Data Science Course

With the rapid change in the modern technology business, calls for a need for us to grow and enhance our skills according to changes. Companies are struggling to stay in business, which has increased demand for qualified data scientists.

Chandigarh Academy Of Digital Learning (CADL)offers its extensive data science training and placement course which gives ambitious professionals the abilities needed to succeed in the quickly developing field of data science. This course is delivered through the use of AI detection.

Our well-defined curriculum of data analyst classes online at Chandigarh Academy Of Digital Learning covers a wide range of topics required to reach excellence in data science. The course assists learners gain a deeper grasp of data science regardless of experience level.

CADL's Data Science Training program covers a wide range of topics essential for individuals aiming to build a successful career as a data scientist. The curriculum caters to both beginners and professionals seeking to enhance their proficiency in data science Use of Artificial Intelligence is the key to succeeding in data science.

Key Modules :

• Introduction to Data Science
• Understanding the foundations of data science course and its many uses.
• Recognizing the lifetime of data science and its critical role in decision-making.
• Mathematics and Statistical Analysis for Data Science
• Establishing a robust foundation in statistics and mathematical concepts.
• Delving into probability, hypothesis testing, and regression analysis.

Highlight:

• Languages Used in Programming:
• Practical knowledge of programming languages used in data science are Python , writing effective code for analysis, visualization, and data manipulation.
• Preprocessing and Data Cleaning:
• Techniques for proficiently handling missing data.
• Preparing data for analysis through effective preprocessing methods.
• Machine Learning:
• Introduction to machine learning algorithms.
• Engaging in hands-on projects to implement supervised and unsupervised learning.
• Big Data
• Grasping big data concepts and addressing associated challenges.
• Data Visualization:
• Making eye-catching visuals with programs like Tableau, Seaborn, and Matplotlib.
• Effectively communicating insights through visual representation.
• Real-world Projects:
• Applying learned concepts to real-world scenarios through practical projects.
• Gaining hands-on experience and constructing a robust portfolio.

Benefits of Enrolling:

• Expert Faculty: Learning from seasoned instructors and industry professionals with a profound understanding of data science.
• Practical Education: Gaining real-world experience through case studies and practical projects.
• Curriculum Relevant to Industry: Keeping up with industry developments and having a curriculum that fits the needs of the data science labor market today.
• Career Counseling : Taking advantage of career counseling and assistance sessions driven by AI to map out your route in the data science industry.
• Possibilities to Form Networks: Establishing a professional network through interactions with other students and professionals in the subject.
• Internship and Placement Support: Get access to the best data science course with a placement guarantee online after completing the course. Our data science certification gives you an edge while applying for jobs.

Syllabus:

• Module 1: Basics and Foundations
• Python Programming
• Python Installation and Setup
• Basic Syntax and Data Types
• Control Flow (if, else, loops)
• Functions and Modules
• File Handling
• Object-Oriented Programming
• Libraries: NumPy, Pandas
• Mathematics
• Linear Algebra
• Probability and Statistics
• Calculus for Machine Learning
• Discrete Mathematics
• Data Analysis
• Data Wrangling and Cleaning
• Exploratory Data Analysis (EDA)
• Data Visualization (Matplotlib, Seaborn)
• Descriptive Statistics
• Hypothesis Testing
• Module 2: Core Data Science
• Machine Learning
• Supervised Learning
• Unsupervised Learning
• Model Evaluation and Validation
• Ensemble Methods (Random Forest, Gradient Boosting)
• Libraries
• Scikit-Learn for Machine Learning
• TensorFlow/PyTorch for Deep Learning
• Natural Language Processing (NLTK, SpaCy)
• Time Series Analysis (statsmodels)
• Data Engineering
• Database Management (SQL, NoSQL)
• ETL Processes (Extract, Transform, Load)
• Data Warehousing
• Big Data Technologies (Hadoop, Spark)
• Module 3: Advanced Topics and Projects
• Deep Learning
• Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Generative Adversarial Networks (GANs)
• Transfer Learning
• Model Deployment
• Model Serialization (Pickle, Joblib)
• Deploying Models with Flask/Django
• Using Docker for Containerization
• Cloud Deployment (AWS, GCP, Azure)
• Monitoring and Maintaining Models
• Projects
• End-to-End Data Science Projects
• Capstone Project
• Kaggle Competitions
• Real-World Case Studies