Official content for the IITM BS course on Tools in Data Science

Materials for STATS 418 - Tools in Data Science course taught in the Master of Applied Statistics at UCLA

"Tools for Data Science Course" Student Interactive Git Repository

Feng Li's course material for "Tools for Data Science"

Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning. This program consists of 9 courses providing you with latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets. It is a myth that to become a data scientist you need a Ph.D. This Professional Certificate is suitable for anyone who has some computer skills and a passion for self-learning. No prior computer science or programming knowledge is necessary. We start small, re-enforce applied learning, and build up to more complex topics. Upon successfully completing these courses you will have done several hands-on assignments and built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in Data Science. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Data Science.

Materials for a computer science course on tools for data science.

Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.

Welcome to 6.86x Machine Learning with Python–From Linear Models to Deep Learning. Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. We will cover: Representation, over-fitting, regularization, generalization, VC dimension; Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning; On-line algorithms, support vector machines, and neural networks/deep learning. You will be able to: Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models Choose suitable models for different applications Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering You will implement and experiment with the algorithms in several Python projects designed for different practical applications. You will expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link these methods together, equipping you with the tools you need to develop new ones.

course website for data science tools 1

this repo is for linkedin learning course: Advanced Python: Top Tools for Data Science and Engineering

For this project, you will assume the role of a Data Scientist / Data Analyst working for a new startup investment firm that helps customers invest their money in stocks. Your job is to extract financial data like historical share price and quarterly revenue reportings from various sources using Python libraries and webscraping on popular stocks. After collecting this data you will visualize it in a dashboard to identify patterns or trends. The stocks we will work with are Tesla, Amazon, AMD, and GameStop. Dashboard Analytics Displayed A dashboard often provides a view of key performance indicators in a clear way. Analyzing a data set and extracting key performance indicators will be practiced. Prompts will be used to support learning in accessing and displaying data in dashboards. Learning how to display key performance indicators on a dashboard will be included in this assignment. We will be using Plotly in this course for data visualization and is not a requirement to take this course. Watson Studio In the Python for Data Science, AI and Development course you utilized Skills Network Labs for hands-on labs. For this project you will use Skills Network Labs and Watson Studio. Skills Network Labs is a sandbox environment for learning and completing labs in courses. Whereas Watson Studio, a component of IBM Cloud Pak for Data, is a suite of tools and a collaborative environment for data scientists, data analysts, AI and machine learning engineers and domain experts to develop and deploy your projects. Review criteria There are two hands-on labs on Extracting Stock Data and one assignment to complete. You will be judged by completing two quizzes and one peer review assignment. The quizzes will test you based on the output of the hands-on labs. In the peer review assignment you will share and take screen shots of the outcomes of your assignment.

Data Science Certificate: Introduction to R at Data Sciences Institute University of Toronto (Summer & Fall 2022). The vast amount of data produced by evolving information technology requires tools and skills. R is a free, open-source language and an environment that could be used for data sciences. This course covers topics in R and data sciences

Introduction to R R is a programming language, which is an object oriented language created by Statisticians, R provides objects, operators and functions that allow the user to explore, model and visualize data. R is a Programming language Developed at AT&T Bell Lab. It is an open source free language, allowing anyone to use and modify it. R is licensed under the GNU General Public License, with copyright held by The R Foundation For Statistical Computing. It has no need to pay any subscription charges R has a huge active community member. If you have any question about any function any library you can Google it and you would get a proper answer and right the way. As it is an open source language, you, me and lots of Data Scientist, they actually built in all those, inbuilt function and they upload it in a website called CRAN and then you can download all those packages. Over 7800 packages listed on CRAN, here we listed some of the most powerful and commonly used in R packages. R is a cross platform. R can run in different kind of operating system and different hardware. Generally, it is used on GNU/Linux, Macintosh, and Microsoft Windows and running on both 32 and 64-bit processor. R is mainly used for Statistical Analysis and Analytics Purpose, you might be thinking why to learn again another language if you already know many programming languages like JAVA or other programming languages, and think why do you need the language because R is mainly used for all those statistical Analysis and that’s why you should learn the language R. you would understand after doing this course it is actually easy to interpret. R is the leading tool for statistics and data analysis, machine learning as well as. The programming language is more than a statistical package, you can build your own objects, functions, and packages. It is easy to use, the coding style is quite easy. R enables you to interact with many data sources: ODBC -compliant databases (Excel, Access). R also can handle CSV files, SAS, and SPSS, XML and lots of other different files as well. Similarly, it can create a very good visualization. It can produce graphics output in PDF, JPG, PNG and SVG formats and table output for LATEX and HTML. It has a lot of inbuilt functions(packages & Libraries) and the results are also easy to interpret and that’s why lots of industries are using R, it is not about the big or small. Lots of companies like Microsoft, Google are using R actively. It has a big reason, it is free and you can do POC out there. So, be confident about the fact that you are going to learn R and it has huge popularity and your market value is always higher if you know R in Data Science

Course on Data Science and Digital Tools for Transport Planning

Block course for teaching tools (Python) and methods (Statistics, Machine Learning) for Data Science.

This course covers various topics, including the 6-step machine learning modelling framework, tools for machine learning and data science, end-to-end structured data projects, neural networks, deep learning, transfer learning with TensorFlow 2.0, and communicating and sharing your work.

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Jupyter notebooks of the course Tool for Data Science (2017/2018)

The University of California, San Diego, course DSE200x "Python for Data Science" (Summer 2018): Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets. Part 1 of »Data Science« MicroMasters® on edX. Instructors: Ilkay Altintas, Chief Data Science Officer, San Diego Supercomputer Center (SDSC) and Leo Porter, Assistant Teaching Professor, Computer Science and Engineering at University of California San Diego

This is an cross platform 2D radar labeling tool for NCU MRL Data Science course

An autonomous AI agent designed to build, deploy, and update complete web applications from natural language project briefs. Made as a project for the (TDS) Tools in Data Science course IITM.

Solution guides for GA-6 of the Tools in Data Science course (IIT Madras B.S. program). Includes structured READMEs and step-by-step methods to help students understand each question using Excel, SQL, and Python.

This project uses data from the Hospital Compare data set that contains information on over 4,000 Medicare-certified hospitals in the United States. The goal was to create a tool that would help inform people's decisions on what hospital to go to based on why they needed to go to the hospital and how far they were able to travel. We used various measures of hospital performance to recommend to users what hospital to go to, changing which ones were used depending on why the user input they were going to a hospital. You can access the code and wiki pages for the project on Github here. It was created for the INFO 370 Introduction to Data Science course at the University of Washington by Arihan Jalan, Sarthak Turakhia, Kazunori Kasahara, and Adele Miller. Disclaimer: Current limitations with this project limit its ability to provide a definitive recommendation to users, so users of it should not rely solely upon our tool when making a decision. For more information on this, see the Assumptions and Limitations of Analysis section.

Repository for projects in the PhD course Tools for Scientific Software Development and Data Science

A set of tools for the Data Science in Cognitive Neuroscience connector course

Marketing Data Science is a first of a series of courses on Business Data Science. The course was constructed to be a meeting point between Marketing and Data Science. A marketing framework analysis is proposed composed of four blocs: Profiling, Segmentation, Targeting and Recommendation. For each of these blocs a Data Science analysis is applied. The pivoting question is: How to better understand your customer. Throughout the course we will use a single database to apply the different concepts and Data Science techniques. Three tools will be presented and used: SQL, Power BI and Microsoft Azure ML If you are a marketer who want to be introduced to the Data-Driven analysis field, then this course is for you. If you have a technical background (IT professional, Developer) and you inspire to become a Data Scientist, then you can take advantage of the Marketing Framework Analysis to introduce you to the business skills a has to know.

PhD Coursework: Online Coursera Courses - Tools for Data Science and Machine Learning Deployment

Course website for EDS 296 - Data science tools for building professional online portfolios (MEDS @ Bren UCSB)

An autonomous AI agent that solves data-driven quizzes using a state machine workflow. Made as a project for the (TDS) Tools in Data Science course IITM.

Code for the Data Science Tools - R course