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June 11, 2017

‘The Mummy’ Director Alex Kurtzman Is Still Optimistic About the Future of the Dark Universe

The Mummy” was supposed to launch Universal’s Dark Universe, but it opened to largely negative reviews and a disappointing performance at the box office. Nevertheless, director Alex Kurtzman remains optimistic about the future of the Dark Universe in a new interview with THR, arguing that “variety is going to be our good friend when it comes to the evolution” of that shared cinematic universe. (That probably doesn’t mean Variety itself — their review of “The Mummy” wasn’t so hot, either.)

READ MORE: Save Brendan: As a New ‘Mummy’ Arrives, Meet the Memes Trying to Revive Brendan Fraser’s Career

“You obviously want to set a somewhat consistent tone, so that people know what to expect when you see these movies, but it would be ideal for each movie to have its own identity, which is largely going to be dependent on who is directing the films and who is starring in the films,” Kurtzman continues. “I’m really excited to see what Bill Condon does with ‘Bride of Frankenstein.’”

READ MORE: Tom Cruise Has a Future, But It’s Not in the Movies

Condon’s take on that classic is one of several planned by Universal in its ongoing update of the monster movies of yore: “The Invisible Man,” “The Wolfman” and “The Creature from the Black Lagoon” are all forthcoming as well.

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Source: IndieWire film

June 10, 2017

Five Essentials Things Video Editors Should Know How to Do in Premiere Pro

If you’re just getting into shooting and editing video, you’re probably running into the veritable plethora of editing techniques and tricks that you …
Source: CW’s Flipboard Feed

June 10, 2017

How augmented reality could save tech from itself

We’ve all heard the predictions that artificial intelligence, and by extension robotics, is gunning for our jobs.<p>Indeed, as technology marches relentlessly forward, it feels like many of today’s positions could soon be displaced. But just as with past technological inflection points — whether the …
Source: CW’s Flipboard Feed

June 10, 2017

Adobe CEO Hints at Artificial Intelligence on Photoshop

<b>Age:</b> 54<p><b>From:</b> Mumbai<p><b>In cloud we trust:</b> CEO since 2007, Shantanu Narayen has overseen a period of explosive growth for the San Jose software company. …
Source: CW’s Flipboard Feed

June 10, 2017

Premiere Pro Audio Workflow Guide for Editors: Part 2

Source: CW’s Flipboard Feed

June 9, 2017

Will VR Ever Be Mass Entertainment?

Exorbitant costs, confused customers, and fire risks: bringing VR to the people is proving tricky.<p>There’s a lot of talk about virtual reality as the future of entertainment. And with big companies like Facebook, Google, Microsoft, Samsung, and Apple pouring money into the technology’s development, …
Source: CW’s Flipboard Feed

June 9, 2017

Watch: How to Pull Off the 'Wolverine Healing' Effect in After Effects

Whether you’re making a movie about a killing machine made of liquid metal or an ill-tempered mutant mercenary, this VFX tutorial will show you how …
Source: CW’s Flipboard Feed

June 8, 2017


This article originally appeared on Column Five.

Data storytelling is one of the best tools out there for content marketers. But for data noobs it can seem super intimidating. Where do you get data? What do you do when you have it? How do you find stories in data? Relax. We’ve been doing this a while, and we’re here to help you get through it.


Good stories don’t just come from data; they’re actually hidden in data relationships. When you start to play with your data, you begin to see how each data point relates to another. The patterns you see (or don’t) help uncover what—if any—story is there. Understanding what type of data relationships to look for helps you find those stories faster. But first, let’s guide you through the steps to get to that point.


This is where most marketers get tripped up. You have a spreadsheet in front of you with a few or a million data points. The first step? Make sure it’s clean and organized.

Organize your data: Most of the time you’ll be working with data from a spreadsheet. The format of your data depends on what kind you have. Let’s talk about different kinds of data.

  • Is this data one point in time? For example, If you have data from a 2017 survey, you’d have survey questions in the column and answers in the rows.
  • Are there multiple time periods with only one observation? For example, if you have data on Apple stock prices from 1990-2016, the format would have years in the rows and the variable or stock prices in the columns. Note: If years and the variable are switched, no big deal. Spreadsheets have a function where you can paste the values “Transposed.” This will switch the rows and columns of the data.
  • What if your data has multiple observations over a time period? Let’s say you have a dataset that has info on multiple countries from 1990-2016. This data will still have years in the rows, but each column will specify which observation is for that particular year. In this example, you would have a “country” variable that identifies which country the data is referring to.

Identify missing values or bad data: These make you a less credible source since your statistics will be wrong. Do a visual inspection to make sure that the data points make sense. For example, if the data set measures human weights, does it make sense for someone to be 2,000 pounds? Get rid of rows where there are tons of missing data.

Look for outliers in your data: These would be data points that don’t seem to fall into your range of expectations. Outliers are usually thought of as a nuisance, but they could also offer interesting stories and insights. For example, if we expect sales to go down in all counties, then a spike in sales in one county would be an outlier (more on that later).


When we talk about data visualization at this stage, we’re not talking about the beautiful data visualizations your designers create. It’s simply the tools that let you literally “see” your data. (This is why we love data visualization so much—it’s an easy way for our brains to understand what we’re looking at.) Technically, this phase is referred to as exploratory data analysis, but we don’t want you to get too overwhelmed too quick.

For this example, we’re using Google Sheets.

1) Highlight the data you want to visualize.

2) Click on “Insert” and scroll down to “Chart.”

From the “Chart” editor you can use the recommended charts or choose your own graphs by clicking on the “Chart Types” tab. The “Customization” tab allows you to do things like rename your title and axes, change colors, or increase the font size.

Remember that different types of data are best represented with certain types of graphs. In the next section, we’ll cover what kinds of graphs can help you answer your data questions.


This is actually the fun part where you start to search for your story by examining relationships. As you play around with visualizations and analyze according to relationship, you’ll start to see behavior patterns that will lead you in the right direction.

But first, you need to understand what type of relationships to look for.


There are many different data relationships, but we’re going to cover the top 5 most common. These will most likely apply to the data you have at hand, and they’ll help you start to get a sense of what else you might like to explore in other data sets.

As you dive into these, consider what types of interesting angles your findings might support. A few questions to ask yourself as you go:

  • Does the data support or disprove my hypothesis?
  • Does it debunk a widely held belief?
  • Did data increase, decrease, or flatline?
  • Does the data show any differences between groups?
  • What are the top 10 (or bottom 10) observations for a metric or variable?


This is data with two or more variables that may demonstrate a positive or negative correlation to each other.

  • Positive: An increase in one variable results in an increase in the other.
  • Negative: An increase in one variable results in a decrease in the other.

Common chart types:

  • Scatterplot
  • Scatterplot with a fitted line

The strength of a correlation is measured by a correlation coefficient. A popular way to measure this is using the Pearson Correlation Coefficient of Pearson’s R ranging from -1 to 1. This measures how closely the points in your scatterplot resemble a line. A correlation coefficient of 1 means there is a perfect positive correlation. A correlation coefficient of -1 means there is a perfect negative correlation. A correlation coefficient of 0 means there is no correlation.

(In less technical terms, the more the dots on your scatterplot resemble a line, the higher the strength of a correlation.) You can also check out this game, which helps you identify the strength of correlation visually.

Here’s a scatterplot with a fitted line that shows the relationship between GDP per Capita and Coca-Cola prices for different countries. The line shows that there is a positive relationship. This means as GDP per Capita increases, the price of a Coke increases. Through visual inspection we can see the dots don’t make a perfect line, so we can say the correlation is only moderately strong. In fact, after calculating Pearson’s R, the correlation coefficient is 0.51.

What you want to look at here is how they interact. Do both variables influence each other? Do they increase, stay the same, or decrease? Remember: Correlation does not equal causation. (Just because there are more ice cream sales and shark attacks in the summer doesn’t mean that ice cream causes shark attacks.)

Example: You might wonder about the relationship between leads generated by a blog post and the number of hours spent writing the post.

Relationship 2: Trends

Look for noticeable trends, increasing or decreasing, in the data.

Common chart types:

  • Bar chart
  • Line chart

Example: You might look at how many page views your website gets every day in a month to identify which days of the week generate the most traffic.


This shows data distribution, often around a central value. Distributions are useful for understanding the minimum, maximum, mean, median, and range of a specific variable. Looking at a distribution lets you understand the shape of your data by looking at the average and end values.

Common chart types:

  • Histogram

Example: You could group clients by how much revenue they generate for your company in a year. This way you can see what the average client spends, as well as the range a client might be expected to spend.


This is any data that acts unusually or outside the norm.

Common chart types:

  • Scatterplots: Shown by points on the plot that lie away from the trending areas.
  • Histograms: The tails of the histogram show if there are many outliers in the data.
  • Bar charts: Any unusually high or low values.

Example: Going back to our previous example, the trend of the histogram we expect to see is that there are less clients in the first and the last groups. But this histogram shows us an outlier. There are actually a lot of clients that spend $51,000 – $55,000—even though we expected there to be less. It would be interesting to investigate why there are so many clients in that group.


Comparison: This is a simple comparison of the quantitative values of subcategories.

Common chart types:

  • Bar chart

There are many ways to compare data. You can compare sets or look at subcategories within those sets.

Example: You might look data comparing click through rates for different colored CTA buttons. Which get higher clicks, and why?

Ranking: This shows how two or more values compare to each other in relative magnitude.

Example: Which content has the highest page views? Rankings help you easily compare how much traffic a page is generating.


Once you think you’ve found your story, follow these tips to make sure you tell it effectively.

1) Have your audience in mind: Effective data storytelling doesn’t mean you tell whatever story you want. It means you find a story that is interesting for you audience. Consider:

  • Is this relevant?
  • Does it solve a problem or expand their knowledge?
  • Have they heard this story before?

Sometimes you have a story that can be told to multiple (or larger) audiences. If you have the data, hone in on the most interesting angles.

2) Use a credible source: Your data should always be from a credible source and presented without spin. Follow these 5 tips to source correctly.

3) Don’t lie with your data: Data can be powerful; it can also be manipulated, misinterpreted, and misrepresented. Make sure you are telling the full story.

4) Design according to best practices: Data visualization doesn’t just visualize the data; it enhances comprehension. Make sure your designers are presenting it in its most optimized—and accurate—form. For more on this, see our guide to designing the most common graphs and charts.

5) Ditch your story if it isn’t actually there: Sometimes people have an idea for a data story and try to retroactively make their data fit that narrative. If the data isn’t there, the story isn’t there. Luckily, oftentimes searching for one story will lead you to another.

If you need to look for more data, check out:

Data storytelling isn’t always easy, but it’s always worth it. Keep an eye out for more opportunities to flex your skills and you’ll find great stories to turn into great content.

For more on data storytelling:

Source: Visual News

June 8, 2017

4 Cinematic Techniques Iñárritu Emboldens Us To Try

Can Alejandro González Iñárritu’s “visual poetry” push you outside of your cinematic comfort zone?<p>We are big fans of the never-ending fountain of …
Source: CW’s Flipboard Feed

June 6, 2017

5 Freelancer’s Personalities You’ll Meet

From designers to developers; writers to project managers –gigging is in.

The freelance economy is predicted to represent 43% of the workforce by 2020, making it one of the fastest-growing workforces out there, and it’s not limited to just the creative world. Freelancers take all forms, and a recent report by LinkedIn and Intuit suggests that there are 5 types of freelancer “personas” –each with their own drivers and desires.

  1. Side Giggers: see freelancing as a means to make extra cash, and are the most driven by money, compared to the other personas.
  2. Substituters: in the freelance game for temporary work, and don’t see this type of work as a long term thing. They’re least satisfied with gig work, which is often the result of recent job loss.
  3. Business Builders: are focused on building their own business, and pursue freelancing to either supplement or support those efforts. They love the fact that they can be their own boss, and are not the biggest fans of working under someone else.
  4. Career Freelancers: pursue gigs to develop their skills to help them in their careers. The majority of these are millennials and are the most satisfied with freelancing compared to other personas.
  5. Passionistas: value the joy that their work brings them. This group is is a master of their practice, and tends to work less, but earn more, compared to the others.

If you’re a freelancer, which persona do you identify with the most? Comment below!

Learn more about each freelancer persona in the infoGIF below, and then dive into the rich data in part two on the LinkedIn ProFinder blog.


Source: Visual News