The document discusses key concepts in data science including data cleaning, integration, reduction, transformation, and discretization. It defines each concept and provides examples. Data cleaning involves fixing incorrect, incomplete, or duplicate data. Data integration combines data from different sources. Data reduction decreases storage needs by reducing data capacity. Data transformation converts data between formats. Discretization buckets continuous values into a limited number of states.

1. Introduction to
Data science
Presentation
Presented By : Chirag Gautam
22L9MBA34102

2. Data Cleaning
• Data cleaning is the process of
fixing or removing incorrect,
corrupted, incorrectly
formatted, duplicate, or
incomplete data within a
dataset.

3. Steps of Data
Cleaning
• Step 1: Remove irrelevant
data.
• Step 2: Deduplicate your
data.
• Step 3: Fix structural errors.
• Step 4: Deal with missing
data.
• Step 5: Filter out data outliers

4. Data
Integration
• Data integration is the
process of bringing data
from disparate sources
together to provide
users with a unified
view. The premise of
data integration is to
make data more freely
available and easier to
consume and process
by systems and users.

5. Data Reduction
• Data reduction is the process of
reducing the amount of capacity
required to store data. Data
reduction can increase storage
efficiency and reduce costs. Storage
vendors will often describe storage
capacity in terms of raw capacity
and effective capacity, which refers
to data after the reduction.

6. Data
Transformation
• Data transformation
is the process of
converting data from
one format to another,
typically from the format
of a source system into
the required format of a
destination system.

7. Two Ways of
Transformation
Natural transformation :
It describes the uptake and incorporation of
naked DNA from the cell’s natural environment.
Artificial transformation :
It encompasses a wide array of methods for
inducing uptake of exogenous DNA.

8. DATA
DISCRETIZATIO
N
• Discretization is the process of
putting values into buckets so that
there are a limited number of
possible states. The buckets
themselves are treated as ordered
and discrete values. You can
discretize both numeric and string
columns. There are several methods
that you can use to discretize data.

9. TECHNIQUES