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120+ hours

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Practice Datasets

& Case Studies

40+

Quizzes

Learn Anywhere

Anytime

Industry

Relevant

Webinars by

Industry Experts

Certification

by Kaalp

120+ hours Online Sessions

Practice Datasets & Case Studies

40+ Quizzes

Learn Anywhere Anytime

Industry Relevant

Webinars by Industry Experts

Certification by Kaalp

This course will be useful for business professionals from any industry sector and will help learners to develop skills in visualization and business analytics using Tableau and Python.
Tableau is used as a data science and business intelligence application software & is known for its flexible intuitive & interactive visualization in order to get effective business insights.Tableau software has a real-time data analytics capabilities and supports cloud as well.The benefits of using Tableau software are its flexibility to explore data with its advanced visualization feature, drill up or down across multiple dimensions of business that could provide effective measures so that monitoring & controlling could be done after carefully studying the trends & patterns.
Python is a general-purpose programming language which can be used for a wide spectrum of applications such as business data analysis, artificial intelligence and scientific computing. Python is built on OOPS (Object Oriented Programming) and caters to a wide spectrum of analytical needs ranging from data access, data wrangling, munging, dashboards, visualizations & a comprehensive exposure to state of the art Machine Learning and Artificial Intelligence techniques.
This course on Tableau and Python will enable business users to master advanced visualization and statistical analysis skills.
Students from B.Sc, B.Com, BBA, Engineering and MBA disciplines who are looking for better employment opportunities. ​
Research scholars, academicians and scientists who wish use R for quantitative analysis and research projects. ​
Corporate professionals who wish to learn business analytics or pursue careers in data science. ​
Software and ITES professionals who wish to use R for data analysis and statistical modelling with advanced visualization and machine learning techniques. ​
  • Build capacity to create Interactive dashboard reporting critical for any business
  • Master skills to carry out Intuitive data visualization for effective decision making
  • Able to perfectly publish the analyzed data either locally or on the cloud
  • Carry out Visual analytics comprising of Drag and drop calculations, Analytics, reference and trend lines, interactive tables, geography search and lasso selection
  • Coding in Python along with exploring the various functionalities; This course will also introduce you to different Python IDE's like Spyder, Jupyter Notebook
  • Python operators, conditional statements, loop concepts data structures, built in operators & functions.
  • Use of Python in Data Science applications by using Numpy, Pandas and visualizations using matplotlib
  • Inferential statistics, various steps involved in hypothesis framing and testing and skills in p- value approach to hypothesis testing.
Elementary knowledge of mathematics or statistics and basic programming skills will be an added advantage to those who attend this course. Exposure to SQL commands or Excel capabilities will be of added advantage.

Description

This course will be useful for business professionals from any industry sector and will help learners to develop skills in visualization and business analytics using Tableau and Python.

 

Tableau is used as a data science and business intelligence application software & is known for its flexible intuitive & interactive visualization in order to get effective business insights.Tableau software has a real-time data analytics capabilities and supports cloud as well.The benefits of using Tableau software are its flexibility to explore data with its advanced visualization feature, drill up or down across multiple dimensions of business that could provide effective measures so that monitoring & controlling could be done after carefully studying the trends & patterns.

 

Python is a general-purpose programming language which can be used for a wide spectrum of applications such as business data analysis, artificial intelligence and scientific computing. Python is built on OOPS (Object Oriented Programming) and caters to a wide spectrum of analytical needs ranging from data access, data wrangling, munging, dashboards, visualizations & a comprehensive exposure to state of the art Machine Learning and Artificial Intelligence techniques.

 

This course on Tableau and Python will enable business users to master advanced visualization and statistical analysis skills.

Who Should Attend?

Students from B.Sc, B.Com, BBA, Engineering and MBA disciplines who are looking for better employment opportunities. ​

Research scholars, academicians and scientists who wish use R for quantitative analysis and research projects. ​

Corporate professionals who wish to learn business analytics or pursue careers in data science. ​

Software and ITES professionals who wish to use R for data analysis and statistical modelling with advanced visualization and machine learning techniques. ​

WHAT YOU WILL LEARN

PREREQUISITES

Elementary knowledge of mathematics or statistics and basic programming skills will be an added advantage to those who attend this course. Exposure to SQL commands or Excel capabilities will be of added advantage.

Curriculum

Tableau Curriculum

This section gives a high level overview of Business Intelligence and its application.
  • Dashboards,
  • Reporting
  • Visualizations
  • Data Preparation
  • Modern Data Warehousing
  • Self-Service Business Analytics
  • Big Data & Advanced Analytics
  • Planning & Forecasting Systems
This chapters covers an introduction to Tableau and its salient features.
  • About Tableau
  • Why Tableau?
  • Tableau reporting architecture
  • Tableau Products
This section covers the different data types in Tableau.
  • Measures & Dimensions
  • Continuous & Discrete data
The following topics covers various operations in Tableau used for creating dashboards.
  • Dashboards
  • Reporting
  • Visualizations
  • Filter
  • Sort
  • Colours
  • Size
  • Label
  • Detail
Advanced features for creating dashboards in Tableau.  
  • Options In Tableau
  • Worksheets and Dashboards
  • Customizing Filters
  • Filter Actions & Functionalities
  • Row Shelf & Column Shelf
  • Marks cards
  • Tooltip, Path, Sets
  • Creating Parameters & Using Parameters
  • Groups
  • Calculated Columns
The chapters broadly details on  the various types of graphs used to summarize the data in Tableau.
  • Pie Charts , Bar Charts, Stacked bar charts, Dual lines
  • Highlight tables
  • Heat maps, Symbol maps, Tree maps, Filled Maps
  • Circle views & Histogram
  • Area Charts – (Continues & Discrete)
  • Combination charts, Scatter Plots & Box Plots
  • Gantt charts, Bullet Charts, Packed bubble charts
This section discusses the various formatting and data munging tools in Tableau.
  • Building Dashboards
  • Trend Lines & Forecasting
  • Reference Bands & Lines
  • Show Missing Values & Handling Null Values
  • Legend Highlighting & Layout Containers
  • Visually Grouping Data
  • Table Calculations
  • Computing Totals
  • Formatting & Annotating
  • Tiling & floating dashboards
  • Tableau Server & Sharing Server views

Python Curriculum

This chapter provides a brief overview about Python programming language
  • History, Why Python?
In this module you will learn to install Python and set it up.
  • Environment Setup & Variables
  • Getting Python & Setting Path
You will learn the characteristics & salient features of Python
  • My first Python program
  • Identifiers
  • Reserved Words
  • Lines and Indentation
  • Command Line Arguments
In this chapter you will get introduced to a detailed study on various operations performed in Python
  • Arithmetic Operations & Number Methods
  • Accessing a value from Strings
  • Updating Strings & String Methods
  • Comparison & Assignment Operators
Understand how Lists, Tuples and Dictionaries are used in Python
  • Accessing, Updating & Deleting elements
This chapter covers the different types of loops & conditional statements
  • IF, ELSE statements & Nested IF statements
  • While, For, Break, Continue etc.

This chapter describes the various built-in functions & generators

In this chapter you will learn how data transformation is performed in Python
  • List processing & Conversions from 1 type to other

This topic covers simple & advanced regular expression to handle string data

This chapter covers various data accessing features in Python
  • Import Statement
  • Executing and Locating Modules
  • Exception Handling

This chapter provides various summary statistics like mean, median & mode with helps in data compression

In this chapter you will learn high level overview of the important libraries used in Data Science

This chapter describes step by step procedures to construct validate & test hypothesis needed for statistical analysis
  • Hypothesis Testing
  • Confidence Intervals
  • T-Test
  • Contingency Tables
  • Cross Tabs
  • Chi-Square significance test
  • Correlation
This chapter covers the different visualization techniques in Python
  • Bar
  • Line
  • Pie
  • Histogram

This chapter broadly describes various data munging & wrangling techniques using Pandas library

  • Importing and Exporting flat files
  • Operations on Pandas Data frames
  • Summary Statistics
  • Sub setting the Data frames
  • Handling missing values in the Data frames

This chapter introduces you to different Numpy libraries useful for handling quantitative data

The chapter details on high level overview on different machine learning techniques
  • Machine Learning with Supervised
  • Machine Learning with Unsupervised
  • Introduction to Deep Learning
The chapter describes various techniques used to segment data
  • Hierarchical Clustering
  • K-means
This chapter discusses the predictive modeling algorithms used to model multi-variate relationships in the data using Python
  • Simple Linear &  Multiple Linear Regression
  • Logistic Regression
The chapter explains three important algorithms to identify various profiles used in decision making
  • Decision trees
  • Random forest
  • Gradient Boosting

The chapter discusses the different techniques to model historical time series data using Python.

Preview

Level 1

Level 2

Level 1

Level 2

Testimonials

I completed the Introduction to Business Analytics training course conducted by Kaalp Consulting. The course material was easy to understand and I got hands on experience to practice the software commands. The instructors had good knowledge and gave us practical tips on application of analytics in problem solving. I was very satisfied with the training course.
BBA Student, Bangalore
Kaalp Consulting conducted a training program in R Programming for our institute. The course material is very extensive and well designed. The instructors were working professionals who have good experience in using R in business analytics. They explained the topics extremely well and gave us a good practical understanding on how R is used in real life scenarios. We very satisfied with the course and highly recommend it to others.
Lecturer and Trainer, TN.
Kaalp Consulting conducted a course on Introduction to Business Analytics with SQL, Excel and Tableau for our B.Com and BBA students. We were very pleased with the manner in which the courses were presented by their instructors who are industry professionals. The instructors explained the topics very well and shared their practical experience on how the business analytics is applied in real life. The course content is well designed and the program was managed very professionally by Kaalp Consulting. We have no hesitation in recommending them to any institution or individual who are interested in joining their training courses.
Dr. Geethu, KJC, Bangalore

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Applications of TABLEAU and Python in the Industry

Academia

Finance

Banking

Healthcare

Manufacturing

E-commerce

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