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Dur: 45 days
Course fee : 3000 /-

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.


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:

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

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
Who can learn this course

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

  1. Data Scientists: Enhancing their Python skills for data analysis, visualization, and machine learning.
  2. Analysts and Researchers: Developing the ability to derive insights from data through statistical analysis and visualization.
  3. Software Engineers: Acquiring skills to apply Python in data-related tasks and understanding its role in machine learning.
  4. Business Analysts: Leveraging Python to analyze and present data-driven insights for business decision-making.
  5. Students and Graduates: Building a strong foundation in Python for future careers in data science or related fields.
  6. 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)

4% Avg
salary hike
50L Avg
Upcoming Batches
Live Training Batches Timetable
Course Name Faculty Date Time Mode of Training Batch Type Meeting Link
Python for Data Science Mr. K V Rao 15 May 9:00 AM (IST) online Online Training
Python for Data Science Mr. K V Rao 15 May 9:00 AM (IST) offline Classroom Training
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

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