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Description: 

This course in Data Science is beneficial to graduates and postgraduates in the IT industry, particularly developers, test engineers, significant data engineers, and data analysts. 

What does the Course cover?

The course is designed in a way to assist participants in increasing their technical understanding while pursuing BE/BTECH, BSC, BCA, MCA, MTech/ME, or MSc. The skills include creating and developing machine learning and deep learning models, changing and debugging the developed models, data analysis using statistics and probability theory, data segregation and maintenance, and basic working knowledge of SQL databases. 

Objectives:

• To be able to establish a career in becoming a data scientist with relevance in the market, such as Python, SQL, Excel, big data, Machine Learning, Tableau, and Generative AI. 

• To assist learners in exploring many details of data analysis, learning the subject of Machine Learning, and improving technical coding. 

• To offer practical experience in building logic and implementing, for example, machine learning and deep learning, based on the demand.  

Course outcome :

 This course equips the participants with job opportunities in data science, artificial intelligence, and big data analytics. The graduates will be knowledgeable in the processing of structured and unstructured data, the usage of various data science instruments, and the provision of a contribution to an organization's decision-making process. 

Why should you learn this course?

The fact that this program is interdisciplinary allows the participants to fit in the dynamic market of data science and AI, and it is attainable by organizations seeking to harness the ability of data for business operations and development.

Requirements:

1)Basic Computer Knowledge.

2)A Laptop or a Computer for practice.

3)Determination to learn new concepts.

Example:

# Import necessary libraries

import pandas as PD

import seaborn as sns

import matplotlib.pyplot as plt

# Load the Iris dataset

iris = sns.load_dataset('iris')

# Display the first few rows of the dataset

print(iris.head())

# Basic statistical analysis

print(iris.describe())

# Check for missing values

print(iris.isnull().sum())

# Pairplot to visualise relationships between features

sns.pair plot(iris, hue='species')

plt.show()

# Boxplot to visualize the distribution of features

plt.figure(figsize=(10, 6))

sns.boxplot(data=iris, width=0.5, fliersize=5)

plt.show()

# Histogram to visualise the distribution of each feature

iris.hist(edgecolor='black', linewidth=1.2, figsize=(10, 6))

plt.show()