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Data Science Fundamentals
43 hours Certification Training Course

Learn From Industry Experts

* Only Limited Slots Per Batch

* No Prior Coding Experience Required

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KEY FEATURES

Live Classes

43 Hours

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

Who can apply?

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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

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*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews

*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews

*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews

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