Name: Introduction To Master’s Program in Data Science in Bhopal | Henry Harvin
Uploaded: 2023-02-22 15:55:14

Course 1: Python for Data Science
Module 1: Python Basics

In this module, the candidate will learn about Python Basics, which includes understanding Anaconda, IDEs, Git, and Creating and Managing Analytics
- Anaconda - Download & Setup
- IDEs - Jupyter, Spyder, PyCharm
- Git - Setup and Configuration with IDEs
- Creating and Managing Analytics/ ML Projects
Module 2: Python Programming Fundamentals

In this module, the candidate will learn about Data Import and Export and Operators in Python.
- Data import and export
- Operators in Python
Module 3: Python Data Structures

In this module the candidate will learn about the Data Structure of Python, which includes Basic Data Structures and Programming Constructs, Handling Libraries, Numpy, Pandas, and Matplotlib.
- Basic Data Structures & Programming Constructs
- Handling Libraries in Python
- Numpy
- Pandas
- Matplotlib
Module 4: Working with Data in Python

In this module the candidate will learn about Working with Data in Python, which includes Group Summaries, Managing Missing Values, Types of Joins, Merge, Partitioning Data into Train and Test Set, and Scaling of Data.
- Group Summaries
- Managing Missing Values
- Various types of Joins, merge
- Partitioning data into train and test set
- Scaling of Data (useful for Clustering)
Module 5: Working with NumPy Arrays

In this module, the candidate will learn how to Work with NumPy Arrays, which includes an understanding of Attributes, Indexing, Slicing, Reshaping, Joining, and Splitting of Arrays.
- Attributes of arrays
- Indexing of arrays
- Slicing of arrays
- Reshaping of arrays
- Joining and splitting of arrays
Course 2: Data Science with Python
Module 1: Data Science Overview

In these data science classes, you will get an overview of data science, data mining, statistics, and more
- Data Science, Data Mining, Statistics
- Supervised vs Unsupervised Learning
Module 2: Data Analytics and Business Application

In this module learn about data analytics, tools and techniques in analytics, and more
- Analytics Definition and Applications
- Why Analytics and Roles (Application and Roles in various domains..)
- Tools and Techniques in Analytics
Module 3: Python Environment Setup and Essentials

In this module, learn about python environment set up and essentials
- Anaconda - Download & Setup
- IDEs - Jupyter, Spyder, PyCharm
- Git - Setup and Configuration with IDEs
- Creating and Managing Analytics/ ML Projects
Module 4: Mathematical Computing with Python

In this module, learn about mathematical computing with python
- Understanding NumPy Library
- Managing and manipulating data
Module 5: Scientific Computing with Python

In this module, learn about scientific computing with python along with SciPy Library, managing and manipulating data
- Understanding SciPy Library
- Managing and manipulating data
Module 6: Data Manipulation with Pandas

In this module, learn about data manipulation with pandas,group summaries, outliers detection, and more
- Group Summaries
- Crosstab, Pivot and Reshape data
- Managing Missing Values
- Outliers Detection
- Managing indexes in pandas
Module 7: Data Visualization in Python using matplotlib

In this module learn about data visualization in python, graphs, plot parameters, and more
- Selection of Graph
- Libraries (matplotlib, seaborn, plotnine)
- Basic Graphs (histogram, barplot, boxplot, pie, etc)
- Managing plot parameters(size, title, axis, legend, etc)
- Advanced Graphs (correlation, heatmap, mosaic, etc)
- Exporting graphs
Course 3: Machine Learning
In these Data Science classes, we will cover the following

Module 1: Introduction to Analytics
- Introductory Topics to Analytics
  Understanding the need for Analytics in Specific Domain; CRISP Modeling
- Introduction to Data Management
  Properties & Types of Data, Measurement Scale, Basic Statistics on Data
- Basics of Python Programming
  Need for Python, Features of Python, Download, Setup, Installation; Python & Python Studio; Configuration. e.g. Learning to setup Python and share Code
- Data Structures in Python
  Creating and Understanding Basic Data Structures in Python - Vector, List, Matrix, Array, Data Frame & Factors which help in creating data in Python programming
- Data Manipulation & Summarization in Python
  Understanding how data can be summarized in different ways to do Descriptive Analysis which describes features of data
Module 2: Analytical Modeling Overview
- Analytical Modeling
  Understand what is Modeling and how it can be used in Various Domain
- Statistical Tests
  P Value, Z Value, Hypothesis, Null Hypothesis, Alternative Hypothesis, F Test, ANOVA Introduction)
- Linear Regression (Using Python)
  Start of Machine Learning, Develop a Prediction Model for predicting a financial values based on one or more than 1 Independent Variable; Understand the assumptions and measures of goodness of Model, Understand its prediction ability
- Visualization using Graphs
  Creating Graph in Python and understanding which graph to be used when
- Missing Value and Outlier Analysis
  Understanding how missing values & outliers are handled in data summarization & modeling
- Logistic Regression
  Predicting Binary Outcome (Buy or not, Churn or not, Loan Default or not) based on Independent Variables e.g. Predicting Cases for Fraud, Default on Payment etc
Module 3: Clustering & Decision Trees
- Clustering
  Grouping Customers based on characteristics so that they can be target for sale increase
- Decision Trees
  When to use CART & CHAID to create a decision tree based on categorical variables.
- Ensembles (Bagging & Boosting)
  Random Forest, XGBoost: Problems of Decision Tree covered in Random Forest, How group thinking impacts the decisions (from business point of view)
Course 4: Tableau
In these Data Science classes, we will cover the following

Module 1: Introduction to Data Visualization and Power of Tableau

In this module the candidate will learn about Data Visualization, Comparison benefits against reading raw numbers, Real use cases from various business domains, Examples of using Tableau, installing Tableau, Tableau interface, Connecting to Data source, Tableau data types and Data preparation

What is data visualization?
- Comparison and benefits against reading raw numbers
- Real use cases from various business domains
- Some quick and powerful examples using Tableau without going into the technical details of Tableau
- Installing Tableau
- Tableau interface
- Connecting to Data Source
- Tableau data types
- Data preparation
Module 2: Architecture of Tableau

In this module the candidate will learn about Architecture of Tableau, which includes learning of the installation, Desktop Architecture and Interface of Tableau, how to start with Tableau and the ways to share and export the work done in Tableau. The candidate will also be provided with a few Hands-on exercises, which will enhance the understanding of the Tableau

Topics:
- Installation of Tableau
- Desktop Architecture of Tableau
- Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.)
- How to start with Tableau
- The ways to share and export the work done in Tableau
Hands-on Exercise:
- Play with Tableau desktop
- Learn about the interface
- Share and export existing works
Module 3: Working with Metadata and Data Blending

In this module the candidate will learn working with Metadata and Data Blending, which includes understanding Connection to Excel, Cubes and PDFs, Management of the Metadata, preparation of Data, Joins and Unions , and how to deal with NULL Values, Cross- database joining, data extraction etc. The candidate will also be provided with Hands-on Exercises which will enhance the understanding of the candidate, of the topic and its working

Topics:
- Connection to Excel
- Cubes and PDFs
- Management of metadata and extracts
- Data preparation
- Joins (Left, Right, Inner, and Outer) and Union
- Dealing with NULL values, cross-database joining, data extraction, data blending, refresh extraction, incremental extraction, how to build extract, etc.
Hands-on Exercise:
- Connect to Excel sheet to import data
- Use metadata and extracts
- Manage NULL values
- Clean up data before using
- Perform the join techniques
- Execute data blending from multiple sources
Module 4: Creation of Sets

In this module the candidate will learn about Creation Sets, in which you will learn how to Mark, Highlight, Sort, Group, and use Sets, understand what are Constant Sets, Computed Sets, Bins etc. The candidate will also be provided with Hands-on Exercises which will enhance the knowledge of the topics learned

Topics:
- Mark, highlight, sort, group, and use sets (creating and editing sets, IN/OUT, sets in hierarchies)
- Constant sets
- Computed sets, bins, etc.
Hands-on Exercise:
- Use marks to create and edit sets
- Highlight the desired items
- Make groups
- Apply sorting on results
- Make hierarchies among the created sets
Module 5: Working with Filters

In this module the candidate will learn about Filters, how to work with Filters, Filtering Continuous Dates, Dimensions and Measures, create folders in Tableau, sorting in Tableau, Filtering in Tableau and the order of Operations, types of Sorting and Filters. The candidate will also be provided with Hands-on Exercises which will enhance the knowledge of the topics learned

Topics:
- Filters (Addition and removal)
- Filtering continuous dates, dimensions, and measures
- Interactive Filters, marks card, and hierarchies
- How to create folders in Tableau
- Sorting in Tableau
- Types of sorting
- Filtering in Tableau
- Types of filters
- Filtering the order of operations
Hands-on Exercise:
- Use the data set by date/dimensions/measures to add a filter
- Use interactive filter to view the data
- Customize/remove filters to view the result
Module 6: Organizing Data and Visual Analytics

In this module the candidate will learn about Visual Analytics and Organizing Data, in which the candidate will learn about usage of Formatting Pane and how to Format Data using Labels and Tooltips, Edit Axes and annotations, K-means cluster analysis, Trend and Reference lines, Visual Analytics and Forecasting, Confidence Interval, Reference lines and Bands. The candidate will also be provided with Hands-on Exercises which will enhance the knowledge of the topics learned and its working

Topics:
- Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste
- Formatting data using labels and tooltips
- Edit axes and annotations
- K-means cluster analysis
- Trend and reference lines
- Visual analytics in Tableau
- Forecasting, confidence interval, reference lines, and bands
Hands-on Exercise:
- Apply labels and tooltips to graphs, annotations, edit axes’ attributes
- Set the reference line
- Perform k-means cluster analysis on the given dataset
Module 7: Working with Mapping Preview

In this module the candidate will learn about Mapping Preview, which includes Working on Coordinate points and the background image, Plotting Longitude and Latitude, editing unrecognized Location, Customizing Geocoding, Maps etc., Map visualization, Custom territories, Map box WMS map and creating Map projects in Tableau and Dual Axes Maps. The candidate will also be provided with Hands-on Exercises which will enhance the knowledge of the topics learned and its working

Topics:
- Working on coordinate points
- Plotting longitude and latitude
- Editing unrecognized locations
- Customizing geocoding, polygon maps, WMS: web mapping services
- Working on the background image, including add image
- Plotting points on images and generating coordinates from them
- Map visualization, custom territories, map box, WMS map
- How to create map projects in Tableau
- Creating dual axes maps and editing locations
Hands-on Exercise:
- Plot longitude and latitude on a geo map
- Edit locations on the geo map
- Custom geocoding
- Use images of the map and plot points
- Find coordinates
- Create a polygon map
- Use WMS
Module 8: Working with Calculations and Expressions

In this module the candidate will learn about Calculations and functions in Tableau, LOD expressions, aggregation and Replication with LOD expressions, Nested LOD expressions, Levels of details, Quick table calculations, Creation of calculated fields and Predefined calculations and validation
- Calculation syntax and functions in Tableau
- Various types of calculations, including Table, String, Date, Aggregate, Logic, and Number
- LOD expressions, including concept and syntax
- Aggregation and replication with LOD expressions
- Nested LOD expressions
- Levels of details: fixed level, lower level, and higher level
- Quick table calculations
- The creation of calculated fields
- Predefined calculations
- How to validate
Module 9: Working with Parameters Preview

In this module the candidate will learn about Parameters, creating and its calculations, using Parameters with calculations Column and chart selection parameters, usage of Parameters in filter sessions, calculated fields and in reference line. The candidate will also be provided with Hands-on Exercises which will enhance the knowledge of the topics learned and its working

Topics:
- Creating parameters
- Parameters in calculations
- Using parameters with filters
- Column selection parameters
- Chart selection parameters
- How to use parameters in the filter session
- How to use parameters in calculated fields
- How to use parameters in the reference line
Hands-on Exercise:
- Creating new parameters to apply on a filter
- Passing parameters to filters to select columns
- Passing parameters to filters to select charts
Module 10: Charts and Graphs

In this topic the candidate will learn about Charts and Graphs, such as, Dual axes graphs, Histograms, Single and dual axes, Box plot, Charts; motion, pie , bar etc., Maps: tree and heat maps, Market Based Analysis and text and highlighted table. The candidate will also be provided with Hands-on Exercises which will enhance the knowledge of the topics learned and its working

Topics:
- Dual axes graphs
- Histograms
- Single and dual axes
- Box plot
- Charts: motion, Pareto, funnel, pie, bar, line, bubble, bullet, scatter, and waterfall charts
- Maps: tree and heat maps
- Market basket analysis (MBA)
- Using Show me
- Text table and highlighted table
Hands-on Exercise:
- Plot a histogram, tree map, heat map, funnel chart, and more using the given dataset
- Perform market basket analysis (MBA) on the same dataset
Module 11: Dashboards and Stories

In this module the candidate will learn about Dashboard, which includes topics such as, building and formatting, best practices for making creative dashboard, creating stories, updating the story points, Adding annotations with descriptions, Highlight actions, URL actions, types of Joins, Tableau field types, Saving as well as publishing data source, difference between Live and Extract connection and various file types. The candidate will also be provided with Hands-on Exercises which will enhance the knowledge of the topics learned and its working.

Topics:
- Building and formatting a dashboard using size, objects, views, filters, and legends
- Best practices for making creative as well as interactive dashboards using the actions
- Creating stories, including the intro of story points
- Creating as well as updating the story points
- Adding catchy visuals in stories
- Adding annotations with descriptions; dashboards and stories
- What is a dashboard?
- Highlight actions, URL actions, and filter actions
- Selecting and clearing values
- Best practices to create dashboards
- Dashboard examples; using Tableau workspace and Tableau interface
- Learning about Tableau joins
- Types of joins
- Tableau field types
- Saving as well as publishing data source
- Live vs extract connection
- Various file types
Hands-on Exercise
- Create a Tableau dashboard view, include legends, objects, and filters
- Make the dashboard interactive
- Use visual effects, annotations, and descriptions to create and edit a story
Module 12: Tableau Prep

In this module the candidate will learn about Tableau prep, how Tableau prep helps combine join, sharp, and clean data for analysis, creation of smart examples with Tableau prep, and how data preparation is made simple and accessible, integrating Tableau prep with Tableau analytical workflow and a clear understanding the seamless process from data preparation to analysis with Tableau prep.
- Introduction to Tableau Prep
- How Tableau Prep helps quickly combine join, shape, and clean data for analysis
- Creation of smart examples with Tableau Prep
- Getting deeper insights into the data with great visual experience
- Making data preparation simpler and accessible
- Integrating Tableau Prep with Tableau analytical workflow
- Understanding the seamless process from data preparation to analysis with Tableau Prep
Module 13: Integration of Tableau with R and Hadoop

In this module the candidate will learn about Tableau with R and Hadoop, which includes Application and use cases of R, Deploying R on the Tableau platform, R functions in Tableau and The integration of Tableau with Hadoop. The candidate will also be provided with Hands-on Exercises which will enhance the knowledge of the topics learned and its working

Topics:
- Introduction to R language
- Applications and use cases of R
- Deploying R on the Tableau platform
- Learning R functions in Tableau
- The integration of Tableau with Hadoop
  Hands-on Exercise:
- Deploy R on Tableau
- Create a line graph using R interface
- Connect Tableau with Hadoop to extract data
Complementary Module 1: Soft Skills Development
- Business Communication
- Preparation for the Interview
- Presentation Skills
Complimentary Module 2: Resume Writing

Resume Writing

Master’s Program in Data Science in Bhopal

11 Nov 2024

104 Hours

11 Nov 2024

104 Hours

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104 Hours of Instructor-Led Sessions

35 Hours of Live Interactive Doubt Solving Sessions

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192 Hours of Self-Paced Learning

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Job-Ready Portfolio of 10 Capstone Projects

219 Auto-Graded Assessments

20+ Industry Case Studies

156 Guided Hands-On Exercises

10+ Assignments and Mini Projects

2 Mock Interviews and 2 Hackathons

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Master’s Program in Data Science

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Curriculum For Master’s Program in Data Science in Bhopal

Course 1: Python for Data Science

Course 2: Data Science with Python

Course 3: Machine Learning

Course 4: Tableau

Complementary Module 1: Soft Skills Development

Complimentary Module 2: Resume Writing

Admission Closes On 11 Nov 2024

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What you'll Learn in this course

Python Programming Fundamentals

Manipulation of Large Datasets

Working with NumPy Arrays

Utilizing Python for Data Analysis

Methods of Exporting Tableau Work

Role of Filters in Tableau

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