Tasks amenable to AI automation in data science.

1. Tasks
amenable to AI
automation in
data science
Mirza Jahanzeb Beg
Lovely Professional University

2. Introduction
Data science involves using
statistical and computational
methods to extract insights and
knowledge from data.
AI and machine learning can
save time and improve accuracy
by automating tasks in data
science.

3. Data Cleaning
• Data cleaning involves detecting
and correcting errors in data.
• AI can be used to automate
many aspects of data cleaning,
such as identifying missing
values and imputing missing
data.

4. Data Preprocessing
Data preprocessing
involves transforming
raw data into a format
that can be used by
machine learning
algorithms.
AI can be used to
automate many
aspects of data
preprocessing, such as
feature selection and
feature engineering.

5. Exploratory Data
Analysis (EDA)
• EDA involves exploring and
visualizing data to gain insights
and identify patterns.
• AI can be used to automate
many aspects of EDA, such as
identifying correlations and
clustering.

6. Predictive
Modeling
• Predictive modeling involves
building models that can make
predictions based on data.
• AI can be used to automate
many aspects of predictive
modeling, such as selecting the
best model and optimizing
hyperparameters.

7. Natural Language Processing (NLP)
• NLP involves analyzing and
generating human language
using computers.
• AI can be used to automate
many aspects of NLP, such as
sentiment analysis and text
classification.

8. Computer Vision
• Computer vision involves
analyzing and interpreting
images and videos.
• AI can be used to automate
many aspects of computer
vision, such as object detection
and image segmentation.

9. Time Series Analysis
• Time series analysis involves
analyzing and modeling time-
dependent data.
• AI can be used to automate
many aspects of time series
analysis, such as forecasting
and anomaly detection.

10. Anomaly
Detection
• Anomaly detection involves identifying
outliers and unusual patterns in data.
• AI can be used to automate many aspects of
anomaly detection, such as identifying
anomalies in real-time and handling large
volumes of data.

11. Recommender Systems
• Recommender systems involve
predicting and suggesting items
to users based on their past
behavior.
• AI can be used to automate
many aspects of recommender
systems, such as
personalization and improving
recommendations over time.

12. Data Integration
• Data integration involves
combining data from multiple
sources.
• AI can be used to automate
many aspects of data
integration, such as identifying
relationships between data
sources and resolving conflicts.

13. Data Mining
• Data mining involves
discovering patterns and
relationships in large datasets.
• AI can be used to automate
many aspects of data mining,
such as identifying associations
and clusters in data.

14. Feature Extraction
• Feature extraction involves
identifying the most important
features in a dataset for a given
task.
• AI can be used to automate
many aspects of feature
extraction, such as identifying
relevant features and reducing
the dimensionality of data.

15. Model Deployment
• Model deployment involves making models
available for use in production
environments.
• AI can be used to automate many aspects
of model deployment, such as monitoring
model performance and updating models
over time.

16. • Conclusion
• AI has the potential to automate many
tasks in data science, from data
cleaning and preprocessing to
predictive modeling and model
deployment.
• There are limitations and challenges to
the use of AI in data science, but it is
an exciting area of research and
development with many opportunities
for innovation and improvement.

17. Thank You
Happy Learning