Prefigured, Short Film, Review And Interview

There are support groups for many challenges we face. They even exist for those events few people believe happened. Reaching out to find others who have similar experiences can be comforting and can even raise more question.

In Prefigured the director, Zeshaan Younus, introduces one of these groups. Later he talks about being inspired by alien abduction, but I also got a Lovecraft vibe from it. I think that would be enough to let fans of those topics know this should hold some interest (and they are wanting to do a feature of Prefigured).

Prefigured was screened at the 2019 FilmQuest film festival (website). It was nominated for Best Sci-Fi Short, Best Supporting Actor (Nicholas Thurkettle).

Synopsis: A woman experiencing a strange phenomenon seeks out an esoteric support group.

Along with the inspiration for Prefigured, here is some more about what Zeshaan Younus is working on, what inspired them to become a filmmaker, and other things they like to do.

What was the inspiration for Prefigured?

PREFIGURED comes from a lifelong obsession with the abduction phenomenon. I like to think of this film as a grounded exploration of the paranormal. Kind of an open-ended case study into a few of the lives impacted by incomprehensible forces. More recently I've been leaning heavily into cosmic nihilism, religious renouncement, and exploring the ideas of free will. A lot of those feelings were explored and exorcised while bringing this to life.

What project(s) do you have coming up that you're excited about?

Right now, I'm trying my best to enjoy the PREFIGURED ride. We are fortunate enough to have a solid festival run ahead of us and that's super exciting. Outside of that, I have a PREFIGURED feature script I'd love to pitch and an audio-drama that I wrote and directed which will be released on a podcast called Earbud Theater in mid-October.

What was your early inspiration for pursuing a career in film?

Film really was my therapy growing up. When things were incredibly difficult at home I retreated into the countless VHS tapes we had gathering dust in our garage. It became a passion of mine and even a crutch that I never stepped away from. A few decades later here I am, doing my best to add something to the conversation.

What would be your dream project?

Making a feature film in general would be a dream. Though bringing the full-length PREFIGURED project to life is currently my #1 goal. If we are talking pie in the sky, I would love to make a boots-on-the-ground, practical FX heavy, visceral, violent, and incredibly metal GHOST RIDER film. Hell yeah.

What are some of your favorite pastimes when not working on a movie?

I have a career I'm very proud of, do a lot of nonprofit leadership work, serve as the Strategic Director of the Horrible Imaginings Film Festival, and play music. But...someone please give me more movies to work on, thanks.

What is one of your favorite movies and why?

ALIEN is the greatest film of all time. Not the Director's Cut but the original theatrical version. It's so elegantly efficient and truly a marvel in my opinion. I think about it often and use it as a benchmark when attempting to develop strong, independent, and dynamic characters. I'll be naming my first child Ripley.

You can find out more about Prefigured on IMDb (link).

You can watch the trailer on Vimeo (link).

I'm working at keeping my material free of subscription charges by supplementing costs by being an Amazon Associate and having advertising appear. I earn a fee when people make purchases of qualified products from Amazon when they enter the site from a link on Guild Master Gaming and when people click on an ad. If you do either, thank you.

If you have a comment, suggestion, or critique please leave a comment here or send an email to guildmastergaming@gmail.com.

I have articles being published by others and you can find most of them on Guild Master Gaming on Facebookand Twitter(@GuildMstrGmng).

 

UCLan’s cJAM Media Event, Friday 22 November

The games design course was excited to take part in cJAM: Media last week!
The event that enables our talented students to meet face-to-face with senior industry professionals, to share ideas, make connections and pitch for opportunities.
cJAM events are hosted by the Faculty of Culture and the Creative Industries and the objective is to give our students the opportunity to win placements that will help launch their careers.

The day included:
FREE breakfast and lunch

Giant speed pitching session

Chance to win industry placements

Industry guest speakers

Industry Q&A panel

Networking throughout.

We were so proud to welcome our Alumni, Saija Wintersun, now Senior Environment Artist at Rebellion, Oxford.
Saija spent much of the day reviewing student portfolios and offering her expert advice.

The Creative Innovation Zone in UCLan's Media Factory was buzzing with conversation as hundreds of students queued for 'speed dating' style interviews with their industry heroes and mentors.

See details of the programme HERE.

Tania Callagher, UCLan Resources Co-ordinator and Richard Albiston, Creative Producer of The Great Northern Creative Expo, must be given utmost credit for arranging this inspiring and exhuberent event which led to 88 placements being awarded to Media students.

Need For Speed Games Part 4: Need For Speed: Hot Pursuit 2, Need For Speed: Underground

Today on Super Adventures, I've reached the end of the first decade of the Need for Speed games. We're in the EA GAMES TRAX era now, where every time a new song comes on, the TRAX box slides onto the screen to let you know who you're listening to. Even on the title screen.

This also means we're in the licenced soundtrack era, and the sixth gen console era! And Underground brings us to the era of perpetual twilight, where daytime is banned. Unless it's literally set underground, I don't think they ever say.

Anyway this is it, the last part. After this you won't be reading about any racing games here for a long long time, so enjoy it while it lasts (or endure it for just a little longer). Earlier parts are here, here and here.

(If I don't mention what system a screenshot came from, it's from the PC version.)

Read on »

Download City Mod For Gta Sandeas And Hd Graphic Mod

gta sandeas city mod is present by y.yadav gamer ths mod contain hd graphic cheat menu an guns cheat and other use full chetat to use this mod go to mod discription (md)


                                                                                md

cheat menu activation key=ctrl+c
change cloth key=3
to activate guns=GUNS

game passswoed is fulla1

click here to download click
click here to download fast click


                                                                 

visist youtube 

video will soon avlable

                             

Top 5 Reasons Why Your PUBG Is Lagging And How To Solve It?

It has become a common thing to lag in big games like PUBG, Apex legends etc. Especially, if we are in a serious situation 
and at that moment PUBG lags then our frustration and angriness go beyond the limit. This has become a serious problem even for a pro player. So if you are looking for the solution to this problem then you have visited the right place. This post will mention the reasons for which PUBG lags and the solution to it.

Why PUBG lags?

Reasons and solutions to PUBG lag :

1. Running of unnecessary apps in the background:

           Believe it or not but just running unnecessary apps or installing any app or updating an app in the background decreases your internet speed which finally results in PUBG lagging. 

Solution:   So, if you want to keep a distance from lagging make sure that there are no online apps running in the background. And avoid updating or installing any app at that time.

2. Bandwidth limits set by your ISP(Internet Service Provider):

         If your internet speed is good but still your PUBG lags whereas you can run other online apps such as Youtube etc easily then there is a high chance that your ISP might have set limits in that particular game.

For example- Sometimes the ISP set 25% bandwidth only for games and the rest 75% for other online apps. This is done to distribute the internet equally otherwise it would become difficult for normal people to surf the internet.

Solution: In that case, you can change your ISP or simply use a VPN. There are many free gaming VPNs present on the internet. You can use one to increase your internet speed. This can decrease PUBG lagging to a great extent.

3. Excess heating of PC or mobile:

Excess heating of Smartphone

         While playing any game, you must ensure that your CPU or phone should not heat too much. In case of excess heating, your game will surely lag because heat is the biggest enemy of the processor and hence the processor is unable to work properly. For such reasons, gaming PCs are specially designed for proper cooling.

Solution: Ensure the proper cooling of the laptop or computer and in case of mobile you should let it rest for some time to attain normal temperature. 

Note: You should never play games on a mobile while it's charging. This not only makes your phone hot but also it is unhealthy for your battery and sometimes if the battery is a low-quality battery, the battery can even burst. 

You can visit this article for more information on overheating of phones.

You can visit this website to know how to cool down your android phone.
You can visit this website to know how to cool down your PC.

4. Your PC or mobile does not meet the recommended requirements of the game:

Requirements don't meet

      This is a well-known problem. Sometimes PUBG can lag because your device can lack many components including processor or graphics card or GPU or RAM or Memory or any other component. In that case, though your ping may be okay still the PUBG will lag.

Solution: If you are highly addicted to this game and want to play the game anyhow without lagging you can change the components of the PC while in case of mobile you need to buy a new mobile.

To know the minimum and recommended requirements of PUBG PC click here.

5.  Playing in the farthest server:

       Another reason for which your PUBG is lagging is that you are playing in a server far away from you. In PUBG there is an option to select servers like Asia, Europe etc. If you belong to Asia and playing in Europe server then your PUBG may lag since it takes time to communicate and get a response from them. 

Solution: You should play at the nearest server. For example, if you live in the Asian continent you should play at Asia server while if you live in America you should play the South or North America server. You can notice less PUBG lag if you play in your nearest server.

Note: The above reasons are also applicable to the whole internet services. You might have noticed that sometimes though your internet speed is very high but still many websites or pages take too much time to reload. This is because their server is very far and takes too much time to communicate with them.

If you are suffering excess lagging of PUBG, you can complain at the official PUBG support page by clicking here. 

Alien Rage Free Download

Alien Rage Free Download game setup in single direct link. It's tactical shooting game where you face dangerous aliens to counter with.

Alien Rage Overview

Alien Rage is a Sci-Fi first person shooter game. It is developed under the banner of CI Games for Microsoft Windows. It was released on 24^th September 2013. You can also download Evolve PC Game 2015.

This game is set on an asteroid. This asteroid is jointly mined by Aliens and Humans in order to get energy from an amazing source named as Promethium. But the aliens attack

on the human miners and kill them. After this you control the human character Jack who has to go in the mining facility and kill the aliens. Half Life is another game that you can download.

Alien Rage is a first person shooter game and you will have to fight the aliens which are of many types. Also when the level is about to end you will have to defeat a bigger enemy Boss in a fight. You can get extra points if you kill the aliens in short time or if you use special moves to kill them. When you get these points you can use them as a currency to upgrade the character's abilities and his behaviors. You can have two weapons at a time. Also the weapons are of different varieties which are made by humans as well as by alien technology. There will be assault rifles, Sniper rifles, rocket launchers and Shotguns included in this game. Multiplayer mode is also added in this game. Altogether this game is an interesting one and you will probably enjoy it. You can also download Halo Combat Evolved.

Features of Alien Rage

Following are the main features of Alien Rage that you will be able to experience after the first install on your Operating System.

• Awesome Sci-Fi first person shooter game.
• Set on an asteroid.
• Need to take the revenge from the aliens.
• Variety of weapons included.
• Multiplayer mode included.
• Can have two weapons at a time.

System Requirements of Alien Rage

Before you start Alien Rage Free Download make sure your PC meets minimum system requirements.

• Operating System: Windows XP/Vista/7/8
• CPU: 2.6GHz Intel Dual Core processor.
• RAM: 2GB
• Hard Disk Space: 4GB

Alien Rage Free Download

Click on the below button to start Alien Rage. It is full and complete game. Just download and start playing it. We have provided direct link full setup of the game.

Download Link:::Link

Size:1.65 GB

Price:Free

Virus status: scanned by Avast security

A Rare Victory At Glucken Ridge

   I played a game of Team Yankee a few weeks ago with my friends at this local club called Board and Brew. I say "local" but it's a good 30 miles from my house, forty five minutes with traffic on a good day. Anyway, it's a pretty good place with plenty of tables and they have beer. So its good.

   I played a fellow I have known for several years but rarely get to see, named Anthony. I was his first opponent ever in Flames of War. He plays Italians, I play Indian Army British. Anyway, in this game, I played a Czech Force with Soviet allies and he ran an American force.

Soviet T-64s advance on the left flank - daring the Yankees in their flashy M1s to attempt to do something about it. One Hero of the Soviet Union is made.

Minefields slow the Czech armoured advance, but a few turns of rocket artillery have softened up the American infantry defenders and the T-72M assault drives them from their foxholes.

All of my scouts are killed. Because I stupided. And my infantry never disembarked.

Beyond the trees in the background to the left, T-64s keep reinforcing M1s occupied.

Burning platoon of M1 Abrams. A sight sure to please any Warsaw Pact player.

In fact, I got very lucky and took them all out in one turn.

   In the end, I won by pushing the American infantry off the objective. The Americans did destroy enough of my units to make it a 5-2 victory rather than a 6-1 overwhelming victory. But I will take it! Anthony was a great sport about it all and a lot of fun to take on as an opponent.

Cross-compiling Rust To Linux On Mac

In my last blog post I said I wanted to spend some time learning new things. The first of those is Rust. I had previously tried learning it, but got distracted before I got very far.

Since one of the things I'd use Rust for is web pages, I decided to learn how to compile to WebAssembly, how to interface with Javascript, and how to use WebSockets. At home, I use a Mac to work on my web projects, so for Rust I am compiling a native server and a wasm client. But I also wanted to try running this on redblobgames.com, which is a Linux server. How should I compile to Linux? My first thought was to use my Linux machine at home. I can install the Rust compiler there and compile the server on that machine. Alternatively, I could use a virtual machine running Linux. Both of these options seemed slightly annoying.

I've been curious how much work it would take to cross-compile, and I found this great post from Tim Ryan. My setup is simpler than his, so I didn't need everything he did. I started with these commands from his blog post:

rustup target add x86_64-unknown-linux-musl brew install FiloSottile/musl-cross/musl-cross mkdir -p .cargo cat >>.cargo/config <<EOF [target.x86_64-unknown-linux-musl] linker = "x86_64-linux-musl-gcc" EOF 

I then compiled for Linux:

TARGET_CC=x86_64-linux-musl-gcc cargo build --release --target=x86_64-unknown-linux-musl 

Unfortunately this failed with an error about OpenSSL. Tim's post has a solution to this. Before implementing that complicated solution I realized that I should't need SSL/TLS anyway. My server talks regular websockets, not secure websockets, and then I use nginx to proxy them into secure websockets. So I disabled the secure websockets with this in Cargo.toml, the file that has the Rust project configuration:

[target.'cfg(target_arch = "x86_64")'.dependencies] tungstenite = { version = "0.9", default-features = false, features = [] } 

At first I tried features = [] but that wasn't good enough. I needed to also use default-features = false to disable the TLS. With this, the binary built, and I was able to run it on Linux!

So now I have a Makefile that builds the wasm client, the Mac server for local testing, and the Linux server for production. Fun!

BUILD = build  RS_SRC = $(shell find src -type f -name '*.rs') Cargo.toml WASM = target/wasm32-unknown-unknown/debug/rust_chat_server.wasm  run-server: target/debug/chat_server  # local testing server  RUST_BACKTRACE=1 cargo run --bin chat_server  target/debug/chat_server: $(RS_SRC)  # production server  cargo build --bin chat_server  target/x86_64-unknown-linux-musl/release/chat_server: $(RS_SRC)  TARGET_CC=x86_64-linux-musl-gcc cargo build \      --release --target=x86_64-unknown-linux-musl  $(WASM): $(RS_SRC)  cargo build --lib --target wasm32-unknown-unknown  $(BUILD)/rust_chat_server_bg.wasm: $(WASM) index.html  wasm-bindgen --target no-modules $< --out-dir $(BUILD)  mkdir -p $(BUILD)  cp index.html $(BUILD)/ 

My Cargo.toml file is kind of terrible but it works so far for building the three outputs:

[package] name = "rust_chat_server" version = "0.1.0" authors = ["Amit Patel <redblobgames@gmail.com>"] edition = "2018"  [lib.'cfg(target_arch = "wasm32")'] crate-type = ["cdylib"]  [[bin]] name = "chat_server" path = "src/chat_server.rs"  [dependencies] wasm-bindgen = "0.2" serde = { version = "1.0", features = ["derive"] } bincode = "1.2"  [target.'cfg(target_arch = "x86_64")'.dependencies] tungstenite = { version = "0.9", default-features = false, features = [] } 

That's it for now. I'm not a big fan of writing client-server code in large part because I want my pages to still work in thirty years, and that's best if there's no server component. But I want to spend time this year learning things for myself rather than trying to produce useful tutorials, so I'm going to explore this.

Tim's blog post was a huge help. Without it, I would've compiled the server on Linux. Thanks Tim!

I've placed it on github.

The Nuisance Of Link Surfing

Photo by mikael altemark. Some rights reserved. Source: Flickr

This essay's going to be rather short, so I'll just cut to the chase. An awful habit is developing among the essays, articles, blogs, op-eds, or whatever you want to call them, on the Internet. Allow me to explain. Say you are reading an article about the history of the peanut butter and banana sandwich, when the author of said piece, claims that the popular dish was invented by Elvis Presley. Instead of detailing where they found this fact, or giving us the passage, they hyperlink the statement to their source. Confronted with this, you can either click on the hyperlink to verify the truth of their claim, or you can trust them to be honest and not bother. Of course, no one on the Internet can be trusted these days, so you click, but when to click? Do you click immediately and interrupt the flow of the article or wait until you finish the article, while it dangles among the sentences, tantalizing you. Eventually, when you do click, 9 times out of 10, you reach another Internet source, and 9 times out of 10, they'll have numerous claims where the hyperlinks abound. This eventually becomes a rather time consuming and irritating game, chasing source after source in search of the original. It may take hours, and by the time you finish, you may have forgotten what the original article was about.



I like to call the cat-and-mouse that many news junkies are familiar with, "link-surfing." Unfortunately, I can't say that I coined the term, because Urban Dictionary thought of it first, "link surfing: Traversing the Web by clicking on links within web pages. This technique is often used on encyclopedia sites like Wikipedia" (Gunderson). If only this habit could be regulated to websites like Wikipedia, but alas, it has infected the annals of our best magazines and newspapers. Since people prefer to get their information for free (I sure do) I imagine that much of the press is gearing themselves towards an Internet audience. As such, they no longer bother with quotes or paraphrases. You're expected to either follow the link or take them at face value. This, with all due respect, comes off as lazy. This type of format certainly isn't admissible for college papers or non-fiction Pulitzer Prize winners. I've always believed that a text should be self-contained. All the relevant information necessary to understanding the point of your piece should all be within the text itself. Your sources should be appendices to your arguments. Following all those links to verify the correctness of your claims is simply too much work for the average reader to do in one sitting. Some of us have lives outside of the Internet. We can't be bothered to go link-surfing all day. I don't know if these Internet writers actually expect us to click on all of their links. In a way, their professionalism is appears as not much different from the rumor mill.

By the way, I often find that some of these links are ultimately useless. They may lead to magazine that requires a subscription, or an academic study that requires a subscription, or an Error 404, or a Wikipedia article. Some of you may wonder why I listed the Wikipedia article as useless. Well, to its credit, the free encyclopedia has plenty of information and sources listed. In my opinion, the website's entries are only useful insofar as you are able to check their sources. Some require that you go to the library, while others are dead links or uncheckable. Checking the links, again, takes up too much time, and knowing that Wikipedia can be edited by anyone, its claims deserve the highest scrutiny. Will we really be able to verify each and every claim? In other words, not a very reliable source.

This may sound a bit old-fashioned, but I call for an appeal to the past. I realize that not every single claim made needs to be referenced or footnoted, but the big ones do. For those big ones, don't cheapen yourself by only linking the words to another site. Quote or paraphrase it, like you were taught. If you can't find it on the Internet, then fine, source a book if you must. I understand that Internet connection has made information a lot easier to find, and that's a good thing. However, save the rest of us some time, and muscle up your arguments inside of the text, instead of relying solely on links, that frankly, most of us won't even bother to click on. Also, don't source Wikipedia. That makes you look like a slacker.


Bibliography

Gunderson, Bob. "link surfing." Urban Dictionary. May 1, 2007. Web. http://www.urbandictionary.com/define.php?term=link%20surfing

Tech Book Face Off: Effective Python Vs. Data Science From Scratch

I must confess, I've used Python for quite some time without really learning most of the language. It's my go-to language for modeling embedded systems problems and doing data analysis, but I've picked up the language mostly through googling what I need and reading the abbreviated introductions of Python data science books. It was time to remedy that situation with the first book in this face-off: Effective Python: 59 Specific Ways to Write Better Python by Brett Slatkin. I didn't want a straight learn-a-programming-language book for this exercise because I already knew the basics and just wanted more depth. For the second book, I wanted to explore how machine learning libraries are actually implemented, so I picked up Data Science from Scratch: First Principles with Python by Joel Grus. These books don't seem directly related other than that they both use Python, but they are both books that look into how to use Python to write programs in an idiomatic way. Effective Python focuses more on the idiomatic part, and Data Science from Scratch focuses more on the writing programs part.

VS.

Effective Python

I thought I had learned a decent amount of Python already, but this book shows that Python is much more than list comprehensions and remembering self everywhere inside classes. My prior knowledge on the subjects in the first couple chapters was fifty-fifty at best, and it went down from there. Slatkin packed this book with useful information and advice on how to use Python to its fullest potential, and it is worthwhile for anyone with only basic knowledge of the language to read through it.

The book is split into eight chapters with the title's 59 Python tips grouped into logical topics. The first chapter covers the basic syntax and library functions that anyone who has used the language for more than a few weeks will know, but the advice on how to best use these building blocks is where the book is most helpful. Things like avoiding using start, end, and stride all at once in slices or using enumerate instead of range are good recommendations that will make your Python code much cleaner and more understandable.

Sometimes the advice gets a bit far-fetched, though. For example when recommending to spell out the process of setting default function arguments, Slatkin proposed this method:

def get_first_int(values, key, default=0):
    found = values.get(key, [''])
    if found[0]:
        found = int(found[0])
    else:
        found = default
    return found

Over this possibility using the or operator short-circuit behavior:

def get_first_int(values, key, default=0):
    found = values.get(key, [''])[0]
    return int(found or default)

He claimed that the first was more understandable, but I just found it more verbose. I actually prefer the second version. This example was the exception, though. I agreed and was impressed with nearly all of the rest of his advice.

The second chapter covered all things functions, including how to write generators and enforce keyword-only arguments. The next chapter, logically, moved into classes and inheritance, followed by metaclasses and attributes in the fourth chapter. What I liked about the items in these chapters was that Slatkin assumes the reader already knows the basic syntax so he spends his time describing how to use the more advanced features of Python most effectively. His advice is clear and direct so it's easy to follow and put to use.

Next up is chapter 5 on concurrency and parallelism. This chapter was great for understanding when to use threads, processes, and the other concurrency features of Python. It turns out that threads and processes have unique behavior (beyond processes just being heavier weight threads) because of the global interpreter lock (GIL):

The GIL has an important negative side effect. With programs written in languages like C++ or Java, having multiple threads of execution means your program could utilize multiple CPU cores at the same time. Although Python supports multiple threads of execution, the GIL causes only one of them to make forward progress at a time. This means that when you reach for threads to do parallel computation and speed up your Python programs, you will be sorely disappointed.

If you want to get true parallelism out of Python, you have to use processes or futures. Good to know. Even though this chapter was fairly short, it was full of useful advice like this, and it was possibly the most interesting part of the book.

The next chapter covered built-in modules, and specifically how to use some of the more complex parts of the standard library, like how to define decorators with functools.wraps, how to make some sense of datetime and time zones, and how to get precision right with decimal. Maybe these aren't the most interesting of topics, but they're necessary to get right.

Chapter 7 covers how to structure and document Python modules properly when you're collaborating with the rest of the community. These things probably aren't useful to everyone, but for those programmers working on open source libraries it's helpful to adhere to common conventions. The last chapter wraps up with advice for developing, debugging, and testing production level code. Since Python is a dynamic language with no static type checking, it's imperative to test any code you write. Slatkin relates a story about how one programmer he knew swore off ever using Python again because of a SyntaxError exception that was raised in a running production program, and he had this to say about it:

But I have to wonder, why wasn't the code tested before the program was deployed to production? Type safety isn't everything. You should always test your code, regardless of what language it's written in. However, I'll admit that the big difference between Python and many other languages is that the only way to have any confidence in a Python program is by writing tests. There is no veil of static type checking to make you feel safe.

I would have to agree. Every program needs to be tested because syntax errors should definitely be caught before releasing to production, and type errors are a small subset of all runtime errors that can occur in a program. If I was depending on the compiler to catch all of the bugs in my programs, I would have a heckuva lot more bugs causing problems in production. Not having a compiler to catch certain classes of errors shouldn't be a reason to give up the big productivity benefits of working in a dynamic language like Python.

I thoroughly enjoyed learning how to write better Python programs through the collection of pro tips in this book. Each tip was focused, relevant, and clear, and they all add up to a great advanced level book on Python. Even better, the next time I need to remember how to do concurrency or parallelism or how to write a proper function with keyword arguments, I'll know exactly where to look. If you want to learn how to write Python code the Pythonic way, I'd highly recommend reading through this book.

Data Science from Scratch

I didn't expect to enjoy this book quite as much as I did. I went into it expecting to learn about how to implement the fundamental tools of the trade for data science, and that was indeed what I got out of the book. But I also got a lighthearted, entertaining, and surprisingly easy-to-read tour of the basics of machine learning using Python. Joel Grus has a matter-of-fact writing style and a dry wit that I immediately took to and thoroughly enjoyed. These qualities made a potentially complex and confusing topic much easier to understand, and humorous to boot, like having an excellent tour guide in a museum that can explain medieval culture in detail while cracking jokes about how toilet paper wasn't invented until the 1850s.

Of course, like so many programming books, this book starts off with a primer on the Python language. I skipped this chapter and the next on drawing graphs, since I've had just about enough of language primers by now, especially for languages that I kind of already know. The real "from scratch" parts of the book start with chapter 4 on linear algebra, where Grus establishes the basic functions necessary for doing computations on vectors and matrices. The functions and classes shown throughout the book are well worth typing out in your own Python notebook or project folder and running through an interpreter, since they are constantly being used to build up tooling in later chapters from the more fundamental tools developed in earlier chapters. The progression of development from this chapter on linear algebra all the way to the end was excellent, and it flowed smoothly and logically over the course of the book.

The next few chapters were on statistics, probability, and their use with hypothesis testing and inference. Sometimes Grus glossed over important points here, like when explaining standard deviations he failed to mention that this metric only applies to (or at least applies best to) normal distributions. Distributions that deviate too much from the normal curve will not have meaningful standard deviations. I'm willing to cut him some slack, though, because he is covering things quickly and makes it clear that his goal is to show roughly what all of this stuff looks like in simple Python code, not to make everything rigorous and perfect. For instance, here's his gentle reminder on method in the probability chapter:

One could, were one so inclined, get really deep into the philosophy of what probability theory means. (This is best done over beers.) We won't be doing that.

He finishes up the introductory groundwork with a chapter on gradient descent, which is used extensively in the later machine learning algorithms. Then there are a couple chapters on gathering, cleaning, and munging data. He has some opinions about some API authors choice of data format:

Sometimes an API provider hates you and only provides responses in XML.

And he has some good expectation setting for the beginner data scientist:

After you've identified the questions you're trying to answer and have gotten your hands on some data, you might be tempted to dive in and immediately start building models and getting answers. But you should resist this urge. Your first step should be to explore your data.

Data is never exactly in the form that you need to do what you want to do with it, so while the gathering and the munging is tedious, it's a necessary skill that separates the great data scientist from the merely mediocre. Once we're done learning how to whip our data into shape, it's off to the races, which is great because we're now halfway through this book.

The chapters on machine learning models, starting with chapter 12, are excellent. While Grus does not go into intricate detail on how to make the fastest, most efficient MLMs (machine learning models, not multi-level marketing), that is not the point. His objective is to show as clearly as possible what each of these algorithms looks like and that it is possible to understand how they work when shown in their essence. The models include k-nearest neighbors, naive bayes, linear regression, multiple regression, logistic regression, decision trees, neural networks, and clustering. Each of these models is actually conceptually simple, and the models can be described in dozens of lines of code or less. These implementations may be doggedly slow for large data sets, but they're great for understanding the underlying ideas of each algorithm.

Threaded through each of these chapters are examples of how to use each of the statistical and machine learning tools that is being developed. These examples are presented within the context of the tasks given to a new data scientist who is an employee of a budding social media startup for…well…data scientists. I just have to say that it is truly amazing how many VPs a young startup can support, and I feel awfully sorry for this stalwart data scientist fulfilling all of their requests. This silliness definitely keeps the book moving along.

The next few chapters delve a bit deeper into some interesting problems in data science: natural language processing, network analysis (or graph algorithms), and recommender systems. These chapters were just as great as the others, and by now we've built up our data science tooling pretty well from the original basics of linear algebra and statistics. The one thing we haven't really talked about, yet, is databases. That's the topic of the 23rd chapter, where we implement some of the basic operations of SQL in Python in the most naive way possible. Once again it's surprising to see how little code is needed to implement things like SELECT or INNER JOIN as long as we don't give a flying hoot about performance.

Grus wraps things up with an explanation of the great and all-powerfull MapReduce, and shows the basics of how it would be implemented with mapper and reducer functions and the plumbing to string it together. He does not get into how to distribute this implementation to a compute cluster, but that's the topic of other more complicated books. This one's done from scratch so like everything else, it's just the basics. That was all fine with me because the basics are really important, and knowing the basics well can lead you to a much deeper understanding of the more complex concepts much faster than if you were to try to dive into the deep end without knowing the basic strokes. This book provides that foundation, and it does it with flair. I highly recommend giving it a read.


Both Effective Python and Data Science from Scratch were excellent books, and together they could give a programmer a solid foundation in Python and data science as long as they already have some experience in the language. With that being said, Data Science from Scratch will not provide the knowledge on how to use the powerful data analysis and machine learning libraries like numpy, pandas, scikit-learn, and tensorflow. For that, you'll have to look elsewhere, but the advanced, idiomatic Python and fundamental data science principles are well covered between these two books.