LEARNING DATA SCIENCE

Available Dates & Locations

This course is offered exclusively as In-House or Virtual, with no public sessions currently available – please contact us for inquiries.

COURSE OVERVIEW

Learning Data Science course is designed to provide participants with a comprehensive understanding of the foundational concepts, techniques, and tools used in the field of data science. This course is tailored for individuals seeking a career transition to become data scientists, equipping them with the essential skills to analyze, interpret, and draw meaningful insights from data. Through a combination of theoretical knowledge and hands-on practical exercises, participants will develop a strong foundation to embark on a successful journey in the data science field.

COURSE OBJECTIVES

By completely attending this course, participants will be able to:

Understand the core principles and methodologies of data science.
Manipulate, clean, and preprocess various types of data for analysis.
Apply statistical techniques to extract insights and patterns from data.
Utilize popular programming languages and tools for data analysis, such as Python and Jupyter.
Develop data visualizations to effectively communicate findings.
Implement machine learning algorithms for predictive and classification tasks.
Evaluate and interpret the results of data analysis and machine learning models.

TARGET COMPETENCIES

Data Preprocessing Skills
Exploratory Data Analysis
Python Programming Basics
Library Data Visualization
Machine Learning Fundamentals
Model Evaluation Expertise

This course is ideal for professionals from non-technical backgrounds who aspire to transition into data science roles. Individuals with a curiosity for data analysis and its applications. Career changers interested in leveraging data for decision-making.

The course will be delivered through a combination of Interactive Lectures: In-depth theoretical explanations of data science concepts and methodologies. Hands-on Labs: Practical exercises using real-world datasets to apply the learned concepts. Group Discussions: Collaborative sessions to encourage knowledge sharing and problem-solving. Case Studies: Analysis of real-life data science scenarios to bridge theory and practice. Quizzes and Assessments: Regular evaluations to gauge participant progress.

INTRODUCTION TO DATA SCIENCE

Understanding the data science lifecycle.
Role and responsibilities of a data scientist.
Overview of data-driven decision-making.

DATA ACQUISITION AND PREPROCESSING

Sources of data and data collection methods.
Data cleaning, transformation, and feature engineering.

EXPLORATORY DATA ANALYSIS (EDA)

Descriptive statistics and data summarization.
Data visualization techniques

INTRODUCTION TO PROGRAMMING WITH PYTHON

Basics of Python programming.
Data structures, control flow, and functions

DATA VISUALIZATION

Creating static and interactive visualizations using Matplotlib and Seaborn.
Best practices in data visualization

INTRODUCTION TO MACHINE LEARNING

Fundamentals of machine learning.
Supervised vs. unsupervised learning.

MACHINE LEARNING ALGORITHMS

Linear and logistic regression.
Decision trees and random forests.
Model training, validation, and evaluation.

CASE STUDIES AND APPLICATIONS

Applying data science techniques to real-world problems.
Presenting findings and insights.

ETHICAL CONSIDERATIONS IN DATA SCIENCE

Data privacy and security.
Bias and fairness in machine learning.

CAPSTONE PROJECT

Hands-on project applying data science concepts to a real dataset.
Presentation of project results.