We are excited to announce a series of semi-technical data courses and two new data certification programs from Pragmatic Institute. Available in 2024, these courses are designed for data professionals aiming to sharpen their skills and beginners eager to break into the data science field.

Learn About Pragmatic Data

Welcome to Pragmatic Data

In 2019, The Data Incubator officially became a part of Pragmatic Institute, the authority on comprehensive product management, product marketing, and data science training. In 2024 and beyond, all data training will be offered by Pragmatic Institute.

What are the new data offerings?

Pragmatic Institute now offers adapted courses formerly offered by The Data Incubator. Current course topics include, but are not limited to, data wrangling, AI and machine learning, distributed computing, and advanced data storage architectures.

Explore Pragmatic Data Courses

How are Pragmatic Institute data trainings offered?

As of January 2024, all data courses will be offered as private team training. If you are interested in data training for your team, get in touch! We would love to connect with you.

What is Pragmatic Institute?

Founded in 1993 as Pragmatic Marketing, Pragmatic Institute has helped over 250,000 students from more than 10,000 companies across 26 countries and countless industries refine and perfect their corporate strategies. Featuring world-class instructors and trusted by leading companies around the world, Pragmatic Institute is proud to be a leader in data training.

Pragmatic Data: A New Division of Pragmatic Institute

While our name may be different, you will receive the same excellent hands-on data training you’ve come to expect from The Data Incubator. We’re still here to help you reach your career goals with guidance from Pragmatic Institute’s world-class instructors.

Does Pragmatic Institute offer data job placements?

At this time, Pragmatic Institute will not offer job placement services for students enrolled in our data training program.

I have questions – how can I learn more?

We have answers! Get in touch with our team.

Not finding the training you’re looking for?

As you explore Pragmatic Institute’s new data training offerings, you might find that our new courses do not offer the level of comprehensive data science or data engineering training that you’re looking for. Fortunately, there are some great options out there to help you in your educational pursuits:

Simplilearn has a list of 18 updated resources that are currently offering data science training.

Nobledesktop offers some free resources and online tutorials.

Coursera provides advice on how to choose a data science bootcamp.

And, Springboard offers courses and provides a list of data science-centered communities.

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A data pipeline mitigates these risks. Pipelines are the tools and processes for moving data from one location to another. A data pipeline’s primary goal is to maintain data integrity as the information moves from one stage to the next. The data pipeline is a critical part of an organization’s growth as the information helps people make strategic decisions using a consistent data set.

Here are the top 10 technologies you need to build a data pipeline for your organization.

What Technologies Are Best for Building Data Pipelines?

A data pipeline is designed to transform data into a usable format as the information flows through the system. The process is either a one-time extraction of data or a continuous, automated process. The information comes from a variety of sources. Examples include websites, applications, mobile devices, sensors, and data warehouses. Data pipelines are critical for any organization to make strategic decisions, execute operations or generate revenue. Data pipelines minimize manual work, automate repetitive tasks, eliminate errors and keep data organized.

1. Free and Open-Source Software (FOSS)

Free and Open-Source Software (FOSS) is, as the name suggests, both free and open-sourced. This means accessing, using, copying, modifying and distributing the code is free.

There are various advantages of using FOSS over proprietary software. For one, FOSS costs less. Secondly, FOSS offers better reliability and more efficient resource usage. The software allows complete control over the code. As a result, FOSS enables companies to customize the software to meet their needs. Many of the technologies listed below fall into this category.

2. MapReduce

MapReduce is an algorithm for breaking large amounts of information into small chunks and processing them in parallel. Hadoop distributes these tasks across many nodes and allows multiple computers to operate simultaneously. Essentially, MapReduce enables the entire cluster to work as one computer.

3. Apache Hadoop

Apache Hadoop is an open-source implementation of the MapReduce programming model on top of Hadoop Distributed File System ( HDFS ). Hadoop provides a framework for the distributed processing of large data sets across many nodes.

4. Apache Pig

When it comes to expressing dataflow programs, Apache Pig is the go-to tool. Pig is a high-level language programming language. The language is well suited to stream processing tasks such as real-time data processing, machine learning, and interactive analysis. Pig implements the MapReduce model by extending the Java language with new operators and functions. These functions make processing complex jobs more efficient.

5. Apache Hive

Apache Hive is an open-source data warehouse system for storing, manipulating, and analyzing unstructured big data stored in Hadoop clusters. Hive extends SQL with a set of operations for manipulating large datasets stored in HDFS using a SQL-like syntax. Hive provides an abstraction over Hadoop’s file system to allow users to interact with HDFS using familiar SQL syntax without needing to be familiar with Hadoop’s programming model or MapReduce programming model.

6. Apache Spark

Apache Spark is a fast-growing technology based on Hadoop. Like Hadoop, Spark is an open-source framework that provides scalable distributed computing capabilities for big data processing.

7. Apache Flume

Apache Flume is a distributed streaming data collection, aggregation, and integration toolkit for Hadoop. Flume enables companies to collect streaming data from many sources into a central location. Flume can be used for things like monitoring systems where it collects metrics from various devices such as routers or switches and stores them in HDFS for analysis by other tools such as Spark or Hive. Flume is also used to collect log files from various systems into HDFS for processing by other tools, such as MapReduce or Pig. Flume provides a simple yet powerful HTTP API for other applications to interact with the central store of data being collected.

8. Amazon Web Services (AWS)

AWS provides a scalable, highly available infrastructure for building data pipelines. AWS offers S3 as a storage service for large volumes of data. S3 is compatible with standard Hadoop file formats. Amazon DynamoDB is a highly scalable database service also used to store large volumes of data in buckets for real-time predictive analysis. Amazon Redshift provides the ability to query large datasets using SQL.

9. Apache Kafka

Apache Kafka is an open-source distributed messaging system designed for high-throughput applications needing reliable real-time communication between distributed applications. Kafka is used in many production environments that require real-time processing for high availability or streaming data from many heterogeneous sources. It is commonly deployed as an application layer service within a Hadoop cluster or on top of other technologies, such as Spark or Kafka.

Kafka offers similar benefits to Hadoop in terms of batch processing. However, Kafka features better scalability as it scales across many machines, making it more suitable for use cases involving large volumes. Kafka has been gaining popularity due to its simplicity and ease of use compared to other solutions.

Kafka also has some additional benefits. For example, Kafka can be used as an event bus and as a message queue with low latency and high throughput. Kafka also handles larger transactions at once. This feature makes Kafka an ideal solution for large-scale batch processing applications.

10. Python

Python is an easy-to-use high-level programming language. Python can be used to write highly-scalable, maintainable and extensible applications. Python can also be used for scripting and automation purposes, such as building websites or automating tasks. Due to its versatility, Python has been gaining popularity recently, especially among web developers.

As organizations become more reliant on data, the need for efficient data processing becomes increasingly important. A data pipeline transforms data into a usable form as it flows through the system. Companies rely on this information for data-backed decision-making.

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If you plan to become a data scientist, it’s critical to understand the storytelling techniques that will help you present data to marketers, directors, investors and stakeholders. You won’t just tell your audience about algorithms and hard numbers but explain the phenomena behind them. Learn more about data storytelling below.

What Is Data Storytelling?

Here’s a great definition of data storytelling: [It’s] the process of translating data analyses into understandable terms to influence a business decision or action.

As a data scientist, it’s your job to explain the meaning behind data to those who lack your analytical skills. That means providing context for data and presenting data in a way that resonates with your audience.

Data storytelling isn’t a new concept. Books like “Storytelling with Data: A Data Visualization Guide for Business Professionals” by Cole Nussbaumer Knaflic discuss, in great detail, how to use data to create a compelling narrative. However, few data science programs teach students how to communicate information from data analysis, resulting in a labor force that’s competent at determining and evaluating data sets and variables but unable to express information to audiences.

Some data scientists are, by nature, introverted and would rather analyze data sets than talk about them. However, properly communicating your ideas and findings will help your audience understand what lies behind your insights. Most people won’t understand complex processes like data wrangling, programming, deep learning, data manipulation and statistical models, so it’s your job to clear up the confusion and explain why you made the analytical decisions you did.

How to Tell Stories in Data Science

Think of the last great book you read. The author presented a logical flow of events, taking you on a journey. There was a story with a beginning, middle and end.

Storytelling in data science also requires a flow of events that uncover the meaning behind data. That means creating a narrative that explains complicated concepts through the use of powerful data visualizations like reports, graphs, charts and infographics. These visuals will help you communicate your story to an audience and keep them engaged.

Say you work for a large business and discover a way to save the company money after many months of data analysis. Instead of printing off and giving spreadsheets to directors, you can tell a story through visualizations that explain your findings. Showing directors graphs and charts, for example, will help them learn how you came to the conclusion of saving them money.

Think about that last great book again. It almost certainly had a setting, characters and storyline. You can apply these elements to your data story to engage your audience.

Setting

The setting of your data story might be the business your work for. Or, if you work independently, a client using your services.

Characters

The characters in your story are those who need your data science skills to solve a problem. For example, a sales manager who wants to identify the most valuable customers to move through their pipelines. You, the data scientist, will also be a character in your own story.

Storyline

Your storyline will be how you solved a problem in your setting. For example, you used a data algorithm that discovered different ways to identify the most valuable customers for the sales manager. You will describe how you created this algorithm, the challenges you encountered and the results you generated.

Tips for Telling Stories with Data

Here are a few tips for more effective data storytelling:

Know your audience

The type of stories you tell with data depends on the audience. When communicating with marketing teams, you could use more advanced statistical models if marketers are already familiar with them. Presenting ideas and insights to directors might involve the use of simpler charts and graphs that convey the most important facts and figures.

Highlight key data points

There’s no use presenting your story with data insights if your audience can’t understand your visualizations. Showcase key data points that illustrate complicated concepts rather than handing your audience all of your data findings.

Be prepared to answer questions

After telling your story through data, your audience might ask you to explain aspects of your data reports or why you used a particular data model or algorithm. Answering these questions correctly is fundamental if you want your audience to understand the information you are presenting to them.

Final Word About Data Storytelling

Data storytelling is all about communicating the power of data through a compelling narrative. Data visualizations can help you achieve this goal and make it easier for your audience to understand the analytical decisions you made and how they benefit their business.

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But for AI in data science to be successful, you need to feed artificial intelligence tools with well-written and correctly-formatted prompts, which can be a little difficult. The trick — talk to AI like it’s a friend.

To make life easier, here are some data science prompts to get you started. You can even customize these AI prompts to your particular use case. Just scroll down to the topic you need.

General Prompts for AI in Data Science

You can ask AI tools like ChatGPT general questions about data science to support your projects and better understand this discipline. Doing so can be helpful when learning about the different components of data science, especially when studying this subject.

Check out the following AI in data science prompts that will expand your knowledge:

1. Explain the differences between linear regression and logistic regression in simple terms.
2. What is a confusion matrix, and how does it help with machine learning? Explain in 100 words or less.
3. I’m struggling to understand the concept of regularization in machine learning, especially the difference between L1 and L1 regularization. Can you help me?
4. Give me five reasons why data regression is essential for data scientists.
5. What data governance frameworks should I know about as a data scientist? How will these frameworks impact my career? I live in the United States.
6. What are the ten best tools to use for data classification? List the pros and cons of each tool. I prefer to use open-source software.
7. Tell me in 200 words or less the difference between extrapolation and interpolation in a data scientist context. Tell me this information in simple English.
8. I only have time to learn one programming language. Would you recommend R, Python, C+++ or something else? I want to get a high-paying job in data science in the next few years.
9. I have a limited knowledge of data mining. Tell me more about it and give me some real-world examples of when businesses use this technique.
10. What is the best way to segment customers interested in [type of product/service]? How many segments should I use?

Coding AI Prompts

Sometimes, you’ll need to decode, debug, edit, and correct strings of complicated code. Instead of doing this manually or using coding tools, AI can do all the hard work for you if you input the correct prompts!

Here are some AI prompts for coding that will generate valuable responses:

11. I have a piece of Python code, which is supposed to [insert function of code]. Can you decipher it for me? [Insert code]
12. The following piece of code doesn’t run properly in my database management system. Correct it for me, and tell me where I went wrong. [Insert code]
13. What is this SQL code doing? Explain in 100 words or less. [Insert code]
14. Help me understand this complicated R code. What is its purpose? [Insert code]
15. There’s a syntax error in the following script. Identify it for me. [Insert code]
16. A runtime error is causing a program to crash. Fix the error. [Insert code]
17. Complete the following code and convert [type of data structure] into [type of output format]. [Insert code]
18. Identify and fix any logic errors in the following string of code. [Insert code] Then tell me how I can prevent these errors from happening in the future.
19. Criticize the following code and tell me how to improve my coding skills. [Insert code]
20. Are there any deadlock problems in the following code? [Insert code]

Prompts for Data Analysis

Data analysis is one of the biggest responsibilities of a data scientist. While you’ll still perform the bulk of analysis, AI tools can guide data science projects and answer any questions about data sets.

Try these AI in data science prompts for yourself and see what responses you get:

21. I have some text data from a government website. Can you help me identify any patterns and trends in this data? [Insert data]
22. Can you analyze this text data from Twitter and conduct sentiment analysis for me? I want to find out what people think about [name of product/service/brand]. [Insert data]
23. Carry out exploratory data analysis on the following data sets. [Insert data sets]
24. Analyze this data about website traffic and tell me which web pages are most popular. [Insert data]
25. Analyze the following weather data and tell me the most common weather patterns from [date range]. [Insert data]
26. I want to conduct an A/B test on two versions of a new web page. What’s the best way to do this?
27. Review the following data set and explain why customers are churning. [Insert data]
28. Give me five examples of transactional data sets.
29. Pretend you’re a data scientist with ten years of experience. Tell me the best ways to analyze the following data set about retail sales. [Insert data]
30. Give me 20 of the best KPIs for analyzing call center performance data.

Machine Learning Prompts

Machine learning (ML) requires a vast understanding of math and computer science, which can be challenging when starting in data science. Thankfully, AI in data science tools can help you make sense of machine learning models and even create them for you.

Here are some machine learning prompts:

31. I want to create a regression model to predict future sales performance. What tips can you give me to ensure this model generates accurate insights?
32. Should I create a supervised learning, unsupervised learning or reinforcement learning model for my data project? [Insert details of your data project]
33. Design a sentiment analysis model to help me learn what people say about my company on Facebook.
34. Give ten examples of R machine learning scripts.
35. Give me ten pros and cons of using scikit-learn in machine learning.
36. Tell me, in simple English, how to create a machine learning model for the finance industry. Then give me real-world examples of how this machine learning model will benefit the industry.
37. How do you normalize test and training data in an image classification model?
38. Recommend three or four Python libraries for machine learning.
39. Create a supervised learning model for the retail industry and explain, in detail, how to split data into testing sets.
40. Tell me how to apply three different regression algorithms using scikit-lean on the following data set. [Insert data set]

AI in Data Science: Data Visualization Prompts

Data visualization is another task that AI tools can help you with. For example, you can input prompts about how to use your favorite visualization tools and learn about the graphical representation of data.

Here are some prompts for visualization that you should know about:

41. How do I create line charts in Seaborn?
42. How do I create bar graphs in pandas?
43. Create a scatterplot to show the relationship between these variables. [Insert variables]
44. What are the benefits of using a histogram to visualize data compared to a bar graph? Give me five benefits.
45. Tell me some best practices for correlation analysis.
46. I have an upcoming presentation as a data scientist. What data visualizations do the C-suite respond to best?
47. Design a pie chart to show the most popular social media platforms used by my customers. [Insert data for pie chart]
48. Give an example of a histogram for the Mmatplotlib library.
49. What is a stacked area chart in 50 words or less?
50. Create a heatmap for the following data set. [Insert data for heatmap]

Final Word

AI in data science can automate mundane tasks you don’t have time for and provide helpful tips for your upcoming projects. Of course, you’ll have to verify the information AI tools generate because they can make mistakes. However, answers from the above prompts can provide a good starting point for data science projects. You can personalize these prompts, input your requests into AI tools, and make smarter data-driven decisions.

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Ready to start a career in data science? Learn other essential skills, including communication skills for data scientists.

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Why is having strong communication skills so critical in data science?

Here are some of the reasons why communication is a fundamental skill in data science:

Communicate data science results skillfully

As a data scientist, it’s essential to make sure you know how to communicate data science knowledge to individuals who aren’t versed in data. Transferring knowledge across departments is crucial, so it’s vital to share insights and analyses in simple, clear terms that don’t overwhelm individuals with jargon or technical details.

Work with others effectively

You may spend lots of time working alone with a computer, analyzing algorithms and datasets. However, you may also find yourself working with others. You may work alongside data analysts or other scientists as part of a team, especially when handling large datasets or working on big projects. Beyond this, you may also frequently work with other teams of professionals who don’t work with data. Thus, it’s essential to be an excellent communicator to work with others effectively.

Hold attention with excellent data presentation skills

As a data scientist, you may have to present your findings to clients or colleagues with presentations. Hence, clear and effective communication is essential. You need to present complex analyses to others in a short time without rushing. You should also be able to create attention-grabbing and accessible data visualizations.

Seven ways to improve your data science communication skills

Here are a few ways to improve your data science communication skills:

1. Identify your audience and speak their language

Tailoring communication to your audience can increase the likelihood that your recommendation will be convincing. To make the strongest appeal among business stakeholders, consider understanding who they are and what their priorities are. Usually, a company’s decision-makers are very busy with many priorities competing for their attention, especially in fast-growing companies. Thus, connecting the new recommendations and insights to your target audience’s existing objectives and goals is one of the easiest ways to capture their attention. Providing a short explanation of why the insight is important, framed in terms of the possible impact on the critical performance metrics of the audience, is a simple and concise way of highlighting the relevance and value of an insight to their performance success.

For instance, if your insight is about API latency and your audience is the engineering team responsible for that API, it would be best to use relevant domain terminologies or metrics because the audience already has the technical context or knowledge necessary to understand the analysis fully. Likewise, if the audience is finance decision-makers, it would be wise to frame the insight in the context of potential EBITDA (earnings before interest, taxes, depreciation and amortization) impact, a financial metric, making the insight more easily understood and relevant.

2. Use the TL;DR approach to clearly communicate what matters

One way to grab your audience’s attention and highlight the relevance of an insight to the business is to use the TL;DR approach (short for “Too Long; Didn’t Read”) at the beginning of every analysis. This approach is a clear, concise summary of the content (typically one line) that frames essential insights in the context of impact on key business metrics. It helps you define the bottom line, making it easier for the company’s decision-makers to recognize the value of your insights and learn more.

Having clear, actionable titles can give your audience an idea of what’s to come, so they’ll be ready to pay attention to the details of your presentation. You can also apply the TL;DR approach to any subheadings you used in your presentation materials, analyses or charts.

Two strategies can make this approach easier to implement:

• Prevent ambiguity and ensure that all subtitles or analyses look like the title of a newspaper article. Although you may be tempted to have a slide titled “Problem,” that is much less appealing than something more specific like “The problem with decreasing website click-through rates.”
• Consider leading with the recommendation instead of just the data: This gives your audience the bottom line faster and catches their attention. For instance, instead of saying something like, “50% of first-time visitors to a website don’t click on an item”, you can say, “Improving item recommendation can increase first-time visitor click-through rate by 50%”.

3. Use visualizations whenever applicable

Spreadsheets, illustrations, graphs and charts can work wonders for an otherwise dull report. Today’s computers, including laptops and many tablets and smartphones, come preloaded with intuitive design applications for this very purpose. Utilize these applications to your advantage whenever applicable, as they can make it easy to display datasets, highlight statistics and draw attention to the most critical points you’re trying to make.

When utilizing visualizations, make sure to avoid confusing the audience. Avoid presenting unnecessarily complex visualizations, as this can distract your audience from the critical insight and make the overall communication of an insight less effective. For instance, a facet grid or correlation matrix can be an efficient way to explore relationships in data, but presenting a dense visualization might confuse business stakeholders and distract you from communicating the key insights. Even an insight initially discovered using an advanced visualization strategy can often be summarized with a simple table or chart, which will be easier for all audiences to understand.

4. Gather questions and feedback

Before you finalize your project or end your report, consider soliciting direct feedback from your audience. It doesn’t matter if you have to prompt them to ask you questions or if they’re impatient to put your knowledge to the test—this form of interaction can help you improve your communication skills and establish a successful career as a data scientist.

5. Use a structured communication strategy

A structured communication strategy can go a long way in driving alignment with your audience. Consider using a three-step communication strategy:

1. ‘Telling’ your audience the subject of your presentation.
2. Actually ‘telling’ your audience.
3. Synthesizing what they were just ‘told’.

This communication strategy is beneficial for a meeting with cross-functional participants, as analytics recommendations and insights can sometimes get technical or granular, making it harder for all participants to follow along successfully. Thus, it’s essential to summarize the agenda upfront and recapitulate the conclusions at the end of the meeting.

A structured communication model can give your audience many opportunities to understand the top-level topics and not get lost in the details they didn’t fully understand. Additionally, using a framework to communicate the five Ws—What, Who, Why, Where and When—can help you provide consistency to the communication and allow you to put insights into context.

6. Focus on the result

Make sure not to get bogged down with the technical details of any specific project. Also, don’t overload your audience with information from the beginning. Instead, start by drawing attention to the result and work backward. Rather than explaining the technical requirements or specifications of a new process or application, consider describing the final benefits. This allows you to capture your audience’s attention immediately and helps you address any other concerns and gain the necessary approval.

7. Continue communication until the recommended actions are complete

As a data scientist, you may sometimes move on to other projects after sharing your insights. This may create a disconnect between you and the team executing those insights, causing delays or sometimes misinterpretations and driving suboptimal results. Thus, to minimize these risks, it’s crucial to have a proactive communication plan for the later stages of a project.

For instance, for an analysis driving actionable insights, ensuring the communication channels are open and conducting regular follow-ups can help keep track of the progress and provide efficient execution. This regular communication may involve asking for status updates, answering questions, highlighting road blockers or iterating towards an even better solution.

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1. Database Design, Implementation and Optimization

Database design

Database design refers to the process of designing database schemas and tables based on requirements or business rules. It involves deciding whether to use a relational or an object-oriented design, determining what type of database to use, and identifying the information elements that will be used.

Database design is a critical data engineering skill because databases underpin an organization’s information strategy. A properly planned database has the structure and functionality necessary to:

• Store information reliably
• Provide accurate information output for loading into other systems, such as BI tools
• Execute complex queries that load quickly

A poorly designed database can lead to many problems, including poor performance, information integrity issues and security issues. In effect, a poorly designed database renders a company’s information unusable.

Database implementation

Database implementation involves installing database software and performing configuration, customization and testing. The implementation also entails integrating the database with applications and loading initial data. The data could either be new data captured directly or existing data imported from another data source.

Database Optimization

Optimization refers to strategies for reducing system response time. This is especially important as organizations collect massive volumes of data each day. The increased load could slow the database. To ensure the system runs at peak performance, the engineer must frequently monitor and optimize the system.

You may be wondering how all of this is different from a database administrator’s role. The key difference is that a database administrator focuses mostly on database functionality. An engineer needs to understand how the business plans to use the information. Understanding this information helps them determine the best technology and structure for the information.

2. Data Modeling

Data modeling is a process for analyzing and defining how a business plans to use its information. It is a valuable data engineering skill because it outlines which business processes depend on the information and where it comes from. Performing this process ensures the information meets the business requirements. Engineers use a process known as data modeling to outline this information.

Data models are representations of an organization’s information. It is also used to model and map the relationships between concepts or entities that exist in the company’s systems. Modeling can be categorized as conceptual, logical or physical models. Conceptual modeling helps identify how information should be organized for maximum usability. Logical modeling defines how the computer system should store the information. Physical modeling is the most detailed model. It is an actionable blueprint for those who need to build the database.

3. Extract, Transform, Load (ETL)

Most organizations’ information exists in silos and disparate systems. The engineer’s job is to figure out how to consolidate that information to meet business requirements. They do this through a process called Extract, Transform, Load (ET). ETL describes the stages that information goes through to be processed in a data warehouse.

Extract

Extract involves retrieving raw data from source systems such as relational information sources and information from unstructured sources such as social media posts or pdfs.

Transform

At this stage, the information must be converted to a standard format to meet the schema requirements of the target database. The level of transformation required depends solely on the information extracted and business requirements. The transform step includes validation and rejecting information that doesn’t meet requirements.

Load

Transferring the information to the destination system.

Data manipulation skills are important for this process. Often, the engineer needs to run queries to validate the information in the system. To do so, they must understand database programming languages such as SQL and NoSQL.

4. Programming and Scripting

Programming

Programming is the process of designing, writing, testing and maintaining instructions that tell a computer what to do. This data engineering skill is important because sometimes the engineer will need to write custom programs to meet business requirements. There may be times when a requirement can’t be met using existing technology. At which point, the engineer needs to create a solution.

Scripting

Scripting languages, also known as scripting or script programming languages, are a subset of computer programming languages. A scripting language is usually interpreted and can be used interactively within an application without requiring compilation of the entire program. Scripts are often considered more flexible than programs written in lower-level code, such as in C or C++. Scripts help engineers automate things that would have been tedious and repetitive tasks, such as generating reports.

Common scripting and programming languages include:

• JavaScript
• Python
• PHP
• Ruby
• Java
• Perl.

5. Data Visualization

Business users don’t want raw information. They need to understand the information in plain terms and how they can use it to help with their business strategy. Data visualization is the process of representing information in a way that’s easy to understand. It is a great technique to communicate the findings to stakeholders. The most common types of visualizations are histograms, line graphs, bar graphs and scatter plots. They’re used to show how data has changed over time or how different variables relate to each other.

Data visualization tools are a type of application that collects and prepares information for stakeholders to review. These applications are sometimes referred to as business intelligence (BI) tools. Their primary function is to make sense out of volumes of raw information by providing insight through graphical representations. A few of the most common visualization tools include Tableau, Power BI, D3 and Plotly.

6. Communication and Consulting

The engineer’s role is not solely technical. As experts, they play a critical role in helping companies get the most value from their information. As such, they need to serve as consultants. Their role as consultants involves evaluating the business requirements to:

• Determine if the requirements can be met
• Determine how best to meet those requirements
• Negotiate with stakeholders to prioritize requirements
• Help stakeholders understand the risks involved in the approach

Once the engineer makes their recommendations, they need to present those options to stakeholders. The engineer needs to communicate with stakeholders who may not be familiar with the technology. This is an important data engineering skill because the engineer must clearly and patiently explain how their solution meets the requirements.

7. Statistical Modeling

Statistical modeling is the process of constructing a mathematical function that describes an observed set of data. The engineer uses this model for predictive analytics.

Predictive analytics is the process of using information from past events to predict future outcomes. This is especially helpful for modeling human behavior based on previous transactions or interactions with other humans. It relies heavily on probability theory, machine learning techniques such as:

• Decision trees and random forests
• Linear regression
• Time series analysis
• Hidden Markov models
• Bayesian networks
• Clustering algorithms

One of the most common use cases for statistical modeling and predictive analytics is market analysis. Businesses use statistical analysis and predictive modeling to glean insights about how their markets are changing, such as where the most promising opportunities lie. Using information gathered from sales records and other sources, they can predict likely future business outcomes; this is called forecasting. Businesses may also use analytics or predictive models to find patterns in the historical behavior of customers that will help them predict what those same customers might want in the future. For example, by analyzing purchasing habits through retailers’ websites a company can determine new products to offer.

8. AI and Machine Learning

Artificial Intelligence

Artificial Intelligence is a computer science term. Artificial intelligence refers to systems that can do things without human input or independently of humans. This can include tasks such as learning, decision-making, and problem-solving.

Machine Learning

Machine Learning (ML) is the process of building a computer program that can learn from, analyze and make predictions about data. Machine learning techniques include gathering information to create accurate models to recognize patterns in data. There are two types of ML.

The first type is supervised machine learning. This form of ML takes a set of sample data and tries to find an output rule that matches it. Unsupervised machine learning is used when there isn’t a clear target in mind but instead seeks patterns within raw information through techniques like clustering and outlier detection. A few use cases for AI and ML include:

• Predicting how much of a price increase the market can tolerate
• Predict the likelihood a customer may be late on their next payment
• Predict the customers most likely to leave

9. Cloud Computing

Engineers work with massive amounts of information. Companies need a cost-effective system to store this information. It can be expensive to purchase the hardware and software to support their information storage requirements. A more cost-effective solution is cloud computing.

Cloud computing refers to the delivery of computing resources over the internet. Using cloud computing, companies can rent physical servers, storage and databases from cloud providers. This lets companies quickly add more computing resources as needed. Typically within minutes, as opposed to the days it would take to provision a physical server. Providers charge a pay-per-usage model, so companies won’t waste money on resources that aren’t being used.

• Infrastructure as a service (IaaS): IaaS refers to the renting of IT infrastructure, including servers, virtual machines, storage, networks and operating systems.

• Platform as a service (PaaS): PaaS provides an environment for developing and managing web or mobile software applications.

• Software as a service (SaaS): SaaS involves supplying software applications on-demand over the internet.

10. DataOps

DataOps (data operations) is a data engineering skill that involves collaboration between the DevOps team, engineers and scientists to automate and streamline data flows within an organization. The DataOps Manifesto is a set of best practices for achieving these goals. Three of the most critical principles are:

Value Working Analytics

We believe the primary measure of data analytics performance is the degree to which insightful analytics are delivered, incorporating accurate data, atop robust frameworks and systems.

Orchestrate

The beginning-to-end orchestration of data, tools, code, environments and the analytic team’s work is a key driver of analytic success.

Make It Reproducible

Reproducible results are required and therefore we version everything: data, low-level hardware and software configurations, and the code and configuration specific to each tool in the toolchain.

Learn more about data science and business-oriented data science skills from Pragmatic Data.

The post Must-Have Data Science Skills for 2023 appeared first on Pragmatic Institute - Resources.

“Data visualization is about adding a visual channel to make the data more memorable and comprehensible. We remember things in images and stories; we are not number creatures.”

– Nadieh Bremer

In this episode of Data Chats, host Chris Richardson and data visualization expert Nadieh Bremer explore the world of data visualization and unravel its intricacies, offering practical tips and insights into the fusion of art and data science.

Nadieh Bremer is a data visualization designer and artist, working to captivate and engage an audience with the insights that the data reveals, to convince them of the lessons hidden within the numbers, and to take readers along on a journey told through the lens of data.

During this episode with Chris and Nadieh, you will:

• Learn the art of maintaining brand consistency while crafting data visuals
• Understand the importance of providing detailed explanations for data variables
• Delve into the challenge of quantifying return on investment (ROI) for data visualization
• Explore the evolving role of AI in data visualization and its potential impact
• Gain insights into tailoring data visuals for different audiences
• Discover the shift from interactivity to purposeful interaction in data visualization

Learn more about Nadieh here.

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Data plays a crucial role in enabling organizations to build models and harness the power of data to uncover actionable insights that drive business success. However, it’s important for data professionals to analyze the data at hand with the goal of providing actionable insights and concrete next steps for stakeholders.

If you enjoyed this episode and want to learn more, download our eBook, Analyze: Unlock Actionable Insights for Business Growth. In this eBook, you’ll discover how to simplify your findings into a business strategy that can be easily interpreted and put into action.

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The post Mastering the Art of Data Visualization with Nadieh Bremer appeared first on Pragmatic Institute - Resources.

“I’m the garbage lady of the data world. It’s a job no one wants to do, but if it isn’t done, society would fall apart if we didn’t have our rubbish cleared.” – Susan Walsh

In this episode, host Chris Richardson, sits down with the data cleaning expert, Susan Walsh, to explore the overlooked realm of data cleaning. Susan shares her rich experiences and the fulfilling aspects of creating order from data chaos, emphasizing its pivotal role in delivering accurate analysis.

They Discuss:

• How clean data drives profitability, improves efficiency, and enables smarter business decisions
• Why budgeting for data cleaning is crucial and how to factor it into project timelines
• An interesting take on how redefining traditional roles within a team could unearth hidden data-cleaning talents
• How can dirty data adversely affect AI and machine learning outcomes, and why human intervention in data cleaning remains indispensable?

What’s Your Organization’s Data Maturity?

Our Data Maturity Scale and Assessment is a powerful tool for organizations that want to measure—and grow—their level of data maturity. The data maturity assessment helps organizations identify pillars for improvement and best practices to make the most out of the data at hand.

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The post Turning Chaos Into Clarity: The Underrated Value of Data Cleaning appeared first on Pragmatic Institute - Resources.