Data Science with Placement Support

Data Science

• Advanced Python

• Data Analysis

• Data Visualization

• Power BI

• Tableau

• Machine Learning

• CNN

This course provides an in-depth understanding of data science, including data collection, cleaning, analysis, visualization, and machine learning. Students will gain hands-on experience with various data science tools and techniques through practical projects and assignments.

Course Outcome Upon successful completion of this comprehensive data science course, students will be well-equipped with the necessary skills and knowledge to embark on a career in data science or enhance their current professional roles with data-driven decision-making capabilities.

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 Data Science

LEARN FROM INDUSTRY EXPERTS

This course provides a thorough introduction to machine learning, covering both theoretical concepts and practical applications. Students will gain hands-on experience with various machine learning algorithms, tools, and techniques through practical projects and assignments.

CERTIFIED DATA SCIENCE COURSE

Module 1: Introduction to Data Science

Week 1: Overview and Foundations

• Topics:

• What is Data Science?

• The Data Science Process and Lifecycle

• Data Science vs. Data Analytics vs. Data Engineering

• Applications of Data Science across Industries

Module 2: Python Programming for Data Science

Week 2: Python Basics

• Topics:

• Python Syntax and Basics

• Variables and Data Types

• Basic Operators and Expressions

Data Structures and Control Flow

• Topics:

• Lists, Tuples, Dictionaries, and Sets

• Control Structures: Loops and Conditionals

• Functions and Modules

Module 3: Data Collection and Preprocessing

Week 4: Data Collection Techniques

• Topics:

• Data Collection Methods

• Web Scraping with Beautiful Soup and Scrapy

• APIs and Data Retrieval

Module 4: Exploratory Data Analysis (EDA)

Week 6: Introduction to EDA

• Topics:

• Importance of EDA

• Summary Statistics

• Data Visualization Principles

Module 6: Statistical Analysis

Week 10: Descriptive and Inferential Statistics

• Topics:

• Descriptive Statistics: Mean, Median, Mode, Variance

• Probability Distributions

• Sampling and Central Limit Theorem

Module 5: Working with Pandas

Week 8: Pandas Basics

• Topics:

• Introduction to Pandas

• DataFrames and Series

• Importing and Exporting Data

Week 9: Data Manipulation with Pandas

• Topics:

• Data Cleaning and Transformation

• Merging and Joining DataFrames

• Grouping and Aggregating Data

Module 1: Introduction to Machine Learning

Week 1: Overview and Fundamentals

• Topics:

• Introduction to Machine Learning

• History and Evolution

• Types of Machine Learning (Supervised, Unsupervised, Semi-supervised, Reinforcement Learning)

• Applications and Use Case

Module 2: Python for Machine Learning

Week 2: Python Basics

• Topics:

• Python Syntax and Basics

• Data Types and Variables

• Basic Operations

Module 3: Data Preprocessing and Exploration

Week 4: Data Preprocessing

• Topics:

• Data Cleaning and Preparation

• Handling Missing Data and Outliers

• Feature Scaling and Normalization

Module 4: Supervised Learning - Regression

Week 6: Regression Analysis

• Topics:

• Simple Linear Regression

• Multiple Linear Regression

• Polynomial Regression

• Evaluation Metrics: MSE, RMSE, R²

Week 7 : Advanced Classification Algorithms

• Topics:

• Support Vector Machines (SVM)

• Naive Bayes

• Ensemble Methods: Random Forests, Gradient Boosting

• Evaluation Metrics: Accuracy, Precision, Recall, F1 Score

Module 6: Unsupervised Learning

Week 8 : Clustering Techniques

• Topics:

• k-Means Clustering

• Hierarchical Clustering

• DBSCAN

• Evaluation of Clustering Models

Module 8: Model Evaluation and Tuning

Week 10: Model Evaluation Techniques

• Topics:

• Cross-Validation

• ROC/AUC and Precision-Recall Curves

• Confusion Matrix and F1 Score

Module 7: DEEP LEARNINING(CNN)

• Introduction to Deep Learning

• Differences between Machine Learning and Deep Learning

• Key Concepts: Neurons, Activation Functions, Layers

• Applications of Deep Learning

Module 9: Convolutional Neural Networks (CNNs)

• Topics:

• Convolution Operations

• Pooling Layers

• CNN Architectures: LeNet, AlexNet

Duration: 3-6Months

Projects: 15+ Nos

Assignments Every Modules

1:1 Mentorship till Advanced Python

Course Fee: FEE: 36000/-
Fees (EMI Mode) : 36000/-
Fees (One time Payment mode) : 33000/-

Demo classes are available to provide an understanding of our trainers' teaching methods

• Three Days Free Classes

• End to End Doubt Clearance Session

• Scope of Data Science Jobs.