Analytics Insight Presents the list of Top Free Online Courses in Statistics and Data Analysis

Would you like to understand data science statistics without undergoing a time-consuming and pricey class? There’s great news! Using solely free online resources, you may understand basic topics such as probability, Bayesian thinking, and statistical deep learning.

This article will show you the statistical thinking skills you’ll need for data science along with the top free online courses for Statistics and Data Analysis. It will give you a tremendous leg up on other budding data scientists who are attempting to get by without it. After all, after you’ve understood how to programme, it can be appealing to jump right into using machine learning programmes. It’s fine if you want to start with real-world projects at first. However, you should never, ever disregard statistics and probability concepts. It’s necessary if you want to advance as a data scientist.

Statistics Needed for Data Science

Statistics is a vast field with several applications in a variety of fields. The science of the collection, analysis, interpretation, presentation and organising of data is statistics, according to Encyclopaedia. As a result, it should come as no shock that data scientists require statistical knowledge.

Data analysis, for particular, necessitates at the very least descriptive statistics and probability theory. These ideas will assist you in making better company decisions based on data. Probability distributions, statistical significance, hypothesis testing, and regression are all important issues.

Artificial learning also necessitates knowledge of Bayesian thinking. The act of upgrading beliefs when new evidence is gathered is known as Bayesian thinking, and it’s at the heart of many machine learning frameworks. Conditional probability, priors and posteriors, and maximum probability are all important topics. Wouldn’t fret if those terms seem like jargon to you. When you get your hands grimy and actually learn, everything will sound familiar.

How to Learn Statistics for Data Science?

You’ve surely observed that “the self-starter route to learning X” frequently includes skipping classroom instruction in favour of “doing stuff.”

It’s no different when it comes to mastering statistics for data science.

In fact, we’ll be tackling important statistical ideas by programming them with coding! This will be a lot of fun, we promise. If you don’t have a formal math background, you’ll find that this method is far more natural than having a hard time figuring out difficult equations. It works by stimulating through each calculation’s logical phases. If you have a strong math background, this method will assist you in putting theory into practice while also providing some enjoyable programming difficulties.

You’ll be prepared to undertake harder machine learning issues and popular real-world data science applications after finishing these three levels. The three steps to studying the statistics and probability needed for data science are as follows:

Step 1: Core Statistics Concepts

It’s a good idea to start learning statistics for data science by examining how it will be applied.

Now let us look at some real-world studies or implementations that you might encounter as a data scientist:

1. Experimental design: Your firm is launching a new line of products, but it will only be available in brick-and-mortar locations. You’ll need to create an A/B test that accounts for geographic variances. You’ll also have to figure out just how many outlets you’ll need to test in order to get statistically relevant findings.

2. Regression modelling: Your enterprise needs to be able to forecast consumption for certain product lines in its outlets more accurately. Both understocking and overstocking are costly. You’re thinking of creating a set of regularized regression models.

3. Data transformation: You’re evaluating a number of machine learning model options. Several of them include assumptions about input data probability distributions, and you must be able to spot them so that you can either convert the data correctly or determine when the presumptions may be eased.

Step 2: Bayesian Thinking

The disagreement between Bayesians and frequentists is one of the philosophical arguments in statistics. While mastering statistics for data science, the Bayesian side is more important.

Frequentists, in an essence, solely employ probability to model sampling processes. This means that they only allocate probability to data that they’ve already gathered.

Step 3: Intro to Statistical Machine Learning

After you’ve grasped essential principles and Bayesian thinking, there’s no better way to learn statistics for data science than by experimenting with statistical machine learning models.

The sciences of statistics and machine learning are inextricably intertwined, and “statistical” machine learning is the predominant method of current machine learning.

In this stage, you’ll create a few machine learning models from the ground up. This will assist you in gaining a genuine knowledge of their dynamics.

Top Free Courses

1. Coursera (Duke University): Statistics with R Specialisation

• Time Period: 10 weeks
• Background knowledge: No prior programming expertise is necessary; just simple mathematics skills are required.

2.  Udacity (Stanford University): Intro to Statistics

• Time Period: 8 weeks
• Background knowledge: No prior experience is necessary; an introductory course is required

3.  Stanford University: Statistical Learning

• Time Period: 10 weeks
• Background knowledge: A basic understanding of statistics, linear algebra and computing are necessary.

4. Leada: Introduction to R

• Time Period: Self-Paced
• Background knowledge: No prior experience is necessary; an introductory course is required

5. Udacity (San Jose State University): Statistics: The Science of Decisions

• Time Period: Self-Paced; approximately 4 months
• Background knowledge: Basic proportions (fractions, decimals, and percentages), negative values, fundamental algebra (solving equations), and exponential and square roots.

6. Saylor: Introduction to Probability Theory

• Time Period: Self-Paced
• Background knowledge: Topics in single-variable and multivariate calculus, numerical analysis, and differential equations, or equivalents, must be completed.

7. EDX (Columbia University): Statistical Thinking for Data Science and Analytics

• Time Period: 5 weeks
• Background knowledge: No prior experience is necessary; an introductory course is required

8. EDX (University of Texas): Statistics Using R

• Time Period: 6 weeks
• Background knowledge: No prior experience is necessary; an introductory course is required

9. Caltech: Learning from Data

• Time Period: Self-Paced
• Background knowledge: No prior experience is necessary; an introductory course is required

Conclusion

We hope that we were able to provide you with the best free courses for Statistics and Data Analysis. They are ranked from 1 to 9 with short details which will help you pick your courses according to your convenience. So, hurry up and get yourself a course now!

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IIT Kharagpur has invited applications for a free online course on introduction to machine learning this time on the SWAYAM NPTEL platform. The 8-week long course will be conducted from 26 July to 17 September 2021. While the course is free to take, to get a certificate from IIT Kharagpur and NPTEL, participants will have to pay Rs 1000 and take an examination on 26 September 2021. The course also carries two credit points, which can be beneficial for engineering students.

The course will be conducted by professor Sudeshna Sarkar who is a professor and currently the head in the department of computer science and engineering at the Indian Institute of Technology Kharagpur. The professor completed her BTech in 1989 from IIT Kharagpur, MS from University of California, Berkeley, and PhD from IIT Kharagpur in 1995, and her research interests are machine learning, natural language processing, data and text mining.

What the IIT Kharagpur Free Online Course on Machine Learning Will Cover

The course will introduce participants to the basics of computational learning theory, and the various issues related to the application of machine learning algorithms will also be discussed. Furthermore, the course will be accompanied by hands-on problem solving with programming in Python and some tutorial sessions. The following are the topics that will be covered over a period of 12 weeks:

• Introduction: Basic definitions, types of learning, hypothesis space and inductive bias, evaluation, cross-validation.
• Linear regression, Decision trees, and overfitting.
• Instance-based learning, Feature reduction, and Collaborative filtering-based recommendation.
• Probability and Bayes learning.
• Logistic Regression, Support Vector Machine, Kernel function and Kernel SVM.
• Neural network: Perceptron, multilayer network, backpropagation, and introduction to deep neural network.
• Computational learning theory, PAC learning model, Sample complexity, VC Dimension, and Ensemble learning.
• Clustering: k-means, adaptive hierarchical clustering, and Gaussian mixture model.

Who Can Apply for the IIT Kharagpur Free Online Course on Machine Learning

While the course is open to all participants, it could be most beneficial for students and professionals from the following domains:

• Computer Science and Engineering
• Artificial Intelligence
• Data Science
• Programming
• Robotics

It also serves an elective course for undergraduate, postgraduate, BE, ME, MS, MSc and PhD students.

How to Enroll for the IIT Kharagpur Free Online Course on Machine Learning

Interested participants will have to enroll in the course by clicking on the Join button available on the official website before 2 August 2021. Prior to that, participants will have to first sign into the SWAYAM NPTEL platform using their Microsoft, Facebook or Google accounts. Furthermore, an option to login using the SWAYAM account is also available. SWAYAM account can be created by registering with the platform by entering the username, password, email address, and then verifying the account. As far as the certificate for the course is concerned, announcements will be made when the registration form is open for submission, and the online registration form has to be then filled and the certification exam fee needs to be paid.

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The Indian Space Research Organisation has announced a five-day free course on machine learning, between July 5 -9. The course is being offered as part of the Indian Institute of Remote Sensing’s (part of ISRO) outreach program. Central and state government employees, researchers, professionals, and those attached with NGOs can attend the course. Interested candidates must have basic knowledge of remote sensing and GIS.

The short course is designed for professionals engaged in remote sensing data processing in different applications which involves extracting a specific class of interest. 

The course content is planned as follows:

July 5: Remote Sensing and its sensors of various resolutions. Radiometry and Geometric corrections and Basic understanding of Image

July 6: Basic classifier to machine learning-A journey

July 7: Methods in machine learning: Supervised, unsupervised and reinforcement. 

July 8: Fuzzy based machine learning with application in temporal data processing

July 9: Network-based learning algorithms – ANN to CNN/RNN

The course materials such as lecture slides, recordings of the classes, handouts of the demonstrations, etc will be made available to the students and the full video lectures will be uploaded on YouTube.

Candidates can attend the course live via any web browser through the e-class portal of IIRS Dehradun. The participants can also attend the live workshop via IIR’s YouTube channel of IIR.

For receiving the course completion certificate, a student must have attended 70 percent of the sessions via the e-class portal. Students attending the class via IIR’s YouTube channel should mark their attendance via an offline session made available after 24 hours post the class.

The post ISRO Offers A Five-Day Machine Learning Course For Free appeared first on Artificial Intelligence.

1| Natural Language Processing

About: This online course covers from the basic to advanced NLP and it is a part of the Advanced Machine Learning Specialisation from Coursera. You can enroll this course for free where you will learn about sentiment analysis, summarization, dialogue state tracking, etc. The topics you will learn such as introduction to text classification, language modelling and sequence tagging, vector space models of semantics, sequence to sequence tasks, etc. Upon completing, you will be able to build your own conversational chat-bot that will assist with search on StackOverflow website.

2| Natural Language Processing By Microsoft

About: This is a self-paced learning course which will give you a thorough introduction to the cutting-edge technologies applied to NLP. The duration of this course is 6 weeks where you will be given a thorough overview of Natural Language Processing and how to use classic machine learning methods. You will learn about statistical machine translation, deep reinforcement learning techniques applied in NLP, Vision-Language Multimodal language as well as Deep Semantic Similarity Models (DSSM) and their applications.

You will also learn how to apply deep learning models to solve machine translation and conversation problems, deep structured semantic models on information retrieval and natural language applications, deep reinforcement learning models on natural language applications and deep learning models on image captioning and visual question answering.

3| Natural Language Processing With Deep Learning

About: This is a lecture series on NLP provided by Stanford University where you will have an introduction to the cutting-edge research in deep learning applied to NLP. The minimum duration of the series is 1 hour and the topics included are NLP with deep learning, word vector representations, global vectors for word representation, word window classification and neural networks, backpropagation, dependency parsing, introduction to TensorFlow and other such related topics.

4| Natural Language Processing By Carnegie Mellon University

About: This course is provided by Carnegie Mellon University which covers a variety of ways to represent human languages (like English and Chinese) as computational systems and various ways to exploit those representations to write programs that do neat stuff with text and speech data, like translation, summarisation, extracting information, natural interfaces to databases, conversational agents, etc. The course includes some ideas central to Machine Learning and to Linguistics.

5| Deep Natural Language Processing

About: This is a GitHub repository which contains course on deep NLP by the University of Oxford in the form of lecture slides and videos. This course is focused on recent advances in analysing and generating speech and text using recurrent neural networks. You will be introduced with mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions.

6| Natural Language Processing With Python

About: This is an e-book version of the book Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper. This book is more of a practical approach which uses Python version 3 and you will learn various topics such as language processing, accessing text corpora and lexical resources, processing raw text, writing structured programs, classifying text, analysing sentence structure and much more.

7| NLP For Beginners Using NLTK

About: This is a video series where you will learn about the basics of NLP through NLTK. The video basically concentrates on to the very useful feature in NLP called frequency distribution. You will learn how to calculate, tabulate and plot frequency distribution of words.

8| Speech And Language Processing

About: This is an ebook by authors Dan Jurafsky and James H. Martin where you will learn from the basics to advance of language processing. The topics included here are text normalisation, edit distance, regular expressions, language modelling, logistic regression, vector semantics, neural networks, neural language models, and other such related topics.

The post 8 Free Resources For Beginners To Learn Natural Language Processing appeared first on Artificial Intelligence.