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Top 6 Data Science Online Courses in 2021



Top 6 Data Science Online Courses in 2021

As an aspiring data scientist, it is easy to get overwhelmed by the abundance of resources available on the Internet. With these 6 online courses, you can develop yourself from a novice to experienced in less than a year, and prepare you with the skills necessary to land a job in data science.

By Natassha Selvaraj, Student in CS majoring in Data Science.

Photo by Luke Chesser on Unsplash.

As a beginner looking to break into the data science industry, it’s easy to get overwhelmed with all the information presented to you. There are hundreds of data science courses out there, and it is difficult to know where to start.

When I decided to teach myself data science just a year ago, I remember feeling really lost because I didn’t know where to start. I saw advertisements for machine learning courses that promised to make me an expert in three days.  I read articles insisting it wasn’t possible to become a data scientist unless I had a Master’s degree in mathematics and a Ph.D. in statistics. There was just too much information out there and so many conflicting opinions.

I finally managed to create my own data science roadmap and teach myself programming and machine learning. I managed to break into the industry and land a data science job.

Every day, at least one person asks me how I did this, how to learn data science from scratch, and land a job in the industry. I did some research and compiled a list of online courses you can take to learn data science. The syllabi of these courses are good and will give you a strong foundation in programming, SQL, and machine learning. I use almost all the concepts taught in these courses during my day job as a data scientist.


Photo by James Harrison on Unsplash.

If you want to learn data science, you first need to learn how to code. If you have no prior programming experience, I suggest starting out with Python.

There is an abundance of resources on the Internet that teach you Python programming, some of which include:

This is a 5-course specialization that will teach you Python from scratch. The first course in the specialization is called Programming for Everybody. In this course, you will learn the very basics of Python — syntax, conditional statements, iteration, functions, and variables.

This course doesn’t assume any pre-requisite, and you don’t need to come from a technical or mathematical background to get started with this course.

The next course in the specialization will teach you data structures. You will learn how to read data from files and manipulate data structures like lists and dictionaries.

The third course in the specialization teaches you to use Python to access web data. You will learn to use APIs and extract data from websites and then process this data with Python. You will also learn to extract data from strings and clean data using regular expressions.

Next, you will learn to access and manipulate databases with Python. You will learn to work with SQL databases with a Python library called SQLite3. No prior SQL or database experience is required to take this course. You will learn everything from scratch.

The final course is a capstone project. You will utilize all the concepts learned in the other courses and build an end-to-end project in the capstone. If you pass your capstone project, you will get a course certificate.

The biggest upside of this course is that it teaches you a lot of data collection and storage techniques that are essential for a data scientist to know.

Many other Python and data science courses skip over these topics, and you end up with little to no knowledge of how to use APIs or access web data.

This introductory Python course is broken up into four sections — Python Basics, Python Lists, Functions, and Numpy.

This course covers all the basics of Python, including variables, mathematical operations, list manipulation, and functions.

It also teaches you the basics of a library called Numpy, which is often used by data scientists to manipulate arrays.

The Python Basics section of this course is free, so you can try this portion of the course first to see if you’d enjoy it.

After completing the introductory Python course, you can take this intermediate level Python course on DataCamp.

This course will teach you to create visualizations in Python, manipulate dictionaries and lists, work with libraries like Pandas, and filter data frames using logic.

The first section in this specialization — data visualization with Matplotlib is free. You can try this out before deciding to get the entire course.

A major advantage of this course is that it teaches you Python for data science. It takes you through data analysis libraries like Pandas and Numpy, along with visualizations like Matplotlib.


Photo by Caspar Camille Rubin on Unsplash.

The biggest piece of advice I’d give aspiring data scientists is to learn SQL. I never thought of SQL as an important part of data science. However, when I did my first data science internship, most of the work I did involved knowledge of data manipulation with SQL.

To learn SQL, I suggest taking the SQL for Data Science course on Coursera.

This is a 4-week course and assumes no prior database or programming knowledge. The first section of this course starts with data selection and retrieval with SQL.

Then, you will learn to use operators in SQL to filter data. As a data scientist/analyst, filtering data based on client requirements is something I do on a daily basis, so the content of this course is really important to understand.

In the next course, you will learn how joins work in SQL. You will learn to link multiple databases to each other. This is a very powerful technique. I deal with large databases on a daily basis and very often need to use joins to merge them together.

Data Science

Photo by Arseny Togulev on Unsplash.

By now, you should have learned the basics of programming. You should also have an understanding of data analysis using libraries like Numpy and Pandas, along with data visualization using libraries like Matplotlib.

Now, you can step into the territory of machine learning.

This course is a part of the IBM Data Science specialization. You can take it as a standalone course and get a certificate for this course alone, and you don’t have to complete the entire specialization.

This course will provide you with a solid understanding of machine learning algorithms. You will learn to build models to solve supervised machine learning problems like regression and classification. You will also learn unsupervised machine algorithms like hierarchical clustering.

A huge advantage of taking this course over Andrew Ng’s machine learning specialization is that this course is taught completely in Python.

This course also has a final capstone project you need to pass before getting a certificate.

Datacamp’s machine learning track is broken up into multiple separate courses — supervised machine learning, unsupervised machine learning, linear classifiers, and deep learning.

I suggest taking the supervised machine learning course first. The first section of this course is free, so try it out and see if the content is useful. If you enjoy it, you can consider enrolling for the machine learning track.

Most machine learning courses online only cover the basics of different algorithms. A major advantage of this Datacamp track is that it covers topics like hyperparameter tuning and building pipelines.

When I took my first data science course on Udemy, I had a lot of gaps in my knowledge because I didn’t understand topics like parameter tuning and dimensionality reduction. It took me a long time to find the right resources to bridge the gap in my learning.

The content of this Datacamp machine learning track seems extremely comprehensive and covers a lot of ground that isn’t usually taught in other courses.


The list of courses mentioned above will provide you with a very strong foundation in data manipulation and machine learning. However, to really grow as a data scientist, you will need to go beyond these courses.

Start working on data science projects during your free time. Work on building real-life applications based on the concepts learned in these courses. You can go on sites like Kaggle and gain access to publicly available datasets and build machine learning algorithms to make predictions on these datasets.

Taking these courses will equip you with the necessary skill set you need to become a data scientist. You will then need to practice these skills and hone them by working on projects.

This article contains affiliate links. This means that if you click on it and choose to buy a course I linked above, a small portion of your subscription fee will go to me. As a creator, this helps me grow and continue to create content like this. However, I only recommend courses I think are good. The syllabi of the courses recommended above are very closely aligned to the work I do everyday as a data scientist. These are courses I recommend to people who ask me for tips to break into the data industry, and I do believe they will be useful in your data science journey.

Thanks for your support!

Bio: Natassha Selvaraj is pursuing a degree in computer science with a major in data science. Natassha’s interests are in the field of machine learning, having worked on a variety of projects in this domain.


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Big Data

WHT: A Simpler Version of the fast Fourier Transform (FFT) you should know



WHT: A Simpler Version of the fast Fourier Transform (FFT) you should know

The fast Walsh Hadamard transform is a simple and useful algorithm for machine learning that was popular in the 1960s and early 1970s. This useful approach should be more widely appreciated and applied for its efficiency.

By Sean O’Connor, a science and technology author and investigator.

The fast Walsh Hadamard transform (WHT) is a simplified version of the Fast Fourier Transform (FFT.)

The 2-point WHT of the sequence a, b is just the sum and difference of the 2 values:

WHT(a, b) = a+b, a-b. 

It is self-inverse allowing for a fixed constant:

WHT(a+b, a-b) = 2a, 2b 

Due to (a+b) + (a-b) = 2a and (a+b) – (a-b) = 2b.

The constant can be split between the two Walsh Hadamard transforms using a scaling factor of √2 to give a normalized WHTN:

WHTN(a, b) = (a+b)/√2, (a-b)/√2 WHTN((a+b)/√2, (a-b)/√2) = a, b 

That particular constant results in the vector length of a, b being unchanged after transformation since a2+b2 =((a+b)/√2)2+ ((a-b)/√2)2 as you may easily calculate.

The 2-point transform can be extended to longer sequences by sequentially adding and subtracting pairs of similar terms, alike in the pattern of + and – symbols they contain.

To transform a 4-point sequence a, b, c, d first do two 2-point transforms:

WHT(a, b) = a+b, a-b WHT(c, d) = c+d, c-d 

Then add and subtract the alike terms a+b and c+d:

WHT(a+b, c+d) = a+b+c+d, a+b-c-d 

and the alike terms a-b and c-d:

WHT(a-b, c-d) = a-b+c-d, a-b-c+d 

The 4-point transform of a, b, c, d then is

WHT(a, b, c, d) = a+b+c+d,  a+b-c-d, a-b+c-d, a-b-c+d 

When there are no more similar terms to add and subtract, that signals completion (after log2(n) stages, where n is 4 in this case.)  The computational cost of the algorithm is nlog2(n) add/subtract operations, where n, the size of the transform, is restricted to being a positive integer power of 2 in the general case.

If the transform was done using matrix operations, the cost would be much higher (n2 fused multiply-add operations.)

Figure 1.  The 4-point Walsh Hadamard transform calculated in matrix form.

The +1, -1 entries in Figure 1 are presented in a certain natural order which most of the actual algorithms for calculating the WHT result in, which is fortunate since then the matrix is symmetric, orthogonal and self-inverse.

You can also view the +1, -1 patterns of the WHT as waveforms.

Figure 2.  The waveforms of the 8-point WHT presented in natural order.

When you calculate the WHT of a sequence of numbers, you are really just determining how much of each waveform is embedded in the original sequence.  And that is complete and total information with which you can fully reconstruct any sequence from its transform.

The waveforms of the WHT typically correlate strongly with the patterns found in natural data like images, allowing the transform to be used for data compression.

Figure 3.  A 65536-pixel image compressed to 5000 points using a WHT.

In Figure 3, a 65536-pixel image was transformed with a WHT, the 5000 maximum magnitude embeddings were preserved, and then the inverse transform was applied (simply another WHT.)

The central limit theorem (CLT) tells you that adding a large quantity of random numbers results in the Normal distribution with its characteristic bell curve.  The CLT applies equally to sums and differences of a large quantity of random numbers.  As a result, C.M. Rader proposed (in 1969) using the WHT to quickly generate Normally distributed random numbers from conventional uniformly distributed random numbers.  You simply generate a sequence of uniform random numbers, say between –1 and 1, and then transform them using the WHT.

Similarly, you can disrupt the orderly waveform patterns of the WHT by choosing a fixed randomly chosen pattern of sign flips to apply to any input to the transform.  That is equivalent to multiplying the WHT matrix H with a diagonal matrix D of randomly chosen +1, -1 entries giving HD.  The disrupted waveform patterns in HD then fail to correlate with any of the patterns seen in natural data.  As a result, the output of HD has the Normal distribution and is actually a fast Random Projection of the natural data.  Random projections have a wide number of applications in machine learning, such as locality sensitive hashing, compressive sensing, random projection trees, neural network pre and post-processing etc.


Walsh (Hadamard) Transform:

Normal Distribution:

Random Projections:

Other Applications:


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Must-Know Text Operations in Python before you dive into NLP!



Text Operations in Python | Must-Know Text Operations in Python for NLP

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Big Data

Canada’s Rogers Communications beats quarterly revenue estimates



(Reuters) -Canada’s Rogers Communications Inc on Wednesday reported second-quarter revenue that beat analysts’ estimates, helped by a pick up in advertisement sales and as its cable business benefited from a pandemic-driven shift to remote work and entertainment.

The requirement of high-speed broadband networks to carry on remote work helped the telecom operator negate the slow recovery from its wireless business.

The return of live sport broadcasting also played a positive role in boosting the Toronto-based telecom operator’s revenue.

The company’s total revenue rose to C$3.58 billion ($2.82 billion) in the quarter ended June 30, compared with analysts’ average estimates of C$3.56 billion, according to IBES data from Refinitiv.

Earlier in March, Rogers said it would buy Shaw Communications Inc for about C$20 billion ($16.02 billion), aiming to double down on its efforts to roll out 5G throughout the country.

Revenue for its cable unit, which includes internet, phone and cloud-based services, rose 5% during the quarter

Quarterly net income rose to C$302 million, or 60 Canadian cents per share, from C$279 million, or 54 Canadian cents, a year earlier.

($1 = 1.2686 Canadian dollars)

(Reporting by Tiyashi Datta in Bengaluru; Editing by Shailesh Kuber)

Image Credit: Reuters

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Climate friendly cooling tech firm gets $50 million from Goldman Sachs



By Jane Lanhee Lee

(Reuters) – Chemicals used in air conditioning, freezers and refrigeration have long hurt the environment by destroying the ozone layer and polluting water sources, but technology is starting to change the way we keep cool.

Phononic, a startup based in Durham North Carolina using a material called bismuth telluride to make so-called cooling chips, on Wednesday said it raised $50 million from Goldman Sachs Asset Management.

When electricity runs through the chip the current takes heat with it leaving one side of the chip to cool and the other to heat up, said Tony Atti, Phononic co-founder and CEO.

The chips can be as small as a fraction of a fingernail or as big as a fist depending on how much coolants are needed and have been used to create compact freezers for vaccine transportation or for ice-cream at convenience stores like Circle K, he said. A more recent and fast growing use is to prevent overheating in lidars, laser-based sensors in autonomous cars, and optical transceivers for 5G data transmission, said Atti.

“The historical refrigerants that had been used for vapor compression systems, they are both toxic and global warming contributors,” said Atti. While the global warming impact had been reduced, refrigerants still had issues with toxicity and flammability.

Atti said while the bismuth telluride powder itself is toxic, when it is processed into a semiconductor wafer and made into a chip, it is “benign” and can be recycled or disposed as its meets all chip safety and disposal standards.

The cooling chips are manufactured in Phononic’s own factory in Durham and for mass production the company is working with Thailand based Fabrinet. The freezers for vaccines and ice-cream are built in China by contract manufacturers and carry the brands of Phononic’s customers or in some cases are co-branded, he said.

The funding will be used to build out high-volume manufacturing and to expand Phononic’s markets and product line.

Atti declined to share the latest valuation of Phononic but said it was “north of half a billion dollars”. Previous investors include Temasek Holdings and private equity and venture capital firm Oak Investment Partners. 

(Reporting By Jane Lanhee Lee; editing by Richard Pullin)

Image Credit: Reuters

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