Data Science
Professional Certification Course to enter in IT Industry with Knowledge and Experience

Complete Course Curriculum
Course Duration: 6- Months
Course Fees: 30000/-
Data Toolkit
INTRODUCTION TO PYTHON
- Understanding The
Upgrad Coding Console - Basics Of Python
- Data Structures In
Python - Control Structure And
Functions In Python - Oop In Python
PROGRAMMING IN PYTHON
- Logic and syntax
building - Data structures: lists,
strings, dictionaries, and
stacks - Time complexity
- Searching and sorting
- Two pointers
- Recursion
PYTHON FOR DATA SCIENCE
- Introduction to numpy
- Introduction to
- Matplotlib
- Introduction to pandas
- Getting and cleaning data
DATA VISUALIZATION IN PYTHON
- Introduction to data
- Visualization
- Data visualisation using seaborn
EXPLORATORY DATA ANALYSIS
- Data sourcing
- Data cleaning
- Univariate analysis
- Bivariate analysis and multivariate analysis
CREDIT EDA CASE STUDY
- Problem statement
- Evaluation rubric
- Final submission
- Solution
INFERENTIAL STATISTICS
- Basics of probability
- Discrete probability
distributions - Continuous probability
distributions - Central limit theorem
HYPOTHESIS TESTING
- Concepts of hypothesis
testing - i: null and
alternate hypothesis,
making a decision, and
critical value method - Concepts of hypothesis
testing - ii: p-value method
and types of errors - Industry demonstration of hypoproportionting:
two-sample mean and
proprotion test, a/b
testing
DATA ANALYSIS USING SQL
- Database design
- Database creation in mysql workbench
- Querying in mysql
- Joins and set operations
ADVACED SQL & BEST PRACTICES
- Window functions
- Case statements, stored
routines and cursors - Query optimisation and
best practices - Problem-solving using sql
SQL ASSIGNMENT: RSVP MOVIES
- Problem Satement
- Evaluation Rubric
- Final Submission
- Solution
Machine Learning
LINEAR REGRESSION
- Simple Linear Regression
- Simple Linear Regression
In Python - Multiple Linear
Regression - Mutliple Linear
Regression In Python - Industry Relevance Of
Linear Regression
LINEAR REGRESSION ASSIGNMENT
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
LOGISTIC REGRESSION
- UNIVUnivariate Logistic
Regression - Multivariate Logistic
Regression: Model
Building And Evaluation - Logistic Regression:
Industry Applications
UNSUPERVISED LEARNING: CLUSTERING
- Introduction To
Clustering - K-Means Clustering
- Hierarchical Clustering
- Other Forms Of
Clustering: K-Mode,
K-Prototype, Db Scan
BUSINESS PROBLEM SOLVING
- Introduction To Business
Problem Solving - Business Problem
Solving: Case Study
Demonstrations
CLUSTERING ASSIGNMENT (OPTIONAL)
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
CASE STUDY: LEAD SCORING
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
Deep Learning
TREE MODELS
- Introduction To Decision Trees
- Algorithms For Decision Tree Construction
- Truncation And Puning
- Random Forests
BAGGING AND BOOSTING
- Introduction To Boosting And Adaboost
- Gradient Boosting
ADVANCED REGRESSION
- Generalized Linear Regression
- Regularized Regression
ADVANCED REGRESSION ASSIGNMENT
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
PRINCIPAL COMPONENT ANALYSIS
- Prinicipal Component Analysis And Singular Value Decomposition
- Principal Component Analysis In Python
TIME SERIES ANALYSIS
- Introduction To
Time Series And Its
Components - Working With Stationary
Time Series - End-To-End Analysis Of
Time Series
Deep Learning And Neural Networks
INTRODUCTION TO NEURAL NETWORKS
- Structure Of Neural Networks
- Feed Forward In Neural Networks
- Backpropagation In Neural Networks
- Modifications To Neural Networks
- Hyperparameter Tuning In Neural Networks
CONVOLUTIONAL NEURAL NETWORKS - INTRODUCTION AND INDUSTRY APPLICATIONS
- Introduction To Convolutional Neural Networks
- Building Cnns With Python And Keras
- Cnn Architectures And Transfer Learning
- Style Transfer And Object Detection
- Industry Demonstration: Using Cnns With Flowers Images
- Industry Demonstration: Using Cnns With X-Ray Images
CNN ASSIGNMENT
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
RECURRENT NEURAL NETWORKS
- What Makes A Neural Network Recurrent
- Variants Of Rnns: Bidirectional Rnns And Lstms Building
- Rnns In Python
GESTURE RECOGNITION
- Two Architectures: 3d Convs And Cnn-Rnn Stack
- Understanding Generators
- Starter Code Walkthrough
- Problem Statement And Final Submission
Natural Language Processing
TREE MODELS
- Introduction To Decision Trees
- Algorithms For Decision Tree Construction
- Truncation And Puning
- Random Forests
MODEL SELECTION & GENERAL ML TECHNIQUES
- Principles Of Model Selection
- Model Evaluation
- Model Selection: Best Practices
PRINCIPAL COMPONENT ANALYSIS
- Prinicipal Component Analysis And Singular Value Decomposition
- Principal Component Analysis In Python
ADVANCED REGRESSION ASSIGNMENT
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
BAGGING AND BOOSTING
- Introduction To Boosting And Adaboost
- Gradient Boosting
TIME SERIES ANALYSIS
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
LEXICAL PROCESSING
- Introduction To Nlp
- Basic Lexical Processing
- Advanced Lexical Processing
SYNTACTIC PROCESSING
- Introduction To Syntactic Processing
- Parsing
- Information Extraction
- Conditional Random Fields
SYNTACTIC PROCESSING -ASSIGNMENT
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
NEURAL NETS FOR NLP
- Understanding Neural Networks
- Loss Functions And Back Propagation
- Understanding Tensorflow
- Case Study : Imdb Movie Review Classification
SEMANTIC PROCESSING
- Introduction To Semantic Processing
- Distributional Semantics
- Topic Modelling
CASE STUDY: AUTOMATIC TICKET CLASSIFICATION
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
Business Analytics
TREE MODELS
- Introduction To Decision Trees
Algorithms For Decision Tree Construction
Hyperparameter Tuning In Decision Trees
Ensembles And Random Forests
TIME SERIES FORECASTING
- Introduction To Time Series And Its Components
- Smoothing Techniques
- Introduction To Ar Models
- Building Ar Models
RETAIL-GIANT SALES FORECASTING ASSIGNMENT
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
MODEL SELECTION & GENERAL ML TECHNIQUES
- Principles Of Model Selection
- Model Building And Evaluation
- Feature Engineering
- Class Imbalance
ADVANCED EXCEL
- Excel Functions
- Data Analysis In Excel
- Advanced Tools And Visualisations
VISUALISATION USING TABLEAU
- Data Exploration In Tableau
- Visualising And Analysing Data In Tableau With Basic Plots
TELECOM CHURN CASE STUDY
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
Business Requirements
STRUCTURED PROBLEM SOLVING USING FRAMEWORKS
- ProbleIntroduction To Structured Problem Solving
- Interviewing And Frameworks - I: 5w And 5whys
- Interviewing And Frameworks - Ii: Spin
- Industry Demonstrations On Frameworks
- Understanding Business Model Canvas And Issue Tree Framework
- Industry Demonstrations On Issue Tree Framework
- Specialized Frameworks For Business Problems:
- ps, 5cs, Etc.
STRUCTURED PROBLEM SOLVING ASSIGNMENT
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
OPERATIONS RESEARCH
- Introduction & Concepts Of Optimisation
- Optimisation Using Excel
- Optimisation Using Python
- Or In Industry - Warehouse Problem, Assignment Problem, Jobshop Scheduling, Etc.
DATA STORYTELLING
- Introduction To Data Storytelling
- Components Of A Good Story With Data - Understanding Your Stakeholder And Stakeholder Empathy, Levels Of Details For Different Stakeholders - Cxo/Leadership Vs Team Presentations, Visuals, Etc.
- Golden Rules For Data Storytelling
Business Intelligence/
Data Analytics
DATA MODELLING
- Database Design Recap
- Building Blocks Of Data Modelling
- Problem Solving Using Data Modelling
- Data Modelling: Optional Assignment
ADVANCED SQL AND BEST PRACTICES
- Window Functions
- Case Statements, Stored Routines, And Cursors
- Query Optimisation And Best Practices
- Problem Solving Using Sql
ADVANCED EXCEL
- Excel Functions
- Data Analysis In Excel
- Advanced Tools And Visualisations
NOSQL DATABASES AND MONGODB
- Introduction To Nosql Databases And Mongodb
- Querying In Mongodb
- Aggregation In Mongodb-I
- Data Modelling In Mongodb
- Indexing In Mongodb
- Aggregation In Mongodb-Ii
- Replication And Sharding
INTRODUCTION TO BIG DATA AND CLOUD
- Big Data And Cloud Computing
- Amazon Web Services
- Big Data Storage And Processing - Hadoop
- Emr Cluster In Aws
HIVE AND QUERYING
- Advanced Lexical Processing
- Introduction To Hive
- Basic Hive Queries
- Advanced Hive Queries
HIVE CASE STUDY
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
Advanced Visualisations
VISUALISATION USING TABLEAU
- Data Exploration In Tableau
- Visualising And Analysing Data In Tableau With Basic Plots
- Advanced Visualisations Using Tableau - I: Lod Expressions, Hexbin Charts, Sankey Diagrams, Waterfall Charts, Etc.
- Advanced Visualisations Using Tableau - II: Pareto Charts, Bullet Graphs, Highlight Tables, Etc.
- Case Study: Visualising Kpis
SPORTS ANALYTICS - IPL VISUALISATION ASSIGNMENT
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
VISUALISATION USING POWERBI
- Powerbi: Introduction And Setup
- Visualising And Analysing Data In Powerbi
- Data Transformations Using Powerbi
- Making Interactive Dashboards With Powerbi
VISUALISATION USING PLOTLY
- Introduction To Plotly
- Basic Visualisations In Plotly
- Advanced Visualisations In Plotly
DATA STORYTELLING
- Introduction To Data Storytelling
- Components Of A Good Story With Data - Understanding Your Stakeholder And Stakeholder Empathy, Levels Of Details For Different Stakeholders - Cxo/Leadership Vs Team Presentations, Visuals, Etc.
- Golden Rules For Data Storytelling
BUSINESS CASE STUDY
- Problem Statement
- Evaluation Rubric
- Final Submission
- Solution
Data Engineering
INTRODUCTION TO BIG DATA
- 4vs Of Big Data
Big Data: Industry Case Studies
INTRODUCTION TO CLOUD AND AWS SETUP
- Introduction To Cloud
- Aws Setup
INTRODUCTION TO HADOOP AND MAPREDUCE PROGRAMMING
- Concepts Retailed To Distributed Computing
- Hadoop Distributed File System
- Mapreduce Programming In Python
DATA MANAGEMENT AND RELATIONAL DATABASE MODELLING
- Enterprise Data Management
- Relational Database Modelling
- Normal Forms And Er Diagrams
DATA INGESTION WITH APACHE SQOOP AND APACHE FLUME
- Introduction To Data Ingestion
- Structured Data Ingestion With Sqoop
- Unstructured Data Ingestion With Flume
HIVE & QUERYING
- Fundamentals Of Apache Hive
- Writing Hql For Data Analysis
- Partitioning And Bucketing With Hive
AMAZON REDSHIFT
- Data Warehousing With Redshift
- Analyze Data With Redshift
INTRODUCTION TO APACHE SPARK
- Spark Architecture
- RDD, Dataframe API,Sparksql
PROJECT: ETL DATA PIPLINE
- Introduction And Problem Statement
- Grading Rubrics And Submission
AWS CLOUD INFRASTRUCTURE
- The Aws Cloud Platform
- Building And Deploying Virtual Machines
- Aws Cloud Storage Solutions
- Application Deployment
- Cloud Administration And Security
- Load Balancing And Backup Strategies
- Cloud Automation
OPTIMISING SPARK FOR LARGE SCALE DATA PROCESSING
- Running Spark On Multinode Cluster
- Spark Memory & Disk Optimisation
- Optimising Spark Cluster Environment
APACHE FLINK
- Introduction To Apache Flink
- Batch Data Processing With Flink
- Stream Processing With Apache Flink
- Sql Api
REAL-TIME DATA STREAMING WITH APACHE KAFKA
- Intro To Real-Time Data Processing Architectures
- Fundamentals Of Apache Kafka
- Setting Up Kafka Producer And Consumer
- Kafka Connect Api & Kafka Streams
REAL-TIME DATA PROCESSING USING SPARK STREAMING
- Spark Streaming Architecture
- Spark Streaming Apis
- Building Stream Processing Application With Spark
- Comparision Between Spark Streaming And Flink
BUILDING AUTOMATED DATA PIPELINES WITH AIRFLOW
- Fundaments Of Airflow
- Workflow Management With Airflow
- Automating An Entire Data Pipeline With Airflow
ANALYTICS USING PYSPARK
- Exploratory Data Analysis With Pyspark
- Predictive Analysis With Spark Mllib
PROJECT: REAL TIME DATA PROCESSING
- Introduction And Problem Statement
- Grading Rubrics And Submission
ANALYTICS USING PYSPARK
- Exploratory Data Analysis With Pyspark
- Predictive Analysis With Spark Mllib
Data Generalist
TREE MODELS
- Introduction To Decision Trees
- Algorithms For Decision Tree Construction
- Hyperparameter Tuning In Decision Trees
- Ensembles And Random Forests
BOOSTING
- Introduction To Boosting And Adaboost
- Gradient Boosting
MODEL SELECTION & GENERAL ML TECHNIQUES
- Principles Of Model Selection
- Model Building And Evaluation
- Best Practices
PRINCIPAL COMPONENT ANALYSIS
- Prinicipal Component Analysis And Singular Value Decomposition
- Principal Component Analysis In Python
ML LAB 1: CLASSIFICATION
- Classification
- Feature Engineering
- Industry Case Study
ADVANCED REGRESSION + ML LAB 2: REGRESSION
- Generalized Linear Regression
- Regularized Regression
- Application Of Regularisation
TEXT ANALYTICS & PROCESSING + TEXT-BASED PREDICTIVE MODELLING
- Basic Lexical Processing: Tokenization, Bag Of Words, Tf-Idf
- Advanced Lexical Processing: Canonicalization, Phonetic Hashing, Spell Corrector, Pointwise Mutual Information
VISUALISATION USING TABLEAU
- Data Exploration In Tableau
- Visualising And Analysing Data In Tableau With Basic Plots
DATA STORYTELLING
- Introduction To Data Storytelling
- Components Of A Good Story With Data - Understanding Your Stakeholder And Stakeholder Empathy, Levels Of Details For Different Stakeholders - Cxo/Leadership Vs Team Presentations, Visuals, Etc.
- Golden Rules For Data Storytelling
Advanced Programming & Databases
DATA MODELLING
- Database Design Recap
- Building Blocks Of Data Modelling
- Problem Solving Using Data Modelling
- Data Modelling: Optional Assignment
ADVANCED SQL
- Query Optimisation And Best Practices
- Problem Solving Using Sql
DATA STRUCTURES - SETS, DICTIONARIES, STACKS, QUEUES
- In-Built Data Structures
- Stack
- Queue
- Trees
ALGORITHM ANALYSIS + RECURSION
- Time Complexity
- Recursion
SEARCHING AND SORTING
- Searching
- Sorting
- Two Pointers
Course Name | Fees | Duration |
---|---|---|
Complete C Programming Course | 3000/- | 45 Days |
Complete C ++ Programming Course | 3000/- | 45 Days |
Core Java Development Course | 6000/- | 2 Months |
Basic Python Development | 6000/- | 2 Months |
Basic Android Development | 6000/- | 2 Months |
Web Designing | 6000/- | 2 Months |
JavaScript | 6000/- | 2 Months |
Graphic Designing | 6000/- | 2 Months |
Digital Marketing | 6000/- | 2 Months |
PHP & MySQL | 6000/- | 2 Months |
Internet of Things (IOT) | 6000/- | 2 Months |
Angular JS | 6000/- | 2 Months |
Node JS | 6000/- | 2 Months |
React JS | 6000/- | 2 Months |
C # | 6000/- | 2 Months |

Have a look on Exciting Project ideas on Data Science
You can build a simple calculator with C using switch cases or if-else statements. This calculator takes two operands and an arithmetic operator (+, -, *, /) from the user, however, you can expand the program to accept more than two operands and one operator by adding logic. Then, based on the operator entered by the user, it conducts the computation on the two operands. The input, however, must be in the format “number1 operator1 number2” (i.e. 2+4).
Using C language, you can also create a student management system. To handle students’ records (like Student’s roll number, Name, Subject, etc.) it employs files as a database to conduct file handling activities such as add, search, change, and remove entries. It appears a simple project but can be handy for schools or colleges that have to store records of thousands of students.
If you have ever lost track of which day of the week is today or the number of days in that particular month, you should build a calendar yourself. The Calendar is written in the C programming language, and this Calendar assists you in determining the date and day you require. We can implement it using simple if-else logic and switch-case statements. The display() function is used to display the calendar and it can be modified accordingly. It also has some additional functions.
This Phone book Project generates an external file to permanently store the user’s data (Name and phone number). The phone book is a very simple C project that will help you understand the core concepts of capacity, record keeping, and data structure. This program will show you how to add, list, edit or alter, look at, and delete data from a record.
An online voting system is a software platform that enables organizations to conduct votes and elections securely. A high-quality online voting system strikes a balance between ballot security, convenience, and the overall needs of a voting event. By collecting the input of your group in a systematic and verifiable manner, online voting tools and online election voting systems assist you in making crucial decisions. These decisions are frequently taken on a yearly basis – either during an event (such as your organization’s AGM) or at a specific time of the year. Alternatively, you may conduct regular polls among your colleagues (e.g. anonymous employee feedback surveys).
With this voting system, users can enter their preferences and the total votes and leading candidate can be calculated. It’s a straightforward C project that’s simple to grasp. Small-scale election efforts can benefit from this.
Tic-tac-toe, also known as noughts and crosses or Xs and Os, is a two-person paper and pencil game in which each player alternates marking squares in a three-by-three grid with an X or an O. The winner is the player who successfully places three of their markers in a horizontal, vertical, or diagonal row. You can implement this fun game using 2D arrays in the C programming language. It is important to use arrays while creating a Tic Tac Toe game in the C programming language. The Xs and Os are stored in separate arrays and passed across various functions in the code to maintain track of the game’s progress. You can play the game against the computer by entering the code here and selecting either X or O. The source code for the project is given below.
Mathematical operations are an everyday part of our life. Every day, we will connect with many forms of calculations in our environment. Matrices are mathematical structures in which integers are arranged in columns and rows. In actual life, matrices are used in many applications. The most common application is in the software sector, where pathfinder algorithms, image processing algorithms, and other algorithms are developed. Some fundamental matrix operations are performed in this project, with the user selecting the operation to be performed on the matrix. The matrices and their sizes are then entered. It’s worth noting that the project only considers square matrices.
Library management is a project that manages and preserves electronic book data based on the demands of students. Both students and library administrators can use the system to keep track of all the books available in the library. It allows both the administrator and the student to look for the desired book. The C files used to implement the system are: main.c, searchbook.c, issuebook.c, viewbook.c, and more.
The Electricity Cost Calculator project is an application-based micro project that predicts the following month’s electricity bill based on the appliances or loads used. Visual studio code was used to write the code for this project. This project employs a multi-file and multi-platform strategy (Linux and Windows). People who do not have a technical understanding of calculating power bills can use this program to forecast their electricity bills for the coming months; however, an electricity bill calculator must have the following features:
- All loads’ power rating
- Unit consumed per day
- Units consumed per month, and
- Total load calculation
The project’s goal is to inform a consumer about the MOVIE TICKET BOOKING SYSTEM so that they can order tickets. The project was created with the goal of making the process as simple and quick as possible. The user can book tickets, cancel tickets, and view all booking records using the system. Our project’s major purpose is to supply various forms of client facilities as well as excellent customer service. It should meet nearly all the conditions for reserving a ticket.
Snakes and ladders, also known as Moksha Patam, is an ancient Indian board game for two or more players that is still considered a worldwide classic today. It’s played on a gridded game board with numbered squares. On the board, there are several “ladders” and “snakes,” each linking two distinct board squares. The dice value can either be provided by the user or it can be generated randomly. If after moving, the pointer points to the block where the ladder is, the pointer is directed to the top of the ladder. If unfortunately, the pointer points to the mouth of a snake after moving, the pointer is redirected to the tail of the snake.
This system is built on the concept of booking bus tickets in advance. The user can check the bus schedule, book tickets, cancel reservations, and check the bus status board using this system. When purchasing tickets, the user must first enter the bus number, after which the system will display the entire number of bus seats along with the passengers’ names, and the user must then enter the number of tickets, seat number, and person’s name.
We will be using arrays, if-else logic, loop statements, and various functions like login(), cancel(), etc. to implement the project.
Pacman, like other classic games, is simple to play. In this game, you must consume as many small dots as possible to earn as many points as possible. The entire game was created using the C programming language. Graphics were employed in the creation of this game. To create the game, you have to first define the grid function to manage the grid structure. To control the movement, you can define functions such as move_right(), move_left(), move_up() and move_down(). C files to add ghosts and their functionalities, positions check, etc. can be added to make the game more fun. The customers will find this C Programming game to be simple to comprehend and manage.
Have a look on Exciting Project ideas on Data Science
Fake News Detection Using R Language
Creating your First Chatbot In Python
Detecting Frauds of Credit Cards via Python
Using Deep Learning for the Classification of Breast Cancer
Movie Recommendation Platform with R Packages
Sentiment Analysis Backed by R Dataset
Prediction of Age & Gender through Deep Learning
Segmentation of Customers’ Groups with ML
Music Recommendation System
Sentiment Analyzing
Face Mask Detection
Vehicle Detection & Recognition
Frequently Asked Questions

Who can apply for this course?
- All branch graduates students or undergraduate students who want to become professional in Data Science.
- All stream students eg. Engineering, BCA, BSc, BBA, Bcom, BA.
- Any who wants to learn a new skill or improve skill for there career.
One should have any prior knowledge?
- Basic computer knowledge.
- General knowledge of what are programming languages.
- Computer Science Background.
- Good in Mathamatics & Statistics.
Any Completion Certificate is there?
- Yes, a course completion certificate is given after the completion of this course.
Is the course offline or online?
- Both the offline and online modes are available for this course.
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