- Applying statistics for descriptive and inferential understanding
- Exploring, wrangling, and analysing a dataset
- Applying machine learning for prediction
- Draw conclusions which motivate others to act on your results
Online
4 Months
Quick facts
particular | details | |||
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Collaborators
IBM,
+4 more
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video Based
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Learning efforts
10 Hours Per Week
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Course overview
The Data Scientist Nanodegree Programme by Udacity has been designed for individuals who wish to learn and upskill with the advanced features and aspects of Data Science and get ahead in the field. The course will help you explore the field of Data Science in-depth and prepare you for the challenging yet exciting role of a Data Scientist.
The Data Scientist Course by Udacity focuses on concepts such as running building recommendation systems, Data pipelines and deploying solutions to the cloud. Also, the Data Scientist. The syllabus explores the path of Data Science in detail and delves deeper into it. Students who are new to Data Science and are not familiar with the basics can opt for the introductory courses provided by Udacity to prepare themselves for this advanced-level course.
Furthermore, the Udacity Data Analysis Course is a self-paced online learning course that allows the candidate to learn at their convenience. Students can get a custom learning plan that is tailored to fit their busy schedules. Data Scientist. classes give you an opportunity to learn and master skills that are in high demand across many job profiles.
The highlights
- Flexible Learning
- Career Coaching Sessions
- Real-world Projects
- Project Reviews
- Project feedback from experienced reviewers
- Technical mentor support
- Student Hub
- Interview preparations
- Resume Services
- Custom Study Plans and Progress Trackers
Program offerings
- Real-world projects
- Instructor-led sessions
- Self-paced learning
Course and certificate fees
Candidates can pay the full Data Scientist training Course fee by Udacity, either upfront for four months or every month. You can also avail of a 15% discount on the total Data Scientist course fee. Moreover, a free seven-day trial is available once you sign up for the online course.
Data Scientist Certification Fee Structure
Training Option | Fee in INR |
Self-paced online training (Pay Upfront) | Rs 91,396 for 4-month access |
Self-paced online training (Pay as you go) | Rs 22,849/month |
certificate availability
certificate providing authority
Eligibility criteria
The Data Scientist Nanodegree programme by Udacity is an advanced programme designed to prepare you for the position of a data scientist in various organisations. As it is not an introductory level course, candidates need to have familiarity and comfort in a variety of topics – such as SQL and Python programming, Probability and Statistics, and Mathematics (Calculus, Linear Algebra).
Besides, candidates also need to understand data wrangling, data visualisation with matplotlib, and Machine Learning.
Certificate Qualifying Details
To get a Data Scientist Nanodegree Programme certificate, the candidate must submit a project for review, based on which the reviewer will provide the approval.
What you will learn
Upon completion of the Data Scientist Certification Course by Udacity, you will have:
- Ability to apply the principles of statistics and probability for design and execution of A/B tests as well as recommendation engines to assist businesses to make data-driven decisions
- Proficiency in using Python and SQL to access and analyse data from several different sources of data
- Skills to manipulate and analyse distributed datasets using Apache Spark
- Fluency in communicating results effectively to the stakeholders
Admission details
To apply to the Data Scientist online programme by Udacity, follow these steps:
Step 1. Open the page– https://www.udacity.com/
Step 2. In the “Programs” option, locate “Data Science”. Choose “Data Scientist” from the courses available on the list.
Step 3. Click on the “Enrol Now” button. You will find two options to apply for the course.
Step 4. Next, you can sign up with your email address, or use your Google or Facebook account.
Filling the form
There is no application form to enrol in the Data Scientist online course, but candidates can sign up to enjoy a seven-day free trial using their email ID, Google account, or Facebook account.
The syllabus
Solve Data Science Problems
The Data Science Process
Communicating with Stakeholders
- Learning what makes a data science blog great
- Implement the best practices in sharing your code and written summaries
- Learning how to ideate with the data science community
Project – Write a blog post on Data Science
Software Engineering for Data Scientists
Software Engineering Practices
- Refactoring code for efficiency
- Track actions and results of processes with logging
- Write modular, clean, well-documented code
- Writing useful programs in multiple scripts
- Create unit tests to test programs
- Conducting and receiving code reviews
Object-Oriented Programming
- Understand magic methods
- Understand when to use object-oriented programming
- Writing programs which include multiple classes, and follow good code structure
- Learning how large, modular Python packages, such as pandas and scikit-learn, using object-oriented programming
- Build and use classes
- Portfolio Exercise – Building your Python package
Wen Development
- Build a web application which uses Plotly, the Bootstrap, and Flask framework
- Learn about the components of a web app
- Portfolio Exercise – Building a data dashboard using a dataset of your choice and deploying it to a web application
Data Engineering for Data Scientists
ETL Pipelines
- Building an SQLite database to store clean data
- Access and combine data from JSON, logs, CSV, APIs, and databases
- Understand what ETL pipelines are
- Handling outliers, missing values, and duplicating data
- Engineering new features by running calculations
- Normalise data and create dummy variables
- Standardise encodings and columns
Natural Language Processing
- Use scikit-learn to vectorise and transform text data
- Build an NLP model to perform sentiment analysis
- Building features with a bag of words and tf-idf
- Prepare text data for analysis with lemmatisation, tokenisation, and removing stop words
- Extract features with tools such as named entity recognition and part of speech tagging
Machine Learning Pipelines
- Use feature unions to perform steps in parallel and create more complex workflows
- Chaining data transformations and an estimator with scikitlearn’s Pipeline
- Grid searching over the pipeline to optimise parameters for the entire workflow
- Understand the advantages of using machine learning pipelines to streamline the modelling process and data preparation
- Complete a case study to create a full machine learning pipeline which prepares data and builds a model for a dataset
Project – Building Disaster Response Pipelines using Figure Eight
Experimenting with Design and Recommendations
Experiment Design
- Define control and test conditions
- Know how to set up an experiment and the ideas associated with experiments vs. observational studies
- Choose testing and control groups
Statistical Concerns of Experimentation
- Establishing key metrics
- Apply statistics in the real world
- SMART experiments – Measurable, Realistic, Actionable, Specific, Timely
A/B Testing
- Sources of Bias – Novelty and Recency Effects
- Multiple Comparison Techniques (Bonferroni, FDR, Tukey)
- How it works and its limitations
- Portfolio Exercise – Using a technical screener from Starbucks, analyse the results of an experiment and record your findings
Introduction to Recommendation Engines
- List business goals associated with recommendation engines, and become capable of recognising which goals are most easily met with existing recommendation techniques.
- Implement each of these techniques in python.
- Distinguish between common techniques for creating recommendation engines, including content-based, knowledge-based, and collaborative filtering based methods.
Matrix Factorisation for Recommendations
- Interpret the results of matrix factorisation to understand latent features of customer data better
- Understand the pitfalls of traditional methods and pitfalls of measuring the influence of recommendation engines under traditional regression and classification techniques.
- Create recommendation engines using matrix factorisation and FunkSVD
- Determine common pitfalls of recommendation engines such as the cold start problem and difficulties associated with usual tactics for assessing the effectiveness of recommendation
- Engines using usual techniques, and potential solutions
Project – Design a Recommendation Engine using IBM
Data Science Projects
Dog Breed Classification
- Deploy your model to allow others to upload images of their dogs and send them back the corresponding breeds.
- Complete a popular project in Udacity history, and show the world how you can use your deep learning skills to entertain an audience!
- Use convolutional neural networks to classify different dogs according to their breeds
Starbucks
- Identify groups of individuals that are most likely to be responsive to rebates.
- Use purchasing habits to arrive at discount measures to acquire and retain customers
Arvato Financial Services
- Top performers have a chance to get an interview with Arvato or another Bertelsmann company.
- Work through a real-world dataset and challenge provided by Arvato Financial Services, a Bertelsmann company
Spark for Big Data
- Take a course on Apache Spark and complete a project with a massive, distributed dataset to predict customer churn
- Learn to deploy your Spark cluster on either AWS or IBM Cloud
Project – Data Science Capstone Project
How it helps
The Data Scientist Course by Udacity primarily trains you to become a successful Data Scientist and get employed in various organisations of your choice. The course explores advanced aspects of Data Analysis and using tools such as Python and SQL to visualise Data. You will have an in-depth knowledge of Data Analysis to be able to make successful professional decisions.
During the Udacity Data Scientist Course, you will get practical experience through real-world projects and studies crafted carefully by industry experts. Students will also be provided with technical mentor support and career coaching advice and services. On completion of this Data Scientist online course, you will have the necessary skills needed to join a good organisation as an adept Data Scientist and make a successful career for yourself.
Instructors
Mr Josh Bernhard
Data Scientist
Freelancer
Ms Juno Lee
Curriculum Lead
Udacity
Mr Luis Serrano
Instructor
Freelancer
Ph.D
Mr Andrew Paster
Instructor
Udacity
Ms Mike Yi
Content Developer
Freelancer
Ph.D
Mr David Drummond
Vice President
Freelancer
Ph.D
Ms Judit Lantos
Senior Data Engineer
Netflix Inc.
FAQs
As a graduate of the Data Scientist Nanodegree Online Program by Udacity, you will be able to work as not only a Data Scientist but also a Data Analyst, Statistician, Engineer, and more. Some people even choose to specialise as Database Administrators
The Data Analyst program has been crafted for people with a little data analysis experience but almost no programming experience. On the other hand, the Data Scientist Program by Udacity is designed for students who possess strong data analysis and programming skills.
Yes, Udacity provides many FREE courses that can help you prepare for Data Scientist online certification; including Introduction to Data Science Online Course, Introduction to Python Course, SQL for Data Analysis Course, Statistics Course, and Linear Algebra Course.
Udacity also provides services such as career coaching sessions, interview preparations and Resume services to help candidates build an Impressive Resume. Students will also receive guidance on how to negotiate and prepare for future job interviews.
Yes, it requires the candidates to do several projects throughout the Data Scientist online certification along with a capstone project that will be reviewed by the Udacity reviewer platform. You will also be given feedback based on your performance and will have to resubmit the project if failed the first time.