Post Graduate Program in Data Science

Post Graduate Program in Data Science


  • 12-Month Online Program
  • Multiple Live Projects
  • 100% Job Guaranteed*


  • Online Lab Sessions
  • Multiple Capstone Projects
  • 25+ Industry Graded Projects

Data Science

Learn how to analyze and interpret data correctly.



Data Science

Data is the bedrock of innovation, but its value comes from the information data scientists can glean from it, and then act upon. Accelerate your profession in Data Science with the specific Data science Analytics program. Experience global-magnificence education by way of an enterprise leader at the maximum in-call for Data science capabilities. Benefit hands-on publicity to key technologies including Python, System Gaining Knowledge of, Statistics Visualization, Square and Synthetic Intelligence and come to be an professional information technological know-how professional today.

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The data science practitioners are called data scientist that contains full-fledged knowledge and skills about pure science and computer science. Data scientists must know list
  • Can apply mathematics, stats, and scientific tactics
  • Have a hands-on range of tools and tactics for validating and forming data from SQL form towards data mining and from that to data integration methods.
  • Pulls out visions with the help of data using assumable analytics and AI along with machine learning and depth learning replicas.
  • Ability to write-down application that consists of data processing and data calculation
  • Explain and narrate the meaning of the result to the deciders and stakeholders in each level of technical knowledge and comprehension.
  • Helps to understand how these results can help in solving the business griefs.

Post Graduate Program in Data Science

The PGP-DS (submit Graduate software in facts technological know-how) gives you extensive coverage to fundamental ideas and techniques from Python, Exploratory records evaluation to machine getting to know, Deep studying and greater. Realistic labs and task paintings convey those thoughts to existence with our instructors and assistants to oversee you with the course.
Equip your profession with this commended PG program in Data Science know-how with Learn n lead and the crew.

AI and Machine Learning

Information Science and Artificial Intelligence have transformed the sector completely. Companies round the arena are leveraging artificial intelligence to keep away from repetitive obligations and improve purchaser experience. Robots are taking over the sector with the aid of typhoon and are continuously constructing intelligence comparable to human brains. Artificial Intelligence and Machine Learning are the very best paying jobs inside the world.

Why Data Science?

As in keeping with a current estimate, more than 90% of the businesses will use artificial intelligence in a single way or the alternative to construct or beautify their services and products. Those agencies are looking for folks that are skilled in facts technological know-how and AI. Regrettably, the enterprise are facing an acute shortage of tremendously skilled people to fill the void.

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Why Data Science

Data is the key to making good business decisions.


35%

Analytics professionals in India have a work experience of less than 3 years.


65%

Jobs are open for Data Science Professional candidates with 0-5 years experience.


3.7 Million

Global Estimate of 3.7 million job postings for analytics and data science roles.


30%

Indian data science work industry is growing at very healthy rate of 29.5 percent CAGR


2023

India to become one of the top five markets for Big Data Professionals by 2023.

On the completion this program, you will:
In-depth knowledge of various latest technologies such as Python, Tableau, SQL
Hands on experience in various industry used ML techniques Regression, Predictive Modelling, Clustering, Time Series etc.
Learn how to use data modelling for solution of a business problem.
Hands on experience in data transformation operations.
You will find yourself good to go for multiple job roles like analyst, data engineer etc. within leading analytics companies.
 
Program Highlights

See which benefits you can derive by joining this program.

Online Program
12-month online program
Online Lab Sessions
Collaborations
Learn N Lead has collaboration with many eminent Universities and Organizations across the Globe to exchange the knowledge.
Dedicated Placement Team
Industry leaders guidance for better aspects
Multiple mock interview sessions in between the program
Leading companies access for numerous opportunities
Become Job-ready
Practical knowledge enhanced by taking real world case studies.
Get yourself ahead with knowledge of tools like Python, Tableau.
Various industry leader sessions to get in-depth industry insights
Program Curriculum

An overview of what you will learn from this program.

  • Hello World
  • Variables
  • Basic Arithmetic & logical operators (int, float)
  • Data Types – numbers, boolean & strings
  • Concat, Subset, Position, length etc.
  • If-else, loops
  • Logic Flowcharts (Intuitive understanding of code flow)
  • Pseudo Code
  • Basic Programming syntax
  • List, Tuples, Sets & Dictionaries ​
  • Default functions
  • Default methods
  • Intro​ to Conditional statements (if-else, elif), Nested Conditional in Python ​
  • Intro​ to Basic For, While Loops, Break in Python​
  • Convert pseudo codes from Day 1 into programs using Loops and if-else. ​
  • List Comprehension​
  • Use cases vs Loops
  • Write Programs including both loops and If-else
  • Practice list comprehensions
  • Lab Exercises
  • Exploring commonly used built in functions (min, max, sort etc.)
  • Programming user defined functions
  • Working with functions with and without arguments
  • Functions with return items
  • Understanding lambda functions
  • Overview of map, reduce and filter functions
  • Introduction to DBMS
  • An Introduction to Relational Database
  • Concepts and SQL Accessing
  • Data Servers MYSQL/RDBMS Concepts
  • Extraction, Transformation and Loading (“ETL”) Processes
  • Retrieve data from Single Tables-(use of SELECT Statement) and the power of WHERE and ORDER by Clause. Retrieve and Transform data from multiple Tables using JOINS and Unions
  • Introduction to Views Working with Aggregate functions, grouping and summarizing Records Writing Sub queries
  • Sampling
  • Probability distribution
  • Normal distribution
  • Poisson’s distribution
  • Bayes’ theorem
  • Central limit theorem
  • Type 1 and Type 2 errors
  • Hypothesis testing
  • Types of hypothesis tests
  • Confidence Intervals
  • One Sample T-Test
  • Anova and Chi-Square
  • Introduction To Machine Learning
  • Introduction To Regression
  • Linear Regression- A Brief Introduction
  • Metrics of Model performance
  • How To Divide the Data For Training & Testing?
  • Training & Testing Of Model
  • Using R^2 to Check the Accuracy of Model
  • Using the adjusted R^2 to compare model with different number of independent variables
  • Feature selection
  • Forward and backward selection
  • Parameter tuning and Model evaluation
  • Data transformations and Normalization
  • Log transformation of dependent and independent variables
  • Dealing with categorical independent variables
  • One hot encoding vs dummy variable
  • Introduction To Logistic Regression
  • The sigmoid function and odds ratio
  • The concept of logit
  • The failure of OLS in estimating parameters for a logistic regression
  • Introduction to the concept of Maximum likelihood estimation
  • Advantages of the maximum likelihood approach
  • Case study on Linear & Logistic Regression
  •  
  • What is Unsupervised learning?
  • The two major Unsupervised Learning problems – Dimensionality reduction and clustering.
  • Clustering algorithms.
  • The different approaches to clustering – Hierarchical and K means clustering.
  • Hierarchical clustering – The concept of agglomerative and divisive clustering.
  • Agglomerative Clustering – Working of the basic algorithms.
  • Distance matrix – Interpreting dendograms.
  • Choosing the threshold to determine the optimum number of clusters.
  • Case Study on Agglomerative clustering
  • The K-means algorithm.
  • Measures of distance – Euclidean, Manhattan and Minowski distance.
  • The concept of within cluster sums of squares.
  • Using the elbow plot to select optimum number of cluster’s.
  • Case study on k-means clustering.
  • Comparison of k means and agglomerative approaches to clustering.
  • Noise in the data and dimensional reduction.
  • Capturing Variance – The concept of a principal components.
  • Assumptions in using PCA.
  • The working of the PCA algorithm.
  • Eigen vectors and orthogonality of principal components.
  • What is complexity curve?
  • Advantages of using PCA.
  • Build a model using Principal components and comparing with normal model. What is the difference?
  • Putting it all together.
  • The relationship between unsupervised and supervised learning.
  • Case study on Dimensionality reduction followed by a supervised learning model.
  • Case study on Clustering followed by classification model.
  •  
  • Introduction To Classification
  • Types of Classification
  • Binary classification vs Multi class classification.
  • Introduction To Decision trees –
  • Decision trees – nodes and splits.
  • Working of the Decision tree algorithm.
  • Importance of Entropy and Gini index.
  • Manually calculating entropy using Gini formula and working out how to split decision nodes
  • How To Evaluate Decision Tree models.
  • Accuracy metrics – precision, recall and confusion matrix
  • Interpretation for accuracy metric.
  • Building a a robust decision tree model.
  • k-fold cross validation.
  • CART – Extending decision trees to regressing problems.
  • Advantages of using CART.
  • The Bayes theorem.
  • Prior probability.
  • The Gaussian NAÏVE’S BAYES Classifier.
  • What are Assumptions of the Naive Bayes Classifier.
  • Evaluating the model – Precision, Recall, Accuracy metrics and k-fold cross validation
  • ROC Curve and AUC
  • Extending Bayesian Classification
  • Introduction to Visualization, Rules of Visualization
  • Data Types, Sources, Connections, Loading, Reshaping
  • Data Aggregation
  • Working with Continuous and Discrete Data
  • Using Filters
  • Using Calculated Fields and parameters
  • Creating Tables and Charts
  • Building Dash Boards and story boards
  • Sharing Your Work and Publishing for wider audience
  • What is Time Series?
  • Regression vs Time Series
  • Examples of Time Series data
  • Trend, Seasonality, Noise and Stationarity
  • Time Series Operations
  • Detrending
  • Successive Differences
  • Moving Average and Smoothing
  • Exponentially weighted forecasting model
  • Lagging
  • Correlation and Auto-correlation
  • Holt Winters Methods
  • Single Exponential smoothing
  • Holt’s linear trend method
  • Holt’s Winter seasonal method
  • ARIMA and SARIMA
  • Feature Engineering on Text Data Lesson
  • Natural Language Understanding Techniques
  • Natural Language Generation
  • Natural Language Processing Libraries
  • Natural Language Processing with Machine Learning
  • Introduction to Deep Learning
  • Neural Networks Basics
  • Shallow Neural Networks
  • Deep Neural Networks
  • Forward Propagation and Backpropagation.
  • How to Build and Train Deep Neural networks, and apply it to Computer Vision.
  • Introduction to Perceptron & Neural Networks
  • Activation and Loss functions
  • Gradient Descent
  • Hyper Parameter Tuning
  • Tensor Flow & Keras for Neural
  • Networks
  • Introduction to Sequential data
  • RNNs and its mechanisms
  • Vanishing & Exploding gradients in RNNs
  • LSTMs – Long short-term memory
  • GRUs – Gated recurrent unit
  • LSTMs Applications
  • Time series analysis
  • LSTMs with attention mechanism
  • Neural Machine Translation
  • Advanced Language Models:
  • Transformers, BERT, XLNet
  • Introduction to R Language
  • How to install R
  • Documentation in R
  • Hello world
  • Package in R
  • Data Types in R
  • Data structures
  • Conditional statement in R
  • Loops in R
  • Subsetting
  • Reading Data from csv,excel files
  • Creating a vector and vector operation
  • Initializing data frame
  • Control structure
  • Data VIsualization in R
  • Creating Bar Chart
  • Creating Histogram and box plot
  • Plotting with base graphics
  • Plotting and coloring in R
  • Machine Learning Algorithms Using R
  •  
  • Reading the Data
  • Cleaning the Data
  • Data Visualization in Python
  • Summary statistics (mean, median, mode, variance, standard deviation)
  • Seaborn
  • Matplotlib
  • Population VS sample
  • Univariate and Multivariate statistics
  • Types of variables – Categorical and Continuous
  • Coefficient of correlations, Skewness and kurtosis
  •  
  • Bagging
  • Boosting
  • Bagging & Boosting Examples
  •  
  • Introduction to Model Deployment
  • Introduction to Flask in Python
  • How to deploy Applications in Flask?
  • Types of Model deployment
  • Introduction to Visualization
  • Introduction to Google Data Studio
  • How Data Studio Works?
  • Data Types, Sources, Connections, Loading, Reshaping
  • Data Aggregation
  • Working with Continuous and Discrete Data
  • Report Edit Mode in Data Studio.
  • Using Filters in Data Studio
  • Using Calculated Fields and parameters
  • Creating Tables and Charts
  • Building Dash Boards and story boards
  • Building Dash Boards and Story Boards in Data Studio
  • Text cleaning, regular expressions, Stemming, Lemmatization
  • Word cloud, Principal Component Analysis, Bigrams & Trigrams
  • Web scrapping, Text summarization, Lex Rank algorithm
  • Latent Dirichlet Allocation (LDA) Technique
  • Word2vec Architecture (Skip Grams vs CBOW)
  • Text classification, Document vectors, Text classification using Doc2vec
  • Introduction to Reinforcement Learning
  • Reinforcement Learning Framework and Elements
  • Multi-Arm Bandit
  • Markov Decision Process
  • Q-value and Advantage Based Algorithms
  • Introduction to Convolutional Neural Networks
  • Convolution, Pooling, Padding & its mechanisms
  • Forward Propagation & Backpropagation for CNNs
  • CNN architectures like AlexNet,
  • VGGNet, InceptionNet & ResNet
  • Transfer Learning
  • Advanced Computer Vision
  • Object Detection
  • YOLO, R-CNN, SSD
  • Semantic Segmentation
  • U-Net
  • Face Recognition using Siamese
  • Networks
  • Instance Segmentation
  •  
Capstone Projects

Test your skills and mettle with a capstone project.

Retail
Techniques used: Market Basket Analysis, RFM (Recency-Frequency Monetary) , Time Series Forecasting


Insurance
Techniques used: NLP (Natural Language Processing), Vector Model, Latent Semantic Analysis


Web & Social Media
Techniques used: Topic Modeling using 9 Latent Dirichlet Allocation. K-Means & Hierarchical Clustering


E-commerce
Techniques used: Text Mining, Kmeans Clustering, Regression Trees, XGBoost, Neural Network


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Finance & Accounts
Techniques used: Conditional Inference Tree, Logistic Regression, CART and Random Forest


Retail
Techniques used: Market Basket Analysis,
Brand Loyalty Analysis


Banking
Techniques used: Linear Discriminant Analysis, Logistic Regression, Neural Network, Boosting, Random Forest
Entrepreneurship /Start Ups
Techniques used: Univariate and Bivariate Analysis, Multinomial Logistic Regression, Random Forest


Supply Chain
Techniques used: Text Mining, Kmeans Clustering, Regression Trees & Algorithms, XGBoost Clustering


Healthcare
Techniques used: Logistic Regression, Random Tree, ADA Boost, Random Forest, KSVM






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Why Learn N Lead

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Data Science Batch Profile

Experience Distribution

Our students include freshers and experienced professionals from across industries, functions and backgrounds.
Placement Assistance*

Take advantage of Learn N Lead’s partnerships with India’s leading IT companies.

enterprise

Opportunities with Leading Companies

resume

Workshops on Resume Review

discussion

Career Guidance & Mentorship

Career Highlights

Take Advantage of various Job Roles after Data Science.

200+
Participating Companies



$122K PA

Average CTC



$250K

Highest CTC



87%

Average Salary Hike


Future of Data Science

Few highlights of data science future.

Hiring Partners

Learn N Lead promises a job at the end of the program.

How to Get Certified

Find the steps below to get certified.



Application Form
Apply by filling a simple online application form.


Application Review
Admissions committee will review and shortlist..


Application Shortlisting
Shortlisted candidates need to appear for an online aptitude test


Application Finalization
Screening call with Alumni/ Faculty.
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FAQs & Answers

Find all the answers for your doubts

What is the PGP in Data Science course from Learn N Lead and what makes it different from individual courses?

The course is carefully designed learning path that has been created by some of the leading industry experts. The structure of the course curriculum has been set up in a way that even a complete beginner to Data Science will be able to follow the course progression and understand all the course materials clearly. Our world-class instructors will ensure that you become a master of Data Science by the time you complete the course.

Can Learn N Lead provide the PGP in Data Science course at a location near me??

Our course content is intended to reach learners globally. Whether you reside in any country be it India, Canada, USA, Philippines, Singapore, Malaysia, Australia, or European countries like UK, Germany, Netherlands, Germany or somewhere else, you will be able to access our content 24/7, at any time of the day. The reason for this is that our content is available 100% online. You can access all of our content sitting at your home or office.

Can I ask for a support and doubt clearing session if I want to understand the topics at a deeper level?

Learn N Lead offers 24/7 query resolution and you can file a ticket with a dedicated support team at any time. We provide both email and video chat support for all the queries. If your query does not get resolved in a suitable amount of time through email, we will arrange live one on one sessions with our world-class instructors who would be more than happy to guide you through your doubts.

Does Learn N Lead provide any kind of job assistance?

Learn N Lead supports its learners by providing placement assistance to all learners who successfully complete the course and pass all the exams, projects, and assignments. We have partnerships with lots of MNCs and other top employers around the world. Through our networks of contacts, you can successfully land a job in several outstanding organizations and equally great enterprises. We also conduct several free seminars and workshops on how to create one’s resume and how to prepare for job interviews. We will also conduct counselling sessions which will be for Career mentoring and participating in Career fairs.

Is Data Scientist a good Career choice?

Data Science is in huge demand in many industries, ranging from IT to Finance to Ecommerce to Manufacturing to Healthcare to Retail. It is the fastest growing job on Linkedin and is predicted to create 11.5 million jobs by 2026. This makes Data Science a very lucrative career choice. Also, the number of people who actually possess the requisite skillsets to become a complete Data Scientist is very little. Thus, there is a high demand of Data Scientists but a low supply of qualified people. So Data Scientists can demand as big a salary as they want and the companies will have to comply with their demands.

What are the skills required to start a job in the field of Data Science?

The skills you will require to land a job in the field of Data Science are - Python Coding, Hadoop platform knowledge, SQL Database/Coding, working domain-specific knowledge of Machine Learning and AI, Data Visualization skills, Statistics, Multivariable Calculus and Linear Algebra.

What are the tools and technologies used to teach this PGP in Data Science course from Learn N Lead?

Python, Tableau, SQL..

What is the duration of this PGP in Data Science course?

The PGP in Data Science course from learn N Lead is a 12 month long online program.

Which topics are covered in the course curriculum of the PGP in Data Science course from Learn N Lead?

There are several topics pertinent to the field of Data Science which are covered in this PGP in Data Science course. Some of them are as follows – Regression, Predictive modelling, Clustering, Time Series Forecasting, Classification etc. There are exercises which require one to structure a business problem into an analytics framework using Statistics and Data Modelling. There are also data cleaning, data transformation, Deep learning, and Natural Language Processing (NLP) topics.

Which sectors are the capstone projects based on?

Retail, Web and Social Media, Supply Chain, Entrepreneurship, E-commerce, Banking, Healthcare, Insurance, Finance and Accounts etc.

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