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Data Science Training Course

Data Science Professional Course from BIT provides high-quality instruction combined with real-world experience through applied projects. You’ll gain a deep understanding of cutting-edge topics like P...

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Course Description

Data Science Professional Course from BIT provides high-quality instruction combined with real-world experience through applied projects. You’ll gain a deep understanding of cutting-edge topics like Python Programming, Machine Learning, Deep Learning, Artificial Intelligence. The Data Science with Python training course will give you a detailed overview on developing machine learning using python...

Data Science Professional Course from BIT provides high-quality instruction combined with real-world experience through applied projects. You’ll gain a deep understanding of cutting-edge topics like Python Programming, Machine Learning, Deep Learning, Artificial Intelligence. 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.

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 and technical computing using SciPy package and its sub-packages such as Integrate, Optimize, Stat...
  • Perform data analysis and manipulation using data structures and tools provided in Pandas package
  • Gain an in-depth understanding of supervised learning and unsupervised learning models like linear regression, logistic...
  • Make predictions using linear regression, polynomial regression, and multivariate regression
  • Build artificial neural networks with Tensorflow and Keras
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
  • Data Visualization with MatPlotLib and Seaborn

Covering Topics

1
Part 1: Python Programming
2
Part 2: Python for Data Science
3
Part 3: Machine Learning
4
Part 4: Deep Learning
5
Part 5: Artificial Intelligence
6
Case Studies

Curriculum

      Part 1: Python Programming
Lecture-1 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

·      Practical Exercise              

Lecture-2 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

·      Practical Exercise              

Lecture-3 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

·      Practical Exercise
      Part 2: Python for Data Science
Lecture-4 Introduction to Data Science for Python 
·      What is Data Science?

·      History of Data Science

·      Methodologies

·      Data Science Applications

·      Image Recognition

·      Speech Recognition

·      Business Intelligence vs. Data Science

·      Data Science Life-Cycle

·      Practical Exercise              

Lecture-5 Python Data Science Environment Setup 
·      Install Python

·      Getting Anaconda for Data Science Environment Setup

·      Anaconda Navigator

·      Installing Anaconda

·      Install Miniconda

·      Setting up a Virtual Environment

·      Important Python Data Science Packages

·      How to Get Jupyter Notebook?

·      Practical Exercise              

Lecture-6 Python Data Cleansing by Pandas & Numpy 
·      Python Data Cleansing – Prerequisites

·      Python Data Cleansing Operations on Data using NumPy

·      Python Data Cleansing Operations on Data Using pandas

·      Dataframe

·      Panel

·      Series

·      Python Data Cleansing

·      Ways to Cleanse Missing Data in Python

·      Python Data Cleansing – Other Operations

·      Practical Exercise              

Lecture-7 Python Data File Formats 
·      How to Read CSV, JSON, and XLS Files

·      Python Data File Formats

·      Prerequisites

·      Read CSV File in Python

·      Read JSON File in Python

·      Practical Exercise             

Lecture-8 Working with Relational Database with Python 
·      Introduction

·      Prerequisites for Relational Database

·      Reading a Relational Table

·      Insert Values in Relational Database with Python

·      Delete Values in Relational Database with Python

·      Practical Exercise             

Lecture-9 Work with NoSQL Database in Python using PyMongo 
·      What is NoSQL Database?

·      Need for NoSQL Database in Python

·      Database Types with NoSQL

·      Document Databases

·      Graph Stores

·      Key-Value Stores

·      Wide-Column Stores

·      Benefits of Using NoSQL Database

·      NoSQL vs. SQL

·      Installing the Prerequisites of NoSQL Database in Python

·      Operations Perform in NoSQL Database in Python

·      Practical Exercise             

Lecture-10 Python Stemming and Lemmatization 
·      Prerequisites for Python Stemming and Lemmatization

·      Python Stemming

·      Python Lemmatization

·      Practical Exercise              

Lecture-11 Aggregation and Data Wrangling with Python 
·      DataFrames

·      Python Data Wrangling – Prerequisites

·      Why we need Data Wrangling with Python

·      Dropping Missing Values

·      Grouping Data

·      Filtering Data

·      Pivoting Dataset

·      Melting Shifted Datasets

·      Merging Melted Data

·      Reducing into an ABT

·      Concatenating Data

·      Exporting Data

·      How Python Aggregate Data?

·      Practical Exercise              

Lecture-12 Python Statistics 
·      Introduction

·      p-value in Python Statistics

·      T-test in Python Statistics

·      KS Test in Python Statistics

·      Correlation in Python Statistics

·      Practical Exercise              

Lecture-13 Python Descriptive Statistics 
·      Data Analysis

·      Descriptive Statistics in Python

·      Central Tendency in Python

·      Dispersion in Python

·      Pandas with Descriptive Statistics in Python

·      Practical Exercise              

Lecture-14 Python Probability Distributions 
·      What is Python Probability Distribution?

·      Implement Python Probability Distributions

·      Normal Distribution in Python

·      Binomial Distribution in Python

·      Poisson Distribution in Python

·      Bernoulli Distribution in Python

·      Practical Exercise              

Lecture-15 Introduction to Python Anaconda 
·      What is Anaconda?

·      Benefits of Using Python Anaconda

·      Python Anaconda Installation

·      Installing Python Anaconda Libraries

·      Anaconda Navigator

·      Practical Exercise              

Lecture-16 Python Matplotlib 
·      What is Python Matplotlib?

·      Python Matplotlib – Pyplot

·      Python Matplotlib Keyword Strings

·      Categorical Variables to Python Plotting

·      Some Line Properties of Matplotlib

·      Showing a Grid in Python Plot

·      Practical Exercise              

Lecture-17 Python Scatter Plot & Python BoxPlot 
·      What is Python Scatter & BoxPlot?

·      Create Python BoxPlot Using Matplotlib

·      Create a Python Scatter Plot

·      Practical Exercise              

Lecture-18 Python Charts 
·      Prerequisites for Python Charts

·      Bubble Charts

·      3D Charts

·      Python Charts Properties

·      Styling your Python Chart

·      How to Save Python Charts File?

·      Practical Exercise              

Lecture-19 Python Heatmap and Word Cloud 
·      What is Python Heatmap & Word Cloud?

·      Create a Heatmap in Python

·      Normalizing a column

·      Create a Word Cloud Python

·      Practical Exercise              

Lecture-20 Python Histogram and Python Bar Plot 
·      Introduction to Python Histogram

·      Displaying Histogram, Rug, and Kernel Density

·      Customizing the rug

·      Customizing the density distribution

·      Vertical Python Histogram

·      Python Histogram with multiple variables

·      Introduction to Python Bar Plot

·      Horizontal Python Bar Plot

·      Adding Title and Axis Labels

·      Practical Exercise              

Lecture-21 Geographic Maps & Graph Data 
·      Prerequisites for Python Geographic Maps and Graph Data

·      Python Geographic Maps

·      Python Graph Data

·      Sparse graphs

·      Practical Exercise              

Lecture-22 Python Time Series Analysis 
·      What is Time Series in Python?

·      Plotting a Python Histogram

·      Plotting a Density Plot in Python Time Series

·      Autocorrelation Plot in Python Time Series

·      Plotting a Lag Plot in Python Time Series

·      Practical Exercise              

Lecture-23 Python Linear Regression 
·      What is Python Linear Regression?

·      Chi-Square Test

·      Practical Exercise
      Part 3: Machine Learning
Lecture-24 Introduction to Machine Learning with Python 
·      Supervised Learning

·      Unsupervised Learning

·      Steps in Python Machine Learning

·      Applications of Python Machine Learning

·      Practical Exercise              

Lecture-25 Environment Setup and Installation Process 
·      How to Install Python?

·      Starting and Updating Anaconda

·      Installing Needed Python Libraries

·      Practical Exercise             

Lecture-26 Data Pre-processing, Analysis & Visualization 
·      Data Pre-processing in Python Machine Learning

·      Python Data Pre-processing Techniques

·      Analyzing Data in Python Machine Learning

·      Visualizing Data-Univariate Plots in Python Machine Learning

·      Visualizing Data-Multivariate Plots in Python Machine Learning

·      Practical Exercise              

Lecture-27 Train and Test Set 
·      Training and Test Data in Python Machine Learning

·      How to Split Train and Test Set in Python Machine Learning?

·      Plotting of Train and Test Set in Python

·      Practical Exercise              

Lecture-28 Machine Learning Techniques with Python 
·      Machine Learning Techniques vs. Algorithms

·      Machine Learning Regression

·      Linear Regression and Non-Linear Regression

·      Machine Learning Classification

·      Decision Tree Induction

·      Rule-based Classification

·      Classification by Back propagation

·      Lazy Learners

·      Clustering

·      Anomaly Detection

·      Practical Exercise              

Lecture-29 Machine Learning Algorithms in Python 
·      Linear Regression

·      Logistic Regression

·      Decision Tree

·      Support Vector Machines (SVM)

·      Naive Bayes

·      kNN (k-Nearest Neighbors)

·      Random Forest

·      Practical Exercise
      Part 4: Deep Learning
Lecture-30 Introduction to Deep Learning with Python 
·      What is Deep Learning with Python?

·      Characteristics of Deep Learning With Python

·      Deep Neural Networks

·      Deep Learning Applications

·      Practical Exercise              

Lecture-31 Python Deep Learning Environment Setup 
·      How to Install Python?

·      Python Libraries

·      Python Text Editor

·      Python Hardware

·      Practical Exercise              

Lecture-32 Libraries and Frameworks 
·      TensorFlow Python

·      Keras Python

·      Apache mxnet

·      Caffe

·      Theano Python

·      Microsoft Cognitive Toolkit

·      PyTorch

·      Eclipse DeepLearning4J

·      Lasagne

·      Nolearn

·      PyLearn2

·      Practical Exercise              

Lecture-33 Deep Neural Networks With Python 
·      Define Deep Neural Network with Python

·      Artificial Neural Networks

·      Deep Neural Networks

·      Structure of Deep Neural Network

·      Types of Deep Neural Networks with Python

·      Challenges to Deep Neural Networks

·      Deep Belief Networks

·      Practical Exercise              

Lecture-34 Computational Graphs 
·      Deep Learning Computational Graphs

·      Need of Computational Graph

·      Composite Function

·      Visualizing a Computation Graph in Python

·      Dynamic Deep Learning Python Computational Graphs

·      Forward and Backward Propagation in Computational Graphs

·      Practical Exercise
      Part 5: Artificial Intelligence
Lecture-35 Introduction to Artificial Intelligence with Python 
·      What is Artificial Intelligence?

·      Python AI–Approaches

·      Artificial Intelligence Tools

·      Search and Optimization

·      Logic

·      Probabilistic Methods for Uncertain Reasoning

·      Classifiers and Statistical Learning Methods

·      Artificial Neural Networks

·      Evaluating Progress

·      Applications of Artificial Intelligence

·      Practical Exercise              

Lecture-36 NLTK Python 
·      What is NLTK?

·      How to Install NLTK?

·      NLTK Tokenize Text

·      Find Synonyms From NLTK WordNet

·      Find Antonyms From NLTK WordNet

·      Stemming NLTK

·      Lemmatizing NLTK Using WordNet

·      NLTK Stop Words

·      Speech Tagging

·      Practical Exercise              

Lecture-37 Python Speech Recognition – Artificial Intelligence 
·      What is Python Speech Recognition?

·      Reading an Audio File in Python

·      Reading a Segment of Audio

·      Python Speech Recognition – Dealing with Noise

·      Working With Microphones

·      Practical Exercise              

Lecture-38 Natural Language Processing (NLP) 
·      Introduction to Natural Language Processing

·      Components of NLP

·      Benefits of NLP

·      Libraries for NLP

·      Glossary in NLP

·      Tasks in NLP

·      NLP Applications

·      Practical Exercise              

Lecture-39 Heuristic Search 
·      What is a Heuristic Search?

·      Heuristic Search Techniques in Artificial Intelligence

·      Hill Climbing in Artificial Intelligence

·      Features of Hill Climbing in AI

·      Types of Hill Climbing in AI

·      Problems with Hill Climbing in AI

·      Constraint Satisfaction Problems (CSP)

·      Simulated Annealing Heuristic Search

·      Best-First Search (BFS) Heuristic Search

·      Practical Exercise              

Lecture-40 Python Genetic Algorithms 
·      What are Genetic Algorithms With Python?

·      Operators of Python Genetic Algorithms

·      Benefits of Python Genetic Algorithms

·      Limitations of Python Genetic Algorithms

·      Applications of Python Genetic Algorithms

·      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.