Post Graduate Program In Data Science by Careerera: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based

Course overview

Careerera's Post Graduate Program In Data Science training gives new wings to the career. This certification training is specifically created for young professionals and recent graduates who want to explore and secure high-reward and low-investment employment in data science. The course also provides placement assistance to the students.

The Post Graduate Program In Data Science online course covers a wide range of concepts and techniques, including exploratory data analysis, Python, deep learning, machine learning, and more. Assignment work and practical laboratories bring these concepts to life, teachers, and assistants guide students along the way.

The Post Graduate Program In Data Science syllabus has been meticulously developed to give learners a simple course path of organic growth in which new subjects and ideas are gradually introduced to the candidates and they can relate Data Science aspects such as SQL, Python, Tableau, Deep learning and neural networks, Machine Learning techniques, Exploratory Data Analysis, Artificial Intelligence, Data Visualization, etc.

Course and certificate fees

Fees information

$ 1,499 $1,999

certificate availability

Yes

certificate providing authority

Careerera

Who it is for

Data analyst Data scientist Research associate

The course will be beneficial for Data analysts, Reporting analysts, Analytics consultants, Data engineers, Data scientists, AI engineers, ML engineers, Research associates, Reporting analysts, Statisticians, etc

Eligibility criteria

Fresh Graduates

A degree in BCA, MCA or B.Sc (Mathematics or Statistics), B.Tech/M.Tech (Any Trade), BA (Economics, Maths or Stats), B.Com.

Working professionals

More than 1 year of professional experience in R, Python, SAS, Data warehousing, Business intelligence, SQL.

Certification Qualifying Details

To qualify for the Post Graduate Program In Data Science certification, candidates will have to qualify for the certification examination conducted by the Careerera.

What you will learn

Data science knowledge Knowledge of python Tableau knowledge

After completing the Post Graduate Program In Data Science certification course from Careerera, candidates will understand technologies and analytics tools such as Tableau, Python, and SQL and learn to use machine learning approaches applicable to the business, such as predictive modeling, regression, time series forecasting, clustering, classification, and so on. Learners will be able to create an analytics framework for business challenges by utilizing statistics and data modeling.

The syllabus

Foundations

Introduction to programming using Python

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 the map, reduce and filter functions

Introduction to programming using R

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

Database Management System using My SQL

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 Subqueries

Data Analysis

Statistics For Data Science

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

Exploring Data Analysis

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

Machine Learning Techniques

Supervised Learning - Regression

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

Ensemble Techniques

Bagging
Boosting
Bagging & Boosting Examples

Unsupervised Learning

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.

Machine Learning Model Deployment using Flask

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

Supervised Learning - Classification

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

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 DashBoards 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

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

Computer vision

Introduction to Convolutional Neural Networks
Convolution, Pooling, Padding & its mechanisms
Forward Propagation & Backpropagation for CNN’s
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

Admission details

To get admission to the Post Graduate Program In Data Science tutorial by Careerera, follow the steps mentioned below:

Step 1. Open the Careerera course page by following the link below.

(https://www.careerera.com/data-science/post-graduate-program-in-data-science)

Step 2. Click on the ‘Upcoming Batches’ button and choose your batch.

Step 3. Tap the ‘Enroll Now’ button to start the registration process.

Step 4. Fill in the required information and submit the essential documents.

Step 5. The admission committee will shortlist candidates and selected candidates will be called for the online aptitude test.

Step 6. After passing the aptitude test, candidates can start the course by paying the fee.

How it helps

Candidates pursuing Post Graduate Program In Data Science online certification training will be benefited in the following ways:

Learn to use a variety of tools and approaches, perform data transformation and cleaning activities.
Understand Natural Language Processing (NLP) and Deep Learning.
Present yourself as a strong candidate for data engineer, analyst, and data scientist positions at top analytics firms.

FAQs

What is the duration of the Post Graduate Program In Data Science course?

The duration of the Post Graduate Program In Data Science course is 12 months.

Is there any exam to qualify for the Post Graduate Program In Data Science certification?

Yes, candidates will have to pass the exam conducted by Careerera to qualify for the Post Graduate Program In Data Science certification.

What industries are the basis of the Post Graduate Program In Data Science capstone projects?

The capstone project of the Post Graduate Program In Data Science course is based on various industries such as Web and Social Media, Retail, Supply Chain, E-commerce, Entrepreneurship, Banking, Insurance, Healthcare Management, Finance, and Accounts, etc.

Is there any placement support after completing the Post Graduate Program In Data Science course?

Yes, the course provides placement support to the students who complete the Post Graduate Program In Data Science course.

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Post Graduate Program In Data Science

Online

12 Months

$ 1499 1999