Python for Data Science Online Training | Master with Data Science Online Course- NareshIT

Training Type

Select faculty

Select Date

Dur:
Course fee : /-

Download Curriculum

Python for Data Science

Course Overview

Python for Data Science is a specialized course that focuses on leveraging the Python programming language for various aspects of data analysis, manipulation, visualization, and machine learning. Python's extensive ecosystem of libraries makes it a powerful tool for professionals aiming to extract meaningful insights from data and build predictive models.

Learn software skills with real experts, either in live classes with videos or without videos, whichever suits you best.

Description

This course covers the fundamental Python libraries used in data science, such as NumPy for numerical computing, Pandas for data manipulation, Matplotlib and Seaborn for data visualization, and Scikit-learn for machine learning. Participants will learn to clean and preprocess data, perform exploratory data analysis, and apply statistical methods. The course also delves into machine learning techniques, enabling participants to build and evaluate predictive models using Python.

Course Objectives

The objectives of the Python for Data Science course include:

Building a Foundation: Establishing a solid understanding of Python programming fundamentals and its role in data science.
Data Manipulation: Learning to use Pandas for efficient data cleaning, transformation, and manipulation.
Data Visualization: Gaining proficiency in creating informative and visually appealing data visualizations with Matplotlib and Seaborn.
Exploratory Data Analysis (EDA): Developing skills in exploring and understanding datasets through statistical and visual analysis.
Statistical Analysis: Applying statistical techniques using libraries like Statsmodels for hypothesis testing, regression, and other analyses.
Machine Learning: Understanding the basics of machine learning and utilizing Scikit-learn for building and evaluating models.
Real-world Applications: Applying Python for solving real-world data science problems and making data-driven decisions.

Prerequisites

- Basic understanding of Python programming language.
- Familiarity with data structures (e.g., lists, dictionaries).
- Knowledge of arrays and matrices.
- Understanding of data manipulation and analysis.
- Awareness of data visualization principles.
- Experience with using and navigating code editors or IDEs.

Enroll Now

Course Curriculum

Basic Introduction to the Numpy
Creation of Numpy Arrays

array()
arange()
linspace()
zeros()
ones()
full()
eye()
identity()
diag()
empty()
random library functions

Array attributes & Numpy Data Types
View vs Copy
Indexing, Slicing and Advanced Indexing

Basic Indexing-->To select only one element.
Slicing-->To select multiple ordered elements
Advanced Indexing-->To select multiple arbitrary elements
Condition Based Selection-->a[condition]

How to iterate elements of the nd array.

By using pythons loop
By using numpy nditer() function
By using numpy ndenumerate() function

Arithmetic operators

Arithmetic operators for Numpy arrays with scalar
Arithmetic operators for Arrays with Arrays.

Broadcasting
Array manipulation functions

reshape()
resize()
flatten()
flat variable
ravel()
transpose()
swapaxes()

Joining of multiple arrays into a single array

concatenate()
stack()
vstack()
hstack()
dstack()

Splitting of arrays

split()
vsplit()
hsplit()
dsplit()
array_split()

Sorting elements of ndarrays
Searching elemenets of ndarray
How to insert elements into ndarray?

insert()
append()

How to delete elements from ndarray
Matric multiplication by using dot() function
Importance of matrix class in numpy library
Linear algebra functions from linalg module

inv()-->To find inverse of a matrix
matrix_power()-->To find power of a matrix A^n
det()-->To find determinant of a matrix
solve()-->To solve linear algebra equations

I/0 operations with Numpy
Basic statistics with Numpy
Numpy mathematical functions
How to find unique items and count

Introduction
Environment Setup
Introduction to Data Structures

Dimension & Description
Series
DataFrame
Data Type of Columns
Panel

Series

Pandas.Series
Create an Empty Series
Create a Series f
rom ndarray
rom dict
rom Scalar
Accessing Data from Series with Position
Retrieve Data Using Label (Index)

DataFrame

pandas.DataFrame
Create DataFrame
Create an Empty DataFrame
Create a DataFrame from Lists
Create a DataFrame from Dict of ndarrays / Lists
Create a DataFrame from List of Dicts
Create a DataFrame from Dict of Series
Column Selection
Column Addition
Column Deletion
Row Selection, Addition, and Deletion

Panel

pandas.Panel()
Create Panel
Selecting the Data from Panel

Basic Functionality

DataFrame Basic Functionality

Descriptive Statistics

Functions & Description
Summarizing Data

Function Application

Table-wise Function Application
Row or Column Wise Function Application
Element Wise Function Application

Reindexing

Reindex to Align with Other Objects
Filling while ReIndexing
Limits on Filling while Reindexing
Renaming

Iterating a DataFrame

iteritems()
iterrows()
itertuples()

Sorting

By Label
Sorting Algorithm

Working with Text Data
Options and Customization

get_option(param)
set_option(param,value)
reset_option(param)
describe_option(param)
option_context()

Indexing and Selecting Data

.loc()
.ix()
Use of Notations

Statistical Functions

Percent_change
Covariance
Correlation
Data Ranking

Window Functions

.rolling() Function
.expanding() Function
.ewm() Function

Aggregations

Applying Aggregations on DataFrame

Missing Data

Cleaning / Filling Missing Data
Replace NaN with a Scalar Value
Fill NA Forward and Backward
Drop Missing Values
Replace Missing (or) Generic Values

GroupBy

Split Data into Groups
View Groups
Iterating through Groups
Select a Group
Aggregations
Transformations
Filtration

Merging/Joining

Merge Using 'how' Argument

Concatenation

Concatenating Objects
Time Series

Date Functionality

Timedelta

Categorical Data

Object Creation

Visualization

Bar Plot
Histograms
Box Plots
Area Plot
Scatter Plot
Pie Chart

IO Tools

read.csv

Sparse Data
Caveats & Gotchas
Comparison with SQL

Introduction to Matplotlib

Matplotlib Introduction
Need of Data visualization
Basic Introduction to Matplotlib
Installing Matplotlib

Line Plot- Basics

Types of Plots
Matplotlib Anatomy
Line plots
Creation of line plot by passing 2 nd-arrays
What is figure?
How to add title to the line plot
How to add xlabel and ylabel to the line plot

Line Plots-Advanced

Line properties
Marker property
Linestyle Property
color property
default color
Shortcut way to set color, marker and line style
alpha property
linewidth and marker size
Components of Line plot
Sequence of Activities of plot() function
To get all default settings in Matplotlib
How to save line plot to a file
To know the supported files types for saving figure object
Creation of line plot by passing a single ndarray
Multiple lines on the same Plot
Shortcut way
How to customize title properties
Customization of xlabel and ylabel

How to add grid lines to plot

How to add grid lines to plot
How to add grid lines to plot
Various cases of grid() function usage
which property
Difference between major and minor grid lines
Axis property
Passing other keyword arguments

Adding Legend

Legend
legend()
legend(labels)
legend(handles, labels)
How to adjust legend location
How to specify number of columns in the legend
Adding title to legend
How to add legend outside of the plot

Customization of Tick Location and Labels

Customization of tick location and labels
Without customizing xticks() and yticks() -->default values are based on the input
customizing xticks()
customizting both xticks() and yticks()
disabling xticks() or yticks()

How to set limit range of values on x-axis and y-axis by using xlim() and ylim() functions

How to set limit range of values on x-axis and y-axis
xlim()
ylim()

How to set scale of x-axis and y-axis?

Scaling: How to set scale for x-axis and y-axis
Linear scaling
Logarithmic scaling
How to customize base value in logarithmic scaling

Plotting Styles

Plotting styles
To know the plotting styles available in matplotlib
How to use the style
Default styling

Functional/Procedural Oriented Vs Object Oriented Approached of plotting

Approaches to create Plot
Procedural or Functional Oriented Approach
OOP Approach

Bar Chart/ Bar Graph/ Bar Plot

Bar Chart/Bar Graph/Bar Plot
Simple bar chart/vertical bar chart
Represent the number of movies of each hero by using bar chart
Mobile Sales of Nokia Company from 2011 to 2020
How to add labels to the bar
pyplot.text()
Adding Labels for the data points of lineplot
pyplot.annotate()
How to add labels to the bar chart
With more readable labels
Plotting bar chart with data from csv file
Horizontal bar chart
Stacked Bar chart
Clustered Bar chart/Grouped Bar Chart/Multiple Bar Chart

Pie Chart

Pie Chart
Adding Labels ==> labels argument
autopct
explode
shadow
colors
VIBGYOR colors
startangle
counterclock
Adding legend
wedgeprops
edgecolor
edgecolor and linestyle
edgecolor, linestyle and linewidth
Examples

Histogram

Histograms
How to change color of bars in histogram
How to change color of the each bars in histogram
To change color of first bar
To change color of each bar
How to get the real data sets
How many movies and TV shows are released on Netflix year-wise
Number of movies and TV shows are released on Netflix from 2000 to 2021
Number of movies and TV shows are released on Netflix from 2000 to 2021
only from 'India'
Number of movies and TV shows released in past decade

Scatter Plots

Scatter plot
Changing the color and size of the markers
How to change color of the marker
Different colors for each markers
Changing the size of the marker
Different sizes for the markers
cmap ==> colormap
colormap ==> viridis
colormap ==> summer
colormap ==> winter
colormap ==> cool
colormap ==> hot
colormap ==> rainbow
colormap ==> rainbow_r ==> reverse of rainbow
colormap ==> prism
colormap ==> flag
usage of keyword argument c with cmap
usage of keyword argument c without cmap
Line plot vs scatter plot
How to add labels to scatter plot data points
Using plt.text(x,y,text)
Using plt.annotate(text,(x,y))
How to add color legend to the scatter plot
Kaggle Case Study : Latest Covid-19 India Statewise Data

Subplots

Subplots
Need of subplots
How to create subplots
pyplot.subplot() function
pyplot.subplots() function

Plotting Geographic Data with Basemap:

Plotting Geographic data with Basemap
How to install basemap
Important Theoritical Terminology
Important methods of Basemap
How to select particular area of the map
How to select only India
from csv file
Top 5 cities with more covid cases in india

Three-Dimensional(3-D) Plotting in Matplotlib

Three-Dimesional plotting(3-D) in Matplotlib
Creating of 3-D axes object
Creation of 3-D line plot
Creation of 3-D Scatter plot
Creation of 3-D Bar charts
Wireframes and surface plots
Surface plots

Animations

Reuse the same line object with different data sets
Demo program for falling line animation
Line falling down and up animation
Demo Program for Growing Line Animation
Demo Program for Burning Coil Animation
How to save animations to a file
Rain Simulation from matplotlib documentation

APPLY NOW !

Who can learn this course

This course is suitable for a diverse range of individuals, including:

Data Scientists: Enhancing their Python skills for data analysis, visualization, and machine learning.
Analysts and Researchers: Developing the ability to derive insights from data through statistical analysis and visualization.
Software Engineers: Acquiring skills to apply Python in data-related tasks and understanding its role in machine learning.
Business Analysts: Leveraging Python to analyze and present data-driven insights for business decision-making.
Students and Graduates: Building a strong foundation in Python for future careers in data science or related fields.
Professionals in Transition: Individuals transitioning to data science roles who want to acquire practical Python skills for the field.

Overall, the course aims to empower participants with the necessary skills to proficiently use Python in the context of data science, catering to a broad audience with varying levels of technical backgrounds.

Average package of course (Python for Data Science)

100% Avg
salary hike

4 - 8L Avg
Package

Training Features

Comprehensive Course Curriculum

Elevate your career with essential soft skills training for effective communication, leadership, and professional success.

Experienced Industry Professionals

Learn from trainers with extensive experience in the industry, offering real-world insights.

24/7 Learning Access

Enjoy round-the-clock access to course materials and resources for flexible learning.

Comprehensive Placement Programs

Benefit from specialized programs focused on securing job opportunities post-training.

Hands-on Practice

Learn by doing with hands-on practice, mastering skills through real-world projects

Lab Facility with Expert Mentors

State-of-the-art lab facility, guided by experienced mentors, ensures hands-on learning excellence in every session

Our Trainees are Working with

Reviews

I am studying Python for Data Science. The course is excellent, with a strong focus on problem-solving and a clear understanding of programming concepts. Thank you, team!

Sangram Rout

course : Python for Data Science

Talk to Advisor +91 8179191999

Top 5 Technologies to learn Register for the Course !

Full Name

Email address

Mobile Number

By Providing your contact details, you agree to our Terms of use & Privacy Policy

Python for Data Science

Course Overview

Description

Course Objectives

Prerequisites

Course Curriculum

Who can learn this course

Training Features

Comprehensive Course Curriculum

Experienced Industry Professionals

24/7 Learning Access

Comprehensive Placement Programs

Hands-on Practice

Lab Facility with Expert Mentors

Our Trainees are Working with

Reviews

Our Relevant Courses list

Numpy | Pandas | Matplotlib

Hadoop Online Training

Data Analytics

Chat GPT

MLOPS

Data Science with AI