What are the benefits of Master's in Data Science?

A data science master's program helps you in acquiring skills to collect, manage and analyze data, its types, trends, and deliver the results accordingly. This advanced skill set is spread out throughout the M. Sc.

Who is eligible for Master's Program in Data Science?

Candidates wanting to pursue Data Science should have completed their graduation or equivalent preferably in Science or Computer Science from a recognized university.

Is Python enough for Data Science?

Python is a high-level, general-purpose programming language known for its intuitive syntax that mimics natural language. You can use Python code for a wide variety of tasks, but three popular applications include: Data science and data analysis. Web application development.

Is Data Scientist an IT job?

A Data Scientist job is most definitely an IT-enabled job. Every IT professional is a domain expert responsible for handling a particular technical aspect of their organization.

What qualification is required for Data Scientist?

A great way to get started in Data Science is to get a bachelor's degree in a relevant field such as data science, statistics, or computer science.

Master’s Program in Data Science

Q: What are the benefits of Master's in Data Science?

A data science master&#39;s program helps you in acquiring skills to collect, manage and analyze data, its types, trends, and deliver the results accordingly. This advanced skill set is spread out throughout the M. Sc.

Q: Can a Data Scientist get job easily?

Getting a job as a data scientist isn&#39;t easy and you need to be very persistent to succeed in this field. One does not become a data scientist overnight. It takes a lot of learning, experience, and understanding of the concepts, especially if you want to start a career in data science as a fresher.

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
- Quiz Module 1
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
- Quiz Module 2
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
- Quiz Module 3
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)
- Quiz Module 4
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
- Quiz Module 5
Module 6: Data Science Overview
In this module, get an overview of data science, data mining, statistics, and more
- Data Science, Data Mining, Statistics
- Supervised vs Unsupervised Learning
- Quiz Module 6
Module 7: 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
- Quiz Module 7
Module 8: Mathematical Computing with Python
In this module, learn about mathematical computing with python
- Understanding NumPy Library
- Managing and manipulating data
- Quiz Module 8
Module 9: 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
- Quiz Module 9
Module 10: 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
- Quiz Module 10
Module 11: 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
- Quiz Module 11
Module 12: 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
- Quiz Module 12
Module 13: 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
- Quiz Module 13
Module 14: 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)
- Quiz Module 14
Module 15: 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
- 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
- Quiz Module 15
Module 16: 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
- 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
- Play with Tableau desktop
- Learn about the interface
- Share and export existing works
- Quiz Module 16
Module 17: 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
- 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
- Quiz Module 17
Module 18: 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
- 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
- Quiz Module 18
Module 19: 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
- 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
- Quiz Module 19
Module 20: 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
- 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
- Quiz Module 20
Module 21: 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
- How to create map projects in Tableau
- Hands-on Exercise:
- Use images of the map and plot points
- Custom geocoding
- Edit locations on the geo map
- Plot longitude and latitude on a geo map
- 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
- Creating dual axes maps and editing locations
- Use WMS
- Create a polygon map
- Find coordinates
- Quiz Module 21
Module 22: 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
- Quiz Module 22
Module 23: 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
- 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
- Quiz Module 23
Module 24: 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
- 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
- Quiz Module 24
Module 25: 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.
- 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
- Quiz Module 25
Module 26: 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
- Quiz Module 26
Module 27: 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
- 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
- Quiz Module 27

Know the complete offerings of our Post Graduate Program in Master’s Program in Data Science

Is Master's Program in Data Science worth it?

In addition to getting an edge during the job application and interview process, pursuing a high-quality, accredited Master of Data Science degree is worth it if data is transforming your industry, you want to transition careers, you're looking to boost your salary, or you simply just love gaining knowledge.

Can a Data Scientist get job easily?

Getting a job as a data scientist isn't easy and you need to be very persistent to succeed in this field. One does not become a data scientist overnight. It takes a lot of learning, experience, and understanding of the concepts, especially if you want to start a career in data science as a fresher.