Contemporary Data Analysis: Survey and Best Practices from Coursera

Despite a large variety of different courses on analytics, the courses that offer a broad overview of the field are rare. From practice of teaching statistics, it became clear that it is difficult for learners to put together a broad field map if they have taken only a few of the different topics on analytical tools. As a result, they do not see the overall picture of everything that the field of data analysis has to offer. This course is designed to fill this gap. It is a survey course on state-of-the-art in interdisciplinary methods of data analysis, applicable to business and academia alike. Unlike other statistical courses, which focus on specific methods, this course will focus on the broader areas within statistics and data analytics. There are five major topics it will cover. It will start with the root of it all - the data – and some of the problems with the data. Then it will move through the contemporary approaches to descriptive, inferential, predictive and prescriptive analytics. Within each broader topic, the course will offer the theoretical foundation behind the methods without focusing too much on the mathematics. It will provide historical references, examples, explanations and case studies to illustrate the main concepts within each broader topic. In doing so, it will introduce the applied, problem-based approach to using specific tools. Then, it will discuss some of the specific of a particular approach. Overall, after taking this course, the students will get a good understanding of the state-of-the-art tools that the field of data analysis currently has to offer. The course consists of two parts. There is a review part with six lectures, providing the description of the major data analysis areas. This 6-lecture course is offered as part of the “Network analytics for business” specialization. For students of the “Master of data and network analytics” program, there are six additional lectures on specific topics. They are designed to illustrate some of the specific state-of-the-art approaches within the broader areas. This Course is part of HSE University Master of Data and Network Analytics degree program. Learn more about admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/WMKM6.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops data analytics skills, which are core for career growth in business and academia

Builds foundation for learners in state-of-the-art data analysis methods

Explores contemporary approaches to descriptive, inferential, predictive and prescriptive analytics

Offers a broad overview of the field of data analysis

Provides historical references, examples, explanations, and case studies to illustrate main concepts

Reviews summary

Theoretical concepts done well

With only one student review, it's difficult to provide an overall description of the course's sentiment. However, the single student review did mention that the course provided a good base of theoretical concepts, but the student felt that the information presented could have been provided through a textbook rather than through a lecture.

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Contemporary Data Analysis: Survey and Best Practices with these activities:

Review statistical concepts

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Ensure a strong foundation in vital statistical concepts to support understanding advanced topics in data analytics.

Browse courses on Statistics

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Revise core statistical concepts such as descriptive statistics, probability distributions, and hypothesis testing.
Practice solving statistical problems using online resources or textbooks.

Practice data cleaning and manipulation

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Develop proficiency in data cleaning and manipulation techniques to prepare data for analysis.

Browse courses on Data Cleaning

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Find practice datasets online or use datasets provided in the course materials.
Use programming tools (e.g., Python, R) to clean and manipulate data.
Experiment with different data cleaning and manipulation techniques.

Follow tutorials on advanced statistical methods

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Gain exposure to cutting-edge statistical methods and learn how to apply them in real-world scenarios.

Browse courses on Machine Learning

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Identify advanced statistical methods relevant to your field of interest.
Find online tutorials or courses that provide guidance on these methods.
Work through the tutorials and practice implementing the methods.

Three other activities

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Join a study group for data analysis

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Collaborate with peers to discuss concepts, solve problems, and enhance understanding of data analysis techniques.

Browse courses on Data Analysis

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Find or form a study group with fellow students in the course.
Set regular meeting times and establish a study schedule.
Take turns presenting topics, leading discussions, and solving problems.

Participate in data analysis competitions

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Test and improve data analysis skills by participating in real-world competitions and challenges.

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Identify data analysis competitions or hackathons relevant to your interests.
Form a team or participate individually.
Analyze the provided data and develop innovative solutions.

Contribute to open-source data analysis projects

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Gain practical experience and contribute to the data analysis community by participating in open-source projects.

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Find open-source data analysis projects that align with your interests.
Identify areas where you can contribute, such as code development, documentation, or testing.
Submit pull requests or collaborate on issues to contribute to the project.

Career center

Learners who complete Contemporary Data Analysis: Survey and Best Practices will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians collect and analyze data to help organizations make informed decisions. The Contemporary Data Analysis course is highly relevant to a career as a Statistician. The course covers topics like descriptive analytics, inferential analytics, predictive analytics, and prescriptive analytics, all of which play a critical role in the field of statistics.

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Data Scientist

Data Scientists use programming to solve problems and generate insightful solutions. Enroll in this course if you want to enter a career as a Data Scientist. Being able to successfully complete this course will show you have the analytical thinking skills in this field. The course covers topics like data descriptive analytics, inferential analytics, predictive analytics, and prescriptive analytics, which are part of an integral Data Scientist toolkit.

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Data Analyst

Data Analysts study the raw data and use their knowledge of analytics to present their findings to stakeholders. If you want to launch a career as a Data Analyst, enrolling in the Contemporary Data Analysis course will help build a foundation of statistical tools. This course covers topics like predictive and prescriptive analytics that can help you understand how data can be used within a variety of business scenarios.

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Quantitative Analyst

Quantitative Analyst use computational models and statistical methods to help make meaningful conclusions. This Contemporary Data Analysis course may be useful if you want to become a Quantitative Analyst. The course covers topics like inferential analytics, which is useful for drawing conclusions from data.

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Market Research Analyst

Market Research Analysts analyze marketing data to help businesses better understand the needs of their target audiences. This Contemporary Data Analysis course may be useful if you want to become a Market Research Analyst. The course covers topics like descriptive analytics, which provide insights into historical data that can inform strategic decisions.

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Business Analyst

Business Analysts provide insights and solutions to help organizations improve their performance. If you want to launch a career as a Business Analyst, this Contemporary Data Analysis course may be useful. The course covers topics like descriptive analytics, inferential analytics, predictive analytics, and prescriptive analytics, each relevant to being able to effectively solve business problems.

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Operations Research Analyst

Operations Research Analysts use advanced analytical techniques such as mathematical modeling and optimization to help businesses improve their efficiency. This Contemporary Data Analysis course may be useful if you want to become an Operations Research Analyst. The course covers topics like predictive analytics and prescriptive analytics, which are core to the Operations Research Analyst toolkit.

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Data Architect

Data Architects design and manage data architectures. If you are looking to become a Data Architect, you may find the Contemporary Data Analysis course useful. The course covers topics like data descriptive analytics, inferential analytics, predictive analytics, and prescriptive analytics, all of which are applicable to data architecture.

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Machine Learning Engineer

Machine Learning Engineers implement and maintain machine learning models. If you want to become a Machine Learning Engineer, you might consider this Contemporary Data Analysis course. The course covers topics like predictive analytics, which is an integral part of building and deploying machine learning models.

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Financial Analyst

Financial Analysts offer advice to businesses and individuals on financial matters and make investment recommendations. If you want to become a Financial Analyst, you may find the Contemporary Data Analysis course helpful. The course covers topics like inferential analytics, which is used in analyzing historical financial data to uncover valuable insights.

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Data Engineer

Data Engineers design, build, and maintain data management systems. If you are looking to become a Data Engineer, the Contemporary Data Analysis course may be useful. The course covers topics like data descriptive analytics, inferential analytics, predictive analytics, and prescriptive analytics, each of which are applicable in data management systems.

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Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. The Modern Data Analysis course may be useful if you want to become an Actuary. This course covers topics like inferential analytics and predictive analytics, which are utilized in measuring risk and uncertainty.

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Web Developer

Web Developers design, develop, and maintain websites. If you want to become a Web Developer, the Contemporary Data Analysis course may be useful. This course covers topics like descriptive analytics, which is useful for analyzing website traffic data to inform design and development decisions.

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Database Administrator

Database Administrators maintain and optimize database systems. The Contemporary Data Analysis course may be useful if you want to become a Database Administrator. The course covers topics like data descriptive analytics, which is applicable to analyzing database performance and identifying areas for improvement.

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Software Engineer

Software Engineers design, develop, and maintain software systems. If you are looking to become a Software Engineer, the Contemporary Data Analysis course may be useful. This course covers topics like data descriptive analytics, which is applicable to data-driven software design.

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