Data science combines scientific methods, algorithms, and expertise to extract knowledge from data, applied in various fields like healthcare and finance. The process involves data collection, cleaning, exploratory analysis, modeling, and visualization, incorporating machine learning techniques. Despite its challenges, data science offers valuable insights and growing career opportunities.

1. INTRODUCTION TO
DATA SCIENCE

2. • Data Science is the field that uses scientific
methods, processes, algorithms, and systems to
extract knowledge from structured and
unstructured data.
• It combines concepts from statistics, computer
science, and domain expertise to solve complex
problems and make data-driven decisions.
• Applications: Healthcare, Finance, Marketing,
Retail, etc.
WHAT IS DATA SCIENCE?

3. • Data Collection: Gathering data from various sources (e.g., databases,
sensors, web scraping).
• Data Cleaning: Removing duplicates, handling missing values, and
correcting errors.
• Exploratory Data Analysis (EDA): Analyzing data to find trends,
patterns, and anomalies.
• Modeling: Applying machine learning algorithms to predict outcomes
or classify data.
• Data Visualization: Presenting findings visually (charts, graphs,
dashboards).
KEY COMPONENTS OF DATA SCIENCE

4. THE DATA SCIENCE WORKFLOW

5. ⚬ Machine Learning: A subset of data science that
uses algorithms to learn patterns from data and
make predictions.
⚬ Types of Machine Learning:
￭ Supervised Learning: Training models on labeled
data (e.g., classification).
￭ Unsupervised Learning: Finding patterns in
unlabeled data (e.g., clustering).
￭ Reinforcement Learning: Learning through trial
and error (e.g., game AI).
MACHINE LEARNING IN DATA SCIENCE

6. TOOLS AND TECHNOLOGIES
Python: Primary language for data manipulation and
modeling.
R: For statistical analysis and visualization.
SQL: For querying and managing databases.
Hadoop: For big data storage and processing.
Tableau: For data visualization and creating dashboards.

7. • Data Quality: Incomplete, inconsistent, or noisy data.
• Overfitting: When models become too complex and fit
the noise rather than the signal.
• Model Interpretability: Difficulty in understanding and
explaining complex models (e.g., deep learning).
• Scalability: Handling large volumes of data efficiently.
CHALLENGES IN DATA SCIENCE

8. CONCLUSION
• Data Science is a powerful field that transforms data into
valuable insights.
• Its applications continue to grow across various industries.
• The field offers exciting career opportunities but also presents
challenges, requiring continuous learning and adaptation.

9. !!THANK YOU!!