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Computer Science · PG program

Post Graduate Program inData Analytics and Data Science

Our data science course syllabus includes training in data analysis software, machine learning and advanced mathematics. Work on industry-relevant data sets and build skills to help you get your dream job.

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Duration
24 weeks
Content
207 hours
Courses
9
Projects
18
Mode
Online
Overview

What you'll
be able to do.

As data and statistics run operations across all industries, data scientists with a practical understanding of programming, statistics, and machine learning algorithms are in demand. This program is designed to give engineers the skills they need to secure job roles in the industry.

By enrolling in this program, you will

  • Understand the mathematics behind different machine learning algorithms
  • Learn about the analysis and visualisation of data using Excel and Tableau
  • Explore SQL extensively, and understand its application in data science
  • Learn about the mathematics and statistics involved in data sciences
  • Gain extensive hands-on experience in the use of Python, Power BI, Tableau, and Anaconda.

Why this program

  • You will gain an in-depth understanding of data analytics, data science, machine learning, programming, and deep learning in order to become an expert in the domain.
  • You will be exposed to modern trends and standard practices followed in the industry.
  • You will extensively work with software tools like Python, Power BI, Tableau, and Anaconda to give you the job ready skills needed to thrive in the industry.

After completing this program, you will have the skills needed to apply for the following job roles:

  • Data Scientist
  • Machine Learning Engineer
  • Data Analyst
  • Business Analyst
  • Data Engineer

You can also pursue higher education in the following domains:

  • Masters in Data Science
  • Masters in Artificial Intelligence
  • Masters in Data Science with a Specialisation in Data Analytics
Tools you'll use
PythonMySQLAnacondaTableauSQLiteMS ExcelPower BIJupyter Notebook
Curriculum

2 tracks to choose from,
9 courses each.

Get the full syllabus ↓
  1. 01Core and Advanced Python Programming12 modules
    1. Introduction to Python, Python BasicsW01
      • Features and uses of Python
      • Program execution
      • Installation of IDE
      • Identifiers and keywords
      • Types of comments
      • Data types
      • Variables
      • Arithmetic operators
      • Assignment operators
      • Input and print statements
    2. Strings, Decision Control StatementsW02
      • Definition of string
      • Operations accessing string elements
      • Relational operators
      • Logical operators
      • Conditional expressions
      • If, If..else, If..elif
    3. Repetition Statements and Console Input-OutputW03
      • Use of while and for
      • Break and continue
      • Pass and else statements
      • Formatted input and output
    4. Lists, Tuples, Sets, DictionaryW04
      • Use of while and for
      • Break and continue
      • Pass and else statements
      • Formatted input and output
    5. Functions and Recursion, Functional Programming and Lambda FunctionsW05
      • Defining a function
      • Types of arguments
      • Global and local variables
      • Functions as arguments
      • Implementing Lambda functions
      • Map, Reduce, and Filter functions
    6. File Input-Output and ModulesW06
      • Read-write operations
        • With the keyword
        • File opening modes
        • Moving within a file
      • Serialization
        • File and directory operations
      • Importing a module
        • Variations of import
        • Third-party packages
    7. Classes and ObjectsW07
      • Class variables
      • Methods
      • Operator overloading
      • Reuse
      • Containership
      • Inheritance
    8. Exception Handling, Iterators and GeneratorsW08
      • Iterables and iterators
      • Syntax errors and exceptions for:
        • try-except
        • else
        • finally blocks
    9. Data Analysis with PandasW09
      • Installing Pandas
      • Loading files
        • CSV files
        • JSON files
      • Dataframes
    10. Numeric and Scientific Computing using NumPyW10
      • NumPy: Introduction
      • OpenCV
      • Images and NumPy Arrays
    11. Graphical User Interfaces with TkinterW11
      • Introduction to Tkinter
      • Setting up a GUI with widgets
      • Connecting GUI widgets with callback functions
    12. Interacting with DatabasesW12
      • SQLite: Introduction
      • Connecting and inserting data to SQLite via Python
      • Selecting, deleting, and updating SQLite records
  2. 02SQL for Data Science12 modules
    1. IntroductionW01

      In this week, students will learn about

      • Data Science: Introduction
      • Data Science Applications
      • Why SQL is required for Data Science
      • Database Management System (DBMS)
      • Relational Database Management System (RDBMS)
      • Basic terminology in RDBMS
      • Data Constraints
      • Entity Relationship Model
      • What SQL is
      • Categories of SQL Commands
      • Hands-on execution of simple SQL statements on RDBMS tool
    2. Database Creation and ManipulationW02

      In this week, students will learn about

      • Detailed SQL Data types
      • Creating databases
      • Create Tables
      • Using Constraints
      • Inserting Table
      • Altering Table structure
      • Dropping Database and Table
      • Deleting and Updating
      • Hands-on importing of sample database schema
    3. Database SelectionW03

      In this week, you will learn about

      • Select statements
      • Removing Duplicate use of Alias
      • Use of Where
      • Use of Wildcards
      • Limit clause
      • Arithmetic Operators
      • Mathematical Functions
      • Hands-on creating of backups and restore for large database
    4. Database SelectionW04

      In this week, students will learn about

      • Generating Strings
      • String Functions
      • Date Functions
      • Conversion Functions
    5. Database SelectionW05

      In this week, students will learn about

      • Comparison Operators
      • Logical Operators
      • Order By
      • Group By
      • Aggregate Functions
      • Using aggregate functions with Group by clause
      • Union Operator
      • Sub-query
    6. Querying Multiple TablesW06

      In this week, students will learn about

      • The need to Join Multiple Tables
      • Cartesian Product
      • Inner Join
      • Left Join
      • Right Join
      • Self Join
      • Delete Join
      • Update Join
      • Hands-on demonstration of joining more than two tables in a sample database
    7. Data ExplorationW07

      In this week, students will learn about

      • What Data Exploration is
      • Structure of Data
      • Understanding the E-R Diagram
      • How to Use SQL for Data Exploration
      • Significance of
        • Joins
        • Sub queries
        • Inbuilt functions
      • Other important capabilities of SQL for data exploration
      • Hands on demonstration
      • Working with NULL values
      • Making trends in Data
      • Identifying Outliers
      • Creating Data Summary
    8. Index, View, TransactionW08

      In this week, students will learn about

      • Creating Index
      • Use of Index
      • Type of Index and Ine
      • X Strategies
      • Views
      • Views for Data Analysis
      • Multi-user database
      • What is Transaction
      • Save points
      • Hands-on working on Multi user database environment
    9. Querying with ConditionsW09

      In this week, students will learn about

      • Querying with Conditions
      • The Searched Case Expression
      • The Simple Case Expression
      • Applications of Case Expression
      • Common Error Codes
      • Hands-on working with Json type data
    10. Stored ProceduresW10

      In this week, students will learn about

      • Stored Procedures for Data Analysis
      • Creating Stored Procedures
      • Removing Stored Procedures
      • Altering Stored Procedures
      • Conditional Statements
      • Loops
      • Hands-on working with cursors
    11. Integrating SQL with ExcelW11

      In this week, you will get a hands-on understanding of the following

      • Accessing MySQL data with MS Excel
      • Running SQL statements with Excel
      • Combining Excel and SQL statements for data representation
    12. Integrating SQL with PythonW12

      In this week, you will get a hands-on understanding of the following

      • Working with Python
      • Accessing SQL data with Python
      • Running basic SQL statements with Python
      • Running inbuilt python functions on SQL data
  3. 03Data Analysis and Visualization with Tableau12 modules
    1. Introduction to TableauW01

      Tableau software allows businesses to clearly visualize, analyze, and report large data sets. In the industry, Tableau is a business intelligence tool used to make decisions supported by data. In order to develop effective dashboards and data visuals that can be studied and interpreted, learners must understand the UI and basic features.

      This week will cover

      • Basics and Installation of Tableau
      • Types of Products and Roles
      • Interface and Workflow
      • Tableau Dashboards vs PPT
      • Connecting to Data
      • Data Source & its Types
      • Live and Extracts Connections
    2. Shelves, Cards, and Analytics PaneW02

      The Tableau workspace has features such as shelves, cards, the Data Pane, Analytics Pane, toolbars, and menus. Each of these features has a specific function that learners must understand how to use when working with Tableau.

      This week will cover

      • Basics of Data Pane
      • Quick Visualisations
      • Marks and its Properties
      • Pages Shelf
      • Menu and Toolbar
      • Analytics Pane
    3. Data Types, Sorting, GroupingW03

      As not all types of data are the same, Tableau supports 7 different data types, which can be sorted and grouped according to requirements. In order to present and organize data visually, learners should understand the features of Tableau related to data types and sorting.

      This week will cover

      • Sheet Interface
      • Data Types
      • Dimensions and Measures
      • Sorting & Custom Sort and Grouping
      • Sets & Combines Sets and Hierarchies
      • Highlight Data
      • Split & Custom Split
    4. Filtering & Table CalculationsW04

      There are six different filters available for users on Tableau, each with their own unique function. By using filters, datasets can be condensed to illustrate concise information, thus improving the efficiency of dashboards. Data in a table can also be analyzed through performing table calculations. These features are essential for learners, as they can be used to develop efficient and concise dashboards.

      This week will cover

      • Filters, Filter Types, and Order Process
      • Highlighted Data
      • Context Filter and its Uses
      • Calculated Fields
      • Aggregations and their Uses
      • Table Calculations
      • Level of Detail Expression (LODs)
      • Bins and its Uses
    5. Complete Dashboard Building & Best PracticesW05

      Spreadsheets with data can be transformed into visually interactive dashboards that present the information. Dashboards can be combined with spreadsheets to tell a story and narrate performance through measured parameters. By learning the best practices, learners can design and develop effective dashboards and stories for businesses.

      This week will cover

      • Parameters
      • Action Filters
      • Dashboard Layout
      • Sizing and Object
      • Formatting
      • Stories
      • Best Practices
    6. Distributing & PublishingW06

      Tableau workbooks can be shared, exported, and published as required. As many individuals may work on a dashboard together, it is important for learners to understand how distribution and publishing work.

      This week will cover

      • Distribution
      • Exporting
      • Workbook File Types
      • Publishing
      • Replacing Data Sources
    7. Joins and BlendingW07

      Tableau is also used to perform data blending and data joining. Through joining, data in tables and sheets from the same source can be combined. Data blending lets users combine data from different sources. It is important for learners to understand joins and blending as both are often required in the industry.

      This week will cover

      • Joins
      • Inner and Outer Join
      • Left and Right Join
      • Blending
      • Union
    8. Relationships (2020.2 and Later Versions)W08

      When analyzing data on Tableau, Relationships let you combine data from different tables. It is important for learners to understand how to work with multiple sets of data for effective analysis and visualization.

      This week will cover

      • Relationships
      • Data Models
      • Relationships vs Joins
      • Creating and Optimizing a Relationship
      • Types of Relationships
      • Smart Aggregations and their Types
    9. Data Preparations, Parameter & Set ActionsW09

      Before data is used in business intelligence, it goes through data preparation. This involves collecting, combining, and systematically organizing the dataset before it gets processed. It is important for learners to understand the parameters considered when preparing data.

      This week will cover

      • Pivot
      • Custom Split
      • Hide & Unhide Fields
      • Sampling the Data
      • Interactivity
      • Parameter Actions
      • Set Actions
    10. Advanced Calculations and AnalyticsW10

      When working with large sets of data, Tableau lets users perform advanced calculations and analytics to draw accurate inferences. It is important for learners to understand how to perform advanced calculations using Tableau to meet industry standards.

      This week will cover

      • Advanced Calculations
      • Regular Expressions
      • Table Calculations
      • Use Cases
      • Trend Lines
      • Clustering and Forecasting
    11. Nested LODs and Mapping FunctionsW11

      Nested LOD is an advanced topic in Tableau that lets users assess data sets in-depth. Users can also build maps using Tableau, and understand the related data. As certain data sets may require mapping functions, and analysis in-depth, it is important to know about these concepts.

      This week will cover

      • Nested LOD and Use Cases
      • Geo Mapping
      • Geospatial Data and Use Cases
      • Standard Maps
      • Map Hierarchies
      • Map Layers
      • Background Maps
      • Common Issues
    12. Dynamic Designs, Extensions, and Tooltip VisualizationsW12

      Through dynamic designs, values in dashboards can automatically change when related data is modified. As it is common for data sets to have changing values, knowing how to work with dynamic designs is an essential skill for learners.

      This week will cover

      • Dynamic Designs
      • Ways to Create
      • Interactivity
      • Extensions
      • Tooltip in Visualizations
      • Performance Recording
      • Design Principles
  4. 04Data Analysis and Visualization using Excel12 modules
    1. Course Introduction and FundamentalsW01

      To use Excel for data analysis and visualization, learners must first understand how to navigate the UI. It is also important to know about the basic formatting, shortcuts, and features of Microsoft Excel.

      In this week, you will learn about

      • Launching Excel and Version Check
      • Basics of the Interface
      • Saving an Excel Document
      • Common Shortcuts
      • Entering and Editing Data
      • Relative and Absolute Cell References
      • Formatting
      • Customizing the Quick Access Toolbar
      • Excel Self Help
    2. Basic Excel Functions and Modification of WorksheetsW02

      Functions in Excel can be used to execute logical, mathematical, or statistical calculations on data in the spreadsheet. For ease of analysis and interpretation, data should be arranged in a legible manner using rows and columns. Learners must understand the basic Excel features to effectively work with data on the spreadsheet.

      In this week, you will learn about

      • Basic Functions
        • Design of Excel Functions
        • Understanding SUM(), MIN() & MAX()
        • Understanding AVERAGE()
        • Understanding COUNT()
        • AutoSum and Autofill/Flashfill
      • Modification
        • Inserting New Sheets, Rows and Columns
        • Deleting Sheets, Rows and Columns
        • Moving or Copying Sheets
        • Sheet formatting
        • Excel Options
        • Customize Ribbon
    3. Data Formatting, Working with Shapes & Images, Creating your First ChartW03

      When working with data sets, it is important to format the information according to a given set of specifications. This lets users organize, understand, and process the information with ease. Users can also insert charts to visualize the information in the data set. Knowing how to organize and present data is a vital skill for learners.

      In this week, you will learn about

      • Data Formatting
        • Cell formatting – Merge, Alignment
        • Borders
        • Currencies and Percentages
        • Format Painter
        • Conditional Formatting
        • Find & Search
      • Shapes & Images
        • Inserting Shapes & Images
        • Formatting Excel Shapes
        • SmartArt
      • Creating your First Chart
        • Creating Chart
        • Chart Options
        • Types of Charts
        • Chart formatting
      • Date Functions
        • DATE() & DATEVALUE()
        • NETWORKDAYS()
        • EOMONTH() & EDATE()
    4. Excel Templates, Excel Options and Printing your Excel Worksheets, Introduction to TablesW04

      Excel Templates let users create spreadsheets with a common format. It is important to understand how to use these templates, as well as how to format the layout of a spreadsheet to print it. In addition to this, learners must also know how to insert tables into their spreadsheet to present their data and findings.

      In this week, you will learn about

      • Excel Templates
        • Importing Excel Template
        • Custom Template
      • Printing Worksheets
        • Print Preview
        • Margins and Scaling
        • Page Layout
        • Headers and Footers
        • Specific Range
      • Tables
        • Table Options
        • Table Formatting
        • Table References
        • Working with Formulas
        • Benefits of Tables
    5. Conditional Functions and Other FunctionsW05

      Conditional functions are commonly used in logical operations when cells must fulfill specific conditions. When working with data, it is important to understand how conditional functions are integrated into an Excel spreadsheet.

      In this week, you will learn about

      • Conditional Functions
        • Understanding IF()
        • Understanding SUMIF() & SUMIFS()
        • Understanding COUNTIF()
        • Use of AND & OR with IF()
        • Understanding IFERROR()
      • Other Functions and Text Functions
        • Understanding COLUMN() & ROW()
        • Understanding TEXT()
        • Understanding LEFT(), RIGHT() & MID()
        • Understanding LEN(), CONCAT()
        • Understanding TRIM(), PROPER(), FIND()
    6. Pivot Tables and Lookup FunctionsW06

      Pivot Tables are used to summarize, calculate, and study a dataset in order to identify patterns or draw comparisons. When sorting through large sets of data, Lookup functions are used to identify values of data in a column or row that matches the value in a particular position. It is important for learners to understand the use of Pivot Tables and Lookup Functions when working with data sets in Excel.

      In this week, you will learn about

      • Pivot Tables
        • Data source
        • Pivot Table Structuring
        • Pivot Table Options
        • Pivot Table Formatting
      • Lookup Functions
        • Understanding VLOOKUP()
        • Understanding HLOOKUP()
        • Understanding INDEX & MATCH
        • Understanding XLOOKUP()
        • Understanding OFFSET()
    7. New Functions, Data Tab, and Introduction to Power QueryW07

      Power Query is a tool in Excel that lets users import data from a variety of sources. Using this tool, users can organize, reshape, and transform the data as needed in order to then create reports or visual charts. Learners must understand how the Power Query tool works as it is essential in business and industries that are driven by data.

      In this week, you will learn about

      • New Functions
        • Understanding UNIQUE()
        • Understanding FILTER()
        • Understanding IFS()
        • Understanding SORT(), SORTBY()
        • Understanding SWITCH()
      • Data Tab
        • Data Import
        • Data Cleaning and Transform
        • What Power Query is
        • Use of Power Query
    8. Advanced Pivot Table Functions and PowerPivot ToolsW08

      Using Power Pivot in Excel, users can import large sets of data, and analyze it without reductions in speed or functionality. This feature allows users to develop data models and merge data from different sources. Power Pivot is an essential skill for learners to have when it comes to working with large data sets.

      In this week, you will learn about

      • Advanced Pivot Table Functions
        • Fields, Items & Sets
        • Pivot Charts
        • Slicers and Timeline
        • Group & Ungroup
        • GETPIVOTDATA()
      • PowerPivot Tools
        • Basics of Excel Power Pivot
        • More Tables and Relationships
        • Creating Data Models
        • KPIs
    9. Large Data Sets, File Protection, Named Ranges, and More in Data TabW09

      Information collected in the industry is often in the form of large data sets with raw information. It is important for learners to know how to sort through this data, and validate it to ensure maximum quality.

      In this week, you will learn about

      • Working with Large Data Sets
        • Freeze Panes
        • Grouping Data
        • Consolidating Data from Multiple Worksheets
      • File Protection
        • Protecting Specific Cells in a Worksheet
        • Protecting the Structure of a Workbook
        • Adding a Workbook Password
      • Data Tab
        • Custom Sorting
        • What-If Analysis
        • Text to Columns
      • Data Validation
        • Understanding Data Validation
        • Custom Data Validation Error
        • Dynamic Formulas in Validation
    10. List Functions and Excel Automation using MacrosW10

      Operations can be performed on lists of data which are often found in rows or columns of an Excel spreadsheet. Repetitive tasks can also be automated using macros in Excel. It is important for learners to know the functions used to analyze data in lists, as well as the use of Macros to automate tasks.

      In this week, you will learn about

      • List Functions
        • Understanding DSUM()
        • Understanding DSUM() with AND/OR()
        • Understanding DAVERAGE()
        • Understanding DCOUNT()
        • Understanding SUBTOTAL()
      • Excel Automation using Macros
        • Understanding Macros
        • Creating Macro with Macro Recorder
        • Editing Macros with VBA
        • Creating Buttons
    11. VBA BasicsW11

      Using Visual Basic for Applications (VBA), users can automate tasks by making macros. This optimizes the efficiency of a spreadsheet, and lets users carry out many functions at once. It is important for learners to understand how and why VBA is used in Excel.

      In this week, you will learn about

      • Basics of VBA
      • VBA Editor
      • Adding Code to VBA procedure
      • VBA variables
      • IF Statements
      • Loops
      • Automating Tasks
      • Input and User Messages
      • Handling Errors
    12. Email Automation and Industry ApplicationW12

      With Excel, users can send out emails to a set of people automatically through email automation. This feature optimizes time and efficiency, which is important in the industry when working with a large number of people.

      In this week, you will learn about

      • Email Automation
        • Basics of Emailing Section
        • Understanding Email Routine
        • Email loop
      • Excel Tips and tricks
      • Industry Standards
      • How to Apply your Learning
  5. 05Statistics and Probability for Data Sciences10 modules
    1. Introduction to Machine LearningW01
      • This week we will learn about
        • Introduction to Artificial Intelligence
        • Introduction to Machine Learning
        • Supervised, Unsupervised, and Reinforced Learning
        • Introduction to Deep Learning
        • Modules needed to implement a Machine Learning model
    2. Set TheoryW02
      • This week we will learn about
        • Set Theory
        • Algebra of Sets
        • Venn Diagrams
    3. ProbabilityW03
      • This week we will learn about
        • Introduction to Probability
        • Axioms of Probability
        • Independent events
        • Mutually exclusive events
        • Conditional Probability
        • Bayes Theorem
    4. StatisticsW04
      • This week we will learn about
        • Measures of Central Tendence
        • Measures of Dispersion
        • Measures of Symmetry
    5. Probability Distribution W05
      • This week we will learn about
        • Concept of Random variable
        • Bernoulli distribution
        • Binomial distribution
        • Negative Binomial distribution
        • Geometric distribution
        • Hypergeometric distribution
        • Poisson distribution
        • Uniform distribution
        • Probability mass function and cumulative distribution function
        • Brief intro to Gamma exponential and normal distribution
    6. Continuous Probability DistributionW06
      • In this week, we will learn
        • Continuous distributions
        • Normal Distribution
        • Gamma Distribution
        • Exponential Distribution
        • Lognormal Distribution
        • Weibull Distribution
        • F Distribution
        • T Distribution
        • chi square Distribution
        • Probabiltiy Density Function
        • Cumulative Distribution Function
    7. Inferential StatisticsW07
      • This week we will learn about
        • Sampling
        • Probabilistic and Nonprobabilistic methods of Sampling Estimation
        • Estimation
        • Sample size estimation
    8. Hypothesis TestingW08
      • This week we will learn about
        • Introduction to hypothesis testing
        • Rejection region
        • Critical value
        • p-value
    9. Hypothesis TestingW09
      • This week we will learn about,
        • z - test
        • f - test
        • t - test
        • Anova - test
    10. Non-Parametric TestsW10
      • This week we will learn about
        • Chi square test
        • Mann Whitney U test
        • Kruskal Wallis test
        • Sign test
        • Correlation
        • Chi square
        • Karl Pearson
        • Spearman Coefficient
        • Regression between variables
        • Implementation of statistical functions in Jupyter notebook
  6. 06Introduction to Machine Learning Algorithms and their Implementation in Python12 modules
    1. Introduction to Data Science and Programming Languages (Tools) for Data ScienceW01

      This week will cover

      • Data Science and Big Data: Introduction
      • Importance of Data Science and Big Data
      • Introduction to Different Programming Languages (Tools) for Data Science
    2. Basics of ProgrammingW02

      This week will cover

      • Variables
      • Operators
      • Data Types
      • Data Structures
      • Control Structures in Python
      • Function File in Python
    3. Essential Python LibrariesW03

      This week will cover

      • NumPy
      • SciPy
      • Pandas
      • Matplotlib
      • Seaborn
    4. Introduction to Machine Learning Cross-Validation, Bias-Variance TradeoffW04

      This week will cover

      • Basics of Machine Learning
      • Classification
      • Fitting of Model with Cross-Validation
      • Bias Variance Tradeoff
    5. Evaluation MetricsW05

      This week will cover

      • Evaluation Metrics for Model Validation
    6. Importing Data and Hands-On Imported DataW06

      This week will cover

      • Exploratory Data Analysis (EDA)
      • Correlation
      • Feature Extraction
      • Hyper Parameters
    7. Univariate and Multivariate Linear RegressionW07

      This week will cover

      • Univariate Linear Regression
      • Multivariate Linear Regression
      • Implementation in Python
    8. Principal Component AnalysisW08

      This week will cover

      • Eigen Values
      • Eigen Vectors
      • Singular Value Decomposition
      • Principal Component Analysis (PCA)
    9. Logistic Regression and k-nearest NeighborW09

      This week will cover

      • Explanation and Implementation of Logistic Regression and k-nearest Neighbor in Python
    10. Decision Tree and Random ForestW10

      This week will cover

      • Explanation and Implementation in Python
        • Decision Tree
        • Random Forest
    11. K-Mean and Hierarchical ClusteringW11

      This week will cover

      • K-Mean
      • Hierarchical Clustering
    12. Neural NetworksW12

      This week will cover

      • Logistic Regression with Neural Network Mindset
  7. 07Machine Learning Fundamentals In Depth12 modules
    1. Basics of Probability and StatisticsW01

      This week, you will learn about

      • Basics of Probability
      • Basics of Statistics
      • What ML & AI is
    2. Basics of Machine Learning (ML) & Artificial intelligence (AI)W02

      This week, you will learn about

      • Normal Distribution & Standard Normal Distribution: Introduction
      • Business Moments: Introduction
      • Artificial Intelligence
    3. Supervised Learning - PredictionW03

      This week, you will learn about

      • Supervised learning: Introduction
      • What linear regression is
      • One hot encoding
      • Cost function and gradient descent
    4. Supervised Learning - ClassificationW04

      This week, you will learn about

      • Classification problems: Introduction
      • What logistic regression is
      • Cost function and gradient descent
    5. Supervised Learning - ClassificationW05

      This week, you will learn about

      • Decision tree
      • Entropy
      • Information gain
    6. Random Forest & Model EvaluationW06

      This week, you will learn about

      • Random forest
      • Bootstrapping and majority rule
      • Evaluation of classifiers
    7. Supervised Learning - ClassificationW07

      This week, you will learn about

      • Support Vector Machines (SVM)
      • Mathematical intuition behind SVM
      • How SVM is different from other classifiers
    8. Supervised Learning - ClassificationW08

      This week, you will learn about

      • K-Nearest Neighbor
      • Lazy Algorithm
      • Single-layer Neural Network
    9. Unsupervised Learning - K-MeansW09

      This week, you will learn about

      • What clustering is
      • Why clustering is important
      • K Means and elbow curve
    10. Unsupervised Learning - HierarchicalW10

      This week, you will learn about

      • Hierarchical Clustering
      • Dendrogram
      • Evaluation of clustering algorithms
    11. Unsupervised Learning - PCAW11

      This week, you will learn about

      • Feature Selection
      • Principal Component Analysis (PCA)
      • Mathematical intuition behind PCA
    12. Supervised Learning - ClassificationW12

      This week, you will learn about

      • Artificial Neural Networks
      • Deep learning
      • Different activation functions
      • Understanding back propagation
  8. 08Advanced Deep Learning using Python12 modules
    1. Artificial Neural Network (Feed Forward Neural Network)W01

      This week will cover

      • Neural networks
      • Different architectures of Neural Networks
      • Importance of Neural Networks
      • Hyperparameters in Neural Networks
      • Different types of Gradient descent methods
    2. Activation Functions in Neural NetworksW02

      This week will cover

      • Conic sections
      • Hyperbolic trigonometric functions
      • Sigmoid activation function
      • Tanhx activation function
      • Relu activation function
      • Softmax activation function
    3. Deep LearningW03

      This week will cover

      • Deep learning terminologies
      • Nomenclature
      • Order of vectorized forms
      • Forward propagation derivation with 1 layer
      • Back propagation derivation with 1 layer
      • Batch size, iteration and epoch
    4. Evaluation of ModelsW04

      This week will cover

      • Underfitting
      • Overfitting
      • Lasso regularization
      • Ridge regularization
      • Elastic Net regularization
    5. Improvising the ModelW05

      This week will cover

      • Ensemble methods
      • Sparse and convex functions
      • Bagging to avoid overfitting
      • Boosting to avoid underfitting
      • Stacking to avoid underfitting
    6. OptimizersW06

      This week will cover

      • Frobenius norm regularization
      • Data augmentation
      • Early stopping
      • Adam optimizer
      • Tensorflow 2.0
    7. Convolutional Neural Network (CNN) - Part 1W07

      This week will cover

      • Basics of CNN
      • Edge detection
      • Padding
      • Stride
      • Simple CNN
      • Difference between CNN & ANN
    8. Convolutional Neural Network (CNN)- Part 2W08

      This week will cover

      • Pooling layers
      • Transfer learning
      • Examples of CNN architecture
      • Combination of different Neural network architecture
      • CNN in Python
    9. Recurrent Neural Network (RNN) - Part 1W09

      This week will cover

      • RNN Model
      • Different types of RNN
      • Gradients in RNN
      • Back propagation
      • Difference between RNN & ANN
    10. Recurrent Neural Network (RNN) - Part 2W10

      This week will cover

      • Gated Recurrent Unit (RNN)
      • Long short term memory (LSTM)
      • Bidirectional RNN
      • RNN Implementation in Python
    11. Basics of Natural Language Processing (NLP)W11

      This week will cover

      • Stop words
      • Stemming
      • Lemmatization
      • Word2vec
      • Implementation of word2vec in Python
    12. End-to-End ML Project StepsW12

      This week will cover

      • Descriptive Analytics
      • Diagnostic Analytics
      • Predictive Analytics
      • Prescriptive Analytics
  9. 09Effective Dashboards using Power BI11 modules
    1. Introduction to Power BIW01

      This week will cover

      • Power BI: Introduction
      • Why Power BI
      • Installing Power BI
      • Power BI Query Editor: Introduction
      • Types of Power BI Data Connectors
      • Basic Table Transformation
    2. Transform Data (Query Editor) - Part 1W02

      This week will cover

      • Data Transformation in Power BI
      • Basic Table Transformation
      • Text, Number & Date Tools
      • Index & Conditional Columns
      • Grouping & Aggregating Data
      • Pivoting & UnPivoting
    3. Transform Data (Query Editor) - Part 2W03

      This week will cover

      • Merging, Modifying & Appending Queries
      • Connecting to Folders
      • Defining Hierarchies & Categories
      • Best Practices of Query Editing & Power BI
    4. Relational Data Model - Part 1W04

      This week will cover

      • Data Model
      • Database Normalization
      • Data Table Types
      • Creating Power BI Table Relationships
      • Table Schemas
      • Relationships
    5. Relational Data Model - Part 2W05

      This week will cover

      • Relationship Cardinality
      • Connecting Multiple Data Tables
      • Filter Flow
      • Two Way Filter
        • Filtering & Cross-Filtering
      • Hiding Fields from the Power BI Report View
      • Data Modeling & Power BI Best Practices
    6. Basics of DAX - Part 1W06

      This week will cover

      • Data Analysis Expressions (DAX): Introduction
      • Calculated Columns vs Measures
      • DAX Syntax and Operators
      • Common DAX Functions
      • Basic Date and Time Formulas
    7. Basics of DAX - Part 2W07

      This week will cover

      • DAX Syntax
      • Common DAX Functions
      • Text Functions
      • Related
      • Basic Mathematical & Statistics Functions
      • Calculate
      • All
      • Filter
      • Time Intelligence Functions
    8. Advanced DAX FunctionsW08

      This week will cover

      • Calculated Joins between Tables
      • Relationship Functions
      • Advanced Time Intelligence
    9. Creating Interactive Reports - Part 1W09

      This week will cover

      • Power BI Report View: Introduction
      • Adding Basic Charts to Power BI Reports
      • Create a Visualization
      • Filtering Options
      • Matrix Visualization
      • Slicers & Timelines
      • Key Performance Indicator (KPIs) Chart
      • Power BI Map Visuals (Basic, Fill, ArcGIS)
    10. Creating Interactive Reports - Part 2W10

      This week will cover

      • TreeMaps, Lines, Areas & Gauges
      • Editing Report Interactions
      • Adding Drill through Filters
      • Linking to Report Bookmarks
      • Using "What-If" Parameters
      • Managing & Viewing Roles
    11. Creating Interactive Reports - Part 3W11

      This week will cover

      • Power BI Service: Introduction
      • Power BI Service Administration
      • Connecting to Multiple Types of Data
Industry projects

18 projects
you'll ship.

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 18 Projects that are available in this program.

English Dictionary App
Core and Advanced Python Programming

English Dictionary App

You will have to create an English Dictionary App that responds with definitions of English words according to the given input.

Library Book Management System
Core and Advanced Python Programming

Library Book Management System

You have to develop a Library Book Management System based on given inputs. The Management System will be implemented with the front-end GUI developed using Tkinter and the backend…

C2C E-Commerce User’s Behaviour Analysis
SQL for Data Science

C2C E-Commerce User’s Behaviour Analysis

In this project, students are expected to analyze user behavior in a C2C E-Commerce business. Analyzing the user’s database will help understand the business perspective. The…

Analyzing the choice of career for the Alumni of Engineering College.
SQL for Data Science

Analyzing the choice of career for the Alumni of Engineering College.

In this project, students are expected to analyze the career choices of alumni from an institution based on given data.

Data Cleaning and Transformation using Pivot Table and Charts
Data Analysis and Visualization using Excel

Data Cleaning and Transformation using Pivot Table and Charts

In this project, you will be given a large set of data from ABC Private Limited that is manufacturing and selling various types of automobiles. With this information, you will have…

Report Creation using PowerQuery, Macro VBA, List functions, and Data Validation Functions
Data Analysis and Visualization using Excel

Report Creation using PowerQuery, Macro VBA, List functions, and Data Validation Functions

You will be given a set of data about loan applicants that will have to be analysed and presented with Dashboard in Excel. With the given data, you will have to make separate sprea…

Supply and Demand Gap Analysis
Effective Dashboards using Power BI

Supply and Demand Gap Analysis

Learners will have to identify the gap between demand and supply so that organizations can take action to minimize those gaps. The candidates can choose any domain, like Human Reso…

COVID-19 Vaccinations Trend Analysis
Effective Dashboards using Power BI

COVID-19 Vaccinations Trend Analysis

In this project, learners have to track and analyze COVID-19 vaccination trends to see doses administered by manufacturer, country, region, age, and gender. This project requires l…

Descriptive Analysis and Regression Analysis
Machine Learning Fundamentals In Depth

Descriptive Analysis and Regression Analysis

In this project, you will perform descriptive analysis on given data regarding cars. Using the data, you will have to classify cars based on various parameters and analyze the pric…

K-Means Clustering
Machine Learning Fundamentals In Depth

K-Means Clustering

In this project, you will have to clean a data set regarding cars and implement the clustering algorithm using K-Means. You will then have to explain the results and find the optim…

Logistic Regression and Gradient Descent
Advanced Deep Learning using Python

Logistic Regression and Gradient Descent

In this project, you will perform logistic regression and gradient descent on a given dataset using Python.

ANN, CNN and RNN
Advanced Deep Learning using Python

ANN, CNN and RNN

In this project, you will have to work on prediction of machine failure for a given dataset using ANN (Hyperparameters is completely dependent on individuals to come up with the be…

+ 6 more projects in the full syllabus

Placements

178 learners placed
from this program.

Companies
108
Median CTC
3.6 LPA
20255 LPA
Gfk
Junior Data Analyst
Tulsi K.
20255 LPA
Amazon
Analyst
Aswin M.
20253.5 LPA
Fluentgrid Tech
Data Analyst
Sanjay P.
20254 LPA
BrightChamps
Manager-Data Analysis
Mohit V.
20254.5 LPA
AssetPlus
Data Analyst
Vignesh K.
20254 LPA
FlatWorld
Data Analyst
Vijay V.
Fee

One fee.
Everything included.

Become job ready with our comprehensive industry focused curriculum for freshers & early career professionals

Premium
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Inclusive of all chargesEMI from ₹14,375/month
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FAQ

Questions,
answered.

What is Data Science and Analytics?

Data Science and Analytics lets you analyze Big Data and use it for various purposes like research, marketing, advertising, etc.

Which industries hire Data Science professionals?

Data Science professionals generally work in industries like Information Technology, BFSI, Healthcare, Telecommunications, Retail, Digital Marketing, Automotive, Cyber Security etc.

What is blended learning in a Data Science course?

Blended learning refers to a combination of instructor-led, live classes and online classes. This post-graduation in data science offers the right mix of online and offline sessions, along with practical projects. 

What is a Data Science engineer's salary?

Depending on experience and course quality, a Data Science engineer may earn anything between INR 6 lakh and INR 25 lakh per annum. Courses in data science and analytics will give you a chance to pursue a lucrative career.

Who can study Data Science?

Almost anyone with a degree in Science or Technology may study Data Science. Business graduates can also take up this program in Data Science. 

What is the duration of the Post Graduate Program in Data Science?

The data science course duration is 24 weeks.

What is the mode of delivery of the Post Graduate Program in Data Analytics and Data Science?

Skill-Lync delivers data science courses online.

What will I learn in a PG Program in Data Science and Analytics?

Through the data science certification course, you will learn Machine Learning, Artificial Intelligence, Python, Tableau, and Power BI. You will also understand advanced mathematics and Excel, alongside data visualization.

Can you tell me more about Skill-Lync?

Skill-Lync is among India's leading EdTech platforms dedicated to transforming engineering education. We equip young engineers with the latest skill sets and cutting-edge tools in new-age technologies.

The brainchild of two engineers from Chennai, Skill-Lync, is on a mission to bridge the skill gap between aspiring professionals and the industry's demands through job-oriented courses.

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