Data Science Fundamentals 43 hours Certification Training Course

126% Avg. CTC Hike Top 1% Industry Instructors 900+ Placement Partners

Data Science Fundamentals
43 hours Certification Training Course

Learn From Industry Experts

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* No Prior Coding Experience Required

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KEY FEATURES

Live Classes

43 Hours

Virtual Lab Support

For Practicals

Industry Expert

Learn From Top Industry Expert

Live Projects

Work On Capstone Projects

Overview of this Certification Course

Data Science is an interdisciplinary field that combines techniques from statistics, mathematics, computer science, and domain knowledge to extract insights and knowledge from data. This comprehensive training program is designed to provide participants with a solid foundation in data science concepts, tools, and techniques. Through a blend of theoretical explanations, hands-on exercises, and real-world case studies, participants will learn how to analyze data, build predictive models, and derive actionable insights.

Prerequisites of this Course

Course objectives

Understand the fundamentals of data science and its applications
Learn data manipulation and cleaning techniques
Master exploratory data analysis (EDA) methods
Build and evaluate machine learning models for classification and regression tasks
Understand techniques for feature engineering and selection
Gain proficiency in data visualization to communicate insights effectively
Learn best practices for model evaluation and validation
Understand the ethical considerations in data science

Who can apply?

Professionals seeking to transition into a career in data science.
Data analysts looking to enhance their skills in data manipulation and modeling.
Software engineers interested in machine learning and predictive analytics.
Studentspursuing degrees in computer science, mathematics, or related fields.
Anyone with a keen interest in data analysis and its applications

Our Hiring Partners

Skills You Learn From this Course

Fundamental Cybersecurity Concepts

Cyber Threat Analysis

Network Security

Operating System Security

Secure Coding Practices

Data Protection and Encryption

Identity and Access Management (IAM)

Cybersecurity Compliance and Ethics

And Many More...

Course Curriculum

Chapter 1 Introduction to Data Science - 4 hours:

Overview of data science and its applications
Introduction to Python for data science
Understanding the data science workflow

Chapter 2 Data Manipulation and Cleaning - 3 hours:

Importing and exporting data
Handling missing values and outliers
Data transformation and normalization

Chapter 3 Exploratory Data Analysis (EDA) - 4 hours:

Descriptive statistics
Data visualization techniques
Identifying patterns and trends in data

Chapter 4 Machine Learning Fundamentals - 4 hours:

Introduction to supervised and unsupervised learning
Model training and evaluation
Overfitting and underfitting

Chapter 5 Classification Models - 4 hours:

Logistic regression
Decision trees and ensemble methods
Support Vector Machines (SVM)

Chapter 6 Regression Models - 4 hours:

Linear regression
Polynomial regression
Evaluation metrics for regression models

Chapter 7 Feature Engineering and Selection - 3 hours:

Handling categorical variables
Feature scaling and transformation
Dimensionality reduction techniques

Chapter 8 Data Visualization - 3 hours:

Matplotlib and Seaborn libraries
Plotting techniques for exploratory analysis
Creating interactive visualizations with Plotly

Chapter 9 Model Evaluation and Validation - 4 hours:

Cross-validation techniques
Hyperparameter tuning
Model selection criteria

Chapter 10 Ethics in Data Science - 2 hours:

Privacy and data protection
Bias and fairness in machine learning
Ethical considerations in data collection and usage

Chapter 11 Project Work - 12 hours (assuming 6 hours per week for 2 weeks):

Real-world data science project development under the guidance of instructors
Applying learned concepts to solve practical problems
Presenting findings and insights to peers

126% Avg. CTC Hike Top 1% Industry Instructors 900+ Placement Partners