AI News, 9 Mistakes to Avoid When Starting Your Career in Data Science

9 Mistakes to Avoid When Starting Your Career in Data Science

If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes.

Many beginners fall into the trap of spending too much time on theory, whether it be math related (linear algebra, statistics, etc.) or machine learning related (algorithms, derivations, etc.).

This approach is inefficient for 3 main reasons: This theory-heavy approach is traditionally taught in academia, but most practitioners can benefit from a more results-oriented mindset.

Thanks to mature machine learning libraries and cloud-based solutions, most practitioners actually never code algorithms from scratch.

While a strong degree in a related field can definitely boost your chances, it's neither sufficient nor is it usually the most important factor.

Many positions are not labeled as 'data science,' but they'll allow you to develop similar skills and function in a similar role.

In addition, many hiring managers will specifically look for your ability to be self-sufficient because data science roles naturally include elements of project management.

To avoid this mistake: Currently, in most organizations, data science teams are still very small compared to developer teams or analyst teams. So while an entry-level software engineer will often be managed a senior engineer, data scientists tend to work in more cross-functional settings.

To avoid this mistake: In this guide, you learned practical tips for avoiding the 9 costliest mistakes by data science beginners: To jumpstart your journey ahead, we invite you to sign up for our free 7-day email crash course on applied machine learning.

5 machine learning mistakes – and how to avoid them

For organizations with the ambition and business need to try modern machine learning, several innovative techniques have proven effective: What makes machine learning algorithms difficult to understand is also what makes them excellent predictors: They are complex.

Some example hybrid strategies include: Effective use of machine learning in business entails developing an understanding of machine learning within the broader analytics environment, becoming familiar with proven applications of machine learning, anticipating the challenges you may face using machine learning in your organizations, and learning from leaders in the field. 

7 Common Data Science Mistakes and How to Avoid Them

This is true in most cases, but in case of data scientists, making mistakes help them discover new data trends and find more patterns in the data.

Without assessing the quality of data they have, the kind of outcome they want and how much profit they are expecting from this kind of data analysis – it becomes difficult to correctly figure out which data science projects will be profitable and which will not.

The best example here is the analysis of Freakonomics in which getting correlation for causation wrong, led Illinois to send books to every student in the state because the analysis revealed that books available at home are directly correlated to high test marks.

This helped make corrections in the earlier assumptions with the insight that houses wherein parents usually buy books have an exhilarated learning environment.

The value of even the best machine learning models is diluted if a data scientist does not choose the right kind of visualizations to model development, to monitor exploratory data analysis or to represent the results.

Even if a data scientist develops an optimum and best machine learning model it will not scream out saying “Eureka”- all that is needed is effective visualization of the results to understand the difference between a data pattern and realizing its existence to be utilized for business outcomes.

As the popular saying goes “A picture is worth a 1000 words.”- It is necessary that data scientists not only familiarize themselves with data visualization tools but also understand the principles of effective data visualization to render results in a compelling way.

crucial step towards solving any data science problem is to get an insight on what the data is about, by representing it through rich visuals that can form the foundation for analysis and modelling it.

To avoid this, the best practice for any data scientist is to ensure that they score their data models with new data every hour, every day or every month based on how fast the relationships in the model change.

The predictive power of models often suffer decay due to several factors and hence there is a constant need to for data scientists to ensure that the predictive power of the model does not drop below the acceptable level.

Most of the data science projects end up answering the “what” kind of questions because data scientists do not follow the ideal path of doing analysis by having the questions at hand.

To avoid this, data scientists should focus on getting their analysis results right by defining the design, variable and data accurately and clearly understanding what they want to learn from this analysis.

As the popular quote by Voltaire goes – “Judge a man by his questions than by his answers.”- Having well defined questions beforehand is extremely important to achieve data science goals for any organization.

Data scientists often get excited about having data from multiple data sources and start creating charts and visuals to report analysis without developing the required business acumen.

If the goal of a data science project is to model the customer influence patterns, then merely considering the behavioural data of customers who are highly influential, is not a good practice.

Overfitting in Machine Learning: What It Is and How to Prevent It

Did you know that there’s one mistake… …that thousands of data science beginners unknowingly commit?

But don’t worry: In this guide, we’ll walk you through exactly what overfitting means, how to spot it in your models, and what to do if your model is overfit.

Next, we try the model out on the original dataset, and it predicts outcomes with 99% accuracy… wow!

In predictive modeling, you can think of the “signal” as the true underlying pattern that you wish to learn from the data.

If you sample a large portion of the population, you’d find a pretty clear relationship: This is the signal.

it has too many input features or it’s not properly regularized), it can end up “memorizing the noise” instead of finding the signal.

In statistics, goodness of fit refers to how closely a model’s predicted values match the observed (true) values.

model that has learned the noise instead of the signal is considered “overfit” because it fits the training dataset but has poor fit with new datasets.

Underfitting occurs when a model is too simple – informed by too few features or regularized too much – which makes it inflexible in learning from the dataset.

too complex (high variance) is a key concept in statistics and machine learning, and one that affects all supervised learning algorithms.

key challenge with overfitting, and with machine learning in general, is that we can’t know how well our model will perform on new data until we actually test it.

For example, it would be a big red flag if our model saw 99% accuracy on the training set but only 55% accuracy on the test set.

Then, we iteratively train the algorithm on k-1 folds while using the remaining fold as the test set (called the “holdout fold”).

It won’t work everytime, but training with more data can help algorithms detect the signal better.

We provide a starting framework for data cleaning in our free crash course on applied machine learning.

This is like the data scientist's spin on software engineer’s rubber duck debugging technique, where they debug their code by explaining it, line-by-line, to a rubber duck.

For example, you could prune a decision tree, use dropout on a neural network, or add a penalty parameter to the cost function in regression.

Bagging uses complex base models and tries to 'smooth out' their predictions, while boosting uses simple base models and tries to 'boost' their aggregate complexity.

While these concepts may feel overwhelming at first, they will ‘click into place’ once you start seeing them in the context of real-world code and problems.

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