# AI News, Aspiring Data Scientists! Learn the basics with these 7 books!

- On Sunday, June 3, 2018
- By Read More

## Aspiring Data Scientists! Learn the basics with these 7 books!

In the last few years I spent a significant time with reading books about Data Science.

If you combine this knowledge with the right online data science courses, it’s already a good-enough level for an entry level Data Scientist position.

You will learn, why is it so important to select the One Metric That Matters as well as the 6 basic online business types — and the data strategy behind those.

If Lean Analytics is about business + data for startups, this book is business + data for big companies.

This book comes with many stories and you will learn how not to be scammed by headlines like “How we pushed 1300% on our conversion rate by changing only one word” and other BSs.

It goes deeper into topics like regression models, spam filtering, recommendation engines and even big data.

You can do almost everything in Python, when it comes to analysis, predicting and even machine learning.This is a heavy book (literally: it’s more than 400 pages), but covers everything with Python.

Most probably as an analyst or data scientist you won’t use this kind of knowledge directly, but at least you will be aware, what the data infrastructure specialists of the company do.

If you want to try out, what it is like being a junior data scientist at a true-to-life startup, check out my new 6-week online data science course: The Junior Data Scientist’s First Month!

- On Sunday, June 3, 2018
- By Read More

## Every single Machine Learning course on the internet, ranked by your reviews

The ideal course introduces the entire process and provides interactive examples, assignments, and/or quizzes where students can perform each task themselves.

Here is a succinct description: As would be expected, portions of some of the machine learning courses contain deep learning content.

If you are interested in deep learning specifically, we’ve got you covered with the following article: My top three recommendations from that list would be: Several courses listed below ask students to have prior programming, calculus, linear algebra, and statistics experience.

Several top-ranked courses below also provide gentle calculus and linear algebra refreshers and highlight the aspects most relevant to machine learning for those less familiar.

Though it has a smaller scope than the original Stanford class upon which it is based, it still manages to cover a large number of techniques and algorithms.

Ng explains his language choice: Though Python and R are likely more compelling choices in 2017 with the increased popularity of those languages, reviewers note that that shouldn’t stop you from taking the course.

Columbia’s is a more advanced introduction, with reviewers noting that students should be comfortable with the recommended prerequisites (calculus, linear algebra, statistics, probability, and coding).

It covers the entire machine learning workflow and an almost ridiculous (in a good way) number of algorithms through 40.5 hours of on-demand video.

Eremenko and the SuperDataScience team are revered for their ability to “make the complex simple.” Also, the prerequisites listed are “just some high school mathematics,” so this course might be a better option for those daunted by the Stanford and Columbia offerings.

few prominent reviewers noted the following: Our #1 pick had a weighted average rating of 4.7 out of 5 stars over 422 reviews.

- On Sunday, June 3, 2018
- By Read More

## How to Actually Learn Data Science

There's huge demand for data scientists — average compensation in SF is well north of 100 thousand dollars a year.

The data science skills gap means that many people are learning or trying to learn data science.

The first step to learning data science is usually asking "how do I learn data science?".

The response to this question tends to be a long list of courses to take and books to read, starting with linear algebra or statistics.

can't fully explain how immensely unmotivating it is to be given a huge list of resources without any context.

You need something that will motivate you to keep learning, even when it's midnight, formulas are starting to look blurry, and you're wondering if this will be the night that neural networks finally make sense.

It can be figuring out new and interesting things about your city, mapping all the devices on the internet, finding the real positions NBA players play, mapping refugees by year, or anything else.

The great thing about data science is that there are infinite interesting things to work on — it's all about asking questions and finding a way to get answers.

Here are some good places to find datasets to get you started: Another technique (and my technique) was to find a deep problem, predicting the stock market, that could be broken down into small steps.

I then created some indicators, like average price over the past few days, and used them to predict the future (no real algorithms here, just technical analysis).

I didn't just learn SQL syntax — I used it to store price data, and thus learned 10x as much as I would have by just studying syntax.

It's hard to get good at communicating complex concepts effectively, but here are some things you should try: It's amazing how much you can learn from working with others.

If you find yourself getting too comfortable, here are some ideas: This is less a roadmap of exactly what to do that it is a rough set of guidelines to follow as you learn data science.

We teach Python because it's the most beginner-friendly language, is used in a lot of production data science work, and can be used for a variety of applications.

Remember, resources on their own aren't useful — find a context for them: This post is adapted from my Quora answer on how to become a data scientist.

- On Sunday, June 3, 2018
- By Read More

## 18 New Must Read Books for Data Scientists on R and Python

There are numerous open courses which you can take up right now and get started. But, acquiring in-depth knowledge of a subject requires extra effort. For example: You might quickly understand how does a random forest work, but understanding the logic behind it’s working would require extra efforts.

Almost, every data scientist I’ve come across in person, on AMAs, on published interviews, each one of them have emphasized the inevitable role of books in their lives.

Here is a list of books on doing machine learning / data science in R and Python which I’ve come across in last one year.

This book introduces you to details of R programming environment using interesting projects like weighted dice, playing cards, slot machine etc.

It’s a decent book covering all aspects of data science such as data visualization, data manipulation, predictive modeling, but not in as much depth.

You can understand as, it covers a wide breath of topic and misses out on details of each. Precisely, it emphasizes on the usage criteria of algorithms and one example each showing its implementation in R.

This book is written by Teetor Paul. It comprises of several tips, recipes to help people overcome daily struggles in data pre-processing and manipulation.

It covers a wide range of topics such as probability, statistics, time series analysis, data pre-processing etc.

Having a solid understanding of charts, when to use which chart, how to customize a chart and make it look good, is a key skill of a data scientist.

This book doesn’t bore you with theoretical knowledge, but focuses on building them in R using sample data sets.

It discusses several crucial machine learning topics such as over-fitting, feature selection, linear &

It comprises of in-depth explanation of topics such as linear regression, logistic regression, trees, SVM, unsupervised learning etc.

It talks about shrinkage methods, different linear methods for regression, classification, kernel smoothing, model selection etc.

It’s a relatively shorter book than others, but aptly brings out sheer importance of every topic discussed.

After reading this book, I realized that the author’s mindset is not to go deep in a topic, still making sure to cover important details.

None of the books listed above, talks about real world challenges in model building, model deployment, but it does.

The author doesn’t move her focus from establishing a connect between theoretical world of ML and its impact on real world activities.

Pandas and provides a useful description of importing data from various sources into these structures. You will learn to perform linear algebra in Python and make analysis by using inferential statistics.

Later, the book takes onto the advanced concepts like building a recommendation engine, high-end visualization using Python, ensemble modeling etc.

There isn’t any online course as comprehensive as this book. This book covers all aspects of data analysis from manipulating, processing, cleaning, visualization and crunching data in Python.

In addition, it also covers advanced methods for model evaluation and parameter tuning, methods for working with text-data, text -specific processing techniques etc.

In this book the authors have chosen a path of, starting with basics, explaining concepts through projects and ending on a high note.

It lets you rise above the basics of ML techniques and dive into unsupervised methods, deep belief networks, Auto encoders, feature engineering techniques, ensembles etc.

With an interesting title, this book is meant to introduce you to several ML algorithms such as SVM, trees, clustering, optimization etc using interesting examples and used cases.

You might feel puzzled at seeing so many books explaining similar concepts. What differentiates these books is the case studies &

- On Thursday, October 17, 2019

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