Business Analytics Using Python Training Course Online Course

Business Analytics using Python Training Course

All levels
English

Course Description

Python is the most popular language used in the field of Business Analytics. Even industry giants like Google and Netflix use it to generate insights and build better products. It can be quickly learnt and is versatile, making life easy for people who work with tonnes of data. BIT’s comprehensive training in Business Analytics using Python is tailored to train you on all aspects of Business Analytics; starting from exploratory data analysis, statistical and quantitative analysis, testing analytics models and forecasting through predictive modelling using Python. This Business Analytics courseencompasses basic statistical concepts to advanced analytics and predictive modeling techniques. You will learn all the skills required for a promising career as a Business Analyst and solve real-world business problems.

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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
Use basic statistical concepts on multiple types of data to prepare reports.
Optimize business situations that involve whole numbers, take decisions that involve multiple input variables to predict...
Compute correlation between data points in a time series.
Test hypothesis for experiments involving different treatments and Identify the source of differences to pinpoint which...
Solve complex problems with Python - the most essential tools for Finance and analytics-driven companies.
Model decisions under a variety of future uncertain states, depending on the decision maker’s proneness or aversion to r...
Compute the regression model for time series data that has a correlation within itself.

Covering Topics

Lecture-1 Introduction to Business Analytics

Lecture-2 Understanding Data

Lecture-3 Business Analytics, Business Intelligence and Data Mining

Lecture-4 Social Media Analytics

Lecture-5 Python for Analytics

Lecture-6 Python Basics

Lecture-7 Python Programming

Lecture-8 FILE Input/Output

Lecture-9 Pandas

Lecture-10 numpy

Lecture-11 Basic Statistical Concepts and Types of Data

Lecture-12 One-way Analysis of Variance

Lecture-13 Correlation

Lecture-14 Linear Regression

Lecture-15 Time series

Lecture-16 Linear Programming

Lecture-17 Linear Programming – Covering Models

Lecture-18 Text Mining

Lecture-19 Text mining modeling using NLTK

Case Studies

Curriculum

  Lecture-1 Introduction to Business Analytics
Live Lecture 
·      Business Analytics

·      Describe the evolution of analytics

·      Describe the differences between analytics and analysis

·      Explain the concept of insights

·      Describe the broad types of business analytics

·      Describe how organisations benefit from using analytics

·      Practical Exercise

  Lecture-2 Understanding Data
Live Lecture 
·      Importance of data in business analytics

·      Differences between data, information and knowledge

·      The various stages that an organization goes through in terms of data maturity

·      Practical Exercise

  Lecture-3 Business Analytics, Business Intelligence and Data Mining
Live Lecture 
·      Differences between Business Analytics and Business Intelligence

·      Describe the two major components within Business Analytics and Business Intelligence

·      Data Mining technique helps both Business Intelligence and Business Analytics

·      Analytical Decision-Making Process

·      Analysing Business Problems

·      Practical Exercise

  Lecture-4 Social Media Analytics
Live Lecture 
·      Capabilities social media analytics

·      Common goals of social media analytics

·      Practical Exercise

  Lecture-5 Python for Analytics
Live Lecture 
·      Introduction to Python Installation

·      Jupyter Notebook Introduction

·      Practical Exercise

  Lecture-6 Python Basics
Live Lecture 
·      What is Python?

·      Progress of Python

·      Success of Python

·      Programming Model of Python

·      Python Programming Features

·      Commands for common tasks and control

·      Essential Python programming concepts & language mechanics

·      Python Installation

·      Introduction to Python using Jupyter Notebook

·      Simple Input/Output

·      Basic Data Types

·      Control Structures

·      Arithmetic Operators

·      Logical Operators

·      Practical Exercise

  Lecture-7 Python Programming
Live Lecture 
·      Strings,

·      Lists

·      Tuples

·      Dictionaries

·      Functions

·      Parameters

·      Arguments

·      Recursion

·      Data Processing using Pandas and Nampy

·      Introduction to Modules & Packages

·      Generators

·      Errors & Exception Handling

·      Practical Exercise

  Lecture-8 FILE Input/Output
Live Lecture 
·      Path and Directory

·      File Operations

·      Reading and Writing to Files

·      Advance File I/O

·      Practical Exercise

  Lecture-9 Pandas
Live Lecture 
·      Pandas Introduction

·      Series, Data Frames and csvs

·      Data from urls

·      Describing Data with Pandas

·      Selecting and Viewing Data with Pandas

·      Selecting and Viewing Data with Pandas Part 2

·      Manipulating Data

·      Manipulating Data 2

·      Manipulating Data 3

·      Practical Exercise

  Lecture-10 numpy
Live Lecture 
·      Mathematical Computing with Python (numpy)

·      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

·      Comparison Operators

·      Sorting Arrays

·      Turn Images Into numpy Arrays

·      Practical Exercise

  Lecture-11 Basic Statistical Concepts and Types of Data
Live Lecture 
·      Statistics and its use in business

·      Types of data

·      Basic statistical concepts

·      Various techniques for sampling

·      Frequency distributions

·      Various measures of central tendency

·      Different measures of dispersion

·      Different measures of shape

·      Practical Exercise

  Lecture-12 One-way Analysis of Variance
Live Lecture 
·      Explain the concept of ANOVA

·      Calculate ANOVA using Python

·      Test a hypothesis using ANOVA

·      Practical Exercise

  Lecture-13 Correlation
Live Lecture 
·      Statistical relationships

·      Understand the measure of correlation

·      Correlation between two datasets using Python

·      Concepts of correlation versus causation

·      Practical Exercise

  Lecture-14 Linear Regression
Live Lecture 
·      Two data series using linear regression

·      To forecast values using linear regression in Python

·      K-Means Clustering

·      What is clustering?

·      K-Means Clustering using python

·      NbClust

·      Practical Exercise

  Lecture-15 Time series
Live Lecture 
·      Introduction to time series data

·      Time series forecasting using Moving Average

·      Time series forecasting using Naïve forecasting

·      Practical Exercise

  Lecture-16 Linear Programming
Live Lecture 
·      Explain the concept of linearity

·      Describe linear programming

·      Formulate a linear programming problem

·      Linear Programming – Allocation Models

·      Describe allocation models in linear programming

·      Solve allocation model problems in linear programming using Python

·      Practical Exercise

  Lecture-17 Linear Programming – Covering Models
Live Lecture 
·      Describe covering models in linear programming

·      Solve covering model problems in linear programming using Python

·      Practical Exercise

  Lecture-18 Text Mining
Live Lecture 
·      The concepts of text-mining

·      Use cases

·      Text Mining Algorithms

·      Quantifying text

·      TF-IDF

·      Beyond TF-IDF

·      Data Mining vs. Text Mining

·      Text Mining and Text Characteristics

·      Predictive Text Analytics

·      Text Mining Problems

·      Prediction & Evaluation

·      Python as a Data Science Platform

·      Practical Exercise

  Lecture-19 Text mining modeling using NLTK
Live Lecture 
·      Text Corpus

·      Sentence Tokenization

·      Word Tokenization

·      Removing special Characters

·      Expanding contractions

·      Removing Stopwords

·      Correcting words: repeated characters

·      Stemming & lemmatization

·      Part of Speech Tagging

·      Feature Extraction

·      Bag of words model

·      TF-IDF model

·      Text classification problem

·      Building a classifier using support vector machine

·      Practical Exercise

  Case Studies

Frequently Asked Questions

basic understanding of Computer Programming Languages. A basic understanding of statistics

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.

Ans: 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.