What is Data Management?
The administrative process that involves collecting and keeping the data safely and cost-effectively is called data management. Data management is a growing field as companies rely on it to store their intangible assets securely to create value. Efficient data management helps a company use the data to make better business decisions.
How is Data Management used?
Zippia reviewed thousands of resumes to understand how data management is used in different jobs. Explore the list of common job responsibilities related to data management below:
- Provided data management expertise by performing data entry contained on case report forms and clarifying discrepant information, updating clarification forms.
- Deliver high quality data to study teams through identification and resolution of data management issues.
- Provide direct supervision to data management co-workers during numerous projects.
- Provided data management troubleshooting and training for medical devices.
- Train, coach, and mentor colleagues with less experience in the overall day to day activities of provider data management.
- Train new or temporary Data Entry Operators in the use of the data management process and data entry guidelines.
Are Data Management skills in demand?
Yes, data management skills are in demand today. Currently, 38,135 job openings list data management skills as a requirement. The job descriptions that most frequently include data management skills are data management associate, data management manager, and database administrator assistant.
How hard is it to learn Data Management?
Based on the average complexity level of the jobs that use data management the most: data management associate, data management manager, and database administrator assistant. The complexity level of these jobs is basic.
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What jobs can you get with Data Management skills?
You can get a job as a data management associate, data management manager, and database administrator assistant with data management skills. After analyzing resumes and job postings, we identified these as the most common job titles for candidates with data management skills.
Data Management Associate
Job description:
A data management associate is responsible for maintaining and updating databases, spreadsheets, documents, and other data storage systems while adhering to company standards and protocols. Their daily tasks usually include gathering and organizing data, conducting reviews to identify errors or inconsistencies, taking corrective measures, and producing regular reports, presenting them to managers. They may also participate in troubleshooting issues, planning data migration procedures, and enforcing data security protocols. Moreover, a data management associate must be proactive at dealing with issues to maintain an optimal workflow.
- Data Management
- Data Quality
- Data Analysis
- PowerPoint
- Data Collection
- Data Entry
Data Management Manager
Job description:
A data management manager is responsible for maintaining the safety and security of all the organization's database networks and systems to prevent unauthorized access and activities that may pose risks to the business' reputation and performance. Data management managers conduct regular quality checks and updates for the networks, ensuring its efficiency and stability to support business functions and operations. A data management manager must have excellent technical and organizational skills, especially in resolving network issues and recommending data storage systems improvement.
- Data Management
- Analytics
- Data Quality
- Data Governance
- Project Management
- MDM
Database Administrator Assistant
- Data Management
- Data Entry
- SQL Server
- Travel Arrangements
- PowerPoint
- Database Access
Configuration Management Lead
Job description:
Configuration management leads support configuring project teams with the baselining and configuring items involved in their project. These leads recommend improvements to perform on existing configuration management processes and give reviews from the user's point-of-view. They develop guidelines to prevent unauthorized use and damages to project items. Their job includes ensuring that only secured and accurate data are entered in the database and proposing resolutions for configuration issues. They also prepare documentation on configuration projects to present to the top management.
- Data Management
- Architecture
- BI
- Infrastructure
- Java
- Master Data
Database Manager
Job description:
A database developer/database administrator specializes in designing and developing database programs and systems, maintaining and updating them regularly. They are in charge of understanding project needs and guidelines, establishing and implementing test systems to identify potential risks and issues, fixing and upgrading components, and storing data according to protocols. They may also produce and present reports to managers and participate in creating security and recovery plans to protect company data. Moreover, as a database developer/database administrator, it is vital to be proactive at dealing with issues while adhering to company standards.
- Data Management
- Data Entry
- SQL Server
- Project Management
- Data Integrity
- Data Analysis
Data Administrator
Job description:
As a data administrator, they support the marketing, sales, finance, and operations departments by providing accurate, complete, and current data to the customer, product, inventory, and vendor. It is the data administrator's responsibility to implement and execute data mining projects and makes reports to provide understanding into sales, marketing, and purchasing opportunities and business trends. The role would also include updating information to the company's database and official company website. Moreover, they also do reports about data analysis, forecasting, and other research activities that lead to decision making.
- Data Management
- Java
- Profiling
- DB2
- Unix
- Sybase
Senior Information Technology Systems Technician
- Troubleshoot
- Data Management
- Linux
- DOD
- Computer System
- Unix
Database Designer
- Database Design
- Data Management
- JavaScript
- PL/SQL
- SAS
- Microsoft SQL Server
Clinical Data Coordinator
Job description:
A clinical data coordinator is primarily in charge of managing and organizing data gathered from various clinical research programs. Their responsibilities revolve around coordinating with different departments, updating databases with accurate information, identifying errors and inconsistencies, performing corrective measures, and maintaining records of all transactions. They must also handle the documentation procedures, review sites, liaise with external vendors and suppliers, conduct audits, and train new staff. Furthermore, as a clinical data coordinator, it is essential to lead and encourage the team to reach goals, all while implementing the company's safety policies and regulations.
- Patients
- Data Management
- Data Collection
- GCP
- Data Validation
- FDA
Senior Data Technician
- Emerging Technologies
- Data Management
- SQL
- Troubleshoot
- Data Collection
- Data Processing
Data Process Specialist
Job description:
A data processing specialist is a data entry professional who specializes in collecting, interpreting, and organizing data according to company standards and policies. They usually work on spreadsheets, documents, databases, and presentations to arrange and convey data in an easy-to-understand format, all while adhering to deadlines. They may also conduct their own reviews and assessments to identify inconsistencies and errors, performing corrective measures right away. Moreover, a data processing specialist must maintain an open and transparent communication line with managers and co-workers for an efficient workflow.
- Data Entry
- Data Management
- Data Accuracy
- SQL
- Assistance Program
- Computer System
Deputy Campaign Manager
- Community Outreach
- Policy Research
- Press Releases
- Data Management
- Campaign Strategy
- Campaign Events
Configuration Management Specialist
Job description:
Configuration management specialists are professionals who are responsible for performing tasks related to the process of configuration management. Under the direction of the configuration management consultant, these specialists must execute product configuration changes of lower complexity and volume. They must ensure that the production and delivery of hardware and software systems are completed according to schedule. Configuration management specialists must also work with the project management and development team so that they can ensure proper change process and software baseline schemes.
- Data Management
- DOD
- Configuration Audits
- Logistics
- Version Control
- Software Development
Clinical Statistical Programmer
Job description:
Clinical statistical programmers collect data, execute statistical analysis, and analyze data sets based on the needs of the clients or employers. The programmers use SAS programming for data set development and analysis during clinical trials. They integrate data for reports after statistical analysis or clinical research. The skills they need to develop include analytical thinking, attention to detail, research, and strategic planning. They should also need to know statistical programming in clinical research.
- Macro
- Data Management
- Adam
- Efficacy
- SAS Programs
- FDA
Data Assistant
Job description:
A data assistant's role is to perform support tasks in data management procedures. Their responsibilities often revolve around coordinating with different departments to gather data, maintaining and updating databases, processing and organizing documentation, preparing progress reports, and analyzing data as needed. They may also participate in devising strategies to optimize data management operations. Furthermore, a data assistant must also monitor the operations of databases, performing regular maintenance checks, and reporting to the information technology department should there be any issues and concerns.
- Data Entry
- Data Collection
- Patients
- Data Management
- Research Data
- Access Database
Lead Data Architect
Job description:
Lead Data Architects are experienced employees who manage the data architecture needs of the company. They also manage the data architects who fulfill these needs. They are in charge of creating data management systems based on the requirement of the company. They work hand in hand with data engineers and information technology professionals to create these systems. Lead Data Architects design data systems based on the internal and external data sources of the company. They would then create a blueprint to centralize these systems to streamline the different processes of the company.
- Analytics
- Data Management
- Java
- Data Governance
- Data Quality
- Emerging Technologies
Clinical Data Associate
Job description:
A clinical data associate is responsible for documenting and recording data from clinical research programs for various purposes, such as validation and future studies. Their responsibilities revolve around understanding the needs of every program, coordinating with different teams to gather accurate data, utilizing special tools and software, and preparing and processing data according to protocols and standards. Moreover, a clinical data associate typically works in a team setting, which requires an active communication line for a smooth and efficient workflow.
- Data Review
- Data Management
- FDA
- SAS
- Clinical Trial Data
- Data Quality
How much can you earn with Data Management skills?
You can earn up to $119,281 a year with data management skills if you become a data management associate, the highest-paying job that requires data management skills. Data management managers can earn the second-highest salary among jobs that use Python, $119,331 a year.
| Job title | Average salary | Hourly rate |
|---|---|---|
| Data Management Associate | $119,281 | $57 |
| Data Management Manager | $119,331 | $57 |
| Database Administrator Assistant | $36,750 | $18 |
| Geoscience Technician | $77,535 | $37 |
| Configuration Management Lead | $94,123 | $45 |
Companies using Data Management in 2025
The top companies that look for employees with data management skills are U.S. Department of the Treasury, Deloitte, and Pwc. In the millions of job postings we reviewed, these companies mention data management skills most frequently.
Departments using Data Management
| Department | Average salary |
|---|---|
| Engineering | $95,036 |
| IT | $92,012 |
20 courses for Data Management skills
1. Data Product Manager
Leverage data to build products that deliver the right experiences, to the right users, at the right time. Lead the development of data-driven products that position businesses to win in their market...
2. The Data Driven Manager
In the Data Driven Manager specialization, you will learn how to first understand the type of data that you have (or want to generate), then describe it with numbers and graphs to communicate with your audience. You will practice using probability and distributions to understand the underlying nature of your data to make decisions and solve problems in a way that increases the likelihood of a desired outcome. You will learn the steps to create a plan to answer business and engineering questions and reduce risk when making decisions. You’ll study how to determine best- and worst-case scenarios using data. Finally, you’ll acquire data analysis skills to answer business and engineering questions that will help you make appropriate decisions.\n\nThis specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder...
3. Data Management Essentials
The course Data Management Essentials is based on the Data Management framework created by DAMA, the most important association of Data Managers at international level. The lectures explore the various components of Data Management, blending theoretical lessons with a very practical content, a case study of application of interventions and solutions on the topic of data management. The course is intended for all those who wish to enrich their competence and their mindset on the subject of data, going beyond the more traditional topics of data analysis, touching with hand how many and what are the fundamental components to make real data management in organizations...
4. Business Data Management and Communication
In a world driven by big data, it is crucial to understand how valuable information can be extracted from large volumes of data. Further, it is pivotal to utilize information to its full potential. This not only requires one to extract value from data but also to effectively communicate the information obtained. The Business Data Management and Communication Specialization is tailored to deliver a well-balanced curriculum that focuses on all three aspects - Understanding Data, Extracting Valuable Information, and Effective Communication. The courses in this Specialization will focus on advanced accounting, working with big data, communicating data and information analysis.\n\nTopics covered include:\n\nUnderstand the basics of how to analyze balance sheet and cash flow statements Understand how accrual accounting and fundamental accounting concepts work Learn how to collect, analyze, and visualize data and use the same in decision making processes Learn how to use R to communicate data analytics results Explore various ways that information can generate economic value Understand how crucial information is and learn how to gauge the potential of valuable information...
5. Managing, Describing, and Analyzing Data
In this course, you will learn the basics of understanding the data you have and why correctly classifying data is the first step to making correct decisions. You will describe data both graphically and numerically using descriptive statistics and R software. You will learn four probability distributions commonly used in the analysis of data. You will analyze data sets using the appropriate probability distribution. Finally, you will learn the basics of sampling error, sampling distributions, and errors in decision-making. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder...
6. Data Management with Databricks: Big Data with Delta Lakes
In this 2-hour guided project, "Data Management with Databricks: Big Data with Delta Lakes" you will collaborate with the instructor to achieve the following objectives: 1-Create Delta Tables in Databricks and write data to them. Gain hands-on experience in setting up and managing Delta Tables, a powerful data storage format optimized for performance and reliability. 2-Transform a Delta table using Python and leverage SQL to query the data for creating a comprehensive dashboard. Learn how to apply Python-based transformations to Delta Tables, and use SQL queries to extract the necessary insights for building a Supply Chain dashboard. 3-Utilize Delta Lake's merge operation and version control capabilities to efficiently update Delta Tables. Explore the capabilities of Delta Lake's merge operation to perform upserts and other data updates efficiently. Additionally, learn how to leverage Delta Lake's built-in version control to track and access previous versions of Delta Tables as needed. Throughout a real-world business scenario, you will use Databricks to build an end-to-end data pipeline that integrates various JSON data files and applies transformations, ultimately providing valuable insights and analysis-ready data. This intermediate-level guided project is designed for data engineers who build data pipelines for their companies using Databricks. In order to be successful in this guided project, you need prior knowledge of writing Python scripts including importing libraries, setting-up variables, manipulating data frames, and using functions. You will also need to be familiar with writing SQL queries such as aggregating, filtering, and joining tables...
7. Data Base Management System
In this course, we will learn:1. What is the meaning of Data? What is the meaning of Information? What is the difference between Data and Information? 2. What is meant by Database? 3. Databases can be classified into two types. What are those types? Discuss. or What is Flat file database and Relational Database?4. What is DBMS? What are the features of DBMS? What is the difference between DBMS and Flat File?5. DBMS is used for various business activities. Give examples. OrWhat are the applications of DBMS?6. What are the various advantages of database or DBMS?7. What is the meaning of data redundancy?8. What is meant by data consistency?9. What are the various disadvantages of DBMS?10. What are the various components of DBMS?11. What is the meaning of database servers?12. What is meant by RDBMS?13. What are the advantages of Relational Databse?14. What is the meaning of i) Item ii) Record iii) field iv) value15. What are the key features of a database?16. What is meant by DML, SQL and DDL?17. What is the meaning of Primary Key and Foreign Key?18. What is the meaning of Composite Primary Key?and much more......
8. Big Data for Managers
This course covers the required fundamentals about big data technology that will help you confidently lead a big data project in your organization. It covers the big data terminology like 3 Vs of big data and key characteristics of big data technology that will help you answer the question 'How is big data technology different from traditional technology'. You will be able to identify various big data solution stages from big data ingestion to big data visualization and security. You will be able to choose the right tool for each stage of the big data solution. You will see the examples use of popular big data tools like HDFS, Map reduce, Spark, Zeppelin etc and also a demo of setting up EMR cluster on Amazon web services. You will practice how to use the 5 P's methodology of data science projects to manage a big data project. You will see theory as well as practice by applying it to many case studies. You will practice how to size your cluster with a template. You will explore more than 20 big data tools in the course and you will be able to choose the tool based on the big data problem. I have recently(24th July 2023) added one hour of content on chatGPT with Top 10 AI tools and four demos on using chatGPT for project managers and data analysis providing visualization of data as well as prescriptive analysis of data. These lectures and 4 demos will provide the latest information on the AI technologies and how it can benefit an organization. I am sure you will be amazed by this content. Top companies now offer this course to their employees. I am glad to share some of the five star comments about the course: This course really exceeded my expectations! Not only it covers the concepts and the overall view of a Big Data project landscape but it also provides good examples of real case studies, that help reinforce the contents presented. Great course! This course is great! I have learnt many useful things. The case studies are very enlightening. I strongly recommend. Thank you very much. otimo tecnicamente, excelenteDidatica muito boa e o conteudo conforme esperado...
9. IBM COBOL Data and File Management
Welcome to IBM COBOL – Data and File Management! By enrolling in this course, you are taking a big step in increasing your knowledge and hands on experience with IBM COBOL data and file management. In this course, you will learn the fundamental elements of COBOL code. You will learn the process of working with COBOL data. You will learn handling COBOL files. This course also relational databases in a mainframe, COBOL context. So let’s get started!...
10. Oracle Cloud Data Management Foundations Workshop
In this course you will learn how Oracle's cloud data platform extends and complements its core databases. Understand how to architect data management platforms that work together to simplify and maximize productivity, while reducing cost and improving performance, reliability, and security...
11. Fundamentals of Data Product Management and Data Strategy
Welcome to the Fundamentals of Data Product Management and Strategy, an essential course tailored for those who aspire to tap into the immense power of data for strategic advantage. If you're considering a career in data product management or if you're in search of a holistic understanding of how data can steer your business objectives, this course is your perfect companion. In the world that is increasingly centred around data, its value cannot be overstated. Data products, in particular, have become vital tools for organisations, harnessing raw data and transforming it into meaningful insights that guide decision-making and strategic planning. The advent of Machine Learning and Artificial Intelligence products has only further accentuated this trend, promising unprecedented opportunities for those ready to seize them. However, there's a notable void in the learning marketplace - while many courses focus on data-driven product management, few truly address data product management. Our course intends to bridge this gap, providing an in-depth exploration of the crucial elements of data product management without overwhelming you with excessive technical details. We offer an immersive learning experience into the world of data strategy, a pivotal aspect often overlooked in conventional courses. Understanding how to strategise and operationalise data can significantly boost your organisation's business success, making it an indispensable part of your learning journey. But our course doesn't stop at that. It delves deeper, exploring the complete lifecycle of data products - from identification and design to development, testing, and deployment. We also equip you with the vital skills you'll need as a data product manager, including mastering roadmaps, managing expectations, collaboration tactics with internal and external teams, and leveraging agile methodologies. In the Fundamentals of Data Product Management and Strategy, we aim to demystify the complexities of data product management and present it as a strategic differentiator in today's data-centric world. Join us on this enlightening journey today and step confidently into the future of data...
12. Data Analytics in Sports Law and Management
Athletes, lawyers and team executive experts provide insight in how law and regulations intersect with data analytics and sports management techniques for best practices in the sports industry. Several courses provide the basis for how data analytics function within player evaluation, team performance, and roster management. The specialization introduces various regulatory, managerial and legal frameworks that includes athlete representation, facilities, and event management and conclude with various jobs available in sectors of the sports industry...
13. Clinical Trials Data Management and Quality Assurance
In this course, you’ll learn to collect and care for the data gathered during your trial and how to prevent mistakes and errors through quality assurance practices. Clinical trials generate an enormous amount of data, so you and your team must plan carefully by choosing the right collection instruments, systems, and measures to protect the integrity of your trial data. You’ll learn how to assemble, clean, and de-identify your datasets. Finally, you’ll learn to find and correct deficiencies through performance monitoring, manage treatment interventions, and implement quality assurance protocols...
14. Basic of Clinical Data Management
The course would give you a detail of all crucial topics related to Clinical Data Management and it's basics. The 10 modules would lead to systematically to the depth of each specific subject. At the end you will come out with filled with knowledge about Clinical Data Management which would be extremely useful when you start working in CRO or Pharma companies as clinical research professional.1)Introduction to CDM2)Clinical Trial Phases in Clinical Data Management3)Case Report Form4)Clinical Data Management System5)Data Entry6)Data Cleaning7)Medical Coding8)Data Cleaning External Data9)SAE Reconciliation10)Database Lock...
15. Clinical Data Management: An overview
This course provides an easy understanding of clinical data management and processes involved for generation of high quality clinical trials data that helps in faster commercialization of product Learn Basics Of Clinical Data Management With This Comprehensive Course Get familiar with Clinical data Management Learn Phases & activities in CDM Identify tools & roles in CDM Master to manage clinical data by CDM activities Clinical Data management is essential to the overall research function, as its key deliverable is the data to support the submission. CDM has evolved in response to the ever-increasing demand from pharmaceutical companies to fast-track the drug development process and from the regulatory authorities to put the quality systems in place to ensure generation of high-quality data for accurate drug evaluation. Contents and overview This course is designed to introduce the concept of clinical data management and data management activities for beginners in healthcare industry covering 24 video lectures with 2 hours content & 2 end of module quizzes. Section 1 starts with an overview of clinical data management, discussing what is clinical data management and regulations, standards implemented in CDM. Section 2 outlines CDM process, data management plan and describe activities involved in different phases of data management. Section 3 gives an overview of variable tools used in CDM and key members involved in CDM team with their responsibilities At the end of the course, you will able to Understand clinical data management and it's role in clinical researchDescribe activities involved in CDM processDemonstrate functions of key members in CDM and variable tools available for CDM...
16. Getting Started with Data Management
Data Management is one of the most important competencies your company has. With Digital Transformation at the top of the strategic agenda for many large organizations, Data Governance and Data Management are vital to building a strong foundation for integration, analysis, execution, and overall business value. Business and data professionals are currently facing The Fourth Industrial Revolution's convergence of megatrends around Customer 360, Artificial Intelligence, Big Data, programmatic marketing, and globalization. To survive these unrelenting business pressures, it's more critical, and strategic, than ever to put your data to work! In this course, you will learn about the various disciplines of data management. First, you will discover what Data Governance is and why you might want to implement a governance program for your organization, after which you will go through some very basic exploratory Data Analysis using the Python programming language. Next up, you'll cover basic Database Design, Data Quality essentials, and the fundamentals of the Structured Query Language. Then, you will get hands-on with some rudimentary Data Integration ETL, as well as Big Data with Hadoop. Finally, you will explore the various disciplines in the Data Management space. By the end of the course, you will have a firm understanding of enterprise data management and what the various disciplines do...
17. Sports Management: Data and Analytics
SummaryAs the world of professional sports has become more competitive than ever before, organisations and athletes must look for new ways to gain an advantage over their rivals. Data science is one of the most important areas of a lot of modern sports organisations for this very reason. In short, data analytics allow teams or athletes to make better use of their resources and talents, potentially helping them to beat the competition. Sports are often all about the smallest of margins, and if data analytics can help an athlete shave a few seconds off of their time or help a team discover top talent for cheaper, it can mean the difference between success and failure. In professional sports, revenues and profits are driven by success on the pitch, first and foremost. Athletes gain higher wages, and teams bring in more revenue from ticket sales and sponsorships, all of which can be affected by results on the pitch. Data analytics seeks to collect, record and study sports data in order to look for patterns, areas that can be improved on and specific advantages. The use of data has grown across lots of different industries over the past few decades. Computing power and improvements in how we capture and understand data have led to large companies setting up their own data analytics departments. Data analytics doesn't just help to identify strengths and weaknesses on the pitch but can also provide insights into how to improve a business through marketing and other means. With more modern businesses making the most of their data to address shortcomings and make the most out of their resources, sports businesses have been quick to follow. The potential for data analytics has only improved as the technology has gotten better, and today, sports organisations use a variety of techniques to improve performances on and off the pitch. This course aims to provide a foundation on the ideas and methods of sports data analytics, showing how data science can be applied to the sports industry. Mathematics play an important role in sports data analytics. Statistics form the basis of many data analysis projects, and the number of statistics collected in sports has increased over the years. Both fans and coaches have an interest in statistics as they help provide deeper insights into sports, as well as helping to predict outcomes through probability. Data science involves making and testing a hypothesis using data, and data analysts need to understand and apply various methods to achieve their aims. Programming languages are at the heart of data analytics. Languages such as Python, R, Scala and SQL allow users to store, sort and analyse large volumes of data. These languages make it much easier to process, analyse and visualise data, which would have been done manually beforehand. A basic understanding of these languages is important, and all good data analysts should be competent in computer programming. On-field analytics refers to the use of sports data to improve performances on the pitch, and it's become an important process in helping coaches get more out of their athletes. Today, coaches make use of modern technology, including wearable devices and cameras, to capture data during training and matches, which they can then use to highlight areas for improvement and make tactical decisions. As well as improving athletic performances, data can be used by sports organisation to gain a competitive advantage in other ways. Whether it is through increasing fan engagement, scouting new talent or making strategic management decisions, data analytics has a wide range of applications off the field. Through carefully analysing the data of the business, managers can make better decisions that take the organisation to new heights. Machine learning and AI represent the future of data analytics, utilising technology in new ways to not only analyse data but also to predict future patterns and results. While the technology is still in its early stages, it has massive potential to completely change the face of sports data analytics, allowing coaches to predict injuries and helping prevent international match-fixing rings. Data analytics has a wide range of applications across all sports, as we can see in examples such as the Oakland Athletics with sabermetrics and how teams like Bayern Munich have improved fan engagement and moved into new markets. There's also the case of the NBA, which has significantly changed how exciting its games are by making data available to all teams. All kinds of different sports organisations are now making better use of data than ever before, improving performances on the pitch as well as building fanbases and revenue. Technology has improved a lot over the last few decades and is continuing to improve with each passing year. As new technology is developed and current technology continues to drive innovation, the face of data analytics can potentially change a lot. Areas such as the Internet of Things, blockchain and fast data are all predicted to impact the area of sports data in the coming years and offer new ways to collect and analyse sports data. The sports industry is highly rewarding to work in, and as a result, demand for positions is high and there's a lot of competition. Finding your first job in the field of sports data can be difficult, but if you focus on education, experience and your professional network, you can make things a lot easier. To be successful, you need to show potential employers that you have the skills, knowledge and experience required to succeed. You can gain these through a degree, online courses, internships, and even working on your own personal projects. Data analytics is more important than ever before in today's world, and sports organisations are increasingly relying on data when it comes to making important decisions. By studying this course, you have the chance to develop the skills you need to become a successful sports data analyst while also learning more about this fascinating area of the sports industry. What You'll Learn· What is data analysis, and why is it important in sports?· Statistics and their role in sports· What is probability, and how it affects sports· The basics of data science· Python for data analytics· Scala for data analytics· R for data analytics· SQL for data analytics· Video analysis in sports· How wearable technology is used in sports· How data can be used to model and predict performances· What is fan engagement, and how can it be tracked· The importance of data in scouting· How strategic management can be optimised with data analytics· Machine learning for identifying match-fixing· How AI can help athletes avoid injuries· The future of AI in sports· How sabermetrics kickstarted modern sports data analytics· Expanding into new markets with the help of data· How the NBA made use of data analytics· The impact of blockchain on the future of data analysis· How can the Internet of Things influence the future of sports data· Fast data and what it means for sports analytics· What education is needed to get started in sports data analytics· How to get experience in sports data analytics· How to build your professional networkWords from the Author, Saam Momen: I have a true passion for teaching! I have proudly taught university courses in Switzerland, USA and Brazil. My career spans over 15 years in the sporting industry with jobs at the London Olympic Bid Committee, UEFA, CSM and TEAM Marketing. I possess a Master Degree in Sports Management and an Executive Education diploma at Harvard Business School on The Business of Entertainment, Media and Sports. I hope that throughout this course you are able to have a wonderful learning experience! Please do not hesitate to reach out should you have any queries. Why Choose This CourseThis course has been created to give you a strong understanding of sports data analytics and everything it involves. As you progress through the course, you'll be able to learn more about data analytics, the techniques involved and how they can be applied to the sports industry. This will include learning about the role of maths in sport, the types of programming languages used in data analytics and how it can affect decisions on and off the pitch. You'll also be able to learn about artificial intelligence and machine learning in the sports industry, plus the future of sports data analytics, and how to improve your chances of a career in this area of sports. Throughout the course, you won't just be learning the theory of sports data analytics. You'll also be able to see real-world examples of how data analysis has been used in the sports industry and the effect it can have. By looking at examples and case studies of data analytics in action, you can learn a lot more about the advantages of sports data and why it's become such as important issue in the industry. If you plan on becoming a sports data analyst, this course presents an excellent way to gain the foundation of skills and knowledge you need to succeed. The course will teach you everything you need to know about sports data analytics techniques and why it's so important. Not only that but there's also a whole chapter dedicated to starting your career as a sports data analyst, with advice on education, experience and networking. After completing the course, you will be ready to take the first steps towards a successful career in sports data analytics. The sports industry can be a very competitive area to find a job which is why it's important to make sure you stand out compared to other candidates. This course provides real-world examples alongside theory, to help give you the knowledge you need to impress in your interview. You'll learn about a wide range of topics and will gain a complete understanding of the field of sports data analytics. While university education is often required to become a data analyst, the growing demand for applicants with a knowledge of data science techniques can mean that lots of professional sports organisations are also willing to accept alternatives. That means that online courses can be just as beneficial, especially if you already have a degree in another field. Provided you have the skills and experience needed, finding a job as a sports data analyst shouldn't be too challenging. This course gives you the skills and knowledge required to make a start towards your dream career as a sports data analyst...
18. R Data Pre-Processing & Data Management - Shape your Data!
Let's get your data in shape! Data Pre-Processing is the very first step in data analytics. You cannot escape it, it is too important. Unfortunately this topic is widely overlooked and information is hard to find. With this course I will change this! Data Pre-Processing as taught in this course has the following steps: 1. Data Import: this might sound trivial but if you consider all the different data formats out there you can imagine that this can be confusing. In the course we will take a look at a standard way of importing csv files, we will learn about the very fast fread method and I will show you what you can do if you have more exotic file formats to handle. 2. Selecting the object class: a standard data. frame might be fine for easy standard tasks, but there are more advanced classes out there like the data. table. Especially with those huge datasets nowadays, a data. frame might not do it anymore. Alternatives will be demonstrated in this course. 3. Getting your data in a tidy form: a tidy dataset has 1 row for each observation and 1 column for each variable. This might sound trivial, but in your daily work you will find instances where this simple rule is not followed. Often times you will not even notice that the dataset is not tidy in its layout. We will learn how tidyr can help you in getting your data into a clean and tidy format. 4. Querying and filtering: when you have a huge dataset you need to filter for the desired parameters. We will learn about the combination of parameters and implementation of advanced filtering methods. Especially data. table has proven effective for that sort of querying on huge datasets, therefore we will focus on this package in the querying section. 5. Data joins: when your data is spread over 2 different tables but you want to join them together based on given criteria, you will need joins for that. There are several methods of data joins in R, but here we will take a look at dplyr and the 2 table verbs which are such a great tool to work with 2 tables at the same time. 6. Integrating and interacting with SQL: R is great at interacting with SQL. And SQL is of course the leading database language, which you will have to learn sooner or later as a data scientist. I will show you how to use SQL code within R and there is even a R to SQL translator for standard R code. And we will set up a SQLite database from within R. 7. Outlier detection: Datasets often contain values outside a plausible range. Faulty data generation or entry happens regularly. Statistical methods of outlier detection help to identify these values. We will take a look at the implemention of these.8. Character strings as well as dates and time have their own rules when it comes to pre-processing. In this course we will also take a look at these types of data and how to effectively handle it in R. How do you best prepare yourself for this course? You only need a basic knowledge of R to fully benefit from this course. Once you know the basics of RStudio and R you are ready to follow along with the course material. Of course you will also get the R scripts which makes it even easier. The screencasts are made in RStudio so you should get this program on top of R. Add on packages required are listed in the course. Again, if you want to make sure that you have proper data with a tidy format, take a look at this course. It will make your analytics with R much easier!...
19. Data Mesh - A Modern Decentralized Data Management Concept
Every year more data is produced globally. This holds also for companies: more details than ever are recorded from customers, partners, transactions, products and supply chain resulting in more data. According to IDC , "the global datasphere will grow from 45 zettabytes in 2019 to 175 by 2025". This data forms the raw material from which organizations are drawing valuable, actionable insights. But the collection, integration and governance of this data is still one of the main challenges. These organizations are now looking at a relatively new concept called "Data Mesh" to overcome these main challenges and inhibitors. Data Mesh is an emerging hot topic for enterprise software that puts focus on new ways of thinking about data. Data Mesh aims to improve business outcomes of data-centric solutions, as well as to drive adoption of modern data architectures. Top Reasons why you should choose this Course: This course is designed keeping in mind the students from all backgrounds - hence we cover everything from basics, and gradually progress towards elaborate topics. This course can be completed over a Weekend. Wonderful collection of useful resources are shared, that will be updated frequently. All Doubts will be answered. A Verifiable Certificate of Completion is presented to all students who undertake this Data Mesh Fundamentals course...
20. Data Visualization for Management Consultants & Analysts
What is the aim of this course?In consulting you will spend a lot of time creating presentations to show the results of your analyses to the customer. That is why data visualization is so important. With a proper display of data, you have more chances of convincing the customers that your approach makes sense. In this course, I will teach how to use different data visualization techniques to show the results of your analyses during consulting projects. In the course you will learn the following things: What types of slides you should use to present your thoughtsWhat types of charts you should use for data visualizationHow to read the chartsHow to create charts in Excel How to create charts in PowerPointHow to create dynamic charts in ExcelThis course is based on my 15 years of experience as a consultant in top consulting firms and as a Board Member responsible for strategy, performance improvement, and turn-arounds in the biggest firms from Retail, FMCG, SMG, B2B, and services sectors that I worked for. I have carried out or supervised over 90 different performance improvement projects in different industries that generated a total of 2 billion in additional EBITDA. On the basis of what you will find in this course, I have trained in person over 100 consultants, business analysts, and managers who now are Partners in PE and VC funds, Investment Directors and Business Analysts in PE and VC, Operational Directors, COO, CRO, CEO, Directors in Consulting Companies, Board Members, etc. On top of that my courses on Udemy were already taken by more than 181 000 students including people working in EY, Walmart, Booz Allen Hamilton, Adidas, Naspers, Alvarez & Marsal, PwC, Dell, Walgreens, Orange, and many others. I teach through case studies, so you will have a lot of lectures showing examples of analyses, and tools that we use. To every lecture, you will find attached (in additional resources) the Excels as well as additional presentations, and materials shown in the lectures so as a part of this course you will also get a library of ready-made analyses that can, with certain modifications, be applied by you or your team in your work. Why have I decided to create this course?Data visualization is one of the most important skills that you should master during your 1st year of work in consulting. If you are able to analyze data but you don't know how to present them to the customer, you will lose the opportunity to impress him. Data visualization is one of the crucial skills that you should master if you want to get promoted fast. Most firms, don't give you the full toolbox that you need. This may lead to huge frustration during consulting projects and a lot of inefficiencies. Therefore, I have decided to create this course that will help students understand or refresh the main skills and tools that they need during consulting projects when it comes to data visualization. The course will give you knowledge and insight into the types of charts and slides that you will be using during your work to present the data. It will also make your life during a consulting project much easier. Thanks to this course, you will know what and how to present data and visualize them during consulting projects. On top of that, you will also have access to the library of slides and charts that we use ourselves. To sum it up, I believe that if you want to become a world-class Management Consultant or Business Analyst you have to have a pretty decent understanding of data visualization. That is why, I highly recommend this course to Management Consultants or Business Analysts, especially those that present the results of their analyses to customers. The course will help you become an expert in data visualization on the level of McKinsey, BCG, Bain, and other top consulting firms. In what way will you benefit from this course?The course is a practical, step-by-step guide loaded with tons of analyses, tricks, and hints that will significantly improve the speed with which you understand, and analyze businesses. There is little theory - mainly examples, a lot of tips from my own experience as well as other notable examples worth mentioning. Our intention is that thanks to the course you will learn: What types of slides you should use to present your thoughtsWhat types of charts you should use for data visualizationHow to read the chartsHow to create charts in Excel How to create charts in PowerPointHow to create dynamic charts in ExcelYou can also ask me any question either through the discussion field or by messaging me directly. How the course is organized?The course is divided currently into the following sections: Introduction. We begin with a little intro to the course as well as some general info on how the course is organizedTypes of slides you can use. In this section, I will show you what kind of slides you can use for different purposes during consulting projects. How to read charts. In this section, I will show how to read charts and how people can lie to you with chartsWhich chart type you should use? We will discuss in detail what type of charts you can use for different purposes. We have grouped them into 5 different categories. Essential Charts in Excel. In the fifth section, we will move to more technical issues. I will show you how you can create and modify specific types of charts in Excel. Data Visualization using Conditional Formatting. You can do data visualization not only with charts but also with conditional formatting. In this section, we will see how this can be done in Excel. How to create charts in PowerPoint. In the seventh section, we will move to more technical issues. I will show you how you can create and modify specific types of charts in PowerPointPivotCharts. To make the charts more dynamic you can use PivotCharts. We will see in this section how this can be done in practice. PivotCharts Case Studies. In this section, we will see how we can use PivotCharts to present data from analyses. Dynamic Charts in Excel. In this 10th section, we will see how we can make the charts more dynamic. Advanced Charts in Excel. In the last section, I will show you more advanced types of charts that you can create in Excel. As a part of this course, you will getUseful frameworks and techniquesAnalyses shown in the courseAdditional resourcesLinks to additional presentations, articles, and moviesLinks to books worth readingAt the end of my course, students will be able to…How to create charts in ExcelHow to use PivotChartsHow to make the charts more dynamicWhat type of slides and charts to use to present data to your customer?Who should take this course? Who should not?Management ConsultantsBusiness AnalystsFinancial ControllersInvestment AnalystsStartup FoundersProject ManagersWhat will students need to know or do before starting this course?Basic or intermediate Excel...