Post Graduate Program In Data Science

About the Program

Use the specialized Data Science Analytics Program to advance your Data Science profession. Discover the most in-demand Data Science skills with world-class training from industry leaders. Study important technologies like Python, Machine Learning, Data Visualization, SQL, and Artificial Intelligence firsthand. Today, develop your skills as a professional in Data Science.

Data Science Bootcamp Program covers a wide range of fundamental concepts and methods, including Python, Exploratory Data Analysis, Machine Learning, Deep Learning, and other topics. Our instructors and subject matter experts will guide you along the journey while putting these concepts into practice through practical laboratories and assignment work.

Why Careernow?

10000+ Students
50 Thousand Hours of Learning Delivered
Top Ranked Programs
100+ Industry Specialists
Best Data Science Faculty
Resume building and Interview Preparation

Industry has a severe scarcity of highly qualified individuals to fill vacancies. Fortunately, CareerNow has decided to contribute to the issue by launching its PGP program in Data Science so that people can take advantage of our services and get our Online Data Science Graduate Credential.

Give your career a boost by enrolling in this well regarded PG Data Science program with CareerNow and the team.

Why Should You Be a Data Scientist?

Artificial intelligence and data science have completely changed the world. Artificial intelligence is being used by businesses all over the world to reduce repetitive work and enhance the customer experience. Robots are dominating society and are constantly developing intellect on par with that of human brains. The highest-paying professions in the world are those in artificial intelligence and machine learning. According to a recent prediction, more than 90% of businesses will utilize Artificial Intelligence in some capacity to create or improve their goods and services. These businesses are recruiting individuals with data science and AI expertise.

CareerNow's innovative Data Science Bootcamp will give your career new life. This online certification course is specifically developed for young professionals and recent graduates interested in investigating and securing low-investment, high-reward Data Science professions. Individuals from the Information Technology, Consultancy, Manufacturing, Pharmaceutical and Healthcare, and Banking, Financial Services, and Insurance (BFSI) industries are encouraged to attend our data science certification and training program in order to enhance their skills. Our one-of-a-kind online classroom training program offers lectures presented in a technologically enhanced format by our qualified instructors via online video sessions.

How CareerNow can help you

You will be allocated a specialized Program Manager to address all of your questions, uncertainties, and concerns that are not directly related to the course curriculum but rather to the administrative parts of the PGP in Data Science program. Our specialized instructors will perform unique one-on-one doubt clearing sessions via email, voice call, and video call help for all your study material-related questions, allowing you to breeze through our Data Science certification course. You will also be handed numerous assignments and projects as part of the PGP in Data Science Program, which will help you put your newly learned skills and knowledge to practical and constructive use. In addition, the teachers will provide specific and accurate comments on all of the students' tasks and projects, aiding in debugging and promoting active engagement.

We at CareerNow encourage Learner Networking with one another, as well as with our Mentors and Industry Professionals, by providing them with a single platform to speak with one another and enhance this online Data Science Certification Course.

Our PGP in Data Science curriculum has been carefully crafted to provide students with a logical progression through the material, with each new topic and concept introduced at a comfortable pace. Students will be introduced to a wide range of Data Science topics, including Python, SQL, Tableau, Machine Learning Techniques, Deep learning and Neural Networks, Exploratory Data Analysis, Data Visualization, Artificial Intelligence, and more.

By the end of the course, you will be able to:

Be fluent in Analytics Software and languages like Python, Tableau, and SQL
Use machine learning strategies that are applicable to your sector, such as Regression, Predictive Modeling, Clustering, Time Series Forecasting, Classification, and so on.
Using Statistical Analysis and Data Modeling, a Business issue should be structured into an Analytics Framework.
Carry out procedures for the cleansing of data and the transformation of data utilizing a variety of tools and methods.
Be knowledgeable in areas such as Natural Language Processing and Deep Learning (NLP).
Create a compelling case for why you would be an excellent hire for an Analyst, Data Engineer, or Data Scientist post in a renowned analytics company.

Boost your professional prospects by enrolling in this highly respected Data Science Bootcamp offered by CareerNow and team.

Program Curriculum

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 map, reduce and filter functions
.
Central limit theorem
.
Row context
.

Introduction to programming using R

Introduction to R Language
.
How to install R
.
Documentation in R
.
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 Sub queries
.

Statistics For Data Science

Sampling
.
Probability distribution
.
Normal distribution
.
Poisson's distribution
.
Bayes’ 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
.

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 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
.

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 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.
.

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 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
.

Ensemble Techniques

Bagging
.
Boosting
.
Bagging & Boosting Examples
.

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
.

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 Dash Boards and story boards
.
Sharing Your Work and Publishing for wider audience
.

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
.
Filter context
.
Analytics pane
.
Creating columns and measures
.
Data drill down and drill up
.
Creating tables
.
Binned tables
.
Data modeling and relationships
.
Power BI components such as Power View, Map, Query, and Pivot
.

Data Visualization Using Google Data Studio

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
.

Time Series Forecasting

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
.

Text Mining And Sentimental Analysis

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 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
.

Computer vision

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
.

Introduction to Neural Networks and Deep Learning

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 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
.

In Demand skills like ChatGPT