Post Graduate Program In Data Science BY Careerera, Program, Fees, Course, Admission, Syllabus

• 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 the model with different numbers 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

• Bagging
• Boosting
• Bagging & Boosting Examples

• 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 dendrograms.
• 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 Minkowski distance.
• The concept of within-cluster sums of squares.
• Using the elbow plot to select an optimum number of clusters.
• 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 principal components.
• Assumptions in using PCA.
• The working of the PCA algorithm.
• Eigenvectors and orthogonality of principal components.
• What is the complexity curve?
• Advantages of using PCA.
• Build a model using Principal components and comparing with the 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.

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• Introduction to Model Deployment
• Introduction to Flask in Python
• How to deploy Applications in Flask?
• Types of Model deployment

• 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 the 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 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 the 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

Data Visualization

Data Visualization Using Tableau

• 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 Dashboards and storyboards
• Sharing Your Work and Publishing for a wider audience

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Data Visualization Using Google Data Studio

• Introduction to Visualization
• Introduction to Google Data Studio
• How does 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 Dashboards and storyboards
• Building Dash Boards and Storyboards in Data Studio

Data Visualization Using Power Bi

• Introduction to Microsoft Power BI
• The key features of Power BI workflow
• Desktop application
• BI service
• File data sources
• Sourcing data from the web (OData and Azure)
• Building a dashboard
• Data visualization
• Publishing to the cloud
• DAX data computation
• Row context
• Filter context
• Analytics pane
• Creating columns and measures
• Data drill-down and drill-up
• Creating tables
• Binned tables
• Data modelling and relationships
• Power BI Components such as Power View, Map, Query, and Pivot

Introduction To Artificial Intelligence

Time Series Forecasting

• What is the 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
• An 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

Text Mining And Sentiment Analysis

• Text cleaning, regular expressions, Stemming, Lemmatization
• Word cloud, Principal Component Analysis, Bigrams & Trigrams
• Web scraping, Text summarization, Lex Rank algorithm
• Latent Dirichlet Allocation (LDA) Technique
• Word2vec Architecture (Skip Grams vs CBOW)
• Text classification, Document vectors, Text classification using Doc2vec

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Introduction to Natural Language Processing

• Feature Engineering on Text Data Lesson
• Natural Language Understanding Techniques
• Natural Language Generation
• Natural Language Processing Libraries
• Natural Language Processing with Machine Learning

Reinforcement Learning

• Introduction to Reinforcement Learning
• Reinforcement Learning Framework and Elements
• Multi-Arm Bandit
• Markov Decision Process
• Q-value and Advantage Based Algorithms

Introduction to Neural Networks And Deep 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 them 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 their mechanisms
• Vanishing & Exploding gradients
• in RNNs
• LSTMs – Long short-term memory
• GRUs – Gated recurrent unit
• LSTMs Applications
• Time series analysis
• LSTMs with an attention mechanism
• Neural Machine Translation
• Advanced Language Models:
• Transformers, BERT, XLNet

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Computer Vision

• Introduction to Convolutional Neural Networks
• Convolution, Pooling, Padding & its mechanisms
• Forward Propagation & Backpropagation for CNN’s
• CNN architectures like Alex Net,
• VGG Net, Inception Net & Res Net
• Transfer Learning
• Advanced Computer Vision
• Object Detection
• YOLO, R-CNN, SSD
• Semantic Segmentation
• U-Net
• Face Recognition using Siamese
• Networks
• Instance Segmentation

How It Helps

Candidates who enroll in the online Post Graduate Program In Data Science BY Careerera programme will gain the following advantages:

• Learn to execute data transformation and cleaning operations using a number of tools and methodologies.
• Recognize Deep Learning and Natural Language Processing (NLP).
• Make a convincing case for the positions of data engineer, analyst, and data scientist at leading analytics companies.

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Post Graduate Program In Data Science BY Careerera FAQ’S

Is doing PG in data science worth it?

The following benefits of PGDM Data Science give a picture of why the course is worth doing: High demand: Data Science is one of the fastest-growing fields in the world. Completing a PG Diploma in Data Science will make you highly employable and increase your chances of getting a job.

What is the career path for master of Data Science?

With more education under your belt, you can advance to higher-level positions such as machine learning engineer, artificial intelligence engineer, data architect, enterprise architect, or applications architect.

Can I do post graduation in Data Science?

Postgraduate diploma Data Science courses are accessible as a path of specialty in Engineering, Computer Science, and Management. Bachelor’s degree with at least 50 percent marks in aggregate or equivalent, preferably in Science or Computer Science from a recognised university, is the minimum qualifying criterion.

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