Showing posts with label Course. Show all posts
Showing posts with label Course. Show all posts

Monday 19 February 2024

Python and Machine-Learning for Asset Management with Alternative Data Sets


What you'll learn

Learn what alternative data is and how it is used in financial market applications. 

Become immersed in current academic and practitioner state-of-the-art research pertaining to alternative data applications.

Perform data analysis of real-world alternative datasets using Python.

Gain an understanding and hands-on experience in data analytics, visualization and quantitative modeling applied to alternative data in finance

Join Free: Python and Machine-Learning for Asset Management with Alternative Data Sets

There are 4 modules in this course

Over-utilization of market and accounting data over the last few decades has led to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as a substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications. The approach of this course is somewhat unique because while the theory covered is still a main component, practical lab sessions and examples of working with alternative datasets are also key. This course is fo you if you are aiming at carreers prospects as a data scientist in financial markets, are looking to enhance your analytics skillsets to the financial markets, or if you are interested in cutting-edge technology and research as  they apply to big data. The required background is: Python programming, Investment theory , and Statistics. This course will enable you to learn new data and research techniques applied to the financial markets while strengthening data science and python skills.

Thursday 8 February 2024

Post Graduate Diploma in Applied Statistics


Earn a Post Graduate Diploma from a premier institution and build skills for a successful career in data science.

By joining this Postgraduate Diploma program, you will be empowered with the statistical tools required to make data-driven decisions and advance your career in the fields of data science and applied statistics. You will also hone your skills with real-world data from governments and international organisations.

  • Learn how to analyse, visualise, and present large data sets: You will benefit from a 360-degree view into how official data systems are built and learn scientific ways of collecting, analysing and presenting data.
  • Select a specialised track: You will start with the foundations of statistics, economics, and computing skills, leading to a choice between two tracks - data analytics or official statistics.
  • Gain job-ready applied skills: Develop experience with data analysis tools in popular coding platforms like Python and R. You will also acquire skills needed to build, interpret and improve official databases used in policy making.

How will you benefit from this Post Graduate Diploma by ISI?

Learn from the Institution that works with the government and trains key officers of the Ministry :

Functioning under the Ministry of Statistics and Programme Implementation, ISI is a national institution that leads high-impact national projects involving very large data-sets and works very closely with the Government on various projects.

Prepare for the real-world by working with data from large government and international projects :

Work with open databases used in policy and decision-making across national public projects like the Census of India, National Coal Index (used by the Govt. of India in its auction process of coal mines), browser-based data capture technology for the NSSO survey and important crypto security projects.

Interact in live sessions with renowned, globally-recognised faculty :

Faculty at ISI include eminent scientists of global repute, whose contributions have been recognised with awards such as the Padma Shri, S. S. Bhatnagar Award and the Fellowship of Indian Academy of Science. They facilitate policy decisions by working with large datasets and train officers of the Indian Statistical Service.

Develop industry-ready skills and learn from accomplished experts :

You will get regular opportunities to interact with senior officers of the Indian Statistical Service with up to 40 years of experience. Learn from industry experts through live, interactive sessions and leverage insights to solve critical business challenges at the workplace.

Access potential job opportunities facilitated by ISI :

Through the placement committee led by an ISI faculty convenor, you will get the chance to showcase your skills to prospective employers after building your ‘Learner Skills’ profile. Placements for all ISI programmes are an entirely student-driven activity - there is no placement guarantee offered by ISI.

Receive exclusive career readiness support :

The career readiness program is designed to equip you with the essential skills and knowledge needed to thrive in today's competitive job market. You will receive exclusive access to networking opportunities, career workshops, sessions with industry experts, curated workplace success courses and mentorship from industry veterans.You will be equipped with the skills, connections, and knowledge needed to accelerate your career.

Gain access to an exclusive student community and alumni network :

  1. Gain a global perspective to data science - 27% learners are studying from 15 different nations such as the USA, UK, Norway, Germany, Japan, Sweden, Australia, etc.

  2. Learners have rich industry experience - 80%+ of the batch are working professionals seeking to advance their skills and career (36% have 10+ years of experience). 40% of the batch is 35+ years old.

  3. Network with industry leaders - Many learners are senior professionals at large MNCs and PSUs such as Microsoft, IBM, Accenture, EY, Volvo, BlackRock, Tata Motors and Wipro.

ISI on-campus graduates consistently go on to succeed as data scientists, analysts, statisticians, researchers, policymakers, and more, and have taken roles with industry leaders such as Microsoft, Google, Dell, JP Morgan & Chase, KPMG, Amazon, Flipkart, Samsung, and others.

Explore enhanced flexibility features :

  1. Payment flexibility - Choose to pay in instalments rather than paying for the entire program upfront to better plan and finance your education.
  2. Self-paced learning - Complete your studies in up to 36 months at no additional cost - focus on your academic pursuits without compromising on your other commitments.
  3. Classes that suit your schedule - Manage your work and studies better by attending classes in the evenings and on weekends.
  4. Choose your specialisation - Choose amongst either data analytics or official statistics (or both), depending on your area of interest.

JOIN: Post Graduate Diploma in Applied Statistics Indian Statistical Institute

Monday 22 January 2024

Learn to Program: The Fundamentals


There are 7 modules in this course

Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language.

Join free : Learn to Program: The Fundamentals

Skills you'll gain

  • Python Syntax And Semantics
  • Computer Programming
  • Python Programming
  • Idle (Python)

Sunday 7 January 2024

Web Design for Everybody: Basics of Web Development & Coding Specialization


What you'll learn

Add interacitivity to web pages with Javascript

Describe the basics of Cascading Style Sheets (CSS3)

Use the Document Object Model (DOM) to modify pages

Apply responsive design to enable page to be viewed by various devices

Specialization - 5 course series

This Specialization covers the basics of how web pages are created – from writing syntactically correct HTML and CSS to adding JavaScript to create an interactive experience. While building your skills in these topics you will create websites that work seamlessly on mobile, tablet, and large screen browsers. During the capstone you will develop a professional-quality web portfolio demonstrating your growth as a web developer and your knowledge of accessible web design. This will include your ability to design and implement a responsive site that utilizes tools to create a site that is accessible to a wide audience, including those with visual, audial, physical, and cognitive impairments.

Join : Web Design for Everybody: Basics of Web Development & Coding Specialization

Wednesday 13 December 2023

Meta Database Engineer Professional Certificate


What you'll learn

Demonstrate proficiency of SQL syntax and explain how it’s used to interact with a database.

Create databases from scratch and learn how to add, manage and optimize your database.

Write database driven applications in Python to connect clients to MySQL databases.

Develop a working knowledge of advanced data modeling concepts.

Join Free : Meta Database Engineer Professional Certificate

Professional Certificate - 9 course series

Want to get started in the world of database engineering? This program is taught by industry-recognized experts at Meta. You’ll learn the key skills required to create, manage and manipulate databases, as well as industry-standard programming languages and software such as SQL, Python, and Django used for supporting outstanding websites and apps like Facebook, Instagram and more.

In this program, you’ll learn:

Core techniques and methods to structure and manage databases. 

Advanced techniques to write database driven applications and advanced data modeling concepts. 

MySQL database management system (DBMS) and data creation, querying and manipulation.

How to code and use Python Syntax

How to prepare for technical interviews for database engineer roles.

Any third-party trademarks and other intellectual property (including logos and icons) referenced in the learning experience remain the property of their respective owners. Unless specifically identified as such, Coursera’s use of third-party intellectual property does not indicate any relationship, sponsorship, or endorsement between Coursera and the owners of these trademarks or other intellectual property.

Applied Learning Project

You’ll complete a series of 5 projects in which you will demonstrate your proficiency in different aspects of database engineering. 

You’ll demonstrate your skills with database normalization by structuring your own relational database by defining relationships between entities and developing relational schema. 

This is followed by a stored procedure project in which you’ll demonstrate your competency in SQL automation by writing a stored procedure to solve real world problems. After developing your skills in Python, you’ll create a Python application to administer a MySQL database and program its interactions with clients. 

In the next project, you are required to apply data modeling to a real-world project by enacting advanced data modeling concepts such as automation, storage and optimization. 

Finally, you’ll be tasked with creating a MySQL database solution for an app by drawing on the knowledge and skills that they have gained throughout the program.

Tuesday 12 December 2023

Capstone: Retrieving, Processing, and Visualizing Data with Python


What you'll learn

Make use of unicode characters and strings

Understand the basics of building a search engine

Select and process the data of your choice

Create email data visualizations

There are 7 modules in this course

In the capstone, students will build a series of applications to retrieve, process and visualize data using Python.   The projects will involve all the elements of the specialization.  In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find.  Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.

Join Free : Capstone: Retrieving, Processing, and Visualizing Data with Python


Using Python to Access Web Data


What you'll learn

Use regular expressions to extract data from strings

Understand the protocols web browsers use to retrieve documents and web apps

Retrieve data from websites and APIs using Python

Work with XML (eXtensible Markup Language) data

There are 6 modules in this course

This course will show how one can treat the Internet as a source of data.  We will scrape, parse, and read web data as well as access data using web APIs.  We will work with HTML, XML, and JSON data formats in Python.  This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization.  These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files.  This course covers Python 3.

Join Free : Using Python to Access Web Data

Python Data Structures by drchuck


What you'll learn

Explain the principles of data structures & how they are used

Create programs that are able to read and write data from files

Store data as key/value pairs using Python dictionaries

Accomplish multi-step tasks like sorting or looping using tuples

There are 7 modules in this course

This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”.  This course covers Python 3.

Join Free : Python Data Structures

Sunday 3 December 2023

MichiganX: Python Data Structures (Free Course)


About this course

This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook "Python for Everybody". This course covers Python 3.

What you'll learn

How to open a file and read data from a file

How to create a list in Python

How to create a dictionary

Sorting data

How to use the tuple structure in Python

Join Free : MichiganX: Python Data Structures

Data Structures and Algorithms in Python


Data structures and algorithms are among the most fundamental concepts of Computer Science. Whether it’s real-world problems you’re trying to solve or the typical coding question asked in an interview, almost every problem requires you to demonstrate a deep understanding of data structures and algorithms.

This course is a detailed review of some of the most common data structures and algorithms that you’ll see in interviews and your everyday work. With implementation details, thorough explanations, and hands-on coding exercises, you’ll quickly gain the confidence you need to solve any problem, no matter the situation.


  1. Introduction
  2. Insertion
  3. Deletion by Value
  4. Deletion by Position
  5. Length
  6. Node Swap
  7. Reverse
  8. Merge Two Sorted Linked Lists
  9. Remove Duplicates
  10. Nth-to-Last Node
  11. Count Occurrences
  12. Rotate
  13. Is Palindrome
  14. Exercise: Move Tail to Head
  15. Solution Review: Move Tail to Head
  16. Exercise: Sum Two Linked Lists
  17. Solution Review: Sum Two Linked Lists
  18. Quiz

Join Free : Data Structures and Algorithms in Python

Friday 1 December 2023

Create Your First Web App with Python and Flask


What you'll learn

Create Web Applications with Flask

Use WTForms and SQLAlchemy in Flask Applications

Use Templates in Flask Applications

About this Guided Project

In this 2-hour long project-based course, you will learn the basics of web application development with Python using the Flask framework. Through hands on, practical experience, you will go through concepts like creating a Flask Application, using Templates in Flask Applications, using SQLAlchemy and SQLite with Flask, and using Flask and WTForms. You will then apply the concepts to create your first web application with Python and Flask.

This course is aimed at learners who are looking to get started with web application development using Python, and have some prior programming experience in the Python programming language. The ideal learner has understanding of Python syntax, HTML syntax, and computer programming concepts.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Join Free : Create Your First Web App with Python and Flask

Thursday 30 November 2023

Python Basics: Automation and Bots


What you'll learn

Learn how to play faster and looser and more casual with code, skimming and copying code from the Internet.

Discuss code flow and the order that your computer reads the code you write. This introduces us to a whole other level of thinking in code.

Logic can be harnessed to do useful stuff. We'll make it concrete by performing tasks like building an anagram finder.

Apply Python by robocall and spam text yourself through the Twilio API.

There are 4 modules in this course

Understanding the flow of running code is a major part of learning to think in code and of coding itself. In this course we will study the flow of code through several demonstrations and walkthroughs. We'll experience turning logic into useful work by running Python that automatically reads all of Shakespeare, and by setting Python up to give you a call on the phone. In technical terms, this course will demonstrate Python loops, list comprehensions, and conditional statements, while at a higher level we'll discuss code style and good practices for code.

Join Free : Python Basics: Automation and Bots

Creative Thinking: Techniques and Tools for Success (Free Course)


What you'll learn

Understand what creative thinking techniques are

Comprehend their importance in tackling global challenges as well as in everyday problem-solving scenarios

Select and apply the appropriate technique based on the opportunity to seize or the problem to tackle

There are 7 modules in this course

In today’s ever-growing and changing world, being able to think creatively and innovatively are essential skills. It can sometimes be challenging to step back and reflect in an environment which is fast paced or when you are required to assimilate large amounts of information. Making sense of or communicating new ideas in an innovative and engaging way, approaching problems from fresh angles, and producing novel solutions are all traits which are highly sought after by employers.

This course will equip you with a ‘tool-box’, introducing you to a selection of behaviours and techniques that will augment your innate creativity. Some of the tools are suited to use on your own and others work well for a group, enabling you to leverage the power of several minds.  You can pick and choose which of these tools or techniques suit your needs and interests, focusing on some or all of the selected approaches and in the order that fits best for you.

The practical approach of this course enables you to acquire an essential skill-set for generating ideas, with plenty of:

- Fun e-tivities and exercises;

- Practical lectures and tips;

- Video representations of the techniques in action.

By the end of this course you should be able to:

- Pick a type of brainstorming you think will be useful to apply to a challenge

- Use alphabet brainstorming in tackling a challenge

- Use grid brainstorming in tackling a challenge

- Use a morphological chart to synthesise a solution to a challenge

- Use the TRIZ contradiction matrix to identify recommended inventive principles

- Apply SCAMPER to a range of challenges

The greatest innovators aren’t necessarily the people who have the most original idea. Often, they are people- or teams- that have harnessed their creativity to develop a new perspective or more effective way of communicating an idea. You can train your imagination to seize opportunities, break away from routine and habit, and tap into your natural creativity.

Join this course and a community of practitioners in CREATIVITY!

Join Free - Creative Thinking: Techniques and Tools for Success

Saturday 25 November 2023

Introduction to Artificial Intelligence (AI)


What you'll learn

Describe what is AI, its applications, use cases, and how it is transforming our lives

Explain terms like Machine Learning, Deep Learning and Neural Networks 

Describe several issues and ethical concerns surrounding AI

Articulate advice from experts about learning and starting a career in AI 

There are 4 modules in this course

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI.  You will also demonstrate AI in action with a mini project.

This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not. 

Join Free  - Introduction to Artificial Intelligence (AI)

Process Mining: Data science in Action (Free Course)


There are 6 modules in this course

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as "data science in action".

The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.

This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments.

The course covers the three main types of process mining.

1. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log.

2. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa.

3. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases.

Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development.

The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field.

After taking this course you should:

- have a good understanding of Business Process Intelligence techniques (in particular process mining),

- understand the role of Big Data in today’s society,

- be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification,

- be able to apply basic process discovery techniques to learn a process model from an event log (both manually and using tools),

- be able to apply basic conformance checking techniques to compare event logs and process models (both manually and using tools),

- be able to extend a process model with information extracted from the event log (e.g., show bottlenecks),

- have a good understanding of the data needed to start a process mining project,

- be able to characterize the questions that can be answered based on such event data,

- explain how process mining can also be used for operational support (prediction and recommendation), and

- be able to conduct process mining projects in a structured manner.

Join Free - Process Mining: Data science in Action

Introduction to Mathematical Thinking (Free Course)


There are 9 modules in this course

Learn how to think the way mathematicians do – a powerful cognitive process developed over thousands of years.

Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box – a valuable ability in today’s world. This course helps to develop that crucial way of thinking.

Join Free  - Introduction to Mathematical Thinking

Friday 24 November 2023

Mathematics for Machine Learning Specialization


Specialization - 3 course series

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.

The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.

At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

Applied Learning Project

Through the assignments of this specialisation you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.

Join Free : Mathematics for Machine Learning Specialization

Improving your statistical inferences (Free Course)


There are 8 modules in this course

This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles. 

In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. We will experience the problems with optional stopping and learn how to prevent these problems by using sequential analyses. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses. Finally, you will learn how to examine whether the null hypothesis is true using equivalence testing and Bayesian statistics, and how to pre-register a study, and share your data on the Open Science Framework.

Join Free - Improving your statistical inferences

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Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses