As a student, you can improve your data analysis skills

You may be a college student and you are preparing to enter the "big boys" job market.

Data analysis is the area that I always return to when I think of high-value areas to focus my technical skills in.

Data analysis is important.

In today's technology-driven society data in all its forms is increasingly valuable because of the insights it provides. All fields are seeing an exponential increase in the amount of data generated. This is good news for students. You can now learn data analysis to enhance your existing skills in any field, including marketing, computer science, and even music. No matter your background, having the ability to manipulate, process and analyze data will help you get ahead.

What should I consider when learning new skills

It can be daunting to learn new skills and tools in tech, on top of your coursework, jobs, or internships. Trust me, it's not easy. It's important that students are strategic and efficient in determining the best resources for learning.

When I learn new software or skills, there are some factors that I consider.

• What is the estimated cost of this?
• What time will this take?
• What relevance does this have to my job prospects and career?

Price
This is something I don't even pretend to be thinking about. It is essential to know how to manage your finances, especially when you are looking to improve your career.

Time
What about time? That is also a cost. Students value time as much as money. Students have to balance coursework, studying, work, family, extracurriculars, career growth, and sometimes even a job. We are looking for skills that can be learned quickly and that we can do on our own, with a flexible, self-paced work schedule.

Applicability
I also want to be able learn a skill that is relevant to my job search. This will allow me to list it on my resume and make it more appealing to the types of companies I will be applying for. This kind of self-study is essential for your career advancement. This is why I look for opportunities to learn directly with industry-standard software and other services.

Learning data analysis using Google Cloud

My internship at Google has given me ample opportunities to improve my data analysis skills through Google Cloud services. This blog post focuses on two of these services: BigQuery, and Data Studio.

What's BigQuery?

BigQuery allows companies to run analytics on large data sets from the cloud. It is also a great place to learn and practice SQL (the language used for analysing data). BigQuery's "getting started" process is very easy and saves students a lot of time. Instead of installing database software and sourcing data to load it into tables, log in to the BigQuery Sandbox to immediately begin writing SQL queries or copying samples to analyze the data provided by the Google Cloud Public Datasets program. You'll be able to see the difference for yourself soon! ).

What's Data Studio?

Data Studio integrates with BigQuery to allow you to visualize data in interactive and customizable tables, dashboards and reports. It can be used to visualize the results from your SQL queries. However, it is also useful for analyzing data with no SQL and sharing insights with non-technical people.

Data Studio is part of Google Cloud so you don't need to export processed queries to another tool. Direct connections to BigQuery can be used to visualize data. This saves time and eliminates the need to worry about file compatibility and size.

BigQuery and Data Studio are free to use within the Google Cloud Free Tier. The free tier allows users to store a minimum amount of data (or upload your own), and it also processes a set number of queries per month. A BigQuery "sandbox", which is free, can be created. It doesn't need a credit card and you don't have to pay any fees to set it up.

BigQuery and Data Studio are free to use. Let's now talk about their applicability. BigQuery and Data Studio can be used in many industries today for production workloads. You can search BigQuery and Data Studio on LinkedIn to see what I mean.

Get started with BigQuery and Data Studio

Let's get on with the business. Let me show you how easy it is to use both these tools. Here's a quick tutorial to help you get started using BigQuery and Data Studio.

Let's look at an example situation that BigQuery can solve.

Congratulations! This is a new intern that was recently hired by Pistach.io. Pistach.io insists that new employees are allowed to come in for training programs for the first few weeks. You must show up on-time. Pistach.io's office is located in New York City. There is no parking available nearby. So you know that New York City's public bike program has been reinstituted. You have decided to use bikesharing to get to work.

You must arrive on time at work. Here are some key questions to help you answer these questions.

• What stations are nearby that have bicycles you can use in morning?
• Is there a drop-off point that is close to the office?
• Which stations are busiest?

These questions could be answered using a public dataset. BigQuery offers tons of datasets that you can use at no cost. This example uses the New York Citi Bike dataset.

How to get set up

1. First, create a BigQuery Sandbox. This is basically an environment that you can use to do your work. Follow these steps to set one up: https://cloud.google.com/bigquery/docs/sandbox.

2. Go to the BigQuery page in the Google Cloud console ( documentation).

3. Click +Add Data in the Explorer pane > Pin a Project > Enter project name.

4. Type "bigquery-public-data" and click Pin. This project includes all datasets that are available in the public datasets programme.

5. Expand the bigquery–public-data project to see the underlying data. Scroll down until you find "new_york_citibike"

6. Click to highlight the data or to expand the citibike_stations/citibike_trips tables. To see the schema and preview of the data, highlight the tables.

   Visualize the results

   BigQuery's great feature is Data Studio, which allows you to visualize your results with ease. Just click the Explore Data button on the query results page! This will help you get a better understanding of the query you made.

   Next steps

   It's that easy! Google Cloud is simple to use and learn, so you spend less time "getting going" and more time analysing data and creating visualizations. It is easy to see the benefits of using this tool in your professional and personal tech development. There are many ways you can improve your data science skills, such as BigQuery, and help your early career in data science.

   Complementing what you have learned in this article, you can also complete the From data to insights with Google Cloud specialization course on Coursera.

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Cloud Storage: New Prefix and Sufix Lifecycle Rules

Google Cloud Storage is happy to announce powerful new object lifecycle rules (GCS) to help customers manage their storage costs in innovative ways. This launch includes two new features.

Conditions for the prefix/suffix lifecycle

Customers had many lifecycle conditions available before this launch. These included the object's age, version history, custom timestamps, and other details. Customers can now add conditions to the objects' names, such as matching a prefix and a suffix.

The Prefix and Sufix Conditions can be helpful in a variety of situations. Here are two examples:

Separate management of common object prefixes. It is quite common to group objects with a common prefix such as in a data set. Lifecycle conditions can now act on these groups by using a matchesPrefix rule.

Separate management of categories. You can also use extensions on object keys to indicate the format of data:.mp4,.zip, etc. Customers may have multiple of these objects within one bucket and wish to be able to use the extension to separate them. One great example is when customers have many binary objects that are rarely read, but metadata about them using a different extension that's read frequently (think,.mov, and.xml). It is now possible to save costs by keeping the metadata "hot" and the bulk of your data "cold" with a matchesSuffix rule.

Cleaning up multipart uploads that are not complete

We launched the Multipart Upload protocol within our XML API in 2021. Our S3 compatibility was greatly improved, which made it easier for our customers to migrate. Multipart uploads can sometimes be abandoned. This is a common problem. Part storage can be expensive so it is important to prune abandoned uploads. Customers can now "set it and forget it" with GCS Support for Incomplete Multipart Uploads .

All of our customers now have access to these new lifecycle features. These features are available through the GCS API and GCS GUI.

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Improve navigation experience by incorporating hyperlocal context

We know that time is important for both customers and you. It can be the difference between a successful delivery and a customer returning. Long wait times and missed arrivals are key factors. We have made substantial investments in our mobility service over the past year. These services enable seamless pickups and drops for drivers and consumers, as well as last-mile pickups.

Drivers and couriers now have access to updated hyperlocal data from our more detailed basemap. Your Android or iOS apps can now integrate Navigation SDK to show traffic lights along the route and offer more fluid zoom and camera controls. This combination creates an improved navigation experience which can reduce delivery failures and increase average delivery times.

Reduce last mile errors by using enhanced building context

We often depend on nearby businesses and buildings to help us navigate our daily lives. This is especially important for drivers who need to navigate the final mile of a trip, pick up riders or drop off orders. It makes it so much easier for drivers to see the exact location of the dropoff point. You can now highlight the location of the building your driver is heading. The driver can see the building numbers and outline of buildings during his turn-by-turn navigation. This helps to minimize last-mile errors and missed deliveries. The addition of highlighting which building the courier is heading to increases the chance of success in pickup and delivery.

DoorDash allows drivers to reclaim their time and keeps customers satisfied

DoorDash is a technology and logistics firm that started using our On Demand Rides and Delivery Solution to deliver their door-to–door services. To help address the unique challenges in the delivery industry, we worked closely with DoorDash's Product and Data Science Teams.

The time it takes for drivers to get to the customer in the last section of their drive was identified as one of the top challenges. The Google Maps Platform team has updated the following On Demand Rides and Deliveries solutions features to increase delivery times and decrease failed delivery rates:

• By adding building numbers and outline to the map, you can increase driver context upon arrival

• Road snapping logic improvement improves arrival notifications to provide better location accuracy and improved notification quality

• Enhance map details and context with custom zoom and camera controls. This allows for a seamless transition during navigation.

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