Core Concepts of Data Science – A Comprehensive Guide to Data-Driven Decision Making

This Data Science Program 📈 is designed to cover the core concepts of data science, including data collection, pre-processing, exploratory data analysis (EDA), machine learning fundamentals, and real-world applications. 🚀💻 Whether you're a beginner or a professional, this guide will help you master data-driven decision-making and AI-powered analytics for a successful career in data science! 🔍📊🤖

CHECK OUT THE LINK FOR MORE DETAILS

https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/


#MachineLearning #AI #BigData

1. COURSE
CORE CONCEPTS OF DATA
SCIENCE
Data Science and Artificial Intelligence

2. Introduction to Data Science
Data Science is transforming decision-making
by enabling organizations to analyze vast
amounts of data for actionable insights. It
enhances accuracy through predictive
analytics, automates processes with AI, and
optimizes strategies using machine learning.
Businesses leverage data-driven decisions to
improve efficiency, reduce risks, and enhance
customer experiences.

3. Data Collection & Sources
Structured Data:
Organized and stored in a predefined
format (e.g., rows and columns in
databases).
Easily searchable using SQL and other
database management tools.
Examples: Customer records, financial
transactions, inventory data.
Unstructured Data:
Lacks a fixed format and is not easily
searchable.
Includes text, images, videos, and social
media posts.
Examples: Emails, sensor data, social media
content, medical images.

4. Data Pre-processing & Cleaning
Handling Missing Data : Missing data can lead to inaccurate analysis and
biased results. Techniques like imputation (mean, median, mode) and
removing incomplete records help maintain data integrity.
Removing Duplicates & Outliers :Duplicates can skew results, so identifying
and removing them ensures data consistency. Outliers are detected using
statistical methods and visualization to prevent misleading insights.
Data Normalization & Transformation: Normalization scales data to a standard
range (e.g., 0 to 1) for better model performance. Transformation (log, square
root) helps make data more interpretable and improves accuracy.

5. Machine Learning Fundamentals
Machine Learning (ML) is a subset of AI that enables systems to learn from data and
make predictions without explicit programming. It is categorized into Supervised
Learning (uses labeled data), Unsupervised Learning (finds patterns in unlabeled
data), and Reinforcement Learning (learns from rewards and penalties). ML
algorithms, such as regression, classification, and clustering, power applications like
recommendation systems, fraud detection, and image recognition.

6. Data Science Tools & Technologies
- Programming Languages (Python, R, SQL)
- Libraries (Pandas, NumPy, Scikit-learn,
TensorFlow)
- Big Data Technologies (Hadoop, Spark)

7. Thank You!
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