, we conduct doubt-solving sessions within the class to address any questions or uncertainties that arise during the course. These sessions provide an opportunity for participants to seek clarification and gain a deeper understanding of the concepts covered.

      What is the fees of Data Science and Machine Learning Course

      The fees for the Data Science and Machine Learning course is INR 79,000/- + 18% GST.

      What are the different modes of payments available?

      The different payment methods accepted by us are:

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

      Can I pay in instalments for the Data Science and Machine Learning Program?

      Yes, you can pay the fees in instalments by taking a no-cost EMI option for INR 5179/month for a 12-month EMI (Foundation Data Science and Machine Learning Course) and INR 5,890/month for a 12-month EMI ( Data Science and Machine Learning Course). You can choose an interest free loan by submitting Aadhar, PAN, 3-month salary slip and other required documents to our banking parner.

      Are there any installment options available for course fee payment in the Data Science and Machine Learning Course?

      Yes! We provide installment options for course fee payment in the Data Science and Machine Learning Course. We understand that managing course fees is important, and we aim to support our students financially. You can connect with our admissions team to discuss the available payment plans and select the one that aligns with your financial needs.

      Is there any scholarship/discount available?

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

      What is Group Discount?

      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.

      What does the Job Assistance program in the Data Science and Machine Learning Course offer?

      The Job Assistance program in the Data Science and Machine Learning Course is designed to help participants kick-start their careers in the field of data science and machine learning. Through this program, we provide job search support, resume building guidance, interview preparation, and networking opportunities. Our goal is to equip participants with the necessary skills and resources to secure job positions in the dynamic and rapidly growing field of data science and machine learning.

      Our job assistance program is a four step program:

      • Github & LinkedIn Profile building
      • Resume Preparation
      • Mock Interviews
      • Job Referrals

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

      The mock interviews in the  Data Science and Machine Learning Course are conducted online via video mode. Within a week, you will receive feedback on your performance. By reviewing the recorded video of the interview, you can identify areas for improvement in both soft skills and technical skills. This course offers up to 2 mock interviews.

      Will I receive job assistance after completing the Data Science and Machine Learning Course?

      Yes, we provide job assistance to students who have successfully completed the Data Science and Machine Learning Course. Our dedicated placement cell assists students in crafting effective resumes, preparing for interviews, and connects them with suitable job opportunities in the data science and machine learning domain. We are committed to helping our students achieve their career goals in this dynamic field.

      How many job referrals will be provided?

      Our Data Science and Machine Learning Course offers job referral assistance to our students until the end of the subscription period. We connect you with our network of partner companies and consultancies, increasing your chances of finding suitable job opportunities in the data science and machine learning field.

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

      To be eligible for job assistance from 1stepGrow, you need to fulfill certain requirements. These include successfully completing all assessment tests with a score of 70% or higher, submitting assignments on time, completing real-time projects, and Capstone project.

      Will I get a Course Completion Certificate from 1stepGrow?

      Yes, upon successfully completing the Data Science and Machine Learning Course, you will receive a Course Completion Certificate from 1stepGrow. This certificate validates your proficiency in data science and machine learning, showcasing your skills to potential employers.

      Are there academic certifications provided in the course?

      Yes, we provide academic certifications as part of the Data Science and Machine Learning Course. These certifications validate your knowledge and skills in the field of data science and machine learning, giving you a competitive edge in the job market.  We are partnered with Microsoft. On successful completion of the assessment you will be awarded with a globally recognized Data certificate by Microsoft.

      Will I get project experience certification from a company?

      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, highlighting your practical skills and project-based learning.

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

      As a college student or fresher, this course provides you with valuable recognition. You can expect a Course Completion Certificate that demonstrates your knowledge in data science and machine learning. On completion of course and real-time projects.  The project experience gained during the course will also enhance your resume and increase your chances of securing internships and entry-level positions.

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

      As an on-job professional, this course offers recognition that can propel your career forward. You will receive a Course Completion Certificate, validating your expertise in Data Science and Machine Learning. You will also be certified for the project experience and practical skills gained through completion of the capstone project. This will enhance your professional profile, enabling you to take on more challenging roles and responsibilities.

      How valuable is a project experience certification by a company?

      A project experience certification by a company holds significant value in the industry. It showcases your ability to apply Data Science and Machine Learning techniques to real-world projects, demonstrating your practical skills and problem-solving capabilities. This certification enhances your credibility and can make a positive impact on your career growth.

      Will I get project experience certification / internship from a company?

      For working professionals we help our students specialize in domain by working on end-to-end industry projects. The projects will require implementation of concepts and tools you will be trained in the class. On successful completion of the project you will be awarded with a Project experience certificate by our collaborated company.

       

      For college students / freshers, we help you work on an internship program for upto 6 months and get certified for the same.

      Have any questions in mind?

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