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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.
Are you ready to take the reins of the data-driven world? The field of data analytics has revolutionized how businesses operate, opening doors to a realm of insights and opportunities. At Singularis Software Technologies, we invite you to embark on a transformative journey through our comprehensive Data Analytics Training program
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.
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