Data Science With Python Training Course Online Course

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Data Science with Python Training Course

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English

Course Description

The Data Science with Python training course will give you a detailed overview on developing machine learning using python covering the topics like regression, Naive Bayes, Clustering, tensor flow etc. The Data Science with Python course has been designed to provide in-depth knowledge of the various libraries and packages that are required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The data science with python course is based on the live projects, demonstrations, assignments, and the case studies to provide a hands-on as well as practical experience to the aspirants. Moreover, the course insights on PROC SQL and other statistical procedures such as: PROC MEANS, PROC FREQ, etc. along with the advanced analytics techniques to have a clear vision of decision tree, regression and clustering.

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What you’ll learn

Live Class Practical Oriented Training
Timely Doubt Resolution
Dedicated Student Success Mentor
Certification & Job Assistance
Free Access to Workshop & Webinar
No Cost EMI Option
To perform scientific & technical computing using SciPy package & its sub-packages such as Integrate, Optimize, Statisti...
Perform data analysis and manipulation using data structures and tools provided in Pandas package
Gain an in-depth understanding of supervised & unsupervised learning models like linear & logistic regression, clusterin...
Use Scikit-Learn package for natural language processing
How to use the matplotlib library of Python for data visualization
Extract useful data from websites by performing web scraping using Python
Integrate Python with Hadoop, Spark, and MapReduce
Master's concepts like Regression, K-Nearest Neighbors, Naive Bayes, Neural Networks Clustering, Network Analysis, Clas...

Covering Topics

Lecture-1 Introduction to data science

Lecture-2 Statistical Analysis and Business Applications

Lecture-3 Python Programming

Lecture-4 Introduction To Statistics

Lecture-5 Pandas

Lecture-6 Mathematical Computing with Python (NumPy)

Lecture-7 The Scientific computing with Python (Scipy)

Lecture-8 Introduction to Machine Learning

Lecture-9 Machine Learning and Data Science Framework

Lecture-10 Data Science Environment Setup

Lecture-11 Matplotlib + Seaborn: Plotting and Data Visualization

Lecture-12 Scikit-learn: Creating Machine Learning Models

Lecture-13 - Scikit-learn - Regression Model

Lecture-14 - Scikit-learn – Classification

Lecture-15 K Means Clustering

Lecture-16 Text Mining

Lecture-17 Basic Time Series Forecasting

Lecture-18 Natural Language Processing, Text Mining & PCA

Lecture-19 Introduction to Deep Learning

Case Studies

Curriculum

  Lecture-1 Introduction to data science
Live Lecture 
·      Different Sectors Using Data Science

·      The Purpose and Components of Python

·      The Data Analytics Process

·      Exploratory the Data Analysis (EDA)

·      EDA-Quantitative Technique

·      EDA - Graphical Technique

·      The Data Analytics Conclusion or Predictions

·      The Data Analytics Communication

·      The Data Types for Plotting

·      Practical Exercise

  Lecture-2 Statistical Analysis and Business Applications
Live Lecture 
·      Introduction to the Statistics

·      About Statistical and Non-statistical Analysis

·      The Major Categories of Statistics

·      About the Statistical Analysis Considerations

·      The Population and Sample

·      What is the Statistical Analysis Process?

·      The Data Distribution

·      Dispersion

·      Practical Exercise

  Lecture-3 Python Programming
Live Lecture 
·         Introduction of Python

·         The Companies using Python

·         Different Applications where Python is used

·         Discuss Python Scripts on UNIX/Windows

·         Values, Types, Variables

·         Operands and Expressions

·         Conditional Statements

·         Loops

·         Command Line Arguments

·         Writing to the screen

·         Sequences and File Operations
·         Python files I/O Functions

·         Numbers

·         Strings and related operations

·         Tuples and related operations

·         Lists and related operations

·         Dictionaries and related operations

·         Sets and related operations

·         Functions, OOPs, Modules, Errors and Exceptions
·         Functions

·         Function Parameters

·         Global Variables

·         Variable Scope and Returning Values

·         Lambda Functions

·         Object-Oriented Concepts

·         Standard Libraries

·         Modules Used in Python

·         The Import Statements

·         Module Search Path

·         Package Installation Ways

·         Errors and Exception Handling

·         Handling Multiple Exceptions

  Lecture-4 Introduction To Statistics
Live Lecture 
·      Introduction To Statistic

·      Distributions and Hypothesis Tests

·      Distributions of One Variable

·      Hypothesis Testing

·      Typical Analysis Procedure

·      Data Screening and Outliers

·      Normality Check

·      Hypothesis Concept

·      Errors

·      p-Value

·     Sample Size

·      Chi Square

·      ANOVA

·      Practical Exercise

  Lecture-5 Pandas
Live Lecture 
·      Pandas Introduction

·      Series, Data Frames and CSVs

·      Data from URLs

·      Describing Data with Pandas

·      Selecting and Viewing Data with Pandas

·      Manipulating Data

·      Practical Exercise

  Lecture-6 Mathematical Computing with Python (NumPy)
Live Lecture 
·      NumPy Introduction

·      NumPy DataTypes and Attributes

·      Creating NumPy Arrays

·      NumPy Random Seed

·      Viewing Arrays and Matrices

·      Manipulating Arrays

·      Standard Deviation and Variance

·      Reshape and Transpose

·      Dot Product vs Element Wise

·      Exercise: Store Sales

·      Comparison Operators

·      Sorting Arrays

·      Turn Images Into NumPy Arrays

·      Practical Exercise

  Lecture-7 The Scientific computing with Python (Scipy)
Live Lecture 
·      Introduction to the SciPy

·      SciPy Sub Package - Integration and Optimization

·      What is SciPy sub package?

·      SciPy Sub Package - Statistics, Weave and IO

·      Practical Exercise

  Lecture-8 Introduction to Machine Learning
Live Lecture 
·      What Is Machine Learning?

·      AI/Machine Learning/Data Science

·      Practical Exercise

  Lecture-9 Machine Learning and Data Science Framework
Live Lecture 
·      Machine Learning Framework

·      Types of Machine Learning

·      Types of Data

·      Types of Evaluation

·      Features In Data

·      Modelling - Splitting Data

·      Modelling - Picking the Model

·      Modelling - Tuning

·      Modelling – Comparison

·      Practical Exercise

  Lecture-10 Data Science Environment Setup
Live Lecture 
·      Introducing Our Tools

·      Windows Environment Setup

·      Linux Environment Setup

·      Jupyter Notebook Walkthrough

·      Practical Exercise

  Lecture-11 Matplotlib + Seaborn: Plotting and Data Visualization
Live Lecture 
·      Matplotlib Introduction

·      Importing And Using Matplotlib

·      Anatomy Of A Matplotlib Figure

·      Scatter Plot And Bar Plot

·      Histograms And Subplots

·      Subplots Option 2

·      Plotting From Pandas DataFrames

·      Customizing Your Plots

·      Saving And Sharing Your Plots

·      Practical Exercise

  Lecture-12 Scikit-learn: Creating Machine Learning Models
Live Lecture 
·      Scikit-learn Introduction

·      Scikit-learn Cheatsheet

·      Typical scikit-learn Workflow

·      Debugging Warnings In Jupyter

·      Splitting Your Data

·      Clean, Transform, Reduce

·      Convert Data To Numbers

·      Handling Missing Values With Pandas

·      Handling Missing Values With Scikit-learn

·      Choosing The Right Model For Your Data

·      Practical Exercise

  Lecture-13 - Scikit-learn - Regression Model
Live Lecture 
·      Types of Regression Algorithms

·      Simple Linear Regression

·      Multiple Linear Regression

·      Logistic Regression,

·      Polynomial Regression

·      Support Vector Regression

·      Ridge Regression

·      Lasso Regression

·      ElasticNet Regression

·      Bayesian Regression

·      Decision Tree Regression

·      Random Forest Regression

·      Case Studies

·      Practical Exercise

  Lecture-14 - Scikit-learn – Classification
Live Lecture 
·      Types of Classification Algorithms

·      Logistic Regression/Classification

·      K-Nearest Neighbours

·      Support Vector Machines

·      Kernel Support Vector Machines

·      Naive Bayes

·      Decision Tree Classification

·      Random Forest Classification

·      Case Studies

·      Practical Exercise

  Lecture-15 K Means Clustering
Live Lecture 
·      K-Means Clustering

·      A Simple Example of Clustering

·      Clustering Categorical Data

·      How to Choose the Number of Clusters

·      Pros and Cons of K-Means Clustering

·      To Standardize or not to Standardize

·      Relationship between Clustering and Regression

·      Market Segmentation with Cluster Analysis

·      Species Segmentation with Cluster Analysis

·      Advanced Statistical Methods - Other Types of Clustering

·      Types of Clustering

·      Dendrogram

·      Heatmaps

·      Practical Exercise

  Lecture-16 Text Mining
Live Lecture 
·      The concepts of text-mining

·      Use cases

·      Text Mining Algorithms

·      Quantifying text

·      TF-IDF

·      Beyond TF-IDF

·      Practical Exercise

  Lecture-17 Basic Time Series Forecasting
Live Lecture 
·      What is time series?

·      Techniques and applications

·      Time series components

·      Moving average

·      Smoothing techniques

·      Exponential smoothing

·      Univariate time series models

·      Multivariate time series analysis

·      Sentiment analysis in Python (Twitter sentiment analysis)

·      Text analysis

·      Rolling Mean For Detecting Temporal Variation

·      Simple Exponential Smoothing (SES)

·      Holt extended simple exponential smoothing

·      Holt Winters

·      Auto Regression Model (AR): Consider Previous Time Steps

·      Implement a Basic ARIMA Model

·      Automated ARIMA & Account for Seasonality (SARIMA)

·      Practical Exercise

  Lecture-18 Natural Language Processing, Text Mining & PCA
Live Lecture 
·      Introduction to Natural Language Processing (NLP)

·      Introduction to text mining

·      Importance and applications of text mining

·      How NPL works with text mining

·      Writing and reading to word files

·      OS modules

·      Natural Language Toolkit (NLTK) environment & text mining

·      Principal Component Analysis (PCA)

·      Practical Exercise

  Lecture-19 Introduction to Deep Learning
Live Lecture 
·      Introduction to Deep Learning with neural networks

·      Biological neural network vs artificial neural network

·      Understanding perceptron learning algorithm

·      Introduction to Deep Learning frameworks

·      Tensor Flow constants, variables and place-holders

·      Practical Exercise

  Case Studies

Frequently Asked Questions

basic understanding of Computer Programming Languages.

The course offers a variety of online training options, including: Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey

The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively. *Note: Availability of recorded live lectures may vary depending on the course and training provider.