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  • Modeling effective and complex business critical data using drag and drop feature to represent it using interactive dashboards for powerful visualization across multiple Industry verticals for better understanding
  • Writing adhoc queries and build structured and unstructured reports including dynamic sorting and filter options
  • Powerful range of statistical techniques with business applications of machine learning algorithms
  • Knowledge exchange & experiences sharing with – over 2 million users and 1200 libraries available
  • Python frameworks and methods that can be used for routine operations of descriptive and inferential statistics
  • Matplotlib is the most widely used base library in Python forgeneral visualization including how to use this library for generating a box plot, bar chart, histogram, and pie diagram
  • Experience sharing by industry experts form different business sectors 
  • Important skills for Fresher’s and working professionals to enhance their careers
  • Membership to KJA–KAALP Job Assistance program

120+ hours

Online sessions

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 advanced analytics using Tableau, R 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.
R is one of the most leading and effective programming languages used for data analysis and statistical modelling. This R training course provides you in depth learning in understanding concepts of data analytics as well as application of R language in data manipulation, calculation and graphical display. In addition this course covers statistical functions and techniques and machine learning which will enable you to learn how to user R features such as random forest and decision trees for applications to learn from data and arrive at decisions with minimal human intervention.
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.
  • Writing R codes for Data manipulations, Statistical functions, Complex calculations & graphical display of outputs.
  • Mastering various techniques like Assign variables, analyze vectors, matrices, factors, Lists & Data frames , Functions to name a few
  • Basic and advanced statistical functions like, Testing of Hypothesis, Control Charts, Cluster Analysis, Regression Analysis
  • Machine learning Techniques such as - Random Forests, Decision Trees, Artificial Neural Networks, Support Vector Machines, K-Nearest Neighbor , Ensemble, Gradient Boosting.
  • 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.
  • Advanced statistical concepts including regression, forecasting & clustering techniques.
  • Machine learning techniques-both supervised and unsupervised learning which helps you develop real time forecasting models to solve business problems.
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 advanced analytics using Tableau, R 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.

 

R is one of the most leading and effective programming languages used for data analysis and statistical modelling. This R training course provides you in depth learning in understanding concepts of data analytics as well as application of R language in data manipulation, calculation and graphical display. In addition this course covers statistical functions and techniques and machine learning which will enable you to learn how to user R features such as random forest and decision trees for applications to learn from data and arrive at decisions with minimal human intervention.

 

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

  • 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.
  • Writing R codes for Data manipulations, Statistical functions, Complex calculations & graphical display of outputs.
  • Mastering various techniques like Assign variables, analyze vectors, matrices, factors, Lists & Data frames , Functions to name a few
  • Basic and advanced statistical functions like, Testing of Hypothesis, Control Charts, Cluster Analysis, Regression Analysis
  • Machine learning Techniques such as - Random Forests, Decision Trees, Artificial Neural Networks, Support Vector Machines, K-Nearest Neighbor , Ensemble, Gradient Boosting.
  • 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.
  • Advanced statistical concepts including regression, forecasting & clustering techniques.
  • Machine learning techniques-both supervised and unsupervised learning which helps you develop real time forecasting models to solve business problems.

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

R Curriculum

Get an overview of OOPS concepts through R programming
  • R Environment
  • Using R Interactively
  • R commands, case sensitive etc.
Learn how to use operations applicable to simple manipulations; numbers & vectors.
  • Vectors & Assignment
  • Vector Arithmetic
  • Generating regular sequences
  • Logical vectors & Index vectors
  • Missing values
  • Character values
  • Array indexing
  • Index matrices
  • Forming partitioned matrices
  • Frequency tables from factors
  • The concatenation function with arrays
Learn how to use Lists & Data Frames
  • Constructing & modifying Lists
  • Working with data frames
  • Managing search path

Know how Open database connectivity is used in R

Reading data from files & different sources

The chapter gives an overview of introduction various theoretical distribution of data
  • Checking normality/Normal distribution
  • Frequency distributions & Contingency Tables
  • Binning
  • Binomial distributions
  • IQR & Empirical rule for symmetric distributions
  • Probability distributions
  • Proportion tables & Confidence Interval
This chapter covers various parametric tests used in statistics to validate Hypothesis
  • T-test
  • F-test
  • Sampling distributions
  • Chi-Square
  • ANOVA
  • Correlation

The below topics describes various visualization techniques to show the relationship of the variables 

  • Bar-plot
  • Pie chart
  • Stacked Bar chart
  • Histogram
  • Line chart

The below mentioned topics cover unsupervised machine learning techniques

  • Cluster Analysis
  • Principal Component Analysis
  • Factor Analysis
Learn how to use supervised machine learning algorithms Regression Analysis
  • Simple Linear Regression
  • Multiple Linear Regression
  • Logistic Regression

Learn how to build robust machine learning models when data is non linear

  • Random Forests
  • Decision Trees
  • Support Vector Machines
  • K-Nearest Neighbor
  • Ensemble
  • Gradient Boosting
  • Regularization
  •  
This topic introduces to the backbone of artificial intelligence including architecture of neural networks
  • Artificial Neural Networks
 

In this chapter learn easy to use interactive web based dashboards with R

  • R-Shiny

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

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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