Statistics 1 Part 1: Introductory statistics, probability and estimation from edX

Statistics 1 Part 1 is a self-paced course from LSE which aims to introduce you to and develop your understanding of essential statistical concepts, methods and techniques, emphasising the applications of these methods. This course can be taken alone or as part of the LSE MicroBachelors program in Statistics Fundamentals or the LSE MicroBachelors program in Mathematics and Statistics Fundamentals.

Part 1, Introductory Statistics, Probability and Estimation, covers the following topics:

● Mathematical revision and the nature of statistics

● Data visualisation and descriptive statistics

● Probability theory

● The normal distribution and ideas of sampling

● Point and interval estimation

Statistics 1 Part 1 forms part of a series of courses which focuses on the application of statistical methods in management, economics and the social sciences. During this course, you will focus on the interpretation of tables and results, and how to approach statistical problems effectively.

What you'll learn

By the end of this course, you will:

be familiar with some key ideas of statistics that are accessible to a student with a moderate mathematical competence
be able to routinely apply a variety of methods for explaining, summarising and presenting data and interpreting results clearly using appropriate diagrams, titles and labels
have a grounding in probability theory

What's inside

Syllabus

● Mathematical revision and the nature of statistics

● Data visualisation and descriptive statistics

● Probability theory

● The normal distribution and ideas of sampling

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops essential statistical concepts and methods, laying a solid foundation for further study

Introduces probability theory, providing a theoretical framework for understanding statistics

Covers important topics such as data visualization, descriptive statistics, and sampling, equipping learners with practical skills

Emphasizes the interpretation of statistical results, fostering critical thinking and problem-solving abilities

Taught by experienced instructors from the London School of Economics, ensuring high-quality content and instruction

May require a moderate mathematical background, which could be a barrier for some learners

Reviews summary

Lse's foundational statistics & probability

According to learners, this course provides a solid and highly theoretical foundation in introductory statistics, probability, and estimation. Many find the explanations incredibly clear and appreciate the LSE quality, particularly for those in social sciences or economics. The initial mathematical revision is noted as helpful. While widely praised for its academic rigor and focus on interpretation, some students found the pace challenging or desired more practical software application, noting its predominantly conceptual approach. It's often recommended as a strong stepping stone for further statistical studies, though some suggest a moderate mathematical competence is essential.

The course is highly relevant and valuable for students in management, economics, and social sciences.

"The emphasis on application to management and social sciences was very helpful."

"As an economist, this course provided the perfect refresher and deepened my understanding of fundamental statistical concepts crucial for my field."

"It truly laid a solid foundation for further studies in economics."

It offers a robust academic understanding of statistical principles, emphasizing the 'why' behind methods.

"This course helped me grasp the underlying statistical concepts. The section on normal distribution and sampling was particularly well-explained."

"Perfect for anyone serious about understanding the 'why' behind the methods."

"As an economist, this course provided the perfect refresher and deepened my understanding of fundamental statistical concepts crucial for my field."

The course is highly praised for its exceptionally clear explanations of complex statistical concepts.

"The explanations were incredibly clear, especially for someone like me coming from a social science background."

"A fantastic course for understanding the core principles of statistics. The instructors were clear, and the materials were comprehensive."

"The explanations for complex topics were outstanding. I appreciated the careful pacing..."

Some learners find the course's self-paced nature and rapid introduction of concepts challenging.

"I struggled with the pace. It felt a bit rushed at times, especially with the probability theory."

"The 'moderate mathematical competence' requirement felt understated. Many concepts were introduced quickly."

"For true beginners, it might be a steep curve. Needed more supplementary material."

The course primarily focuses on theoretical concepts, with limited practical application using software tools.

"I wish there were more practical exercises involving statistical software, even basic Excel. It's very theoretical..."

"Don't expect to learn any coding or software tools here."

"I felt a strong disconnect between the theory and how to apply it in real-world scenarios, especially without any software integration."

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 Statistics 1 Part 1: Introductory statistics, probability and estimation with these activities:

Review your previous coursework in mathematics

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Refreshing your understanding of mathematics will strengthen your foundation for statistics.

Browse courses on Mathematical Revision

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Go through your old mathematics notes or textbooks.
Focus on topics relevant to statistics, such as algebra, calculus, and probability.
Complete practice problems to test your understanding.

Organize your notes, assignments, and course materials

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Organizing your materials will improve your study efficiency and retention.

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Gather all of your course materials, including notes, assignments, handouts, and readings.
Create a system for organizing your materials, such as using folders or binders.
Review your organized materials regularly to reinforce your learning.

Follow tutorials on statistical distributions

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Guided tutorials will provide clear explanations and examples, helping you grasp the concepts of statistical distributions.

Browse courses on Statistical Distributions

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Search for reputable online tutorials on statistical distributions.
Choose tutorials that align with the topics covered in your course.
Follow the tutorials attentively, taking notes and asking questions as needed.

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Join a study group to discuss course materials

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Engaging in group discussions will clarify concepts, strengthen your understanding, and foster collaboration.

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Find or create a study group with classmates who are also taking the course.
Meet regularly to discuss course readings, assignments, and concepts.
Take turns leading discussions and presenting your perspectives.

Solve practice problems on probability

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Solving practice problems will help you develop fluency in probability theory, a crucial foundation for statistics.

Browse courses on Probability

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Identify a list of practice problems from your course materials or online resources.
Set aside dedicated time for solving these problems, preferably daily.
Work through each problem step-by-step, checking your answers against the provided solutions.

Compile a glossary of statistical terms and formulas

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Creating a glossary will help you retain and quickly reference important statistical terms and formulas.

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Start a document or spreadsheet to organize your glossary.
As you encounter new terms or formulas in the course, add them to your glossary.
Include definitions, examples, and any relevant notes for each entry.

Conduct simulations to demonstrate probability distributions

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Conducting simulations will provide a hands-on approach to understanding probability distributions.

Browse courses on Probability Theory

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Choose a probability distribution to simulate, such as the normal or binomial distribution.
Use a statistical software or online tool to generate random samples from the distribution.
Analyze the simulated data to observe the properties of the distribution.

Create a visualization of a real-world dataset

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Creating a data visualization will enhance your understanding of descriptive statistics and its application to real-world data.

Browse courses on Data Visualization

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Find an interesting dataset that aligns with your interests.
Choose an appropriate data visualization technique, such as a bar chart, scatter plot, or histogram.
Use a software tool or online platform to create your visualization.
Analyze the visualization, draw conclusions, and present your findings.

Career center

Learners who complete Statistics 1 Part 1: Introductory statistics, probability and estimation will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians apply statistical methods to collect, analyze, interpret, and present data. This course would provide a comprehensive introduction to the field of Statistics, covering key concepts such as probability theory, sampling, and estimation. You would gain a strong understanding of the principles and techniques used by Statisticians to draw meaningful conclusions from data.

See salaries and explore the career path for Statistician

Data Analyst

Data Analysts sift through large and complex data sets to transform raw numbers into valuable insights that can be used for decision making. This course's focus on statistical concepts, probability, and estimation would provide a solid foundation for a career in Data Analysis. You would learn how to interpret data, draw conclusions, and make predictions, all crucial skills in this field.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use statistical methods and programming to extract insights from data. This course would provide a solid foundation in the statistical concepts and methods used in Data Science. You would learn how to collect, clean, analyze, and interpret data, as well as how to build and evaluate predictive models.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

Quantitative Analysts use statistical methods to analyze financial data and make investment decisions. This course would provide a solid foundation in probability theory, sampling, and estimation, which are essential concepts in Quantitative Analysis. You would learn how to analyze financial data, assess risk, and make informed investment decisions.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

Market Researchers gather and interpret data about consumer behavior, market trends, and competitor activities. This course would equip you with the statistical skills and knowledge necessary to conduct market research effectively. You would learn how to design and implement surveys, analyze data, and draw insights that can inform marketing strategies.

See salaries and explore the career path for Market Researcher

Actuary

Actuaries use statistical methods to assess risk and uncertainty in financial, insurance, and other areas. This course would provide a strong foundation in probability theory, sampling, and estimation, which are fundamental concepts in Actuarial Science. You would learn how to analyze data, model risk, and make informed decisions in the face of uncertainty.

See salaries and explore the career path for Actuary

Risk Analyst

Risk Analysts use statistical methods to assess and manage risk in various industries. This course would provide a strong foundation in probability theory, sampling, and estimation, which are essential concepts in Risk Analysis. You would learn how to analyze data, model risk, and make informed decisions in the face of uncertainty.

See salaries and explore the career path for Risk Analyst

Biostatistician

Biostatisticians use statistical methods to design and analyze studies in the medical and health sciences. This course would provide a strong foundation in probability theory, sampling, and estimation, which are essential concepts in Biostatistics. You would learn how to design studies, analyze data, and interpret results in the context of medical research.

See salaries and explore the career path for Biostatistician

Operations Research Analyst

Operations Research Analysts use statistical methods to improve the efficiency and effectiveness of operations in various industries. This course would provide a strong foundation in probability theory, sampling, and estimation, which are essential concepts in Operations Research. You would learn how to analyze data, model systems, and make recommendations for improvement.

See salaries and explore the career path for Operations Research Analyst

Epidemiologist

Epidemiologists use statistical methods to investigate the causes and patterns of disease in populations. This course would provide a strong foundation in probability theory, sampling, and estimation, which are essential concepts in Epidemiology. You would learn how to analyze data, identify risk factors, and develop strategies for preventing and controlling disease.

See salaries and explore the career path for Epidemiologist

Economist

Economists use statistical methods to analyze economic data and develop economic models. This course would provide a strong foundation in probability theory, sampling, and estimation, which are essential concepts in Economics. You would learn how to analyze economic data, build economic models, and make predictions about economic trends.

See salaries and explore the career path for Economist

Survey Researcher

Survey Researchers use statistical methods to design and conduct surveys to collect data on a wide range of topics. This course would provide a strong foundation in probability theory, sampling, and estimation, which are essential concepts in Survey Research. You would learn how to design surveys, select samples, and analyze data to draw meaningful conclusions.

See salaries and explore the career path for Survey Researcher

Quality Control Analyst

Quality Control Analysts use statistical methods to ensure that products and services meet quality standards. This course would provide a strong foundation in probability theory, sampling, and estimation, which are essential concepts in Quality Control. You would learn how to design experiments, collect data, and analyze results to identify and correct quality problems.

See salaries and explore the career path for Quality Control Analyst

Business Analyst

Business Analysts use data and statistical methods to identify problems and opportunities in businesses. This course would provide a strong foundation in the statistical concepts and methods used in Business Analysis. You would learn how to collect, analyze, and interpret data, as well as how to make recommendations that can improve business performance.

See salaries and explore the career path for Business Analyst

Financial Analyst

Financial Analysts use statistical methods to evaluate financial data and make investment recommendations. This course would provide a solid understanding of probability, sampling, and estimation, which are essential concepts in financial analysis. You would learn how to analyze financial data, assess risk, and make informed investment decisions.

See salaries and explore the career path for Financial Analyst