Data Scientist by Udacity: 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

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.

Course and certificate fees

The fee for Data Scientist is summarized as follows:

Particulars	Amount
Annual Fee	₹20,500
Monthly fee - pay as you go	₹10,250/month

certificate availability

Yes

certificate providing authority

Udacity

Who it is for

Data scientist Python programmer Machine learning engineer Data architect

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

Knowledge of python Sql knowledge

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

The syllabus

Solve Data Science Problems

The Data Science Process

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

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

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 “Enroll 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.

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

What positions will I be able to apply to after the completion of this program?

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

What is the difference between the Data Scientist and Data Analyst course?

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.

Does Udacity have any Free Courses related to Data Science?

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.

What other services will be provided to aid in the job search?

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.

Does this course provide any practical forms of learning?

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.