Post-Graduate Program in Data Science:- The Post-Graduate Programme in Data Science (PGP-DS) training is a 12-month, in-depth online course that gives you a thorough understanding of many different data science ideas.
Candidates will learn how to use technologies including R, Tableau, Hadoop, Python, Spark, Hive, and SQL as well as data analytics, machine learning, data visualisation, and deep learning during the training process. This course is offered by Amity Future Academy in collaboration with eCornell, the online education division of Cornell University, which is situated in New York.
Post-Graduate Program in Data Science
A virtual job fair, specialised career counselling, and hiring tools like Hiration and CutShort.io are all included in the Post-Graduate Programme in Data Science (PGP-DS) certification course. Candidates also have access to four case studies from Harvard Business Publishing, projects, and top-notch video tutorials taught by professionals in the field.
You will complete a capstone project towards the end of the course, which tries to synthesise all the information learned throughout the online Post-Graduate Programme in Data Science (PGP-DS) training course. After completing all required course modules, assignments, and capstone projects, candidates are awarded a certificate. Additionally, eCornell offers certification, 26 PDUs, and 40 PDHs in the form of professional development hours.
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Post-Graduate Program in Data Science Details
| Article Name | Post-Graduate Program in Data Science (PGP-DS) BY Amity Future Academy |
| Year | 2024 |
| Category | Courses |
| Official site | www.amityfutureacademy.com |
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The highlights
- Personal mentoring sessions
- Certification from eCornell
- 24/7 access to course materials
- 12 months course
- Industry-based projects
- Live interaction with industry experts
- Alumni status for Amity Future Academy
- 7-10 Hours per week
- Career support and assistance
Program offerings
- Course-completion certificate
- Personal mentoring
- Case studies
- 24/7 access
- Hands-on training
- Capstone project
- Live interactive doubt solving
- Career assistance
- Ecornell certification
Course and certificate fees
FEES INFORMATION
₹ 215,000
(Inclusive of GST)
- If you are unable to pay the Post-Graduate Programme in Data Science (PGP-DS) certification cost in full at once, EMI options are available.
- The payment can be made online through net banking, a credit card, or a debit card.
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Post-Graduate Program in Data Science (PGP-DS) fee structure
| Course | Fees in INR |
| Post-Graduate Program in Data Science (PGP-DS) | Rs. 2,15,000 |
Eligibility criteria
Mathematics Statistics
You need a degree in one of the following fields with a minimum cumulative GPA of 50% in order to join in the Post Graduate Programme in Data Science (PGP-DS) online training: BE, BCA, B.Tech, MCA, B.Sc. (Maths), or M.Sc. (Maths). It is ideal if you have a mathematical or statistical background. The requirement for students is a two-year work history. Additionally, Amity Future Academy may relax the work experience requirements if you are a meritorious graduate or post-graduate with a GPA > 7/10.
Certification Qualifying Details
Moreover, candidates must pass all of the program’s assessments, assignments, modules, and quizzes in order to receive the Post Graduate Programme in Data Science (PGP-DS) accreditation from Amity Future Academy. Additionally, you will need to complete the capstone project and all of the case studies by the deadline. When you turn in a comprehensive report on the project and a presentation, the capstone project will be deemed finished.
What you will learn
You will be able to: After completing the online Post Graduate Programme in Data Science (PGP-DS) certification syllabus.
- Learn how to create data management processes, leverage data to inform important company choices, and interpret unintelligible organisational data.
- Identify the best ways to integrate data visualisations and predictive models to increase the accuracy of your forecasts.
- Learn how to formulate a business question as a scientific hypothesis that can be tested using different statistical techniques.
- Create and validate regression models to discover how attributes affect a single decision and forecast potential outcomes.
- Learn how to use a wide variety of software programmes and analytical tools, including R, Python, Hadoop, Spark, Excel, and MySQL.
- Amity offers a free subscription to Tableau, a premium software programme that teaches you how to perform visual analytics.
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Who it is for
Data scientist, Machine Learning Engineer, and Data Analyst Data engineer
A good fit for: The Post Graduate Programme in Data Science (PGP-DS) programme offers extensive understanding in the field of data science.
- Students in their final year of graduation
- Some professionals like the following:
- Data Analyst
- Data Scientist
- Big Data Analyst Engineer
- Big Data Developer
- Machine Learning Engineer
- Data Architect
Step to Apply for Admission details
For information about applying to the Post-Graduate Programme in Data Science (PGP-DS) classes, please see:
- First of all, open the Amity Future Academy’s website using this link- https://amityfutureacademy.com/Home/app/ and log in using your credentials.
- To find the Post-Graduate Programme in Data Science (PGP-DS) course, use the website’s ‘Programme’ menu.
- The course is listed in the catalogue for the 12-month PGP certification programme.
- Complete the application form located at the page’s top.
- The relevant information must be provided, including your name, email address, phone number, nationality, and city of residence.
- The login information will be sent to you through email by Amity Future Academy.
- You must now input information about your present employer, job history (measured in months), and educational background. Continue on to the next.
- Complete a screening call, then send the final application to the admissions director’s office.
Filling the form
Fill out the application form found on the course webpage to enrol in the online Post-Graduate Programme in Data Science (PGP-DS). You will be contacted by an admissions director from the Amity Future Academy’s office to begin the screening process. After analysing your application and profile, the admission committee will reach a final judgement.
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The syllabus
Module I – Introduction to Data Science
- What is Data Science and why is it so important?
- Overview of Data Science and Analytics
Mathematics for Data Science
- Probability and Inferential Statistics
- Linear Algebra
- Calculus
Introduction to R and Python
- Basic Python Programming constructs
- Functions and OOP in Python, NumPy basics
- Learn Pandas basic concepts, work with Series
- Work with Pandas DataFrame (Advanced)
- Fundamentals of R
- Univariate statistics in R
- Data preparation using R
Module II a – Data Visualization with Tableau
Introduction and Overview of Tableau
- Why Tableau?
- Why Visualization?
- Level Setting – Terminology
- Getting Started – creating some powerful
- visualizations quickly
- The Tableau Product Line
- Things you should know about Tableau
Getting Started with Tableau
- Connecting to Data and introduction to data source concept
- Working with data files versus database server
- Understanding the Tableau workspace
- Dimensions and Measures
- Using Show Me!
- Tour of Shelves (How shelves and marks work)
- Building Basic Views
- Help Menu and Samples
- Saving and sharing your work
Analysis
- Creating Views
- Marks
- Size and Transparency
- Highlighting
- Working with Dates
- Date aggregations and date parts
- Discrete versus Continuous
- Dual Axis / Multiple Measures
- Combo Charts with different mark types
- Geographic Map Page Trails
- Heat Map
- Density Chart
- Scatter Plots
- Pie Charts and Bar Charts
- Small Multiples
- Working with aggregate versus disaggregate data
- Analyzing
- Sorting & Grouping
- Aliases
- Filtering and Quick Filters
- Cross-Tabs (Pivot Tables)
- Totals and Subtotals Drilling and Drill Through
- Aggregation and Disaggregation
- Percent of Total
- Working with Statistics and Trend lines
Getting Started with Calculated fields
- Working with String Functions
- Basic Arithmetic Calculations
- Date Math
- Working with Totals
- Custom Aggregations
- Logic Statements
Formatting
- Options in Formatting your Visualization
- Working with Labels and Annotations
- Effective Use of Titles and Captions
- Introduction to Visual Best Practices
- Combining multiple visualizations into a dashboard
- Make your worksheet interactive by using actions and filters
- An Introduction to Best Practices in Visualization
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Module II b – Understanding and Visualizing Data
- Learn to utilise your decision-making frameworks to achieve favourable outcomes
- Assess decisions by looking at KPIs and determining their impact on stakeholders
Module III -Predictive Analytics and Optimization Techniques
- Implementing Scientific Decision Making
- Case Study
- Using Predictive Data Analysis
Module IV – Machine Learning (Supervised Learning) – I
Regression Techniques
- Linear & Multiple Regression
- Support Vector Regressor & Decision Tree Regressor
- Hands-on Regression techniques
Data exploration
- Data Pre-processing
- Data Transformation
- Data Reduction
Evaluation methods
- Hold-out method
- Cross-validation and bootstrapping method
Classification Techniques
- Ensemble Methods
- Decision Trees
- Support Vector Machines
- K Nearest Neighbours
- Logistic Regression
- Naïve Bayes
- Hands-on classification techniques
- Case Study
Module V – Machine Learning (Unsupervised Learning) – II
Clustering
- Hierarchical clustering
- K-Means
- Proximity Measures in Machine Learning
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
Anomaly Detection
Dimensionality Reduction
- Need for dimensionality reduction
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD
- T-distributed Stochastic Neighbor Embedding (t-SNE)
Association Rule Learning
- Apriori Algorithm
- FP growth
- Hands-on on clustering
- Hands-on association rule mining
- Hands-on dimensionality reduction
- Hands-on anomaly detection
- Case Study
- Database Management Systems
Module VI – Database Management Systems
Module VII- Deep Learning
Neural Networks
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory (LSTM)
- Gated recurrent units (GRUs)
Natural Language Processing (NLP)
- Case Study
Module VIII- Big Data Analytics
- Introduction to Big Data and Hadoop
- HDFS and YARN
- MapReduce and Sqoop
- Basics of Hive and Impala
- Working with Hive and Impala
- Types of data formats
- Advanced Hive Concept and Data File
- Apache Flume and HBase
- Pig
- Basics of Apache Spark
- RDDs in Spark
- Implementation of Spark Applications
- Spark Parallel Processing
- Spark RDD Optimization Techniques
- Spark Algorithm
- Spark SQL
- Case Study
Capstone Project
How it helps
Discover the advantages of the Post-Graduate Programme in Data Science (PGP-DS) certification listed below:
The need for Data Analytics specialists has increased twofold on the global market. Industry estimates state that as almost 70% of firms intend to hire data professionals in the future years, the need will grow dramatically.
You will become an expert in data analytics, business intelligence, data science, machine learning, and data engineering after finishing the Post-Graduate Programme in Data Science (PGP-DS) training course offered by Amity Future Academy in association with eCornell. Jobs for applicants can be found in businesses like Google, Amazon, Zomato, Bewakoof, and others.
Post-Graduate Program in Data Science 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.
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.
What is the PG program in Data Science?
The PGP-DS (Post Graduate Program in Data Science) gives you wide coverage of main ideas and techniques from Python, Exploratory Data Analysis to Machine Learning, Deep Learning and more.
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