Problem definition and project scoping Data collection and preprocessing Exploratory data analysis Model building and evaluation Model deployment and monitoring Iterative improvement and optimization

1. "Data Science: Unlocking
Insights and Driving Innovation”
Introduction to Data Science
ZENITHLEARNING

2. Introduction to Data Science
● Define data science and its importance in the era of big
data
● Discuss the role of data scientists in extracting value
from data and making data-driven decisions.

3. Key Components of Data Science
● Data collection and preprocessing
● Exploratory data analysis (EDA)
● Statistical modeling and machine learning
● Data visualization and storytelling

4. Applications of Data Science
● Predictive analytics and forecasting
● Customer segmentation and targeting
● Fraud detection and cybersecurity
● Recommender systems

5. Data Science Workflow
● Problem definition and project scoping
● Data collection and preprocessing
● Exploratory data analysis
● Model building and evaluation
● Model deployment and monitoring

6. Tools and Technologies in Data Science
● Programming languages (Python, R, Scala)
● Data manipulation and analysis libraries (Pandas,
NumPy)
● Machine learning frameworks (Scikit-learn,
TensorFlow, PyTorch)
● Data visualization tools (Matplotlib, Seaborn, Tableau)

7. CONTACT US:
+91 6305149934