How to Transition to Data Science from Computer Science?

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Why Transition to Data Science from Computer Science?

If you’re looking for the best ways to transition into data science, some degrees can give you a massive advantage. And a degree in Computer Science certainly qualifies you for this rewarding and challenging career.

So, in this article, we’ll be making the switch from computer science and explore the steps you need to take to enter one of the hottest career fields.

We’ll answer some of the most important questions that go through your head, like: “Can I”, “Should I” and “How can I” make this switch. We’ll also discuss the pros and cons, and give you some tried-and-tested tips to transition into data science.

How to Transition to Data Science from Computer Science

 

Let’s start with “Can I make the switch?”

Well, if you can’t, then no one else can. A degree in Computer Science prepares you to be a code-savvy professional with strong analytical thinking, and a knack for creative tech solutions – which makes you the top choice of data science employers. Professionals with that degree have outstanding mathematics and problem-solving skills. Not to mention they are already proficient in several programming languages and tools. No wonder 18.3% of current data scientists have majored namely in Computer Science! So, let’s explore in detail the major points computer science helps you score.

The first and the most important advantage a computer science background gives you is spectacular problem-solving skills.

Computer Scientists thrive in challenging situations. And solving complex issues is just a regular part of their lifestyle!  Basically, what they do on a daily basis is identifying a problem, translating it to the computer, and finding the smartest way to deal with it. Over and over again. A Computer Science graduate rushes in and finds solutions where others fear to tread which makes them a leading figure in any data science team.

Second – writing a code that’s reusable and understandable by others.

This is one of the most precious skills for everyone working in data science. Why is that?

For one thing, it saves a lot of time for everyone involved.

If your code is very hard to follow, no one will want to use it. Especially in a fast-paced business environment where data science teammates should work like a well-oiled machine.

On the other hand, writing readable code that complies with the best practices speaks volumes. It shows you’re good at explaining your way of thinking to others, which is undeniably crucial for a data scientist working within a cross-functional team.

As a Computer Science person, you obviously know how to do that, so this box is ticked!

And third – having a super-versatile toolbox.

Data scientists rarely fly solo. That said, your ability to work with TTD or version control systems, like Git, for example, is indispensable to managing the code: including past changes, speed of execution, and development of the project. A data science team needs someone who knows how to monitor timelines or check if the code is labeled properly. Not many people are highly skilled at that, but a Computer Science graduate has the know-how that certainly gives them an edge.

We believe now you know transitioning into data science from computer science is not a question of “Can I?” rather than “Should I?”

Should I transition to Data Science from Computer Science?

Well, every person is different and so are their career choices.

Data science has been recently “discovered” and giving it a worldwide meaning seems to be a problem. Because of that, understanding the data science industry is a tough job. We might say that in most places being a data scientist will require you to work in a chaotic, continuously developing, and challenging environment.

And, yes, 20 years ago, there wasn’t a Data Science job… And you may ask “Why?”

The main reason is that there wasn’t that much data to work with.

But this is not the case now. There are 2.5 quintillion bytes of data created daily and businesses are in dire need of people working on it to improve our lifestyle, health, and more… In fact, the demand for data science professionals is so high that it will be hard for the supply to catch up for many years to come! That also explains the $100,000+ median base salary and why reports like Glassdoor’s 50 Best Jobs have consistently named Data Science the winner for the past few years.

Consider this – data science today is very close to how computer science was perceived back in 2005.

Actually, data science and computer science are very similar in that they are following the same demand and supply laws… But only with a 20-year difference. So, you might as well take advantage of that before the market gets overcrowded with highly trained data scientists and salaries start to plateau.

So, how to transition to Data Science from Computer Science?

Knowing how to code has already put you on the fast track to the data scientist role. What you might miss in terms of knowledge is:

Statistics

Computer Scientists boast a deterministic mindset. This compels them to want to have all possibilities covered. And that’s great, but, to be a data scientist, you need to shift to a statistical or even better – a probabilistic mindset. Why? Well, because of how data science works – events follow distributions and there are probabilities associated with each possibility. So, that’s a whole new way of thinking to adapt to.

Machine and Deep learning

You guessed right -usually, the Computer Science curriculum doesn’t cover these. But namely sharp predictive modeling skills and advanced deep learning techniques will give you a huge competitive edge. Fortunately, there are plenty of post-graduate qualifications and online trainings that will help you get there.

Reading research papers

Math, Statistics, and Data Science majors are very science-oriented. So, reading, understanding, and applying the technical methods in said paper is no challenge for them. But these don’t come naturally to a Computer Science graduate. Being able to apply concepts from papers is the number 1 skill demanded in top companies. That’s why adding research to your reading list is certainly worth the effort.

Data Visualization

Representing whole data research on just a few graphs and tables is a major component of a data scientist’s work. And it’s not an easy task. So, while you may prefer to code, adding software tools like Tableau, Power BI, and Excel are a must for any data scientist. Overlooking these could be the biggest mistake of Computer Science graduates. Remember – in the business world, sometimes it is about completing a task in 5 minutes and not about writing the most parameterized code.

But even with these skills under your belt, data science is no easy street.

In fact, one of the biggest challenges you’ll face is working efficiently with both C-level executives and team members with various backgrounds and fields of expertise.

So, if you think that employers are only looking for top technical talent – you’re wrong. A data scientist should also be a great team player.

According to an internal study run by Google, the most inventive and effective teams within the corporation aren’t the ones full of top scientists. Instead, their best performers are interdisciplinary groups with employees who bring strong soft skills to the table and enhance the collaborative process…

Which brings us to Leadership.

As a data scientist, you will not only plan projects, and build analytic systems and predictive models. You will also be the leader of a data science team. And managing a team of other data scientists, machine learning engineers, and big data specialists requires more than drive and vision.

In a data science team, you can always teach others or be taught yourself, regardless of their level in the hierarchy.

So, keeping an open mind to new and challenging ideas is a must. But don’t worry if you don’t feel you’re cut out to be a leader just yet– as long as you have empathy, integrity, and the desire to listen to your team’s needs and concerns, you can grow to become an outstanding Lead Data Scientist.

All things considered, Computer Science majors can, and should, try to pursue a career in data science because they have the necessary skills and there is high market demand. Surely, programming skills are mandatory for any data scientist. Thus, there is no doubt that you, dear Computer Science major, could be a successful one.

Ready to take the next step towards a data science career?

Check out the complete Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses. Build up a step-by-step experience with SQL, Python, R, Power BI, and Tableau. And upgrade your skillset with Machine Learning, Deep Learning, Credit Risk Modeling, Time Series Analysis, and Customer Analytics in Python. Still not sure you want to turn your interest in data science into a career? You can explore the curriculum or sign up 12 hours of beginner to advanced video content for free by clicking on the button below.

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Answer for Certificate - Power Bi

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Hi Mohsin,

Unfortunately, we do not offer certificates for ‘Free preview’ of courses.

To get a certificate you will need to enroll in our paid program and finish the courses there. 

Note that our paid courses are much bigger than the free preview versions and will help you prepare much better for the job.

Let me know if you have any other questions.

Best,
The 365 Team

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Answer for Rstudio

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Hi Porage, 

thanks for reaching out!

According to the tidyverse support page, to use the package you should have at least R version 3.2.3.

 

Best, 

Eli

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How to Transition to Data Science from Economics?

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Why transition to Data Science from Economics?

Have you ever wondered “So, what’s next for me?”

Well, you’re not alone! Many graduates aren’t too sure what they want to do after graduation. That’s especially true for Econ majors. Trust me – I am one.

And one of the often-overlooked options is data science.

So, in this article, I’ll tell you how to transition to data science from economics.

I’ll examine the good, the bad, and the ugly; answer some of the most important questions running through your mind, like: “Can I”, “Should I” and “How can I” make this switch. And I’ll explain the pros and cons before finding the best way to transition to data science from economics.

How to Transition to Data Science From Economics

Let’s start with “Can I make the switch?”

The answer here is a resounding “Yes!”.

Roughly 13% of current data scientists have an Economics degree. For comparison, the most well-represented discipline is data science and analysis, which takes up 21% of the pie. Therefore, Economics is indeed a competitive discipline when it comes to data science.

This isn’t at all surprising for several reasons.

First, unlike STEM disciplines, social studies help develop great presentational skills that are essential for any data scientist.

Through presentations and open discussions, students learn how to present a topic, as well as argue for or against a given statement. These activities result in developing a confident and credible way of showcasing actionable insights. Moreover, most econ majors deeply care about human behavior and response to different stimuli.

Hence, social-studies majors can capably serve as mediators between the team and management.

Second, economists often have a different approach than Computer Science or Data Science majors.

Due to their superior understanding of causal relations, social-studies graduates can add another perspective when looking at the data and the results. This is extremely important because their casual inference allows them to think beyond the numbers and extract actionable insights.

Furthermore, Economics frequently intertwines with Mathematics, Finance, Psychology, and Politics.

Therefore, an economist’s approach is always meant to be interdisciplinary.

Finally, the technical capabilities of an economist are often quite impressive.

An average economist has a good understanding of Machine Learning without really referring to it as such. Linear regressions and logistic regressions are studied in almost all economics degrees.

I think we are pretty convinced about the “Can I” part. So, let’s move to the “Should I” part.

Should I transition to Data Science from Economics?

Well, the answer here is “Yes” – with a very small asterisk next to it.

Now, any Economics graduate possesses many of the required skills to transition into Data Science, but that doesn’t necessarily suggest they should do it… They might be more suited for something else.

For example, an Economics graduate with an affinity for Political science will most likely thrive better in a policy advisory role in a bank or hedge fund or even in a government position. Similarly, less-coding-savvy social-studies graduates are a finer fit for data analyst positions, where machine learning algorithms are relied upon less frequently. It’s not that either one wouldn’t be able to succeed as a data scientist, but their skills are better suited for different career paths.

So, let’s look at the question like an economist would – through the lens of incentives.

Where does one find the incentives? That’s right – in a job ad.

The main components of a job ad are the level of education, years of experience, and indispensable skills.

We already discussed how popular Economics is compared to STEM degrees, so you know it’s a good choice for a potential career as a Data Scientist. When it comes to economics degrees, 43% of the job ads in our research require a BA and an additional 40% a Master’s. Hence, due to the interdisciplinary nature of social sciences, you don’t need to get a doctorate to be successful in the field.

As for years of experience, if you’re transitioning from another position in business, you’ve probably had to do some analytical thinking already.

Usually, 3 to 4 years in such a setting are enough to ensure a smooth transition. But this is tightly related to your level of education. A Master of Science will need 2 fewer-years of experience in a business setting due to their additional academic qualifications.

However, if you’re trying to make a transition straight out of college, you might want to go for an entry-level job in the field.

When it comes to skills, one of the key parts is understanding statistical results and their implications.

Luckily, economics degrees are often based on statistical study cases and experiments, so you should feel comfortable interpreting the results. Of course, this expands to understanding the intuition behind machine learning algorithms and their limitations. As we already stated, Econometrics incorporates linear and logistic regressions, so Economics graduates have a great grasp of the intuition behind Machine Learning models.

Additional skills listed in such job ads include problem solving and strong analytical thinking.

A lot of economics degrees heavily rely on examining study cases, solving practical examples, and analyzing published papers, so you probably possess these qualities already.

Of course, communication skills are essential when working in a team.

As mentioned earlier, Economics graduates often serve as a bridge between the data science team and higher management.

Lastly, anybody making the switch to data science needs a certain coding pedigree.

Whether it’s R, Python, or both, knowing how to use such software is a must if you want to succeed in the field.

If you’re an Economist in your 20s, we can assume you have seen some Python or R code. Hence, you only need to gather more work experience in a business setting.

If you are above 30 and you aren’t a Computer Science graduate, you most probably didn’t use the computer in your university classes. So, you may think your main challenge is the lack of programming skills. But that shouldn’t be the case.

Just focus on the technical part – programming and the latest software technologies.

Coding has never been easier, and anyone can learn. Especially a person from an economics background. We all know you have seen some very complicated stuff.

We answered the “can” and “should” parts of the discussion, so let’s dive into the “how-to” part.

How can I transition to Data Science from Economics?

There are generally 4 crucial things you need to do to make the switch.

The first one is picking your spot.

As discussed, there is plenty of room for Economics graduates in data science. All you need to make sure you’re ready to fit exactly that role and demonstrate your strengths.

Employers value your understanding of causal inference, so you need to highlight that in your application.

Showcase the analytical part of your work. Mention insights you gained through research or academic work and quote their measurable impact. These bring credibility and provide recruiters with a glimpse of what they’ll be getting once they hire you.

Second  – use your social science advantage.

By knowing how surveys and experiments are constructed, you know where to look when examining the results. You see beyond the data and understand which Machine Learning approach should work best in each case.

In contrast, Data Science and Computer Science graduates often have a mindset of “How can I pre-process the data before I run a machine learning algorithm?”, instead of looking at the way the data was gathered. Your understanding of collinearity, reverse causality, and biases can help you accurately quantify interdependence within the data. Thus, you can have great synergy with the rest of the members on your team.

The third and most crucial change you need to make is to adapt your way of thinking.

Even though the cause & effect mentality will help you settle in your career, you need to be able to look for other things as well. The findings of Neural Networks algorithms can be confusing because they discover patterns rather than causal links. Hence, you need to be ready to demonstrate flexibility in your thinking and adjust accordingly.

Of course, this isn’t a change that can happen overnight, but rather one that happens gradually with experience.

Last but not least, you’ll need to learn a programming language or BI software.

Lucky for you, programming languages such as Python and R aren’t that hard to learn. And once you’re fluent in one programming language, you can easily master another one, despite coming from an economics background.

This also falls into the “learn as we go” area, so just make sure to be proficient in at least one of either Python or R, and your transition into the field should be smooth as butter.

All things considered, Economics majors can, and should, try to pursue a career in data science because they have the necessary skills and there is high market demand. Surely, economics skills are mandatory for any data science team. Thus, there is no doubt that you, dear Econ major, could be that person.

Ready to take the next step towards a data science career?

Check out the complete Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses. Build up a step-by-step experience with SQL, Python, R, Power BI, and Tableau. And upgrade your skillset with Machine Learning, Deep Learning, Credit Risk Modeling, Time Series Analysis, and Customer Analytics in Python. Still not sure you want to turn your interest in data science into a career? You can explore the curriculum or sign up 12 hours of beginner to advanced video content for free by clicking on the button below.

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What Is a Tensor?

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Tensors have been around for nearly 200 years. In fact, the first use of the word ‘tensor’ was introduced by William Hamilton. Interestingly, the meaning of this word had little to do with what we call tensors from 1898 until today.

How did tensors become important you may ask? Well, not without the help of one of the biggest names in science – Albert Einstein! Einstein developed and formulated the whole theory of ‘general relativity’ entirely in the language of tensors. Having done that, Einstein, while not a big fan of tensors himself, popularized tensor calculus to more than anyone else could ever have.

Nowadays, we can argue that the word ‘tensor’ is still a bit ‘underground’. You won’t hear it in high school. In fact, your Math teacher may have never heard of it. However, state-of-the-art machine learning frameworks are doubling down on tensors. The most prominent example being Google’s TensorFlow.

What is a tensor in Layman’s terms?

The mathematical concept of a tensor could be broadly explained in this way.

A scalar has the lowest dimensionality and is always 1×1. It can be thought of as a vector of length 1, or a 1×1 matrix.

It is followed by a vector, where each element of that vector is a scalar. The dimensions of a vector are nothing but Mx1 or 1xM matrices.

Okay.

Then we have matrices, which are nothing more than a collection of vectors. The dimensions of a matrix are MxN. In other words, a matrix is a collection of n vectors of dimensions m by 1. Or, m vectors of dimensions n by 1.

Furthermore, since scalars make up vectors, you can also think of a matrix as a collection of scalars, too.

Now, a tensor is the most general concept.

Scalars, vectors, and matrices are all tensors of ranks 0, 1, and 2, respectively. Tensors are simply a generalization of the concepts we have seen so far.

An object we haven’t seen is a tensor of rank 3. Its dimensions could be signified by k,m, and n, making it a KxMxN object. Such an object can be thought of as a collection of matrices.

How do you ‘code’ a tensor?

Let’s look at that in the context of Python.

In terms of programming, a tensor is no different than a NumPy ndarray. And in fact, tensors can be stored in ndarrays and that’s how we often deal with the issue.

Let’s create a tensor out of two matrices.

Our first matrix m1 will be a matrix with two vectors: [5, 12, 6] and [-3, 0, 14].

The matrix m2 will be a different one with the elements: [9, 8, 7] and [1, 3, -5].

Now, let’s create an array, T, with two elements: m1 and m2.

After printing T, we realize that it contains both matrices.

It is a 2x2x3 object. It contains two matrices, 2×3 each.

Alright.

If we want to manually create the same tensor, we would need to write this line of code.

As you can imagine, tensors with lots of elements are very hard to manually create. Not only because there are many elements, but also because of those confusing brackets.

Usually, we would load, transform, and preprocess the data to get tensors. However, it is always good to have the theoretical background.

Why are tensors useful in TensorFlow?

After this short intro to tensors, a question still remains – why TensorFlow is called like that and why does this framework need tensors at all.

First of all, Einstein has successfully proven that tensors are useful.

Second, in machine learning, we often explain a single object with several dimensions. For instance, a photo is described by pixels. Each pixel has intensity, position, and depth (color). If we are talking about a 3D movie experience, a pixel could be perceived in a different way from each of our eyes. That’s where tensors come in handy – no matter the number of additional attributes we want to add to describe an object, we can simply add an extra dimension in our tensor. This makes them extremely scalable, too.

Finally, we’ve got different frameworks and programming languages. For instance, R is famously a vector-oriented programming language. This means that the lowest unit is not an integer or a float; instead, it is a vector. In the same way, TensorFlow works with tensors. This not only optimizes the CPU usage, but also allows us to employ GPUs to make calculations. What’s more, in 2016 Google developed TPUs (tensor processing units). These are processors, which consider a ‘tensor’ a building block for a calculation and not 0s and 1s as does a CPU, making calculations exponentially faster.

So, tensors are a great addition to our toolkit, if we are looking to expand into machine and deep learning. If you want to get into that, you can learn more about TensorFlow and the other popular deep learning frameworks here.

Ready to take the next step towards a data science career?

Check out the complete Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses. Build up a step-by-step experience with SQL, Python, R, Power BI, and Tableau. And upgrade your skillset with Machine Learning, Deep Learning, Credit Risk Modeling, Time Series Analysis, and Customer Analytics in Python. Still not sure you want to turn your interest in data science into a career? You can explore the curriculum or sign up 12 hours of beginner to advanced video content for free by clicking on the button below.

 

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Answer for Converting Monthly Subscription to Annual

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Hi Adnan,

Please contact team@365datascience.com regarding this issue. There they can help you with making the upgrade! 

In the Q&A Hub we (the instructors) focus on study related materials!

Best,

The 365 Team

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Answer for Loan dataset for credit risk model

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Hi RD,

You can find all Credit Risk Modeling files over here: https://www.dropbox.com/sh/7oslws1xhsm1zbf/AABkdWDKqpdcGmY1NbXAnkrBa?dl=0

Hope this solves your issue!

Best,
The 365 Team

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Answer for Interview Questions

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Hi Anirudh,

Currently we do not offer any courses on SVMs and Ensemble models. In the future you may expect them to be added.

Best,

The 365 Team

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Answer for Combining Python Conditional Statements and Functions Exercise

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Hi Aimen and Prafful!

Thanks for reaching out.

Indeed, can you please support your question with more information, Aimen? Much as Prafful requested.

Looking forward to your answer.
Best,
Martin

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Answer for Error message from pandas_datareader?

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Hi Carl!

Thanks for reaching out.

This is not an error message; this is just a warning message, telling you that you can expect some discrepancy in your future work unless you update your module at some point. Moreover, this message won’t affect our work, therefore you can feel free to proceed with the course while getting this error message.

Should you wish to stop getting this error message after executing such code, you may try updating your pandas module, which is related to the pandas-datareader module, and retry.

Here’s a command that can help you upgrade the pandas package, if you run it in Anaconda Prompt or Terminal, depending on whether you are a PC or Mac user.

pip3 install --upgrade pandas

Hope this helps.
Best,
Martin

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Answer for MySQL workbench not installed

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Hi Mark!

Thanks for reaching out.

There are multiple reasons that can lead you to obtain such an error message. Therefore, can you please support your question with screenshots that clearly explain what the error you’ve encountered is?
In addition, please not that you only need to have installed a single version of Visual C++ on your computer. Oftentimes having several versions of C++ hinders the installation process of MySQL.

The more information you can provide, the better will we be able to assist you. Thank you.

Looking forward to your reply.

Best,
Martin

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Answer for Error while getting data from quandl

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Hi Srajan!

Thanks for reaching out.

Quandl is an online financial data source that currently imposes limits on its free use. You don’t really need to use such data to complete our course, however it might be beneficial for you to know how to use it.

With that in mind, in case you’d like to proceed your work with Quandl, please remember that this error is to say that you have a limit on the number of times you can extract data from Quandl. You need to refer to their website to see how you can deal with these limits at present.

Hope this helps.
Best,
Martin 

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Answer for Is the IFNULL and COALESCE exercise questions wrong?

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Hi Yunfeng!

Thanks for reaching out.

Thank you for looking into the text of the Exercise in such detail.

Nevertheless, the text of the exercise is ok and doesn’t need correction at the minute.

Hope this helps.
Best,
Martin

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Answer for Can you use function inside HAVING clause

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Hi jo!

Thanks for reaching out and please excuse me for not getting back to you sooner.

This is to reflect the way in which the SQL syntax has been designed. It is indeed the use of alias in the having clause that allows us to apply conditions in the HAVING clause.

You can refer to the following lecture for more information about the only_full_group_by mode and see if reverting to the previous, or old, settings, helps you execute your code.
https://365datascience.teachable.com/courses/360102/lectures/11680913

Hope this helps but please get back to us should you need further assistance. Thank you.
Best,
Martin

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Answer for Install Jupyter on Linux

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Hi Simon!

Thanks for reaching out and please excuse me for not getting back to you sooner.

Regarding MySQL – great. Regarding Python, and Jupyter in particular, I think there are two options that will suit your situation best.

1. Please download the relevant installer from the Anaconda website. Currently, there are Linux versions that are available: https://www.anaconda.com/products/individual


2. Should the issue persist, please consider using Google Colab. The latter is not a necessary requirement but might be helpful at least while you are solving the Jupyter installation.

Hope this helps.
Best,
Martin

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Answer for Error Code: 1146. Table 'sales.sales' doesn't exist

https://365datascience.com/dwqa-answer/answer-for-error-code-1146-table-sales-sales-doesnt-exist/ -

Hi Michael!

Thanks for reaching out and please excuse me for not getting back to your sooner.

To assist you better, we need to have the exact code you’ve executed, as well as the entire error message you’ve obtained. Can you please provide them (the latter can perhaps be as a screenshot)? Thank you.

The reason I am asking you for more information is the fact that there are several types of error you may have encountered, and each one of them may reflect numerous other reasons for it to appear. 

Looking forward to your answer.
Best,
Martin

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Answer for Date question on R

https://365datascience.com/dwqa-answer/answer-for-date-question-on-r/ -

Hi Patrice, 

thanks for reaching out! Could you let us know which lecture this is referring to or add a link to it?

 

Best, 

Eli

 

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Answer for sm.add_constants()

https://365datascience.com/dwqa-answer/answer-for-sm-add_constants/ -

Hi Anuridh,

Thanks for reaching out.

OLS requires the input to have a column of ones. We need to add this column manually.

x = sm.add_constant(x1) 

adds a column of ones to the x1 array (data['SAT']). Here is the head of x:


As you can see, a column of ones is added to SAT. This column of ones corresponds to x_0 in the simple linear regression equation: y_hat = b_0 * x_0 + b_1 * x_1. As explained in the lecture video, x_0 is always one and thus the regression equation becomes: y_hat = b_0 * 1 + b_1 * x_1. The x array holds 1 and x_1.

Best,
The 365 Team

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Answer for sklearn or tensorflow installation

https://365datascience.com/dwqa-answer/answer-for-sklearn-or-tensorflow-installation/ -

Hi RD,

Could you please let me know what kind of OS are you running? Is it 32-bit or 64-bit?

If it is 32-bit, I am afraid, you will not be able to run tensorflow. In this case, I would suggest opting for Google Colab as an option: https://colab.research.google.com/

Google Colab is an implementation of the Jupyter Project, where you can code on Google servers. There you can simply use TensorFlow as if it were installed.

Best,
The 365 Team

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Answer for Tensorflow final exercise issue

https://365datascience.com/dwqa-answer/answer-for-tensorflow-final-exercise-issue/ -

Hi Freek,

Did you do the preprocessing exercise (after the preprocessing lecture in the business case)?

Best,

The 365 Team

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Answer for Machine learning

https://365datascience.com/dwqa-answer/answer-for-machine-learning/ -

Hi Porage,

Please refer to this question here: https://365datascience.com/question/couldnt-access-other-kernels/

Best,
The 365 Team

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Answer for New User

https://365datascience.com/dwqa-answer/answer-for-new-user/ -

Hi there,

Unfortunately, we do not offer certificates for ‘Free preview’ of courses.

To get a certificate you will need to enroll in our paid program and finish the courses there. 

Note that our paid courses are much bigger than the free preview versions and will help you prepare much better for the job.

Let me know if you have any other questions.

Best,
The 365 Team

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Answer for Open cv

https://365datascience.com/dwqa-answer/answer-for-open-cv/ -

Hi there,

Could you please try opening Anaconda prompt and installing opencv using this code:

pip install opencv-python

Best,
The 365 Team

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Answer for Topic on Decision Trees in ML

https://365datascience.com/dwqa-answer/answer-for-topic-on-decision-trees-in-ml/ -

Hi Frank,

At this point we do not teach these topics. 

We plan to include them in our program in the future, but they are unfortunately not available yet.

Best,
The 365 Team

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Answer for Unable to download the exercise and resources

https://365datascience.com/dwqa-answer/answer-for-unable-to-download-the-exercise-and-resources-2/ -

Hi there,
We don’t seem the experience that problem.
Therefore, if you have issues with a specific file, just let us know and we will send it to your email directly!
Best,
The 365 Team

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Answer for Statistics course, Problem to open some excel files.

https://365datascience.com/dwqa-answer/answer-for-statistics-course-problem-to-open-some-excel-files-2/ -

Hi Marek,

We don’t seem the experience that problem.

Therefore, if you have issues with a specific file, just let us know and we will send it to your email directly!

Best,
The 365 Team

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Answer for Test for the mean. Independent samples (Part1)

https://365datascience.com/dwqa-answer/answer-for-test-for-the-mean-independent-samples-part1/ -

Hi Marko,

Correct!

hen the z-score is negative, the true value is likely to be lower than the hypothesized one.

hen the z-score is positive, the true value is likely to be higher than the hypothesized one.

Best,
The 365 Team

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Answer for Mathematics downloads

https://365datascience.com/dwqa-answer/answer-for-mathematics-downloads/ -

Hi Matt,

Thanks for reaching out!

I am unsure why these files were not there, but there were in fact supplementary files for the ‘Mathematics’ part.

You should now be able to find them with their respective lectures.

Best,

The 365 Team

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Answer for data set for tableau

https://365datascience.com/dwqa-answer/answer-for-data-set-for-tableau/ -

Hi shagun, 

all downladable resources can be found under the videos, in the Download section. 

 

Best, 

Eli

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Answer for Need dataset

https://365datascience.com/dwqa-answer/answer-for-need-dataset/ -

Hi shagun, 

thanks for reaching out! 

You can download the data set from the Download section, under the video. It’s the GDP-Data.xls file.

 

Best, 

Eli

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Answer for null hypothesis

https://365datascience.com/dwqa-answer/answer-for-null-hypothesis/ -

Hi Tara,

I think that the question is missing several words. Could you please complete that so we can help?

The 365 Team

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Answer for samples

https://365datascience.com/dwqa-answer/answer-for-samples-2/ -

Hi Tara,

I think that the question is missing several words. Could you please complete that so we can help?

The 365 Team

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Answer for statistics

https://365datascience.com/dwqa-answer/answer-for-statistics/ -

Hi Tara,

I think that the question is missing several words. Could you please complete that so we can help?

The 365 Team

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Answer for Deep learning course notes missing

https://365datascience.com/dwqa-answer/answer-for-deep-learning-course-notes-missing/ -

Hi Nagaraj,

Thank you for reaching out and thanks so much for the kind words!

Firstly, the course notes are a great tool to learn and we will work on creating those for the said sections!

Regarding your other requests:

1. Well, TF2 is basically Keras, so we will not be doing that. Historically, Keras was built on TF1 to provide easier syntax (a high-level language). However, when they created TF2, they mostly used Keras, practically consuming it.


2. Working on that actually 🙂


3. That’s right after 2!

Best,
Iliya

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Answer for Bias Importance in Neural Network

https://365datascience.com/dwqa-answer/answer-for-bias-importance-in-neural-network/ -

Hi Sumant,

How far into the course are you right now (so that we know what words to pick).

Best,

The 365 Team

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Answer for Balancing a imbalance dataset

https://365datascience.com/dwqa-answer/answer-for-balancing-a-imbalance-dataset/ -

Hi Venkatesh,

Dealing with imbalanced datasets is generally a hard task and is an active area of research.

There are simple ways to approach it that are, however, limited in their simplicity.
For instance, you can oversample, i.e. repeat samples from the minority class, or undersample which is what we used in our lectures here – throw away points from the majority class(es).
Oversampling is obviously quite naive and not really recommended. I’ve never seen it used in practice.
Undersampling is better in principle but only works if you have large amounts of data so cutting maybe >50% of it still leaves you with a big enough dataset.
An improvement on oversampling is using SMOTE and SMOTE-like techniques, as you mentioned.
I have seen a paper that dealt with NNs for better portfolio diversification that used this method extremely successfully but unfortunately I can’t find the reference now. Still, the point is that it is definitely a viable approach to the problem.

Another way to look at it is to tweak the training process instead of the dataset.
A more or less standard topic we did not go into in this course is regularization.
With it you can impose certain restrictions on what you’re learning by introducing extra terms (basically Lagrange multipliers) to your cost function.
There is tons of information on regularization online but most of it focuses on the typical applications such as L1, L2, etc. and not on “engineering your own restriction”.

The two paragraphs so far describe methods to 1) tweak the dataset or 2) tweak the training process.
The third option is to change the learning algorithm entirely.
Non-neural network approaches such as decision trees inherently handle imbalanced datasets better.
Taking this idea further, I believe a popular approach in recent literature (2017) is variations of boosting and bagging.

There even seems to be things like SMOTEBoost, which I discovered now.
You can read furthere here: SMOTEBoost and later arXiv:1712.06658.

In short, it’s still an open problem of how to truly deal with imbalanced datasets.
You can try basic techniques such as undersampling or more advanced ones like some sort of smartly regularized random forest.
What method will be successful depends highly on the given dataset so you can’t generally know before you have tried applying a few of the aforementioned approaches.

Regards,
The 365 Team

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Answer for Cant access course content after course completion

https://365datascience.com/dwqa-answer/answer-for-cant-access-course-content-after-course-completion/ -

Hi Sen,

Please contact our team at team@365datascience.com for more information regarding that!

Best,

The 365 Team

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Answer for can a NN model run without specifying the epoch_size at the time of fitting the model?

https://365datascience.com/dwqa-answer/answer-for-can-a-nn-model-run-without-specifying-the-epoch_size-at-the-time-of-fitting-the-model/ -

Hi there,

Please refer to this question here: https://365datascience.com/question/running-into-errors-in-mnist-exercise/

Best,
The 365 Team

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Answer for P-value - last practical

https://365datascience.com/dwqa-answer/answer-for-p-value-last-practical/ -

Hi Lucas,

You are almost there!

The one-tailed p-value in this case is 0.26 (the answer that you got).

The two-tailed p-value (which we actually need) is 0.51 – as suggested in the answer.

Best,
The 365 Team

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Answer for Neural network output differentiation wrt input variable

https://365datascience.com/dwqa-answer/answer-for-neural-network-output-differentiation-wrt-input-variable/ -

Hi Sumant,

How far into the course are you right now? 

I assure you that later on the differentiation is always “automatic”.

Best,
The 365 Team

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Answer for Issue with Jupyter Notebooks

https://365datascience.com/dwqa-answer/answer-for-issue-with-jupyter-notebooks/ -

Hi Jorge,

Thanks for reaching out and sorry for the delayed reply.

You can find all Credit Risk Modeling notebooks here: https://www.dropbox.com/sh/7oslws1xhsm1zbf/AABkdWDKqpdcGmY1NbXAnkrBa?dl=0

And all Web Scraping Ones here: https://www.dropbox.com/sh/k9nfy7q6piu1a9g/AACEqzb1cO8LyOi9Z6H-P66oa?dl=0

Best,

The 365 Team

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Answer for Error faced while extracting outputs

https://365datascience.com/dwqa-answer/answer-for-error-faced-while-extracting-outputs/ -

Hi Manav,

Thanks for reaching out to us.

In order to use round on tensors of TensorFlow 2.1, we need to get their numpy arrays:
model.predict_on_batch(training_data['inputs']).numpy().round(1)

Best,
The 365 Team

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Answer for Couldn't access other kernels

https://365datascience.com/dwqa-answer/answer-for-couldnt-access-other-kernels/ -

Hi Anuj,

Could you please open your base environment in Anaconda and write:

pip install ipykernel

and 

conda install nb_conda_kernels

Then restart Anaconda and the Jupyter notebook.

Let me know if the problem persists.

Best,
The 365 Team

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Answer for Doubt regarding credit risk analysis

https://365datascience.com/dwqa-answer/answer-for-doubt-regarding-credit-risk-analysis/ -

Hi Kunari,

Thanks for reaching out.

Unfortunately, this information is not sufficient for us to troubleshoot per se.

Could you please share a bit more from the error and the code that you wrote to get it?

Best,
The 365 Team

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Answer for ipynb Math course

https://365datascience.com/dwqa-answer/answer-for-ipynb-math-course/ -

Hi Gilson,

I am afraid that those notebooks are available only once you subscribe for the course (and are not a part of ‘free preview’).

Best,
The 365 Team

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Answer for modules and dictionaries unicode error 0x9d

https://365datascience.com/dwqa-answer/answer-for-modules-and-dictionaries-unicode-error-0x9d/ -

Hi Maria,

Is it possible for you to share your work with us so we can investigate this further?

Best,
The 365 Team

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Answer for Type-of-Joins.xlsx file is corrupted in the curriculum which is why I am unable to open it. Kindly share the same to me.

https://365datascience.com/dwqa-answer/answer-for-type-of-joins-xlsx-file-is-corrupted-in-the-curriculum-which-is-why-i-am-unable-to-open-it-kindly-share-the-same-to-me/ -

Hi Himani,

Could you please point out the lecture that has this issue?

Best,
The 365 Team

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Answer for Data loding issue

https://365datascience.com/dwqa-answer/answer-for-data-loding-issue/ -

Hi Nikita,

Could you please share a screenshot of the issue you are experiencing?

The 365 Team

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Answer for Credit Risk Modeling in Python Section PD Model 6.2

https://365datascience.com/dwqa-answer/answer-for-credit-risk-modeling-in-python-section-pd-model-6-2/ -

HI Edu,

Did you use the code provided in the lecture? 

If you made some changes, could you please share them with us?

Best,
The 365 Team

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Answer for GIT Problem

https://365datascience.com/dwqa-answer/answer-for-git-problem/ -

Hi Jenish,

Could you please try with /user/ instead of /usr/ 

Best,

The 365 Team

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How To Scrape Data Locked Behind A Login?

https://365datascience.com/scrape-data-locked-behind-login/ -

 

In our last tutorial, we looked into request headers and cookies and their role when you scrape data.

So, what’s the next problem you could encounter when scraping?

Yes, it’s login screens.

Sometimes, you might set your sights on scraping data you can access only after you log into an account. It could be your channel analytics, your user history, or any other type of information you need.

In this case, first check if the company provides an API for the purpose. If it does, that should always be your first choice. If it doesn’t, however, don’t despair. There is still hope. After all, the browser has access to the same tools when it comes to a request as we do.

How to Scrape Data That Requires a Login – Important Disclaimer

Information that requires a login to access is generally not public. This means that distributing it or using it for commercial purposes without permission may be a legal violation. So, always make sure to check the legality of your actions first.

With that out of the way, let’s walk through the steps to get past the login and scrape data.

Depending on the popularity and security measures of the website you are trying to access, signing in can be anywhere between ‘relatively easy’ and ‘very hard’.

In most cases, though, it exhibits the following flow.

First, when you press the ‘sign in’ button, you are redirected to a log-in page. This page contains a simple HTML form to prompt for ‘username’ (or ‘email’) and ‘password’.

When filled out, a POST request, containing the form data, is sent to some URL. The server then processes the data and checks its validity. In case the credentials are correct, most of the time a couple of redirects are chained to finally lead us to some account homepage of sorts.

There are a couple of hidden details here, though.

First, although the user is asked to fill out only the email and password, the form sends additional data to the server.

This data often includes some “authenticity token” which signals that this login attempt is legitimate and it may or may not be required for successful login.

The other detail is related to the cookies we mentioned last time.

If we successfully signed into our account, client-side cookies are set. Those should be included in each subsequent request we submit. That way, the server knows that we are still logged in and can send us the correct page containing sensitive info.

So, how can you do this in practice?

The first piece of the puzzle is to find out where the ‘post’ request is sent to and the format of the data. There are a couple of ways to do that. You can either infer that information from the HTML or intercept the requests our browser submits.

The majority of login forms are written using the HTML tag ‘form’:

The URL of the request can be found in an attribute called ‘action’, whereas the parameter fields are contained in the ‘input’ tags. This is important because the hidden parameters will also be placed in input tags and thus can be obtained.

Another important piece of information is the name of the input field.

As trivial as it may seem, we don’t have that knowledge a priori.

For example, think about the username. What should that parameter be called? Well, it might be simply ‘userName’, or it could be called ‘email’, maybe ‘user[email]’. There are many different options, so we should check the one employed by the developers through the ‘name’ attribute.

This information can also be obtained by intercepting the browser requests and inspecting them.

We do that with the help of the Developer tools. Specifically, in the Chrome developers’ tools, there is a ‘Network’ tab that records all requests and responses.

Thus, all we need to do is fill our details and log in while the Network tab is open. The original request should be there somewhere with all request and response headers visible, as well as the form data.

However, bear in mind that it could be buried in a list of many other requests, because of all the redirects and the subsequent fetching of all resources on the page.

Now that we’ve got the URL and form details, we can make the POST request.

The data can be sent as a regular dictionary. Don’t worry about the subsequent redirects – the requests library deals with that automatically, by default. Of course, this behavior can be changed.

But what if we want to then open another page while logged in?

Well, we need to set our cookies in advance first. That means we have to take advantage of requests’ sessions.

Summarizing all this, a sample code for a simple login may look like this:

Here we define all the form details, then we create the session and submit the POST request for authentication. Note that the request is made through the session variable, in this case, ‘s’.

Some websites employ more complex login mechanisms, but this should suffice for most.

Now you know how to tackle a login when scraping data.

I hope this tutorial will help you with your tasks and web scraping projects.

Eager to scrape data like a pro? Check out the 365 Web Scraping and API Fundamentals in Python Course!

The course is part of the 365 Data Science Program. You can explore the curriculum or sign up 12 hours of beginner to advanced video content for free by clicking on the button below.

 

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